Key Challenges in Choosing a Third-Party E-commerce Site Search Tool (According to Public User Reviews)
To stay competitive and take advantage of the latest on-site search technology, many E-commerce sites are turning to third-party search solutions. This can help take their store’s search capabilities to the next level and take pressure off their developers.
However, when working with third party providers, you need to be aware if they have any weaknesses or limitations, so you can choose the best fitting one for your business.
In this article, we’ve gathered some of the commonly recognized disadvantages that retailers have highlighted to help you better evaluate onsite search providers.
Site search serves as the way through which consumers navigate vast product catalogs, seeking the perfect item to meet their needs on Ecommerce sites. A great search experience can be the difference between a satisfied customer swiftly finding what they seek and a frustrated user abandoning their shopping journey.
“If you’ve been treating on-site search as a necessary function, rather than as one of the most powerful revenue-generating tools in your arsenal, I have three words for you: Please stop that.“
In a previous article, we delved into the essential features necessary for an E-commerce search engine to provide your shoppers with the best experience possible. From intuitive autocomplete and typo correction to advanced filtering options and personalized recommendations, these features are paramount in meeting the evolving expectations of today’s online consumers. You can find this comprehensive guide here.
Why would e-tailers choose third-party site search tools?
Many online stores now opt for search solutions developed by third-party providers rather than investing in developing their own. The main advantages of this approach are the following:
Less effort, more expertise
Specialized search companies are typically more knowledgeable about the latest technologies and can leverage their expertise to offer enhanced search functionality for their partners’ stores. They typically offer pre-built solutions with latest tech or easy-to-implement APIs that can be integrated seamlessly with existing platforms, minimizing development time and effort.
Outsourcing site search to a third-party provider this way allows online retailers to focus on their core competencies, such as product development, marketing, and customer service, rather than diverting resources to non-core activities like search engine development.
Advanced technologies
Third-party providers continuously innovate and integrate advanced features and technologies into their site search solutions, such as
AI-powered search algorithms
Natural Language Processing
Personalization, and
Voice search capabilities.
These features enhance the user experience and drive better conversion rates.Vector search, for example, is the latest technology in search that goes beyond looking for matches in keywords, and understands context.
Machine learning-based solutions can offer users a whole another level of personalization, be it by recommendations or smart search results (e.g. returning women’s shorts if the user had been browsing women’s clothing previously).
Developing and maintaining such advanced capabilities in-house requires significant investment in research and development.
Merchandizing
As Shopify found, many e-tailers look for a third-party search solution because their own setup does not offer advanced merchandizing features. Having the ability to re-rank search results and promote the products with the highest margin is an essential capability for retailers.
Most third-party providers bring a wealth of specialized expertise, advanced technology, and industry insights to the table, making them valuable partners for retailers looking to elevate their merchandizing strategies and drive revenue growth.
In 2024, amongst the most known E-commerce search providers are Algolia, Klevu, DooFinder, Constructor and Luigi’s Box.
While third-party providers undoubtedly offer compelling advantages, it’s crucial for retailers to conduct a comprehensive evaluation before integrating these solutions into their E-commerce platforms in order to make sure their criteria will be met.
Challenges with third-party E-commerce Site Search Tools According to Public User Reviews
Without further ado, let’s delve into the intricacies that retailers encounter while navigating the quest for the perfect search solution.
User Interface / User Experience (UI/UX)
While third-party search solutions offer a range of features, they may not fully align with the specific needs or branding of every online retailer. Customization options might be limited, which could impact the seamless integration of the search function with the overall website design and user experience. For example, retailers may be unable to fine-tune search rankings or adjust filtering options to reflect specific product attributes or customer preferences.
It’s also common that some customization options are available, but require developer effort from the retailer’s side. This goes against the idea that search providers should take weight off retailers’ shoulders, so they can focus their efforts into other areas.
So, if you don’t have a large development team with capacity to work on your search customization, be sure to avoid choosing a third party solution that comes with these possible limitations.
Several users of the biggest Finnish site search provider, for example, consider the limited customization options for the search results layout, and the time needed for styling their store to match the search pages with the rest, as a disadvantage.
Usability
It’s imperative for third-party search providers to prioritize user-centric design principles, streamline on-boarding processes, and offer intuitive interfaces that empower retailers to efficiently configure and manage their search functionalities.
Retailers often face a learning curve when integrating and configuring third-party search solutions, compounded by the lack of comprehensive documentation or user-friendly interfaces.
Complicating matters further, certain settings may only be adjustable with the assistance of the provider, leading to potential delays and frustrations in fine-tuning the search experience.
Accuracy and Relevance Problems
While returning the most relevant results for a search query is a basic functionality of site search engines, achieving this consistently can be challenging.
Third-party site search solutions often come with advanced search algorithms and functionalities with features like autocomplete, typo correction, and faceted search, which help users find products more easily and quickly. However, some of them may struggle with accuracy and relevance due to limitations in their algorithms or indexing methods.
Without advanced technologies like Natural Language Processing (NLP) and vector search, these solutions may rely solely on keyword matching, leading to inaccurate or irrelevant results, especially for complex or ambiguous queries.
E.g. some of the biggest providers’ solutions still return inaccurate results sometimes, based on customer reviews.
To make sure your search always returns relevant results, choose a third-party provider that stays on top of technological advancements and leverages the latest search technology.
Issues with Integration and Technical Requirements
Ensuring seamless integration of third-party search with the existing systems and platforms of the retailer is crucial for maintaining operational efficiency and providing a consistent user experience.
Sometimes however, compatibility issues, API documentation clarity, and ongoing maintenance requirements may pose challenges for the retailer’s development team.
Some of the users of a globally known enterprise search provider, for example, find that the complexity of their features, and the need for developers in the integration phase takes away from the user experience (“It’s hard to know what you can/can’t do. No applied examples of how to use features, makes it hard to test and implement.”)
Similarly, a Finland-based provider’s reviewers mention that the non-seamless integration of the search solution may make it feel like the landing pages come from a connected system and stand out from the rest.
These kinds of issues, partnered with limited technical documentation and lack of compatibility with the retailer’s existing platforms can be quite a let-down in the retailer’s user experience, which is why these are factors to be aware of before making a decision about a search provider.
On the positive side, many providers offer comprehensive support and resources to facilitate seamless integration processes.
Insufficient Speed
Delays in indexing frequency and slow updates to product databases can negatively impact the speed and accuracy of search results. This can result in customer frustration for and lost sales opportunities for retailers. Improving indexing frequency and optimizing search result speed should be a priority for third-party search providers to enhance overall performance.
Weak Analytics Functionality
Access to user-friendly dashboards and robust search analytics are essential for retailers to gain valuable insights into customer behavior and preferences. Monitoring user search behavior, including autocomplete queries, can offer valuable insights into undiscovered content or tasks on your website.
However, some third-party search providers may fall short in providing predictive analytics and actionable insights, leaving retailers without the tools necessary to make informed decisions and drive business growth.
Some of the retailers using a known American search provider’s solution, e.g., mention having to work with a less than user-friendly analytics dashboard, but also give credit to their helpful support team, and their readiness to get the best out of every situation as a mean of compensation.
Customer Support
Effective and responsive consumer support is critical for retailers relying on third-party search providers to address issues and optimize performance. However, it’s not uncommon for some providers to struggle in delivering timely assistance, which can result in frustration and dissatisfaction among retailers.
While providing extensive technical documentation is undoubtedly valuable, it alone is not a sufficient form of support. Retailers often require personalized guidance, troubleshooting assistance, and proactive communication to resolve issues efficiently and ensure optimal performance of the search solution.
Wrap up
All in all, choosing a third-party E-commerce site search tool allows retailers to leverage specialized expertise, advanced features, scalability, and cost-effectiveness to enhance the search experience for their customers and drive business growth.
However, it’s worth noting that challenges may arise depending on the chosen provider, such as usability and customization issues, limited search analytics, and integration and support concerns. Given the framework of your team and the objectives of your company, it’s crucial to assess providers with the potential issues discussed above. This evaluation will enable you to identify the search solution that aligns most effectively with your needs and company goals.
Karolina GecseMarketing Assistant – Prefixbox
Karolina is working with Prefixbox’s Marketing team on projects related to content, online presence and PR. She lives in Budapest, has a Bachelor’s in English, and is currently a Business student.
Common Problems with Shopify Search and How to Solve Them
Did you know that 30-60% of E-commerce site visitors use a search bar when it’s available?
This is one of the many reasons why it’s important to have a powerful search engine with better-than-basic functions, that goes beyond looking for exact matches in keywords.
Shopify Search & Discovery is a search app Shopify offers for store owners looking to enhance their sites’ default search functionalities. While it might be an easy (and free) choice, it also has limitations.
In this article, we’ll explore common pain points experienced by Shopify Search users and discuss potential solutions.
Merchants looking to expand their businesses and set up their own online store often turn to E-commerce platforms like Shopify, WooCommerce, Salesforce and others. Shopify has emerged as one of the most popular e-commerce platforms due to its highly customizable solutions, catering to stores of all sizes and categories.
As we like to highlight every once in a while, the power of search cannot be overstated when it comes to online stores. Just consider the fact that search users spend almost 3 times more money shopping online, compared to non-searchers.
Many of them give Shopify’s own Search & Discoveryapp a chance, considering how it already has some additional search features compared to many themes’ default engines.
Before delving into why this app might be a good but somewhat limited solution for your search problems, here’s a fun fact you might not have heard:
E-commerce search queries can be categorized into 8 (!) very different types.
Shoppers might search for:
exact product names and specifications
features alone
describe the problem they need a solution for, or
use slang and abbreviation for the product they have in mind, among others.
In an ideal world, your search engine could return relevant results for every one of these search query types. However, 72% of retailer sites still can’t.
Limitations to Shopify Search and Discoveryapp
After thorough analysis of the reviews left by Shopify Search & Discovery users on several platforms, let’s examine the most commonly experienced challenges and pain points Shopify platform users have to face:
1. Filtering Issues
Most store owners that reviewed Shopify Search complain of the filtering function in some way. In general, filters on Shopify aren’t very sophisticated or “smart”, even with the app’s added functionalities.
For instance, filtering is not available (or doesn’t work as intended) on every site with Shopify Search & Discovery. For example, filtering is not available for product collections (like men’s clothing) containing more than 5,000 items, and search result pages with more than 100,000 items. This is unfortunate as stores with large product catalog need filters the most in order to facilitate a great online shopping experience.
If a product fits the filter selected by a shopper, it will be shown in the results, even if it’s out of stock and can’t be ordered. There is an “Availability” filter however, which your customer can tick to only see products currently in stock, but this can be quite frustrating and illogical for many.
On the topic of availability, many retailers agree that filter values should only be displayed if they correspond to items that are in stock. For example, if there’s no red women’s t-shirt in stock, the filter value ‘red’ should not be available under women’s t-shirts. However, Shopify’s search does not filter these options out, which means shoppers may still end up with zero results when navigating via filters.
Some users also find it a disadvantage that they can’t set their own minimum and maximum prices on the price filter.
Shopify’s filtering options are also quite rigid, as there’s minimal space to customize them in order to fit a product collection. This could be problematic for stores that sell products with unique features that should be filterable values. Moreover, Shopify Search & Discovery only allows you to display 1000 filter values maximum, which can be a pain point for retailers with a wide product catalog.
2. Typos and Synonym Management
Most traditional search engines rely on keyword matching alone, which is not the latest, state-of-the-art method of search (vector search is). Outdated engines aren’t always able to recognize typos, conjugated versions of the same word, or synonyms that are used to describe the same product.
Unfortunately, Shopify Search & Discovery is not much better in this regard, as it mainly looks for a full-text match between the product titles, descriptions, and the search query.
On the positive side, however, it partially recognizes typos and misspellings. It’s able to decipher a query if it only contains 1 misplaced letter, or 2 exchanged ones. Typo-tolerance is only applied for product titles, product types and product variants. Additionally, the first 4 letters of the search term need to be entered correctly for it to work.
There’s the opportunity to manually add and manage synonyms as well, which could lead to more accurate results with a bit more effort and time invested. However, there is a limit of 20 synonyms per synonym group, and a maximum of 1000 synonyms for the entire store.
The Shopify Search & Discovery team suggests having product tags that contain any words a shopper might type in the search bar when looking for a specific product, as a loophole in the matter of synonyms.
3. Limited Personalization
According to BigCommerce, personalization can boost conversion rates up to 50%.
According to reviewers, Shopify Search & Discovery does not automatically utilize previous user behavior, thus can’t offer your site’s visitor an advanced personalized shopping experience.
In practice this means that if a user searches for “tiles,” the search engine won’t consider that they were exploring bathroom tiles and bathroom decoration just two days ago. It most likely won’t prioritize similar results based on their recent activity; instead, it will simply display everything containing the term “tiles.”
Many Shopify stores are available in more than one language, which means they need a search solution that can support multiple languages.
Shopify Search & Discovery is lacking on this front, as it is not entirely language-agnostic. For example, predictive search is only available in English, and not all filter types can be translated either, as of now.
5. Poor Customer Support
The quality of customer satisfaction plays a key role in a customer deciding whether to keep on using a product or service.
Unfortunately, the users of Shopify Search & Discovery often experience issues with Shopify’s support team, ranging from being hard to reach at times to working with pre-written material and documentation alone.
6. Limited Analytics, No Predictive Analytics
Insights into your customers’ search habits, purchasing patterns and user behavior in general can serve as invaluable assets in your future decisions.
Shopify’s Search & Discovery app is equipped with some insightful metrics, but based on users’ experience, they’re not quite thorough when it comes to analytics.
You can check your:
click and purchase rate,
your customers’ top searches,
top no-result searches,
most searches that result in no click on products.
These can be helpful, but will prove to be insufficient when it comes to providing you with predictions for the future.
Predictive analytics can assist in identifying customer needs, recognizing shopping patterns based on purchase history, and forecasting future customer behavior.
If you’d like to know what other metrics you should be monitoring in order to have deeper insights into different areas of your business, and the performance of your online store, check out our article about E-commerce KPIs.
App Store Alternatives to Shopify Search and Discovery
There are many otherapps available in the Shopify App Store, developed by companies specializing in search and discovery, that can help you take your store’s search capabilities to the next level. Most of them offer a free trial, and different packages for different search needs.1
Implementing an alternative third-party search solution with enhanced features can alleviate stress for you and your team, and most probably lead to an increase in revenue. To help you with discovering them, we’ve gathered a few of these available third-party search apps below.
Fast Simon is available for Shopify and Shopify Plus stores, and offers advanced search functionality for large E-commerce retailers. The platform leverages shopper behavior, store inventory, data and visual signals to enhance the online shopping experience.
They offer 6 different search packages, with the features changing from package to package, so you can choose the best fitting one based on your needs and budget.
Offering Site Search, Filtering, Merchandising, Upsell and Cross-sell, and Analytics features, Searchanise advertises itself as a solution that improves customer experience across all stages of the shopping journey.
Their Search&Filter app is available on a wide array of platforms, such as Shopify, Magento, BigCommerce or WooCommerce. The packages start at $0 (best fit for small businesses) and include custom plans, depending on your product catalog and overall usage.
Prefixbox is an AI-powered, data-based search and discovery solution for E-commerce retailers. Prefixbox’s Rich Autocomplete, Hybrid Search Engine, dynamic filtering, Merchandizing and automated synonym mining functions are made to improve your conversion rate and your customers’ experience on your site.
The solution is fully managed, meaning that their customer support team will optimize your settings to maximize revenue. Prefixbox Search Analytics are the most detailed on the market, and offer unmatched insights.
Prefixbox uses vector search, the latest search technology that sees behind keywords, and learns from user behavior to return the most relevant results.
The Prefixbox Hybrid Search package on Shopify comes with custom plans, each offering a 30-day free trial.
Boost AI advertises itself as a smart search app leveraging AI to ensure swift and relevant search results. Users like it because it’s customizable, offers insightful analytics, and good customer support.
The app integrates with Shopify easily, and prioritizes increasing your conversion rate via relevancy. Their four product suites are: AI-powered Search, Product Recommendation, Merchandising, and Advanced Analytics.
Boost AI is fit for Shopify stores of various sizes, as there are various packages available, starting from basic plans for smaller stores, to custom subscription offers for bigger ones.
Having been working with Shopify since 2015, Findify is a Certified Shopify Plus App. It offers high-level personalization and customization options, utilizes NLP and lets your customers search for content besides your products.
Merchants have the option to rely on Findify’s personalization algorithm for automated assistance, or they can customize the customer experience according to their preferences.
It can be integrated with various platforms, including Shopify and BigCommerce.
Wrap Up
In conclusion, optimizing the search functionality on your E-commerce site is crucial, since the vast majority of shoppers rely on search bars. While Shopify’s Search & Discovery app may provide a convenient starting point, it does come with limitations such as filtering issues, a lack of robust support, and insufficient analytics.
Several third-party alternatives in the Shopify App Store, however, offer advanced features for basic search engines to overcome these challenges. Exploring these allows E-commerce businesses to tailor their search functionalities based on specific needs, leading to improved user satisfaction and potential sales and revenue growth.
Prefixbox AI Search is one of these alternative apps, addressing common pain points such as filtering issues, recognizing typos and synonyms, lack of personalization and limited language support.
Karolina GecseMarketing Assistant – Prefixbox
Karolina is working with Prefixbox’s Marketing team on projects related to content, online presence and PR. She lives in Budapest, has a Bachelor’s in English, and is currently a Business student.
Vector Search enhances the E-commerce experience by making it easier and more accessible for shoppers to find what they’re looking in the most efficient and user-friendly way.
In this article, we’ll explore how vector search impacts E-commerce and why it’s a game-changer.
With brands popping up faster and further than the eye can see, E-commerce platforms need solid digital infrastructure they can rely on to deliver quality shopping experiences to their audience.
These days, consumer expectations for online shopping are high, and they’re changing all the time. Accuracy and speed are top priorities for modern e-shoppers, and vector search is the powerful tool that E-commerce platforms are using to provide them.
Vector search is a type of search technology that utilizes machine learning (ML) to provide faster, more accurate, and more personalized search results to online consumers, thus driving sales and encouraging repeat business for E-commerce brands.
We’re going to unpack how vector search works, what its role in the current E-commerce world is, and various other solutions for enhancing online shopping experiences for online customers across the globe.
Understanding Vector Search: A new era of E-commerce
Before we jump into vector search’s impact on E-commerce, let’s give it some broader context. Vector search is essentially a form of search technology aimed at providing online users with more accurate and relevant search results.
The technology used in vector search indexing relates to Machine Learning. ML is an adaptive technology that continuously learns and evolves as it receives data input in order to become more precise and compatible with the searcher’s goal.
It can be applied to a variety of different websites or platforms that feature search engines, but E-commerce websites are among those that benefit most from this powerful form of technology.
Vector search can also be referred to as similarity search. This is because the technology seeks to understand the inherent relationships and overlapping qualities shared between multiple different datasets.
The result is a search mechanism that identifies and produces search results that align with individual consumer preferences more profoundly than other purely keyword-based search tools offer.
Vector search’s prevalence in the current E-commerce landscape
Vector search is prevalent today because E-commerce shoppers rely heavily on search tools to find and purchase the items they seek. In fact, 69% of online shoppers make a beeline for the search bar as soon as they access an E-commerce platform.
Furthermore, consumers place a high level of importance on the quality of search results provided at an online store. If the results do not immediately surface or are too distant from what they are looking for, it may drive them to exit the page in search of a better site to browse.
As such an integral component of the online shopping experience, search functionality must be outstanding, and vector search technology helps accomplish this.
How does vector search technology influence the E-commerce industry?
The use of vector search technology impacts the E-commerce experience in a number of ways. This is partially due to the fact that the search function is so vitally important to the consumer sales journey, and a satisfying (or dissatisfying) search result can mean the difference between a successful and unsuccessful purchase decision.
However, this unique form of technology also has influence due to its more technical faculties, such as faster loading speeds and correcting common errors.
Bearing all this in mind, let’s take a closer look at six fundamental ways in which vector search positively impacts the E-commerce industry as well as the troves of people who sustain it.
1. Enhanced personalization capacities
One of the biggest and most significant ways in which vector search impacts the E-commerce experience is by creating the potential for more personalized consumer experiences. We know from a plethora of studies that consumers value personalization highly, especially when it comes to online shopping, so using technology like vector search immediately boosts the chances of a satisfying purchase.
Vector search enhances personalization by analyzing customer preferences and using their purchase history as a guideline for gauging which search results to offer on each individual consumer’s device.
This style of personalization extends beyond just making similarly-related product suggestions – it factors in what each consumer is most likely to want to find on the website and offers it to them in an organic and convenient way. This drastically increases the likelihood of a sale and helps drive repeat business.
2. Managing typos and synonyms
A lot of older, more traditional search engine models struggle to identify what consumers are searching for when the words used include misspellings and typos. Vector search can overcome this problem.
When people are searching for a product in a hurry, they may use the wrong word or an inaccurately spelled one to find it, and normally, this disrupts the data sorting process.
Statistic-wise, roughly 20-30% of all searches contain misspellings. But with vector search technology, the engine is able to handle more nuanced queries, thus allowing for faster and more accurate results regardless of how the words themselves have been phrased.
This is related to the fact that, while other search engine systems rely on exact words to recognize products, vector search relies on their inherent features.
3. Uncovering hidden data patterns
Have you ever been recommended a product you weren’t actively looking for, but once you saw it, you knew instantly that you wanted to buy it? If so, you may have been dealing with a vector search engine.
Vector search engines don’t just focus on offering you information on products you are looking for, they also continuously seek out underlying patterns of consumer behavior to determine whether you would like to be recommended certain products.
This function can lead to surprising and delightful experiences for shoppers who feel that the algorithm “intuitively” understands their needs – perhaps even before they themselves do. As an E-commerce platform, this kind of experience is worth its weight in gold.
4. More precise visual search for online shoppers
Visual search is a relatively new approach to search engine input (similar to voice search tools) that is rapidly gaining traction in the E-commerce world. So far, it’s an approach that only works with vector search technology.
With visual search, consumers can opt to use reference images of products instead of using words or their voices to find them. For instance, if a consumer snapped a picture of a t-shirt they really liked at the mall, they could upload or share that picture with an E-commerce platform with vector search tools. It would then present them with a range of options that relate to it as much as possible.
While visual search may not be as common as regular word-based searches, it’s a unique feature of product searching that can provide a lot of value to the consumers who need it.
5. Optimized search speed
Another important and highly valuable way that vector search influences E-commerce experiences is through its seamless and lightning-fast search loading speeds.
Because vector search is founded on mathematical representations, it is able to find the fastest and most efficient way to draw up accurate results. This helps keep consumer’s attention fixed on the site and encourages them to continue their sales journey.
Fast loading speeds are one of the most pivotal and crucial elements of a successful E-commerce website or any website for that matter. If consumers don’t see results in under 2 seconds, they‘re likely to grow frustrated or bored and take their business elsewhere. But vector search aims to prevent that from happening at all costs.
6. Enhances shopper’s overall experience
Ultimately, the value that vector search brings to the consumer experience is multi-faceted. It takes into account speed, efficiency, accuracy, and personalization in order to provide each individual consumer with a map to conversion that they can follow in an easy and natural-feeling way.
By bringing consumers closer to the products they want in as short a time span as possible, vector search technology elevates the consumer experience while simultaneously boosting sales and conversion rates.
Furthermore, vector search is redefining the way consumers engage with E-commerce platforms.
With additional features such as visual search, enhanced personalization, and the ability to anticipate hidden patterns of buyer behavior, vector search is set up to be a hallmark of effective E-commerce business going forward.
Seeking solutions for enhanced E-commerce experiences
Vector search is one of several technological solutions that E-commerce platforms can use to improve their digital infrastructure and make consumer’s shopping experiences better. Software services like Elastic make vector search engine integration simple and offer a range of tech solutions that can improve the efficiency, manageability, and usability of E-commerce platforms, such as:
Threat hunting – Advanced analytics allows E-commerce brands to hunt down, identify, and kill digital threats in order to safeguard both customer and business experiences online.
Generative AI – ElasticSearch offers a range of Large Language Models (LLMs) to produce powerful new applications for online businesses through semantic search tools, transformer model flexibility, and vector database management.
Log monitoring – Elastic’s scalable, centralized log monitoring tools allow E-commerce businesses (and other types of businesses) to deploy and manage logs at a petabyte scale.
These solutions consider observability, security, and fruitful engagement with consumers. By focusing on the integration of cutting-edge E-commerce technology and software tools that both protect and enhance the usability of websites, brands can experience higher ROI, better search engine rankings online, and a much deeper, more lasting relationship with consumers.
Conclusion
Vector search is a cutting-edge form of search technology powered by ML that enables consumers to find the products they are looking for with renewed accuracy and compatibility.
Alongside other E-commerce solutions such as log monitoring, threat hunting, and generative AI, vector search has become a key player in the evolution of the modern E-commerce industry.
Guest authorPowered By Search
The SaaS Marketing Agency that builds reliable demand generation systems. By focusing on revenue and pipeline metrics above all else, Powered By Search is able to drive predictable growth for B2B SaaS companies.
With the E-commerce industry growing at an extraordinary rate globally, businesses are faced with the challenge of getting ahead of their competition and providing the best user experience possible in order to grow their business.
As numbers speak louder than words, the following statistics demonstrate the influence of AI on the E-commerce sphere:
A report by McKinsey & Co. shows that adopting AI can generate 20% extra revenue for your business
“Harnessing the power of Artificial Intelligence (AI) is no longer a luxury – it’s an imperative”
Let’s go over a few of the hot topics in the field, and see how you can leverage these trends to your E-commerce business’s advantage.
Emerging Trends for AI in E-commerce in 2024
1. Conversational Commerce
Conversational AI serves as the basis for chat commerce, as we discussed in a previous article.
Shortly put, conversational shopping aims to recreate the in-store experience online by enabling shoppers to chat with an AI powered shop assistant to get answers to any questions they have (about the store or products).
Conversational AI tools are capable of processing unstructured speech or text inputs and can further enhance their abilities through additional training and human feedback.
Using an AI-powered chatbot can reduce in-house costs, improve the user experience, and open a new online sales channel.
According to Markets&Markets’ recent study, the Conversational AI market is expected to increase from USD 10.7 billion in 2023 to USD 29.8 billion by 2028.
In addition to conversational commerce, Artificial Intelligence unlocks the door to seamless, lightning-fast customer support.
AI-powered chatbots are changing the rules of customer interactions by bringing efficiency and personalization to the forefront. They ensure 24/7 availability, break language barriers, reduce the need for human agents and enable higher degree of customer loyalty at the end.
3. Predictive Analytics
Predictive analytics algorithms are used to analyze various data derived from transactions and customer behavior.
Through the utilization of big data and statistical analysis, predictive analytics can assist in identifying customer needs, recognizing shopping patterns based on purchase history, and forecasting future customer behavior.
With the use of these analytics, you can efficiently generate personalized messages and offers, along with product recommendations that cater to the customer’s interests and preferences.
Providing suggestions tailored to the customer’s needs and preferences is one of the easiest ways to make them feel understood and valued. Customers are more likely to return to a store that prioritizes their satisfaction.
4. Personalized Recommendations
The concept of personalized product recommendations is not exactly new for E-commerce companies. Technological developments (including Artificial Intelligence) however, significantly contribute to making these recommendations more sophisticated and effective.
AI- and ML-based algorithms are the best choice when it comes to analyzing data related to customer interactions, as they can provide deeper insights into their purchasing patterns and behavior overall. Thanks to this, your recommendations can become even more personalized.
All in all, predictive analytics play an important role in customer satisfaction and retention as well.
5. Visual Search and Image Recognition
Computer vision, which is a subset of AI, gives machines the ability to “see” and interpret the world. It serves as a basis for visual search, where instead of a text-based query, the user searches with an image.
How is it helpful in the E-commerce sphere?
The shopper can just take a photo of the product they’d like to buy, and upload it to the search engine, instead of writing lengthy queries, then browsing and filtering to find the best fit.
Target and IKEA are great examples of visual search.
To easily get started, you can make use of ready-made visual search engines like Google Lens or Pinterest Lens. The other method of implementing this technology is to choose a site-search solution that supports visual search.
6. Vector Search
Vector search is the latest development in E-commerce search technology. It is based on a mathematical representation of language that transcends traditional search methods that rely on keywords alone.
Vector Search dives deep into the very nature of products, understanding their features and nuances. This means more relevant search results, better product recommendations, and an altogether more satisfying shopping journey.
Thanks to Natural Language Processing, vector search enables shoppers to search more generally, while prioritizing relevancy, which means even if they cannot exactly explain what product they need, they can search for the concept and receive matching results
Vector search can be combined with traditional keyword search to improve search result relevancy leading to increased conversion rate and revenue.
Live commerce (also known as Livestream shopping or live video shopping) is not yet a mainstream concept in the West.
However, recent studies show that in China, 77% of people have already purchased something via livestream shopping. It is an emerging trend in several Asian countries.
Live commerce is when retailers sell products via videos broadcasted in real time. The main idea is that people can see the products from all angles and in the context before making a purchasing decision.
There are different ways to implement this new avenue of sales, but the two most common are omnichannel live streaming to promote and sell products, and live Q&A sessions to initiate conversation between the viewers and the representative(s) of a business. Either way, there is always a link or another action item in the videos that enables potential customers to reach the store in an instant.
Social media is the most common platform for live video shopping, but some businesses like streaming on their websites as well.
8. Fraud Detection with AI
AI is emerging as a powerful asset in the fight against financial fraud as it can process huge amounts of data and recognize patterns in order to find anomalies and unusual activity.
ML- and AI-powered tools can uncover cases of fraudulent transactions involving several accounts, different locations, devices, and channels.
Natural Language Processing (NLP) plays a key role in AI fraud detection efficacy, by making sense of written communication, such as emails or text messages, related to a suspicious transaction.
This technology can be used for multiple other functions, including: identifying fraud both online and offline, filtering out fake reviews on websites, and blocking bot traffic.
9. Automated Content Generation
Using AI to generate content can save retail businesses a lot of time and effort.
It can be used to augment the product catalog by automatically generating high-quality product descriptions. It can help with automatic translation as well, to adapt content for different regions and languages, ensuring a consistent brand message and catering to a global audience.
Generative AI, like ChatGPT can also be a helping hand in creating content for bundle offers and cross-sell recommendations, directing customers to relevant items.
Anticipating Future Developments in E-commerce AI: 2 predictions
+1 Augmented Reality / Virtual Reality
As customers look for more interactivity with a product before making a purchase, AR and Virtual Reality Shopping are most likely to emerge in the upcoming years.
A VR/AR online shopping experience, for one, lets the customer take a thorough look at the product from all angles, even simulating the feeling of using the item (visual try-on). Journey, for example, a quite well-known shoe store in Northern America, allows their customers to try on most of their shoes virtually.
Foreseeably, this elevated level of convenience and comfort is on the list of future demands from shoppers.
Some stores have created entire virtual stores that mimic their existing physical ones.
Ralph Lauren allows their customers to take a virtual look into their stores in several locations, while Charlotte Tilbury has an entire browsable virtual store.
Virtual or Augmented Reality can help shoppers get educated about products, before making a purchase decision.
+2 Innovative Omnichannel Payments
Merging all kinds of payment methods into one omnichannelstrategy, and blurring the lines between them is another avenue of development in AI. In practice, this means that the customer can handle any transaction related to their shopping journey in whatever form they prefer (browse online, pay in-store with cash, get a coupon via email or text message, return an item through a pickup point, see their refund on their bank account the next day).
Payment integrations like Amazon’s palm scanning, and biometric technology (payment by physical features, fingerprint), as well as social media payments, are examples of innovative ways to utilize AI in stores’ omnichannel payment strategy. As Michael Johnson, the CIO of SVP Worldwide phrased in his post,
“The Future of E-commerce is Here and It’s AI-powered“
Wrap Up
The latest developments in Artificial Intelligence can equip your business with the necessities to enhance the customer experience in your online store, helping to make you a leading retailer.
To maximize the potential of AI, it’s crucial to have a solid understanding of the technology by staying informed to be up-to-date on the latest developments, then take the initiative and invest in AI tools to realize their benefits as soon as possible.
Make sure your efforts align with your objectives and leverage Artificial Intelligence in the most fitting manner for your E-commerce business.
Karolina GecseMarketing Assistant – Prefixbox
Karolina is working with Prefixbox’s Marketing team on projects related to content, online presence and PR. She lives in Budapest, has a Bachelor’s in English, and is currently a Business student.
Conversational commerce has evolved into a multifaceted landscape with numerous channels and platforms available for direct business-to-customer communication.
From chatbots and virtual assistants to voice-enabled interfaces, each type of conversational platform brings its unique strengths to the table. These multi-featured platforms are called conversational experience solutions.
Let’s explore how you can leverage these platforms to make your shopping experience as convenient as possible for your customers.
Heard about conversational commerce, but not sure how to get started?
Don’t worry— we’ve got you covered.
This new trend, often called chat commerce, is reshaping the way businesses connect with their customers, and those who get a jump-start, will be poised to gain a competitive advantage.
Leveraging any of the wider known messenger apps (e.g. Facebook Messenger, WhatsApp Business, Viber, etc.) starts by choosing a conversational commerceplatform that helps you with this kind of implementation. Oftentimes, they are quite versatile and offer other features for E-commerce businesses, besides the opportunity to be in contact with their customers directly.
Why to Use a Conversational Commerce Platform?
Let’s take a quick peek at the most important features of said platforms:
automated conversations through chatbots
Powered by artificial intelligence, they can engage in real-time conversations with customers, answer their queries, and provide support.
personalized experiences and recommendations
This is a standout feature of conversational commerce platforms, thanks to their ability to access customer data, such as purchase history and preferences. It enhances customer satisfaction, customer loyalty, and ultimately increase conversion rate.
omni-channel communication
When choosing a platform, you first need to make sure it’s compatible with the messaging channel you plan to use. To choose the best channel for your business, make sure you consider factors such as your target audience and geographical location, and the specific features offered by each to align with your business objectives.
An omni-channel communication platform is a great choice if you’d like to engage with your customers across multiple messaging channels, be it social media platforms, messaging apps, and ecommerce websites. Ensuring that your customers can interact with you in the way they prefer enhances their convenience and satisfaction.
NLP enables the platform to understand and interpret user queries in a more human-like manner. By comprehending the context and intent behind customer messages, you can deliver more accurate and relevant responses to them.
advanced analytics and reporting capabilities
Thanks to this feature, you gain insights into customer interactions, track key metrics, and measure the effectiveness of your conversational marketing strategy. This can help you refine strategies, optimize customer journeys, and drive better outcomes.
Choosing Your Ideal Platform: A Strategic Decision
When selecting the ideal platform for your business, opt for one that suits your business requirements and objectives, plus, integrates seamlessly with your current systems.
Evaluate aspects such as scalability,customization options tailored to your unique requirements, and compatibility with your E-commerce platform to make an informed decision.
There are two major use cases of conversational commerce platforms that stand out from the rest:
Support
Through the integration of chatbots and AI-driven systems, businesses can provide instant assistance to customers, offer 24/7 support, and address their concerns in real-time.
Intercom’shas long been a popular support platform and is now integrating AI chat capabilities to support conversational scenarios. Their chatbot, called Fin, is powered by GPT-4 and is available 24/7 and chats with your customers to resolve queries based on your support content.
Conversational commerce tools can take the offline shopping experience, online by replicating the experience of sales assistants who engage potential customers in personalized conversations. They can recommend products, answer customer inquiries, and guide users through the purchasing process seamlessly.
Chatfuel is a chat commerce platform that has ready-to-use templates that you can use to engage your customers on other channels. This can help you build workflows to automate cart abandonment reminders, track orders, and more.
Types of Conversational Experience Platforms (and How Can You Benefit from Using Each)?
Each conversational commerce platform can provide your business with unique features and benefits, however, in order to choose one you need to clarify what your objectives are and what purposes you would use it for.
Let’s see the most common solutions that conversational platforms support:
Build-your-own AI Chatbot platform solutions
These messaging platforms enable you to build your own chatbot to leverage the power of AI in customer support and engagement. The communication channels are up to your (and your customers’) preferences.
Instant chatbot support is quite an attractive feature for online shoppers when it comes to getting help with a service.
💡Pro tip: to maintain a balance between automation and human touch, provide the option for users to connect with human agents when they feel their case requires it.
The next level of automated E-commerce messaging is when the entirety of the shopping journey is carried out with the help of chatbots.
AI Customer Engagement solutions (Marketing Automation)
The purpose of these solutions is to assist your business in creating different campaigns for increased customer engagement. They help you find the most relevant content for your target audience, alongside the best communication channels and the recommended time of publication.
Furthermore, automation promotes speedier communication between customers and businesses, allowing for faster problem-resolution times at cheaper costs than human processes.
Cloud Contact Center solutions
As a 2021 study shows, 77% of contact centers that already used AI for multiple purposes back then, reduced their overall costs as a result.
In the context of contact centers, AI-powered bots are able to handle first line of customer interactions and free up some time in your customer service team’s already busy phone line.
Chat commerce solutions can help you connect different digital channels to ensure the same level of service, regardless of the way your online shoppers decided to get in touch. Usually, you can access all relevant channels from one interface, alongside the information related to your customer’s profile.
AI Customer Data platforms
These platforms let you gather, store, and analyze data about users to create customer profiles and enable you to personalize every interaction with them for enhanced user experience. With the help of AI, you can recommend your products and services to the right audience, based on individual profile data.
As we mentioned at the beginning of this article, there are platforms that offer numerous different features for businesses instead of focusing on one of the developmental areas. These are called conversational experience solutions.
Case Study: Infobip
Infobipis a great example of an omnichannel conversational experience solution. They are a CPaaS (communication platform as a service) company that now also empowers businesses to integrate chat commerce seamlessly into their operations.
They enable you to communicate with your customers over their favorite channels. By expanding your communication solutions and channel portfolio, you can create new revenue streams and win new business.
Infobip allows you to craft end-to-end customer journeys powered by AI and build your own Gen AI-based chatbot by your own rules. They also have a solution that lets you create customer profiles as well, for maximum personalization.
“Creating connected customer experiences is at the heart of what we do“
To enable your shoppers to reach out directly to your business with inquiries, be it about general product recommendations, needing support with choosing the right product, or having trouble with payment, you can choose Infobip’s chatbot platform, and imitate the feel of a human conversation with many times fewer effort.
Wrap up
In conclusion, conversational commerce platforms represent a transformative force in the E-commerce landscape.
From instant AI-backed support to personalized sales assistance, these platforms redefine the dynamics of business-consumer relationships. In order to select the platform best for your business, start by identifying your goals and what you want to achieve.
As customer expectations continue to shift towards seamless and personalized interactions, leveraging these platforms becomes not just an option but a valuable addition to any E-commerce strategy.
Paige TyrrellHead of Marketing – Prefixbox
Paige is the Head of Marketing at Prefixbox, a leading eCommerce site search solution. She’s an American who’s been living in Budapest since 2017 and loves giving #alwayslearning sessions to help people optimize their online stores.
Vector Search is a game-changer for retailers striving to enhance their online user experience, boost conversion rates, and increase their online revenue.
In this article we are going to delve deeper into how this transformative technology actually works.
The key difference between vector and keyword search lies in the way data is represented and compared.
Traditional search methods treat data as a collection of words, focusing on the presence or absence of specific terms. Vector search, on the other hand, uses vectorembeddings to represent data points.
Vectors are essentially mathematical representations of objects, whether they be words, products, or images.
Each product in an E-commerce catalog is translated into a vector in a high-dimensional space. These dimensions can represent various product attributes. For example, movies might be represented as vectors where each dimension represents a movie feature (e.g., genre, director, actors, etc.).
By operating in high-dimensional spaces, the search algorithm can capture intricate relationships and similarities between products. For example, products with similar attributes will have vectors that are closer in this multi-dimensional space.
In the E-commerce setup, methods like word embeddings (for textual product descriptions), product embeddings (based on their attributes), session data embeddings (that adds up to a user profile), and hybrid models that combine multiple vectorization methods might be the most beneficial, providing the best search accuracy.
When a user or system submits a query, it is also vectorized in the same manner as the items. The query vector represents the features or characteristics of the query, which then will be used as a base of similarity to find the relevant products in stock.
Similarity Metrics
To find similar items in vector databases, you need a way to measure the similarity between the search query vector and the vectors of items in the dataset. Common similarity metrics include cosine similarity, Euclidean distance, L2 distance and Jaccard similarity, among others.
Closeness in space means more vector similarity, whereas more distance means fewer common characteristics. Businesses using a vector search engine could perform nearest-neighbor searches to determine distance metrics to the closest query-related vectors in a space.
Utilizing the joint power of keyword and vector search
When vector search and keyword search results are blended, it’s called a hybrid search solution.
A hybrid search engine that combines keyword-based and vector-based approaches offers a more robust and versatile solution, providing highly relevant search results across a wide range of scenarios.
But how exactly are keyword and vector search results blended?
There are multiple different ways, but here’s how we do it at Prefixbox:
In a vector-based search result, you can still have traditional keyword-based search scores, like
the matching score, which shows how relevant the result is for a specific search query, and
the popularity score, which shows how popular a specific search result is.
In addition to these scores, the vector-based search results have similarity scores, which are based on the mathematical representation of the keyword, and its distance from the result in the vector space.
Blending is the method of merging the result lists. This can be achieved by scaling and normalizing these scores by applying weights in order to get the best ranking.
Summary
Vector search is a technique used to find similar items in a large dataset based on their vector representations. Applications span from content recommendation to image retrieval and E-commerce product recommendations.
It’s the critical element that empowers recommendation systems to deliver more insightful suggestions and fine-tunes search engines for superior performance.
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What if there was a revolutionary way to help shoppers navigate the vast E-commerce universe? One where search results are not just based on keywords, but on the essence of the products themselves?
In this article, we
Explain what vector search is
Showcase 7 Main Advantages Vector Search has over traditional Keyword Search technology
More often than not, search solutions are currently powered by traditional keyword-based search, which are usually called semantic search engines (when they have advanced capabilities like query understanding). This traditional search method that, as the name suggests, relies on keywords, has its limitations.
Solutions that only leverage keyword matches often require a lot of manual optimization efforts (think: relevance optimization, matching and tuning weight editing, and synonym management), otherwise they lead to irrelevant results, frustrating experiences, and missed revenue opportunities.
This is where vector search enters the scene, a transformative technology that has the potential to redefine the E-commerce search experience.
Vector and keyword-based search can be combined to give you the most powerful search on the market.
But first let’s get started with an overview of vector search.
Vector Search explained
Vector search is a search methodology that provides even more relevant results by taking into account information like: productdescriptions and user behavior to return better quality search results.
Unlike traditional keyword search , vector search seeks to understand the inherent qualities and relationships between items in a dataset. To put it even more simply, it’s like transitioning from looking up words in a dictionary to understanding concepts and their connections.
Vector Search goes beyond mere keywords, diving deep into the very nature of products, understanding their features and nuances. This means more relevant search results, better product recommendations, and an altogether more satisfying shopping journey.
Benefits of Vector Search for E-commerce businesses
One of the primary challenges with traditional E-commerce search methods (keyword-based search) is the heavy reliance on keyword matching.
Until the emergence of vector search, this has been a challenge for shoppers because, in order to receive relevant results, a retailer must have a highly optimized search solution or the shopper must meticulously craft their search queries in order to see relevant results.
However, with a solution solely leveraging keyword-matching search, even the best search queries can still return irrelevant results.
For example, searching for “black leather boots” might return brown boots or other unrelated products simply because they’re black OR leather.
This is where vector search can make a big difference, offering numerous benefits for both customers and E-commerce businesses.
Here are the 7 main benefits you can get from using a search solution that leverages vector search.
Result Relevance Improvement
Vector search enables shoppers to search more generally, while prioritizing relevancy, which means even if they cannot exactly explain what product they need, they can search for the concept and receive matching results.
Take the following queries for example:
home decor items for a minimalist living room
engagement rings with sapphire stones
skincare products for sensitive skin
hiking boots for all-terrain trekking
recommend pet toys for large dogs
A search engine powered by vector search would understand these broadly described concepts, and provide the user with the following results:
furniture, lighting and accessories that align with the minimalist aesthetic
a list of rings specifically designed for engagement, featuring sapphire gemstones
all kinds of skincare products known to be gentle and beneficial for sensitive skin (moisturizers, cleansers, serums)
hiking boots known for their durability and performance on various terrains
a selection of pet toys specifically designed for larger breeds, taking into account durability and safety
By leveraging a technology that can understand such queries, you can boost your search coverage, which will lead to Zero Result Search Rate reduction, and Search Success Rate, Conversion Rate, and Revenue increases.
Vector search enhances the overall shopping experience.
Besides returning more relevant results and enabling shoppers to search using natural language (no need for exact keyword matches), it supports real-time updates, so shoppers always see in-stock products and the most up-to-date product information. Of course, it also helps reduce the appearance of irrelevant products.
All this together can lead to longer sessions, a higher retention rate, and a greater sense of user satisfaction.
Faster Shopping Journey
Vector search streamlines the search process, ensuring a quicker shopping journey for the user. Its ability to understand and interpret intent means that customers spend less time thinking about how to phrase, then rephrase their search queries, sometimes several times in a row.
By using vector representations, the search engine narrows down the search space to focus on items that are semantically similar to the user’s query. This reduces the number of items that need to be considered during the search process, and as a consequence, it can provide results more quickly since it doesn’t need to sift through a vast number of irrelevant items.
Less Manual Synonym Editing Work
Synonym management is an important, but extremely time-consuming, way to optimize your online store’s search results. Most often, tagging products and keywords with relevant synonymous keywords is a manual process.
There are solutions for automatically mining and recommending synonyms for search keywords in order to improve synonym management productivity, but these recommendations still need to be manually reviewed for accuracy.
Vector search can handle synonyms automatically based on the language model it leverages by machine learning techniques. By freeing up your team from this time-intensive process, imagine how much more time they’ll have for other conversion rate- and UX optimizations.
Improved Personalization and Recommendations
By deeply understanding product relationships, vector search powers advanced personalization and recommendation systems. This not only helps customers discover new items they might love but also increases cross-selling and upselling opportunities for E-commerce businesses, and can even be used for digital marketing purposes as well.
Besides creating vector embeddings of the search queries sent and the products in your stock, you can make a vector space of user profiles and offer an even more personalized shopping experience for your customers based on their journey on your site and their behavior in general (categories of interest, previous purchases, etc.)
This is a significant challenge for search engines, as language is constantly evolving with the introduction of new words, phrases, and shifts in terminology.
Traditional keyword-based search relies on predefined lists of keywords, which results in less adaptiveness to linguistic changes. This limitation might lead to a less satisfactory shopper experience.
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Thanks to Natural Language Processing (NLP), vector-based search models can adapt more quickly to changes in language usage and stay current with evolving language through machine learning.
Smart Ranking
Besides the traditional keyword-based search scores, like the matching and the popularity score, vector-based search results have a so-called similarity score. This is computed based on the item’s vector distance from the query’s vector.
A search solution powered by vector databases ranks the search results based on their similarity to the query. The most similar items are retrieved and presented to the user.
Hybrid Search – Blending Vector and Keyword Search
The emergence of vector technology does not mean that it will replace keyword search altogether. Instead, they complement each other, so that more, and more relevant results appear.
When vector search and keyword search results are blended, it’s called a Hybrid search solution.
A Hybrid Setup that combines keyword-based and vector-based approaches offers a more robust and versatile solution, thanks to the benefits mentioned above, providing highly relevant search results across a wide range of scenarios.
But let’s cut the story here for now, we have another blog post exploring this topic.
Wrap Up
Vector search is transforming the search and online shopping experience.
As the E-commerce landscape continues to evolve, businesses that embrace vector search are poised to lead the way.
By adopting this transformative technology, you can revolutionize how customers discover and shop for products while optimizing your operations for the future.
Chat commerce (or conversational commerce) is a new avenue of shopping where people can interact with their brands directly to ask questions, find products, and make purchases all via a messaging platform. It combines the convenience of messaging platforms with the personalized experience of a real conversation.
It is changing the way both retailers and shoppers think about online shopping and those businesses that can move fast will get a competitive advantage.
If you’ve already heard about this topic, but aren’t sure how to get started, you’re in the right place!
We’ll explain the benefits of conversational commerce (both for retailers and shoppers) and have gathered 5 steps below to help you get started in your conversational commerce journey today.
How can conversational commerce contribute to your E-Commerce brand and increase your revenue?
By integrating messaging platforms and artificial intelligence, conversational commerce provides a personalized and interactive experience for shoppers, revolutionizing the way E-commerce businesses engage with their audience.
This avenue of shopping opens a dialogue between retailers and their potential customers.
Customers may interact with one of the company’s human representatives, a chatbot, or a mix of both.
From guiding shoppers through their entire buying journey to nurturing post-purchase customer relationships, chat commerce enables brands to enhance customer satisfaction, increase conversion rates, drive sales, and foster customer loyalty and retention. According to a Hubspot study, 47% of shoppers are already open to buying items via a chatbot.
The most essential benefits in more detail are the following:
Engage shoppers with seamless experience
Conversational commerce tools can be integrated into various platforms, including live chat on websites and social messaging apps (like Facebook Messenger) allowing businesses to meet customers where they already are.
It saves shoppers time by providing instant responses and eliminating the need to browse through search engine results or navigate complex E-commerce websites. This immediacy not only increases customer satisfaction but also increases conversion rates and reduces shopping cart abandonment.
Personalize the buying journey
There are many ways in which chat commerce brings customers closer to businesses. By leveraging messaging platforms and AI-powered chatbots, e-tailers can create a personalized experience that meets the unique needs and preferences of each individual shopper.
Every interaction with your customers via chat commerce allows your E-commerce business to gain further insight into their preferences, interests, and concerns.
By building this consumer profile, you’re able to create a much more personalized shopping experience.
Over time, chatbots can help you engage even more in natural conversations, providing relevant information and guidance in real time. They can remember previous interactions and preferences, ensuring a seamless and consistent experience across multiple customer touchpoints. For example, Sa Sa Hong Kong beauty retailer decreased waiting times by 57% with automated chat commerce solutions according to Facebook.
Improve conversion rates and order value
Chat commerce has the potential to significantly boost conversion rate and increase order value, particularly when automated by AI chatbots.
Thanks to chatbots tailoring their recommendations based on data that the shopper continuously provides, customers find products that they are more likely to purchase.
What’s more, this feature also increases the likelihood of upselling and cross-selling for businesses, thus driving up the order value.
Sometimes, consumers abandon their shopping carts due to distractions or indecisiveness. On the topic of order values, conversational messaging platforms can be leveraged to send shopping cart reminders as well.
By sending these reminders through chat apps (like Facebook Messenger or Viber), businesses can prompt customers to complete their purchases, contributing to a growth in conversion rates and average order value.
Improve the post-purchase experience
Leveraging conversational AI does not have to end upon purchase. AI chatbots can contribute to enhancing customer loyalty by providing customer service and support after shopping, like offering post-purchase recommendations and alternative products upon a return.
By automating this part of the shopping journey, companies can free up human agents to focus on more complex challenges, such as addressing specific consumer queries or handling escalations.
This allows them to provide faster and more efficient support to a larger number of shoppers, enhancing overall customer satisfaction.
How to implement conversational commerce? TL;DR
There are 5 main steps that businesses can take to successfully integrate conversational commerce into their operations.
Get to planning and goal-setting
Choose the right messaging platform
Create a structure for conversations
Implement and train your own AI-powered chatbot
Monitor and adjust relevant KPIs
1. Get to planning and goal-setting
Formulating your plans and objectives for implementing chat commerce in your store might be just as important as the practical implementation itself.
Understanding your business’ needs and goals is essential as it will help you determine how conversational AI can resolve your challenges effectively.
Pro tip: start small and build upon your successes. As for the first few weeks of your plan, set goals like decreasing the customer service queries by 8-10%, or increase online orders by 5-7%, etc.
Moreover, knowing where your customers are is fundamental. Different chat apps attract different demographics. Knowing the target audience’s preferred communication channels helps you with choosing which platforms to be present on.
Customer’s behavioral patterns and interests are also key factors. Analyze data from your existing customer base and identify common characteristics to create personas.
These personas will guide your conversational commerce strategy, allowing you to tailor your messaging and personalized experiences to specific customer segments.
2. Choose the right messaging platform
When it comes to conversational commerce, choosing the right messaging platform is crucial for a successful customer journey. Popular mobile messaging apps like Facebook Messenger and WhatsApp are leading the way in consumer communication and online shopping. As of 2023, mobile commerce or M-commerce already takes up more than 60% of all E-commerce sales worldwide, based on Statista’s numbers.
These platforms offer a range of features that make them effective, such as direct and convenient means of engaging with potential and existing customers.
When choosing a messaging platform, it’s important to note which functionalities are supported and what limitations exist.
Each messaging platform is different, so based on the one you decide to use, you will need to adapt your strategy accordingly.
3. Create a structure for conversation
Creating a structured and effective conversation between a seller and a client involves careful planning and design.
It’s important to maintain a conversational and friendly tone throughout the interaction, and the chatbot or seller should be adaptable to handle unexpected questions or issues that may arise.
Identify the scenarios you want to support via chat commerce and design an ideal conversation flow as the baseline to get started.
Regularly analyze and refine the conversation structure based on customer feedback and evolving business needs to continually improve the chat commerce experience.
A possible guide for your seller-shopper conversation:
Greeting and introduction
Identification and verification
if necessary, verify the shopper’s identity to ensure security
asking for the customer’s name helps with personalizing the conversation
Customer inquiry
use open-ended questions to gather information on what your customer is looking to buy
let the consumer ask clarifying questions about the products, and give informative and timely responses
Order assistance
guide the customers through the order process, when they are ready to make a purchase
Customization or personalization
if possible, offer shoppers the option to customize and personalize their order
Payment and billing information
ensure transparency regarding pricing, taxes, and shipping costs, then assist the shopper in providing payment and billing details securely
Confirmation and order summary
recap the client’s order, and request them to confirm that all the information is valid
Additional services or upselling
offer related services, accessories, opportunities to upsell
Post-purchase chitchat – delivery and shipping information, support and assistance
provide information on shipping options, delivery times, and tracking details, and offer post-purchase support (returns, exchanges, contact information for customer support)
4. Implement and train your AI-powered chatbot
For this to work, you’ll need to choose a conversational commerce platform that offers chatbot capabilities. These platforms often provide easy-to-use tools and integrations to build and deploy your chatbot.
Once you have selected a platform, you can start training your chatbot using natural language processing (NLP) and machine learning (ML) techniques. NLP allows the chatbot to understand and interpret user messages, while ML helps the chatbot learn from past interactions to improve its responses over time.
Additionally, sentiment analysis can be incorporated into the chatbot to understand the emotional tone of customer messages. This allows the chatbot to provide more empathetic and personalized responses, ultimately leading to better customer service.
These AI chatbots act as virtual shopping assistants and with sufficient training, they can replicate the feel of interacting with a store associate, being available 24/7 to answer questions, suggest choices, and guide customers to decisions based on their personal wants and needs.
5. Monitor and adjust relevant KPIs
Businesses that implement any form of chat commerce, require a thorough understanding of the key performance indicators (KPIs). By tracking specific metrics, businesses can gain valuable insights into customer engagement, conversion rate, order value, customer satisfaction, and abandoned cart rate.
Customer engagement measures the level of interaction and involvement customers have with chatbots or customer service representatives. Monitoring the number of conversations initiated, response times, and the overall duration of customer conversations can provide insights into the quality of the customer experience.
Conversion rate plays a significant role in determining the success of conversational commerce. Tracking the percentage of customer interactions that result in a purchase can help identify any bottlenecks or issues in the buying process. By analyzing conversion rates, businesses can optimize their strategies to drive more conversions and increase revenue.
Order value is another important KPI to monitor. It measures the average value of orders made through conversational commerce channels. By analyzing order values, businesses can identify opportunities for upselling and cross-selling to increase average order sizes.
Overall customer satisfaction is a crucial metric for assessing the effectiveness of conversational commerce. Monitoring customer feedback, ratings, or surveys after interactions can provide insights into the quality of customer service and identify areas for improvement.
Finally, tracking the abandoned cart rate is essential in understanding customer behavior and optimizing the shopping experience. By monitoring the percentage of customers who abandon their shopping carts, businesses can identify any pain points or barriers in the purchasing process and take action to improve it.
Wrap Up
Conversational or chat commerce has emerged as a powerful tool for customer engagement in the digital era. It’s a growing trend offering a more interactive and efficient way for businesses to engage with shoppers and drive sales.
Besides granting convenience to shoppers, the data-based personalized product recommendations and the 24/7 availability coming hand-in-hand with chat commerce are amongst the most attractive features for shoppers.
While automation can streamline processes and scale customer service, it is crucial to not lose the personal touch that human agents provide. Striking the right balance ensures that businesses can provide assistance while efficiently handling customer queries.
The effectiveness of chat commerce largely depends on how well businesses address the pros and cons to create a seamless and secure user experience.
For a comprehensive understanding of conversational commerce, check out our other articles on this topic covering its fundamentals, types of platforms and use cases.
Paige TyrrellHead of Marketing – Prefixbox
Paige is the Head of Marketing at Prefixbox, a leading eCommerce site search solution. She’s an American who’s been living in Budapest since 2017 and loves giving #alwayslearning sessions to help people optimize their online stores.
Generative AI and chatbot technologies are revolutionizing the way businesses and shoppers communicate and are shaping the future of retail before our eyes.
Before exploring the benefits of conversational commerce, we must understand what it is and it’s importance in Ecommerce.
Defining Conversational Commerce
Conversational Commerce enables online shoppers to communicate with their favorite brands through their platform of choice. By opening a messaging app and starting a conversation with a chatbot, shoppers can easily purchase items, receive immediate customer support, and view recommendations tailored to them.
Conversational commerce meets shoppers wherever they are and provides them with what they need. When done effectively, conversational commerce enhances the user experience, builds brand trust, and increases revenue.
Conversational commerce empowers businesses to foster meaningful connections with customers. Integrating AI-powered chatbots into online platforms enables stores to interact with shoppers in real-time. Thus, shoppers can easily access information and quickly make purchases with confidence.
By implementing conversational commerce, retailers can improve customer satisfaction, build trust, generate more sales, and stand out from competitors.
Enhancing the Customer Experience
By AI, retailers can use conversational commerce to provide an offline shopping journey online by mimicking the retail assistant’s behavior.
Chatbots and Virtual Shopping Assistants
First, let’s review what chatbots and virtual shopping assistants do. Chatbots and virtual shopping assistants offer support designed to mimic human interaction. They leverage NLP to understand user requests and return intuitive responses that go beyond the scope of pre-programmed replies.
By understanding context and subtle language nuances to provide tailored recommendations, virtual shopping assistants act like knowledgeable human salespeople.
They can answer questions, suggest additional products, and provide real-time product information, pricing, and availability instantly.
There are countless ways conversational commerce enhances the online user experience, all of which revolve around real-time interactions that lead to satisfied customers who feel understood and valued. See Toptal’s article for more chatbot UX best practices.
Product Discovery
AI powered chatbots can help shoppers find the products they’re looking for by understanding natural language and context of shopper queries.
They can make relevant recommendations to help shoppers complete their order and can leverage advanced filtering options to guide shoppers through product catalogs.
It discusses improving e-commerce search experiences through chatbot UX best practices.
Personalized Product Recommendations
By analyzing user behavior, preferences, and needs, chatbots can deepen customer understanding beyond Autocomplete searches, and provide personalized replies and tailored recommendations.
Personalized product recommendations help shoppers quickly find what they’re looking to buy, round out their purchases with complementary items, and inspire product discovery in areas of your catalog relevant to them. They make shoppers feel understood and help them navigate the shopping journey conveniently and efficiently.
Chatbots enable shoppers to track orders in real time, which helps retailers build trust by eliminating uncertainty and offering transparency in the shipping process.
Plus, retailers can offer proactive communication via automated updates and notifications about a delivery. This keeps shoppers in the loop every step of the way.
Ensuring chatbots are attentive and engaging helps manage shoppers’ expectations and reduce frustration, which increases customer satisfaction and loyalty.
Instant Customer Feedback Collection
Chatbots give shoppers a voice.
Instant feedback helps online retailers solve problems that may arise during the shopping process. They also help retailers discover pain points, which is insightful data that be used to optimize the chatbots. Ecommerce businesses can leverage feedback to fine-tune product recommendations, improve the user experience, and enhance customer support.
Collecting feedback helps customers feel like their opinion matters, increases engagement through collaboration, and results in a more satisfying shopping experience.
Sales and Marketing Optimization
Conversational commerce is a powerful tool that can be used to optimize sales and marketing efforts.
Let’s explore how.
Chatbot-Powered Sales Funnels
Chatbots are revolutionizing sales funnels. They’re a cost-effective way to generate leads, increase customer engagement, and offer real-time support.
Customer Engagement: Chatbots offer shoppers the ability to quickly navigate the shopping journey by providing personalized recommendations and support. These interactive conversations can guide shoppers through the sales funnel. By addressing users’ individual interests and needs, chatbots keep shoppers engaged and satisfied.
24/7 Support: Chatbots offer real-time support for common queries at any time of day. By providing instant responses, chatbots keep shoppers satisfied while freeing up human support for more complex requests.
Chatbots automate many parts of the sales process and are reshaping the sales landscape as we know it.
Their ability to leverage data and continuously improve helps sales teams promptly and effectively streamline the sales process and drive significant revenue.
Upselling and Cross-selling Strategies
Many shoppers use chatbots to find products they want to purchase. By analyzing user history, purchase behavior, and preferences, chatbots can identify opportunities to cross-sell and upsell.
The most effective way to do this is to offer recommendations that help shoppers discover relevant products. Personalized recommendations that align with what they’re looking for, upgrades, or complementary items that help complete their purchase are proven to increase conversion rate. Additionally, retailers can use chatbots to promote sales, discounts, or items that further support their bottom line.
Product recommendations increase conversion rate, average order value, and revenue. If you’re not using chatbots to upsell and cross-sell, you’re leaving money on the table.
To make sure your strategy is helping achieve your desired results with customer support, we recommend setting and tracking the relevant Ecommerce KPIs.
Interactive Promotions
Interactive promotions alert shoppers to deals, discounts, and promotions that encourage them to buy. They are a lucrative tool for increasing customer engagement, optimizing conversion rate, driving customer loyalty and retention, and helping your brand stand out.
Promoting personalized sales through chatbots or Ecommerce Merchandising is a proactive way to increase customer engagement. Helping shoppers find suitable deals will help you strengthen relationships with customers, create positive user experiences, and build sustainable sales success.
Streamlined Customer Support
Conversational commerce offers real-time communication, which is extremely powerful for customer support. The moment shoppers have a question or concern, they can get the answer they’re looking for, quickly absolve their frustration, and stay satisfied.
Chatbots can be programmed to answer most inquiries immediately. By automating routine answers and offering personalized 24/7 support, online retailers can reduce wait times, and keep shoppers informed.
Answering support questions quickly and accurately helps build trust, increases customer loyalty, and inspires return visits.
Escalation to Human Agents
If automated replies don’t provide the information shoppers want, chatbots can pass them on to human agents. Human agents can streamline the support process by ensuring that more complex inquiries are handled with the proper care and attention.
Offering human support when needed will ensure shoppers get accurate information regarding their requests. Implementing human support in your conversational commerce strategy will resolve queries faster and improve overall customer satisfaction.
Post-Purchase Support and Returns Management
Chatbots and virtual assistants offer a wide range of post-purchase support. They can provide order information like tracking number or delivery status as well as resolve issues that may arise.
Chatbots can help address modifications to an order, concerns with shipping, and help shoppers with returns. Offering instructions, providing return labels, and passing shoppers onto human customer support ease the post-purchase process.
Plus, chatbots can offer post-purchase recommendations too. If a shopper isn’t happy with a purchase, chatbots can provide suggestions for alternative products based on their return.
Multichannel Communication
Conversational commerce increases online retailers reach by enabling customers to engage with them at various touch points through their preferred communication channels.
Offering real-time interactions across many channels helps meet users where they are, facilitates convenient communication, and maximizes potential for customer engagement and sales.
Here are a few common communication channels.
Social Media Messaging Platforms
Over 4.7 billion people use social media, with the average person spending two and a half hours daily on their preferred platform. So, it only makes sense that online retailers incorporate social media into their conversational commerce strategy.
By offering real-time communication via social media, shoppers can engage with their favorite businesses, explore products, request support, and make a purchase- all within their platform of choice.
Social media integration increases online retailers’ reach and promotes seamless interactions with shoppers by making it easy to communicate through their platform of choice.
SMS Integration
Shoppers can communicate with their favorite businesses via SMS (Short Message Service) too. Instead of navigating to an online store’s site, shoppers can quickly message retailers using the native messaging app on their mobile devices.
With a simple text message, users can receive updates on their orders, view promotions or discounts for products they’re interested in, ask for customer support, and more. Implementing SMS into your conversational commerce strategy is an effective way to offer convenient assistance and enhance the overall perception of your brand.
In-App and On-Site Messaging
While it can be easier for shoppers to communicate with online retailers through their preferred apps, it is crucial to offer chatbots and virtual assistants to shoppers on websites and in apps too.
As users navigate your online store, chatbots can analyze shopping behavior and prompt conversations or answer questions at important touchpoints. Chatbots can answer questions about products, offer personalized recommendations, and answer support questions all from the store’s website or app.
In-Store Retail Applications
Most conversational commerce communication is designed to take place virtually, without the need for a physical storefront. However, chatbots and virtual assistants can be used to enhance the in-store shopping experience as well. Take a look.
5 Examples of Successful Conversational Commerce
Successful conversational commerce use cases keep shoppers engaged and informed at key touch points throughout the shopping journey.
Let’s look at how online retailers have successfully implemented conversational commerce in their stores.
1. Dollar Shave Club
Dollar Shave Club’s chatbot is simple. Shoppers receive 24/7 support by selecting a common support topic about order status, products, and sign-up questions, or they can ask a question.
By providing real-time support, Dollar Shave Club ensures customers receive fast answers to their questions. If the chatbot doesn’t have the answer shoppers need, they do a great job passing them on to human support. Take a look.
Keeping shoppers informed and ensuring they receive the exact support they’re looking for improves the shopping experience and creates a positive impression of your brand.
2. Lego
Lego’s chatbots do a great job of building customer trust from the get-go. They start by ensuring users their data is protected, then ask for a first name to personalize the experience. Plus, they ask for an email address in case follow-up is required later.
You’ll notice their chatbot has a name too. These small details go a long way in creating interactions that feel natural, authentic, and tailored to each shopper.
3. HM
One thing H&M’s chatbots do exceptionally well is ask for customer feedback after every interaction.
Asking users if their query was solved, how satisfied they were with the interaction, and how accurately the chatbot understood what they were looking for is the best way to discover what’s working well and where there is room for improvement.
Chatbots rely on generative AI and are designed to get better over time. Directly asking shoppers for feedback can help streamline this process and continuously optimize chatbots for the best possible user experience.
4. Clinique
Clinique is a prime example of how chatbots can work in conjunction with human agents. They offer support on a handful of predetermined topics or immediately connect you to a live agent.
If a shopper is looking for a product, Clinique’s agents ask questions to personalize their recommendation. Look how efficiently and effectively they help shoppers find what they’re looking for.
Plus, they make it easy to add recommended products to shopping carts and keep you engaged by offering relevant discounts and promotions. This shopper-first approach is an effective way to shorten the path to purchase, build customer loyalty, and drive sales.
5. 1-800-FLOWERS
The 1-800-Flowers chatbot does an excellent job guiding users through the shopping journey.
Shoppers can give the intended recipient’s name and address, explore flowers that fit a specific occasion, and quickly navigate the check-out process- all with help from the chatbot.
One of the best parts about this chatbot is that it offers images of relevant flowers and bouquets to help shoppers narrow down their search. This is a convenient way for shoppers to view the different arrangements offered so they can make confident purchase decisions.
The conversational commerce approaches and techniques mentioned above highlight some of what’s possible with chatbots and virtual assistants. However, keep in mind that customers’ needs vary from industry to industry. When implementing a chatbot in your store, you must understand your specific shoppers so you can program your chatbot accordingly.
Conclusion
Conversational Commerce is a game-changer. By leveraging chatbots and virtual assistants, retailers can meet customers where they are and engage with them in real-time. This enhances the user experience, optimizes sales and marketing efforts, and streamlines customer support.
Many online businesses are already using conversational commerce. However, advances in conversational AI are transforming the Ecommerce industry, reshaping what’s possible, and creating new opportunities for retailers.
If you aren’t already implementing conversational commerce into your Ecommerce strategy, the time to start is now.
FAQ
How can conversational commerce impact my online retail business?
Conversational commerce significantly impacts retail business in many ways. By providing personalized and interactive communication with customers in real-time, retailers can increase engagement and offer a more satisfying shopping experience.
One way to increase engagement is by offering personalized responses and product recommendations on customers’ preferred channels. By showing shoppers they understand them and can meet their needs, retailers can develop stronger relationships and drive more sales.
Additionally, chatbots provide automated customer support, which helps retailers cut costs on overhead (think physical storefronts, staff, etc.). Delivering a seamless multi-channel Ecommerce experience to shoppers can improve customer satisfaction and helps maximize overall business growth.
What are the risks and challenges of implementing conversational commerce?
There are many advantages to conversational commerce, but there are risks and challenges businesses must consider too. It’s important to understand that the lack of human support may hinder the customer journey for certain shoppers. This is largely because chatbots and artificial intelligence algorithms struggle to understand shoppers’ inquiries, which can result in irrelevant or non-useful communication. The best way to prevent this happening is to implement algorithms and machine learning for Ecommerce specifically.
Privacy and security concerns are prominent too. Customers may fear sharing sensitive data like their address or credit card information with a chatbot or virtual assistant. Lastly, the integration process can present problems for Ecommerce sites as well. That said, online businesses can help prevent or mitigate these problems by continuously testing, monitoring, and optimizing their solutions.
How can I ensure my customers have a positive experience with conversational commerce?
The best way to create a positive experience with conversational commerce is by providing clear communication, personalized services, and proactive assistance. Offering convenient, immediate, and relevant answers to shoppers’ inquiries will help build brand trust and a positive impression of your store. It is also important to ask for feedback to continually improve the user experience.
On the technical side, finding conversational chat platforms that integrates seamlessly across all channels will help you maintain a consistent shopping experience. Lastly, make sure to inform shoppers about the security and privacy measures your store takes to protect their data. Each of these steps will help enhance the customer journey with conversational commerce.
Paige is the Head of Marketing at Prefixbox, a leading eCommerce site search solution. She’s an American who’s been living in Budapest since 2017 and loves giving #alwayslearning sessions to help people optimize their online stores.
Conversational commerce is a new channel that enables retailers to interact with customers online, most commonly through messaging platforms, chatbots, and virtual assistants.
Instead of navigating to online retailers’ sites, shoppers can interact with businesses on their preferred social channels (think: Facebook Messenger) and get an immediate answer.
With conversational commerce, shoppers can ask questions, view product recommendations, request support, or purchase products with fast and personalized communication.
Users can open a mobile messaging app like WhatsApp or Facebook, ask an online retailer for wireless headphone recommendations, view relevant products, and immediately purchase what they’re looking for without navigating to the online store.
With advances in conversational AI, shoppers’ queries can be accurately understood, making communication more effective and authentic.
The Evolution of Conversational Commerce
The modern evolution of technology shows that consumers have consistently opted for convenience. When Apple introduced the iPhone in 2007, mobile devices quickly outperformed sales of desktop computers for one simple reason: users could access whatever they needed, when, how, and where they wanted it.
Shortly after the iPhone was introduced, messaging apps like WhatsApp and Facebook Messenger gained popularity, and conversational commerce emerged. Online businesses used Facebook Messenger and other apps to interact with customers via chatbots, which sparked a new era of sales and customer care.
WeChat paved the way too, offering a wide array of Ecommerce services and customer support within their application’s ecosystem. The popular Chinese messaging app made it easy for users to make purchases, book taxis, and pay bills all within the app. WeChat’s success inspired other messaging platforms to develop and offer a multitude of features merging Ecommerce and customer communication.
It didn’t take long for businesses to see the lucrative potential of conversational commerce, with numerous companies exploring new ways to increase sales and the customer support experience. Many initial chatbots and virtual assistants were not intuitive enough to provide the support customers were looking for. However, as technology improved, things changed.
With the introduction of generative AI and advances in Natural Language Processing, chatbots can now understand and respond to complex requests authentically.
Amazon Alexa and Google Assistant are two key examples of success with conversational commerce – both devices help users engage with brands using voice commands and quickly make desired purchases.
Conversational commerce is improving and demand is growing, proving it’s here to stay. With the market set to grow by $290 billion by 2025, Ecommerce chat apps and conversational services will likely become an integral part of our lives before we know it.
Why is Conversational Commerce Important?
Conversational Commerce is a powerful tool that enables online retailers to interact with their customers exactly where they are.
By being present in the same channels their customers regularly use, brands can make themselves more accessible and develop a new sales channel: an online chat shop.
With 73% of shoppers saying the customer experience is a deciding factor in the purchase process, conversational commerce is more than important – it’s crucial.
Convenience
Customers can communicate with businesses via messaging platforms, chatbots, and virtual assistants 24/7. They can ask questions, purchase items, see personalized recommendations, or receive customer support when they want it – all within their preferred channel.
Personalization
Thanks to AI and machine learning for Ecommerce, businesses can provide personalized recommendations based on customers’ preferences, user history, and individual needs. Individualized messages, promotions, and support help create an engaging online experience that increases customer retention.
Efficiency
With chatbots, virtual assistants, and messaging apps, businesses can offer shoppers immediate assistance. Chatbots are adept at executing routine requests like processing orders or answering FAQs, which saves customers time and reduces costs by up to 30%. Nearly 80% of inquiries can be answered by a chatbot.
By reducing the need for human interaction, conversational commerce benefits shoppers and retailers. It allows retailers to meet customer expectations and build stronger relationships efficiently and effectively.
Popular Channels for Conversational Commerce
Businesses can engage in conversations with customers using several different messaging channels, chat platforms, and conversational technologies. Here are the most common types.
Social Messaging Apps
The key to a successful conversational commerce experience is convenience. Social messaging apps are one of the most convenient ways for shoppers to interact with your store because they are already an essential part of our daily lives.
Customers can use apps like WhatsApp or Facebook Messenger to get automatic answers to their questions. They can also receive customer support, explore relevant product recommendations, and navigate the shopping process from search to completed transaction.
If an avid runner needs new shoes, they can open their preferred social messaging app on their phone, message a store, explore personalized recommendations, and purchase a new pair of shoes in minutes.
Alternatively, if an item isn’t delivered on time, customers can quickly learn why on their messaging app of choice.
Messaging apps generally consist of a combination of chatbots and live human agents. Chatbots handle most inquiries and can pass a customer on to receive more personalized human assistance when required. This approach to social shopping cuts customer service costs and ensures shoppers receive the stellar customer support they need.
Chatbots
Chatbots leverage natural language processing to understand user intent and stimulate authentic human conversation in an instant. Advances in generative AI have made it possible for chatbots to understand complex requests, provide answers to user queries, offer 24/7 customer care, and guide users through the shopping journey.
There are two main types of chatbots. Rule-based chatbots follow a set of predetermined rules to respond to customer questions. AI-powered chatbots are more complex, leveraging machine learning algorithms to understand user intent and improve replies over time.
In addition to handling the majority of user requests, chatbots can generate significant Ecommerce revenue for businesses. By seamlessly highlighting relevant promotions, offering personalized product recommendations, and collecting user feedback, chatbots successfully drive sales.
Voice Assistants
Voice chats are a powerful way to improve customer interactions. Apple’s Siri, Amazon Alexa, and Google Assistant are the most popular voice search assistants on the market.
Like other conversational commerce tools, voice assistants help with product discovery, provide information, assist with purchases, and offer customer support.
By integrating voice assistants with additional technology like NLP or sentiment analysis, E-commerce brands can create advanced customer conversations.
Modern voice technology can determine a user’s mood by analyzing their tone of voice and returning responses accordingly.
Digital voice assistants help E-commerce websites further create convenient, personalized shopping experiences by making it easy for users to navigate the purchase journey without needing to type or research what they’re looking for.
Conversational commerce is all about keeping customers’ best interests in mind. As technology continues to evolve, new platforms and channels are sure to surface.
Conversational Commerce Use Cases
The conversational commerce landscape is rapidly growing, evolving, and opening up an entirely new avenue for sales.
Chatbots function as “chat shops,” where shoppers can conveniently navigate the shopping journey through conversation.
Let’s explore two most common, beneficial, and lucrative use cases for conversational commerce: product discovery and customer support.
Improving Product Discovery
Most conversational commerce platforms leverage NLP (natural language processing) to understand user intent. Therefore, shoppers can ask for the products they want however feels natural to them and instantly see relevant results.
By collecting and analyzing user behavior data and interactions, Ecommerce retailers can better understand what shoppers are looking for and effectively tailor a wide range of product recommendations to meet their needs.
Let’s explore how data-driven product recommendations can improve the shopping experience and drive sales.
Showcase Relevant Products
With chatbots or other conversational support offerings, shoppers can state their preferences and quickly explore relevant products for any query.
If a user wants to buy a pair of running shoes but is undecided on brand or style, they can simply tell the chatbot what they’re looking for, their size and color preferences, and explore the most relevant items.
Even more impressive is that chatbots understand colloquial language and detailed queries that could never fit in a search box.
If a shopper types, “Show me a laptop good for gaming that” then specifies “that are easy to carry in my backpack,” chatbots can immediately present the most relevant options.
In addition to helping shoppers feel understood by intuitively responding with relevant products, chatbots can shorten the path to purchase by presenting products shoppers are most likely looking for.
Upsell and Cross-Sell with Product Recommendations
Chatbots are a valuable tool for upselling and cross-selling.
If a shopper is looking for a laptop, chatbots can be programmed with Ecommerce merchandizing capabilities to recommend specific brands or popular models.
Additionally, if a shopper purchases a laptop, chatbots can recommend complementary or additional products like a computer mouse or earphones to round out their purchase.
Provide Product Information
Shoppers often need product information before making a purchase. Users may be interested in sizing information, pricing, discounts, product reviews, or return status. Chatbots streamline the search experience by providing responses instantly.
Keep in mind, just like with Ecommerce search result page design, the product information must be presented in clear and easy-to-understand ways that guide customers through the purchase journey.
In the digital age, shoppers expect to have their needs met immediately. If they don’t find the products or information they’re looking for, they’ll leave your store and head to a competing site.
Chatbots and virtual assistants can improve customer relationships with people by understanding their concerns and providing real-time customer feedback. Let’s look at a few ways chatbots can assist with customer support.
Chatbots understand customers’ questions and immediately respond with answers from your knowledge base. In addition to alleviating frustration and creating a quick user experience in your store, automating support with chatbots saves Ecommerce businesses money and resources because it prevents the need for human interaction.
Connect to Live Support Agents
While chatbots can effectively handle a large number of inquiries, AI-driven conversations are not capable of doing everything.
When online or mobile users need additional support, they can be seamlessly connected to live chat agents on the customer service team.
Having company representatives provide personalized support for necessary requests will help shoppers feel cared for and resolve their issues more efficiently.
Track Deliveries
Chat commerce enables shoppers to communicate in real-time, making it easy for them to inquire about the delivery status of their purchase and receive an immediate answer.
By providing instant notifications, updates, and instant answers to customer concerns, conversational commerce is a powerful way to meet customer demands, reduce frustration, and improve the tracking experience.
How Ecommerce Retailers are Using Conversational Commerce
More and more retailers are adopting conversational commerce in the E-Commerce space. Companies like H&M, Burberry, and American Eagle Outfitters offer chatbots to help their user base find products, offer styling advice, and provide special care on different platforms.
While conversational commerce has been prevalent in the online retail industry, companies have only cracked the surface of what’s now possible with generative AI. Advances in technology have made it possible for online retailers to better understand customer support requests, provide more personalized interactions, improve customer engagement, and increase online sales.
Let’s take a closer look at some of these benefits.
Advantages of Conversational Commerce in Ecommerce
Conversational commerce benefits shoppers and online retailers by offering on-demand support without traditional overhead costs.
Businesses that utilize online messaging platforms, chatbots, and virtual assistants to interact with users improve the customer experience, drive sales, and create significant business impact.
Chatbots can accurately respond to routine customer queries, suggest relevant products that shorten the path to purchase, and offer human-like correspondence that keeps users engaged, satisfied, and coming back for more.
Furthermore, chatbots can offer exclusive discounts and promotions. Targeting deals and offers to individual customers creates a sense of urgency, engages shoppers, and encourages them to purchase.
These interactive experiences help users seamlessly navigate the shopping journey from start to finish and inspire them to return for repeat purchases.
Higher Conversion Rates
By leveraging AI and machine learning algorithms, businesses can understand user intent and accurately provide shoppers with the products, information, and support they need to make confident purchases.
Offering data-based recommendations via conversational commerce effectively inspires catalog discovery, helps you hit key Ecommerce KPIs, and builds loyal customers.
If a shopper wants to buy a laptop on a budget, a virtual assistant can provide recommendations and answer questions about product specifications like size or hard drive space.
By ensuring easy conversation with customers and offering the information they need to make a confident purchase, Ecommerce businesses can achieve a rise in conversion rate, customer satisfaction, and online revenue.
Cost-Savings
Conversational commerce is conducted online, which reduces the need for overhead costs like physical storefronts and employees.
Conversational commerce leverages automation, enabling businesses to answer frequently asked questions and perform routine tasks without physical labor. Automating support and the sales process saves time and resources when handling orders or inquiries.
Additionally, chatbots and virtual assistants can handle high volumes of customer requests at once, making it easy for businesses to scale up or down depending on demand.
Personalized Experiences for Customers
Personalizing the shopping experience shows shoppers you understand them and can meet their needs.
Chatbots use data mining and machine learning to analyze patterns in user behavior and make tailored recommendations that align with their preferences.
Leveraging AI to predict what customers need is an effective way to help shoppers find what they’re looking for and help guide them through the purchase journey.
Chatbots can also send personalized messages and updates to customers. Welcome messages, order confirmations, and shipping updates via chatbots help shoppers stay up to date with previous purchases and engaged as they navigate your site.
As technology continues to evolve, chatbots are getting better at interacting with customers and understanding what they’re looking for. Nowadays, chatbots for Ecommerce fashion stores can ask shoppers about their style preferences, analyze thousands of products in a catalogue, and suggest clothing that aligns with their tastes.
And it’s not just the E-commerce market. These advanced communication methods are transforming the hospitality industry, the banking industry, and so many more.
Valuable User Behavior Data
Conversational commerce provides online retailers valuable insights into user behavior and preferences, which can improve personalization efforts, products, and other services.
Machine learning algorithmscollect and analyze user behavior data, which improves product recommendations and tailored customer service efforts.
Messaging platforms store chat logsof interactions between chatbots and customers, which offer insightful information about customer preferences, behavior, and common pain points.
Chatbots can be programmed to request feedback from users after an interaction. This information can help gauge customer satisfaction and address room for improvement for the chatbot.
Conversational commerce platforms track user demographics and shoppers’ purchase history, which is useful in offering data-driven recommendations and discounts.
By collecting data at all phases of the online shopping journey, Ecommerce businesses can identify room for improvement and make optimizations for increased customer satisfaction and sales.
Reduced Abandoned Carts
As the Ecommerce industry continues growing, so does the shopping cart abandonment rate. About 70% of online shoppers add products to their cart via autocomplete search or along the customer journey and leave the store without purchasing them. However, conversational commerce can help mitigate the problem.
If a shopper is on the verge of abandoning their cart, chatbots and virtual assistants can intervene.
Proactive customer engagement like reaching out to customers with carefully crafted messages can increase the chances of a customer completing their purchase.
Furthermore, AI-based conversational commerce chatbots enable shoppers to purchase their items directly in the conversation window. Streamlining the shopping journey to prevent shoppers visiting another page can help reduce the abandoned cart rate and keep businesses ahead of the curve.
5 Conversational Commerce Best Practices
While implementing conversational platforms into your Ecommerce strategy is crucial, it’s important to keep the following best practices in mind to maximize its potential.
1. Prioritize the Customer Experience
The goal of conversational commerce is to improve the customer experience. In addition to implementing site search best practices in your store, one of the most effective ways to prioritize customers is by creating convenient shopping experiences.
Make sure you put what customers need at their fingertips.
The best way to prioritize customers is by leveraging data like demographics, browsing behavior, and purchase history to inform targeted response algorithms and return relevant results.
Data-driven responses that improve over time to accurately address shoppers’ questions and help them explore relevant products will ensure your conversational commerce solution is convenient, engaging, and creating customer satisfaction.
2. Use a Conversational Approach
Simply integrating a chatbot into your online store and app messaging will not suffice. You must establish rules and guidelines for chatbot responses to create a consistent, reliable, and quality experience. The same applies to voice assistant interactions; communication must feel natural.
The best way to create authentic communication is to keep the customer in mind. Take advantage of key user touch points and offer personalized interactions throughout the most crucial stages of the purchasing journey. Carefully planning your conversational commerce strategy will help ensure you create an engaging experience with a personal touch.
3. Monitor, Test, and Refine Your Conversational Experience
AI-driven conversations are not a set-it-and-forget solution. You must consistently ensure your chatbots are responsive and effective. This will help you provide shoppers with the best possible experience.
To monitor performance, identify your specific goals and track the right metrics. A few insightful metrics are conversion rate, customer satisfaction ratings, and response time. These key metrics will help you identify room for improvement and make adjustments where necessary.
It’s also a good idea to test messaging strategies and tools to get an accurate idea of what keeps shoppers engaged and drives sales. Consistently monitor results and optimize your solution to get the most out of your conversational commerce efforts.
4. Provide Valuable and Relevant Information
Customers trust that information provided through conversational commerce channels is relevant and accurate. Therefore, all interactions, answers, and assistance must align with what shoppers want. If users ask for headphone recommendations and see suggestions for computer mice, they will likely grow frustrated, lose trust in your brand, and not return to your store.
Leverage user behavior data to provide relevant and accurate responses. When shoppers receive factual information that guides them seamlessly through the purchase process, this creates a positive impression of your store and inspires repeat business.
To ensure chatbots and virtual assistants return relevant results, track analytics and monitor performance data. This will show you what’s keeping shoppers engaged and where there’s room for improvement.
Furthermore, the conversational nature of chatbots makes it easy to ask for user feedback. Program your chatbot to ask users if their query was addressed properly with a simple thumbs-up or thumbs-down button. Directly asking for feedback will help you see if shoppers find their interactions valuable and relevant and help you discover room for improvement if necessary.
5. Maintain a Human Touch
While chatbot responses are automated, it is crucial that interactions feel authentic and personal. Even if shoppers ask general questions or perform routine actions, communication shouldn’t feel robotic or impersonal.
Create natural-feeling interactions by using a conversational tone. Refer to customers by name and incorporate personality and your brand identity into your messaging so your chatbot feels like a natural extension of your store.
Make sure your chatbots respond promptly, too. Answering inquiries in a timely matter will show you value customers’ time and help build trust.
Lastly, keep in mind that chatbots need an abundance of data to be most effective. When first implementing conversational chatbots into your conversational commerce strategy, it’s best to run a co-pilot scenario with a human that can review replies. Having humans monitor chatbot interactions during the learning phase will ensure replies accurately address users’ inquiries while feeling authentic and natural.
Conclusion
Conversational commerce is a powerful tool that helps businesses improve the customer experience and drive sales. By providing personalized, accurate, and timely responses to customer requests, chatbots and other types of conversational commerce can keep shoppers engaged, inspire them to purchase more items, and create a positive impression of your brand.
The most prevalent forms of conversational commerce include chatbots, virtual assistants, voice assistants, and social media platforms. However, rapid advances in artificial intelligence will continue to make conversational commerce even more engaging and effective.
When implementing conversational commerce in your store, make sure to adhere to the best practices, consistently measure performance, and stay on top of growing trends to exceed your shoppers’ expectations and keep your Ecommerce store ahead of its competition.
Paige is the Head of Marketing at Prefixbox, a leading eCommerce site search solution. She’s an American who’s been living in Budapest since 2017 and loves giving #alwayslearning sessions to help people optimize their online stores.
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