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.

About Shopify and Shopify Search

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. 

Given that 60% of E-commerce internal search engines offer low-quality search results, and using an advanced search solution can lead to a ~20% boost in sales, it’s no surprise that store owners (including those using Shopify) constantly seek ways to take their search functionalities up a notch.

Many of them give Shopify’s own Search & Discovery app 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 Discovery app

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.”

Check one of our previous posts on the importance of personalized (AI-driven) product recommendations, and best practices to maximize their impact.

4. Weak Multi-language Support

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 other apps 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.

1. Fast Simon

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. 

2. Searchanise

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. 

3. Prefixbox AI Search

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. 

4. Boost AI

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. 

5. Findify

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.

What is Vector Search’s Impact on E-commerce? 

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.  

With the ability to personalize searches, uncover hidden patterns, manage common typing errors, and optimize search speed, vector search technology makes online shopping more satisfying and successful than ever before.  

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.

The Ultimate Guide for AI in E-commerce: 9 Trends for 2024 (+2 future predictions)

Leveraging the latest technological developments such as Artificial Intelligence is undoubtedly a way for online retailers to get ahead.

With increased efficiency, accuracy, and personalization, AI is transforming the way E-commerce businesses operate.

In this article, we guide you through the use cases everyone is talking about for AI in E-commerce in 2024.

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
• Amazon’s AI-powered recommendation engine drives 35% of their annual sales
  

As NexGenAI Solutions phrased in a recent article:

“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.

Read more at: 

• Conversational Commerce Use Cases
• Conversational Commerce Platforms

2. AI-Powered Customer Support

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.

Read more at:

• Vector Search – the Future of E-commerce
• Unveiling How Vector Search Works

7. Live Commerce

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 omnichannel strategy, 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 Platforms and Solutions

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.

For a comprehensive understanding of conversational commerce, check out our previous articles covering its fundamentals, use cases, and implementation strategies.

Leveraging any of the wider known messenger apps (e.g. Facebook Messenger, WhatsApp Business, Viber, etc.) starts by choosing a conversational commerce platform 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.

• the use of NLP (natural language processing) technology

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: 

1. 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’s has 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. 

You can see a demo of Fin in action here.

2. Sales 

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

Infobip is 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.

Infobip’s multifunctional services include all of the above-mentioned features and solutions. 

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.

Unveiling How Vector Search Works

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.

We’ve recently covered the basics and the main benefits of vector search, an emerging technology that’s redefining how we search and shop online.

Check our mini video series on the topic too! Now let’s see the technical aspects of vector search.

Mathematical representation of information

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 vector embeddings 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.

Vectorization methods

To create vector representations, you can use various techniques, such as Word2Vec, Doc2Vec or deep learning models like convolutional neural networks (CNNs) or recurrent neural networks (RNNs). The choice of machine learning method depends on the data and the problem you are trying to solve.

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.

Indexing

Once you have vector representations for all your items, you need to index them for efficient retrieval.

This is often done using data structures like search trees or more advanced techniques like locality-sensitive hashing (LSH) or approximate nearest neighbor (ANN) indexes. These data structures enable fast nearest-neighbor searches.

Search Query Vector

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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Vector Search: The Future of E-commerce Search

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

Importance of E-commerce Search

In the ever-evolving landscape of E-commerce, where countless products are just a click away, the power of search cannot be overstated.

The search bar is the key to directing shoppers to the products they want to buy.

People who search spend 2.6 times more money compared to non-searchers, and on for B2B sites, 92% of purchases start with search!   

Issues with traditional keyword search

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: product descriptions 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:

1. home decor items for a minimalist living room
2. engagement rings with sapphire stones
3. skincare products for sensitive skin
4. hiking boots for all-terrain trekking
5. 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:

1. furniture, lighting and accessories that align with the minimalist aesthetic
2. a list of rings specifically designed for engagement, featuring sapphire gemstones
3. all kinds of skincare products known to be gentle and beneficial for sensitive skin (moisturizers, cleansers, serums)
4. hiking boots known for their durability and performance on various terrains
5. 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.

If you want to dive deeper into measuring your E-commerce store, check our comprehensive guide on the 25 most essential E-commerce KPIs.

Enhanced User Experience

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

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.) 

Learn more about AI-drive product recommendation from our all-inclusive guide.

Adaptation to Evolving Language

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. 

How to Implement Conversational Commerce? 5 Steps to Success [2024]

Are you ready to take your E-commerce business to the next level by opening a new revenue stream and improving your customer experience?

In this article, we discuss:

• the 4 most essential ways how conversational commerce can contribute to your business goals
• 5 steps to successfully implement conversational commerce

Conversational Commerce Basics

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?

Conversational commerce has become increasingly important for E-commerce retailers, as it offers numerous benefits for both the brand and the customers.

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.

1. Get to planning and goal-setting
2. Choose the right messaging platform
3. Create a structure for conversations
4. Implement and train your own AI-powered chatbot
5. 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

Product recommendations

• based on the customer’s needs stated previously, provide tailored product recommendations, including their most important features, pricing and availability

Questions and clarifications

• 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.

The All-Inclusive Guide to AI-Driven Ecommerce Product Recommendations

AI-driven Ecommerce product recommendations are everywhere.

They can increase sales and improve the customer experience, which makes them essential to a thriving Ecommerce business.

In this article, we’ll uncover:

• How AI-driven Product Recommendation Engines work
• Where to place recommendations in your store
• Best practices to maximize impact

Let’s jump in.

What are AI-Driven Ecommerce Product Recommendation Engines?

Ecommerce Product Recommendation Engines display relevant product recommendations to shoppers throughout their journey. To do this, AI algorithms identify patterns in customer behavior to place shoppers into groups and automatically recommend products that match their preferences or are similar to what they’re viewing.

Product Recommendation engines that leverage AI to return data-driven results are the best way to help shoppers discover relevant products. This increases engagement, strengthens customer loyalty, and boosts revenue.

Thanks to recent advances in AI and GPT technologies, recommendation engines are becoming faster, more accurate, and more effective. They are a crucial tool for anyone running an online business.

How do AI-Driven Ecommerce Product Recommendations Work?

Ecommerce Product Recommendation Engines use machine learning and advanced AI-based algorithms to collect data, analyze shoppers’ behavior, and return relevant product suggestions.

They recognize shoppers’ habits, preferences, and browsing history and effectively return personalized recommendations for products they want to buy.

We’ve outlined how AI-based recommendation engine technology works to help you use it to your advantage.

Collaborative Filtering

The most common way to personalize recommendations is with collaborative filtering.

Collaborative filtering is a recommendation system that analyzes customer tastes, preferences, and browsing history to predict what they might want to buy next. The logic is simple. If Shopper A and Shopper B like the same product, then Shopper A might be interested in a product Shopper B has previously purchased.

This system identifies meaningful associations between items and users to provide accurate product recommendations for products they may not have sought out.

You need an abundance of user data for this method to be effective. Without sufficient data and rich customer profiles, recommendations may miss the mark and create a poor perception of your Ecommerce business.

There are two types of collaborative filters you should implement in your store. Let’s take a look at them.

User-Based Collaborative Filtering

User-based collaborative filtering analyzes user behavior to identify similar groups of shoppers and recommend products to those with corresponding purchase habits.

As individual customers navigate your store and make purchases, an algorithm collects that data to calculate a similarity score. Customers with matching similarity scores are then grouped together and provided with similar recommendations.

This approach assumes past users with similar preferences can help determine what similar shoppers will want in the future.

By creating rich customer profiles and accounting for changing user behavior, user-based collaborative filtering effectively tailors recommendations that align with shoppers’ tastes and preferences.

Item-Based Collaborative Filtering

Item-based collaborative filtering analyzes the relationship between types of products rather than users. This method identifies products frequently purchased together and groups them accordingly to make recommendations.

For example, in a Consumer Electronics store, this filtering system will recognize that users often buy cell phones and cases together. The next time a shopper searches for a cell phone, they will see a recommendation for the highest-rated phone cases.

Item-based collaborative filtering is most effective in stores with large product catalogs. The more items there are, the easier it is to determine relationships between them to ensure recommendations are relevant and effective.

Content-Based Filtering

Content-based filtering makes recommendations by taking a product’s attributes, related keywords, and categories into consideration. This method uses keyword-based recommendation algorithms to determine which products best suit users based on their previous activity.

Tools like Prompt Vibes can assist in refining keyword-based recommendation algorithms by generating and optimizing relevant prompts tailored to enhance the accuracy of content-based filtering systems. This ensures that product recommendations resonate better with the shoppers’ preferences.

For example, in a Health and Beauty store, content-based filters will look at a shopper’s purchase history and identify products with similar ingredients, formulas, and certifications.

Content-based filtering is most effective in stores with rich product attributes listed in their catalogs. This filtering method is an effective way to recommend items that align with shoppers’ preferences while providing insights that help better target ads and promotions in your store.

Hybrid Recommendation Systems

Hybrid Recommendation Systems leverage two or more filtering methods to suggest products that encourage catalog discovery and inspire sales.

For example, online stores can implement both content-based and user-based filtering systems (or any other combination) to provide recommendations to shoppers.

Implementing more than one type of recommendation system in your store is the most effective way to improve recommendation accuracy and relevancy.

There are different ways to approach hybrid recommendation filtering. You can use one of these AI-based recommendation techniques to modify or enhance another or combine the outputs of both filtering systems into one long recommendation list.

Next, let’s take a look at how product recommendations can benefit your store.

Benefits of Ecommerce Product Recommendations

AI-driven product recommendations engines are a powerful tool that offer Ecommerce retailers countless benefits. Here are a few of them.

Better Understand Your Customers

Product Recommendation engines leverage AI and data mining to identify meaningful connections between shoppers and products.

Advanced algorithms collect purchase history, browsing habits, ratings, reviews, search activity, and more to create comprehensive profiles of shoppers and return accurate results.

This invaluable data helps you understand your customers’ preferences and interests, which is key to effectively tailoring store offerings to meet shoppers’ needs.

Improved User Experience

If shoppers can’t quickly find the products they’re looking for, chances are they’ll leave your site and go to a competitor’s.

Providing personalized recommendations that align with shopper intent improves the user experience and keeps visitors engaged. By using AI-based personalization techniques and strategically placing relevant product suggestions throughout your store, retailers can save shoppers time and energy, encourage product discovery, and prevent frustration.

Creating a positive personalized shopping experience that makes customers feel understood will help build trust and inspire them to return.

Higher Customer Engagement

Users have very short attention spans. Placing relevant recommendations at every phase of the shopping journey will catch their attention, keep them engaged, and encourage them to spend more time on your site.

When customers are offered personalized recommendations, they are more likely to return for follow-up visits and less likely to compare prices elsewhere – leading us to our next key benefit.

Increase Average Order Value and Online Revenue

Personalized recommendations have proven to play a significant role in boosting revenue. 

Data-driven recommendations that reflect changing user behavior can alert shoppers to relevant products, deals, and promotions and inspire them to explore your catalog.

When visitors see items tailored to their tastes and preferences, they are more likely to make a purchase.

As you can see, AI-driven product recommendations benefit shoppers and businesses alike.

Next, let’s look at where you should place them for maximum impact.

Strategically Placing Ecommerce Product Recommendations

Recommendations should appear seamlessly in your store to enhance the shopping experience, not overtake it. As shoppers search, browse, and add items to their baskets, recommendation types should meet shoppers where they are on their journey and help guide them through it.

Let’s look at how and where Ecommerce websites should place AI-driven product recommendations to increase engagement and shorten the path to purchase.

Search Engine Results Pages

Over 30% of visitors go directly to the search box upon entering an online store. Therefore, encouraging product discovery via Autocomplete search and on Search Engine Results Pages is lucrative and essential.

When a shopper navigates to a search results page, you can assist with their discovery by placing popular products, complementary products, or related product recommendations below the SERP results.

Keep in mind that good Ecommerce search result page design is crucial. Placing recommendations in a visible location on the search page is the most effective way to increase conversion rate.These items can help shoppers refine search intent, explore similar products, and stay in the purchase flow.

Homepages

Data-driven recommendations can help kick-start the shopping journey from the get-go.

For first-time shoppers, we suggest showcasing trending, seasonal, discounted, or new products from the biggest brands on your home page. These recommendations can entice first-time shoppers to explore the products you want to promote.

For repeat customers, personalized recommendations that align with their past preferences, browsing history, and purchase history can help them pick up where they left off. They will also show them you understand what they’re looking for.

For example, products a shopper recently viewed or discounted items that match previous user behavior can work wonders. Take a look at how Selsey recommends personalized products on their homepage.

The homepage offers prime recommendation real estate to highlight products, improve the user experience, and boost sales. Be sure to implement some of the practices listed above to maximize your store’s potential.

Category Pages

Category pages showcase the most relevant items within a given product category. If a shopper searches for a microwave in Autocomplete, your recommendation engine should make it easy to navigate to a category page displaying the most popular options in stock.

Placing relevant products, such as a microwave with a grill or new models offered by a well-known brand on a specific category page can help shoppers quickly find items they want and may not have otherwise seen.

Product Detail Pages

Use the space at the bottom of the product page to your advantage.

If a shopper has decided the item they’re reviewing is not for them, you can keep them engaged by showing similar products in your catalog. Or, if they’ve added the item to their cart, you can encourage additional purchases to help them round out their order.

If a visitor is looking at a laptop, recommend other popular models at the bottom of the page. Once they add the laptop to their cart, suggest complementary items like a mouse or keyboard.

Product detail pages are a prime location to keep shoppers engaged, encourage further product discovery, and boost average order value.

Checkout Pages

If a shopper has decided to buy an item in your store, that’s great! But their purchase journey doesn’t have to end there. You can increase average order value by upselling or cross-selling relevant items on the checkout page.

Offering advanced models with a bigger profit margin or data-driven recommendations for frequently purchased together products can help with your bottom line.

Suggesting products you know a shopper will be interested in can inspire last-minute purchases at the most critical decision-making point.

Zero Result Search Pages

Shoppers expect to find desired products immediately. Therefore, unsuccessful product searches are one of the most frustrating experiences users can have in your store. If they land on a zero result research page, you risk losing them to a competitor.

That said, placing recommendations on the zero result pages can help ease the pain. We recommend checking out these no result page examples for Ecommerce and implementing the best practices.

Suggesting products that align with a customer’s intent can show you understand them and make it easy for them to stay in the purchase flow.

404 Pages

404 pages, also known as “error pages” or “page not found” pages, are a bummer that can reflect poorly on your business. Conversely, you can use them to your advantage.

Placing recommendations for popular, discounted, or personalized products on 404 pages can intrigue shoppers and create additional viewing opportunities for a wide range of products.

This will create a seamless shopping experience instead of driving shoppers away.

How Can You Assess If Your Product Recommendation Strategy is Successful?

The best way to measure success is by setting recommendation-specific Ecommerce KPIs. Here are a few examples.

• Number of product clicks
• Add to basket actions
• Product checkouts
• Unique purchases
• Product revenue

These metrics are more insightful than measuring conversion rate or average order value and can help you identify if your recommendations are effective. Customer feedback, return visits, and positive reviews can also indicate how shoppers feel about them too.

Keep in mind success is defined differently from company to company. So once you identify what you want to achieve with product recommendations, track the corresponding KPIs and measure them consistently to see if you’re achieving your goals.

6 Strategies to Maximize the Impact of Product Recommendations in Ecommerce

In the highly competitive Ecommerce industry, AI-driven product recommendations have become standard. Providing personalized suggestions enhances the experience in your store, increases the likelihood they’ll become paying customers, and drives customer retention.

However, not all product recommendations are equal.

The following tips and strategies will help you properly leverage recommendations to create a seamless shopping experience, inspire purchases, and boost revenue.

1. Leverage all paths to conversion, not just cross-selling and upselling

Using product recommendations for cross-selling related items and upselling products is an effective way to increase conversion rate, but there are other strategies too.

Offer personalized Ecommerce recommendations. Product suggestions that reflect customers’ browsing history, demographics, location, and previous purchases are more likely to drive clicks.

Don’t undermine the power of social proof. Shoppers are more likely to buy products when they see others have already purchased and liked them. Include social proof in your product recommendations by highlighting good reviews and showcasing trending products.

Recommend related products. If a shopper searches for a sofa bed, recommend the most popular items in stock that align with their initial purchase intent. For example, a u-shaped sofa bed or a corner sofa bed.

Show complementary products. If shoppers search for a video game console, recommend controllers or trending games. Encouraging shoppers to round out their purchases with related items is an effective way to drive conversions and increase average basket size.

Use scarcity. Highlighting limited-time promotions or items running out of stock with recommendations can create a sense of urgency and encourage shoppers to make faster purchase decisions.

Take advantage of E-commerce personalization and all other recommendation opportunities and use cases in your store. This will help you offer a positive customer experience that keeps shoppers coming back for more while improving your bottom line.

2. Ensure personalized product recommendations are data-driven

Recommending any old products to shoppers is not enough. Recommendations should help shoppers continue their journey effectively. One effective way to do this is by creating personalized experiences.

To create a personalized online experience, Ecommerce businesses must invest in the proper technology. AI and machine learning will analyze user behavior data to understand shoppers’ preferences and automatically return accurate product recommendations. 

Recommendations that reflect customer behavior, taste, and purchase patterns are more likely to resonate with shoppers. If you want to increase conversion rate, customer satisfaction, and sales, you need to leverage the right technology to maximize the potential of your personalization efforts.

3. Strategically place product recommendations throughout your store

Item recommendations must be visible to be effective, which means placement is paramount. Offering relevant product suggestions throughout every step of the shopping journey is important. However, you must ensure the right recommendations are in the right places.

Above, we discussed the different areas you can place recommendations in your store. We also highlighted which recommendation types most effectively assist shoppers at different stages in their purchase journey.

For example, related products help shoppers explore other options in your store. It’s best to offer them on the SERP, during the consideration stage where visitors browse products, rather than on a checkout page when they’ve already made a decision.

For checkout pages, complementary product recommendations are the most effective. These will allow shoppers to round out their purchases with items that align with what they’re about to buy.

Recommendations should streamline the shopping process. To create an efficient and enjoyable customer experience, you must place them strategically.

4. Create Category Pages for specific audiences

Category pages for specific audiences are unique landing pages designed for groups of users with similar tastes, interests, and shopping behavior. They help all kinds of customers navigate to the areas of your catalog most relevant to them.

You’re probably familiar with the category pages found in online fashion stores. These stores generally offer pages for men, women, and on-sale items to help shoppers quickly narrow down search intent based on their interests.

You can add more specific category pages too. For example, if someone clicks on the women’s category page in a fashion store, they can further narrow their search intent by clicking on a page for dresses. This page will show the most relevant products in the dress category and should give shoppers the ability to filter results per their desired attributes too.

In addition to being useful, category pages enable you to tailor recommendations, discounts, and promotions to the people most likely to be interested in them.

5. Use merchandizing to your advantage

Merchandizing tools allow you to promote the products you want to sell. For example, you can recommend products on sale, in high demand, or with a high-profit margin to help you improve your bottom line.

For these reasons, we recommend implementing a product recommendation solution with merchandizing capabilities that make it easy to create custom banners and campaigns. Leveraging Ecommerce merchandizing best practices in your recommendation strategy will increase product visibility and inspire shoppers to explore parts of your catalog they might not have been interested in.

Merchandizing and searchandising (search-specific merchandizing) are powerful tools for using product recommendations to your advantage. Just make sure to keep an eye on your inventory and monitor your profit margins to ensure your promotions are financially worth your time and energy.

6. Optimize your recommendations by running A/B tests

Optimization is essential to all recommendation practices. Continuously experimenting with placement, recommendation type, and visual appearance will help you identify what recommendations are engaging your audience and where there’s room for improvement.

Incorporating A/B testing into your recommendation strategy can be time-consuming, especially if you test new ideas frequently. But this optimal approach is worth it. Dedicating the proper time and resources will help ensure your recommendations drive desired business results.

What factors should you consider when choosing a product recommendation solution?

When considering Ecommerce product recommendation vendors, it is important to consider the following criteria.

Choose a data-driven solution

As we mentioned, product recommendations must effectively guide shoppers through your store. Your solution should leverage AI, machine learning, and robust automations capable of analyzing all facets of user behavior. This will best return accurate results that meet visitors’ needs and streamline their shopping journey.

While your Ecommerce product recommendation system project may focus solely on product discovery, the best-known approach is to find an all-in-one enterprise Ecommerce search provider. Finding an AI-driven product recommendation engine is a convenient way to optimize your search and discovery with data-driven results.

Make sure it’s easy to integrate

Before investing in a product recommendation engine for Ecommerce, make sure the solution you’re interested in will integrate with your platform easily. This will save you time and money, create the best possible customer experience, and allow you to scale down the road if necessary.

Ensure recommendation settings are customizable

Every business has different needs. Therefore, your solution should be user-friendly and offer easy-to-fine-tune recommendation settings. A customizable solution will help you tailor suggestions to your shoppers and give you more control.

This is crucial to targeting specific users and customer groups with personalized products or items you want to promote. It will also allow you to adapt to changes in your industry and ensure you’re always working toward your business goals.

Pick a solution that provides detailed analytics

Detailed analytics will show you how customers interact with your recommendations. These invaluable insights will help you identify where to optimize your algorithms to better meet visitors’ needs.

Recommendation engine providers offer different analytics.

Do your research to ensure your solution tracks the metrics you need to continuously improve your solution and stand out in a crowded market.

AI-driven E-commerce Product recommendations are an invaluable tool to optimize the user experience and increase sales. The right recommendation engine can set your store up for success, so be sure to choose one that adheres to the criteria above.

Conclusion

In the current Ecommerce landscape, AI-driven Ecommerce product recommendations are essential.

Product recommendations that leverage artificial intelligence most effectively catch shoppers’ attention and shorten the path to purchase. Therefore, enabling retailers to improve the customer journey, increase conversion rates, boost sales, and build customer loyalty.

The best recommendations are data-driven by a mix of filtering methods to best meet shoppers’ needs at every point in their search. So be sure to invest in the proper technology to provide relevant suggestions in your store.

Implementing a product recommendations solution and adhering to the best practices above will help you maximize your store’s potential and be a win-win for your customers and your business.

Rebecca PacunCopywriter – Prefixbox

Rebecca is the Copywriter at Prefixbox, a leading Search and Discovery solution for Enterprise Ecommerce retailers. Originally from California, Rebecca works at Prefixbox’s office in Madrid.