What is Composable Commerce?

Composable commerce is made possible by headless technology, but the two are not interchangeable.

Elastic Path describes composable commerce as the process of composing the best possible commerce components to create a cohesive solution that meets your unique business needs.

These components, often referred to as “composable building blocks” and Packaged Business Capabilities (PBCs), handle specific E-commerce functionalities such as product catalog management, search, checkout, payments, etc., and are connected via APIs.

Businesses can easily put together multi-vendor commerce solutions based on their preferences and business requirements.

This modular approach has allowed online businesses to gain more control over the customer experience across multiple touchpoints. Composable commerce platforms also enable retailers to quickly adapt to changing market demands or experiment with new features by easily swapping out or adding new components without disrupting the entire system.

MACH architecture

We can’t talk about composable commerce without mentioning MACH architecture.

The letters stand for Microservices, API-first, Cloud-native SaaS, and Headless. MACH is a set of technology principles that enable businesses to build best-of-breed, modern platforms, and is the first building block of composable commerce.

Let’s take a look at each of these “letters”:

M – Microservices: These are individual, independently developed and managed pieces of software. The composability of Packaged Business Capabilities (PBCs) is enabled by a microservice-based architecture, as PBCs are built up by one or several aggregated microservices.

A – API-first: The storefront is connected to the back-end system through APIs, enabling several applications or services to tie together.

C – Cloud-native SaaS: Software-as-a-Service that leverage cloud features, like automatic scaling and high availability. This software automatically updates, so you don’t need to worry about making upgrades.

H – Headless: Headless architecture is the base of the composable approach. As the front-end of the storefront is completely decoupled from the back-end, it allows retailers to easily update the user interface. The application can be tied to several endpoints, different channels or devices.

Salesforce is a proponent of composable commerce. It enables retailers to work with a composable storefront (“an industry-standard framework for creating innovative Progressive Web Apps”).

Salesforce recommends composable storefronts to all those businesses that:

• need more operational agility to stay ahead of customer expectations
• want flexibility and freedom in choosing which third-party solutions to integrate that match their objectives best
• strive to create unique digital experiences.

Benefits of Composable Commerce

Increased speed

By separating the back-end and front-end systems, you eliminate the need for requests to pass through both. With the help of headless solutions and/or a composable commerce approach, many requests can be handled on the front-end. This results in faster loading times, which corresponds to a better user experience for your shoppers.

Greater flexibility and agility

The modular approach of composable commerce architecture allows businesses to easily swap out or add components as needed, without interrupting the rest of the system.

It heavily relies on APIs (Application Programming Interfaces) to facilitate communication between different components, which defines how they interact with each other, allowing for seamless integration and interoperability. APIs make it easier to connect with external systems and third-party services, further enhancing flexibility and agility.

For example, if a business wants to change its payment processor or add a new feature to the checkout process, it can simply integrate a new component via APIs without needing to rebuild the entire platform.

Reduced cost

The pay-as-you-go model that the modular architecture of composable commerce enables means that retailers can choose to only pay for the components they need, avoiding overinvestment in features their business needs don’t require.

This is a significant advantage compared to monolithic E-commerce platforms.

More scalability

Traditional, non-composable E-commerce platforms often present challenges with scalability since they are usually originally built as a single, tightly integrated system. As a retailer’s business grows, they may encounter limitations in terms of system performance, capacity, and the ability to accommodate increasing traffic or transaction volumes. Scaling such monolithic architectures and systems typically requires significant investments in hardware, software, and infrastructure upgrades.

In contrast, composable commerce solutions enables retailers to scale their technology stack more dynamically and efficiently as they grow, minimizing unnecessary costs by this all-in-one platform approach.

They can scale

• horizontally by adding additional instances of specific components to handle increased loads
• vertically by upgrading individual components to handle higher demands.

Challenges and considerations for retailers

While composable architectures offer many advantages, it’s always beneficial to be aware of any challenges that may arise with adapting a new, modern approach.

Challenges rooted in integration

Integrating multiple software components from different vendors or providers requires careful planning and coordination. Each of these components may come with their own set of APIs, data formats, and protocols, especially if they are developed by different teams instead of a single vendor.

Testing the separate components together might be the best way to ensure that they’re 100% compatible and interoperable, which also requires effort and time.

Maintenance of individual components

It can happen that the different components of a system have different update cycles, dependencies, and compatibility requirements, which can increase the overall maintenance burden.

Dealing with several vendors can also add to the complexity and the cost of the processes, but an efficient coordination of communication and support helps a lot with ensuring the smooth operation of a composable system.

Does composable commerce impact your customers?

In short: yes!

Composable commerce can have several significant impacts on your online shoppers.

As 80% of shoppers agree that the experience a company (including their website) provides is as important as the products and services they offer and sell, now is the best time for brands to embrace innovation and stay on top of trends.

Now, let’s see how composable commerce can assist in this matter:

Faster and smoother customer journey

Increased speed is not only beneficial for your business operations, but contributes to higher customer satisfaction and an increased conversion rate. By decoupling the front-end and back-end systems through headless commerce architecture, composable commerce allows for faster loading times and smoother user interactions. Shoppers experience quicker page load times, seamless checkout processes, and reduced friction when navigating the website or app.

Personalized shopping experiences

With access to a wide range of specialized third-party tools and technologies (and by choosing those that offer data analytics and utilize AI algorithms), you can personalize the shopping experience for each customer, deliver product recommendations based on previous user behavior, display promotions and special offers.

Access to innovative features

AI-powered product recommendations are not the only innovative feature shoppers can enjoy access to.

If you decide to utilize other best-in-breed components in your store, your shoppers can have virtual try-ons, ask for assistance from interactive chatbots, or engage in an entire social commerce experience.

By prioritizing personalization, performance, and innovation, you can build loyalty, drive sales, and differentiate your store and brand in a competitive landscape.

Wrap up

In summary, composable commerce marks a significant evolution in E-commerce strategy, enabling businesses to swiftly adjust and innovate according to market changes.

By breaking down commerce functionalities into interchangeable modules, companies gain flexibility, scalability, and agility in their online endeavors. Embracing this method unlocks avenues for tailored solutions, seamless integrations, and enriched customer interactions, positioning businesses at the forefront of digital commerce innovation.

Prefixbox’s solution optimizes the search experience on all headless and composable storefronts. What’s more, we are deeply integrated into many of these platforms, including native integration with Salesforce Commerce Cloud.

Introduction to Headless Commerce

In the traditional E-commerce setup, the website we see (the front-end) and the system managing inventory, orders, and payments (the back-end) are tightly connected.

Headless commerce systems, on the other hand, work with a type of E-commerce architecture where the website’s front-end layer is separated from the backend E-commerce functionality. In practice, this means that any changes in the User Interface (UX/UI) are independent from the back-end, and vice versa.

Everything on the front-end is developed separately and receives data from the back-end through intermediaries called Application Programming Interfaces (APIs).

Think of it as a multi-head tool (screwdriver kit or kitchen mixer) – you can change either the base (back-end) or the head (front-end) without disturbing the other part, and losing compatibility. Headless commerce architecture offers the same convenience, versatility, and functionality for retailers.

With headless commerce platforms, developers have the freedom to use any tech stack or programming language to build the front-end, including traditional web technologies, mobile apps, etc. This freedom of greater flexibility, scalability, and agility is why headless commerce architecture is becoming preferred over old-school monolithic architecture.

Headless commerce solutions can be a great choice if your business is striving to be quickly adaptable to the ever-changing market conditions, support omnichannel experiences, and ensure high customer satisfaction.

Benefits of Headless Commerce

Unlimited customization, wide variety of tools

The freedom to create customized and personalized experiences is critical in E-commerce. While a monolithic system with a tightly connected front- and back-end can be an ideal starting point for launching online stores quickly, it can present challenges when it comes to customization later on, as your brand grows. To offer your customers dynamic and personalized experiences, and support your brand’s expanding digital identity, your storefront needs to transition to a more flexible, decoupled architecture.

Updating the appearance of your store in line with current market trends, and doing that swiftly, can be a key differentiator of your business. Due to the separation of the front-end experience from the back-end, making changes and experimenting is simpler and less risky with the headless method – since these experiments on your storefront don’t change anything in the underlying back-end logic of your site.

Headless architectures also allows for instant testing of whatever you build, which helps you to continuously optimize the customer experience on your site, and increase conversions.

As headless architecture relies heavily on APIs, it keeps all content centralized and easily accessible, thus allows for faster delivery times this way as well.

IT efficiency

Traditional monolithic architectures often suffer from complexity and rigidity, lacking agility and innovation.

Headless architecture, on the other hand, enhances operational efficiency by enabling seamless integration with third-party services and devices. Using APIs, you can integrate all of your existing systems (ERP, IMS, PIM, e.g.) into your workflows, and build out your ideal E-commerce set-up.

The headless model simplifies development processes and reduces dependencies with its modular approach, and allows IT teams to centralize their focus on optimizing and maintaining the separate components, leading to faster development cycles.

Cost effectiveness

With a headless architecture, businesses can scale resources according to demand, avoiding unnecessary expenses associated with over-provisioning.

Additionally, the ability to leverage a diverse ecosystem of pre-built components and services reduces development overhead and quickens time-to-value. By maximizing efficiency and minimizing overhead costs, headless commerce enables you to achieve a higher return on investment and maintain a competitive edge in the market.

Flexibility

Headless commerce enables rapid iteration and experimentation with new features, designs, and functionalities. You can easily A/B test different variations of the front-end to optimize conversions, explore innovative ideas, and respond quickly to customer feedback and market trends.

Headless Commerce is a Good Fit for Your Online Store, IF:

• your current framework is hindering your ability to innovate or bring new experiences to market quickly
• you find it difficult to integrate new tools into your established system infrastructure (this challenge is what makes headless commerce appealing for so many retailers)
• you want to take effort off your IT and development departments as they are already overwhelmed with current projects
• you have the development resources to support the transition.

How does Headless Commerce Impact Your Customers?

A positive shopping experience goes a long way. Retail Brew found that 82% of American adults are likely to return to a store if they had an overall good experience while shopping, and 65% of them would not most probably stop shopping at a store if their experience was negative (even after just one time).

The same report shows that almost half of American Gen Z and Millennials like to use advanced technology (such as chatbots) while shopping online.

If your architecture is inflexible, it can be hard to move fast enough to keep up with the latest trends. However, a headless set-up can help you deliver a high-quality online shopping experience that meets customer expectations.

Here are a few examples of how headless commerce affects your shoppers:

Site speed, faster loading times

In today’s digital age, speed can make or break a visitor’s experience on a website. What’s more, your site speed can impact your business’ bottom line.

A 2023 report shows that loading time impacts the purchasing decision of 82% of consumers, and 40% of online shoppers wait no longer than 3 seconds before they close the tab. It’s a proven fact that bounce rates increase drastically at this 2-to-3 second mark.

Headless commerce architectures can scale more efficiently than traditional, monolithic ones, and handle the quick rises in traffic, even during peak times like sales or holidays. This results in faster loading times and higher availability, across different devices, desktop or mobile; keeping your shoppers happy.

Changes and optimization in the blink of an eye

Any changes in content on the back-end are instantly reflected in the front-end, if you work with a headless architecture. This means that product promotions show up instantly, and new products are suggested as soon as they become available.

Cross-channel and omni-channel experiences

Headless commerce allows you to integrate the newest channels, devices, and technologies, as they emerge on the market. Think of chat commerce, AR for shopping, image search or voice assistants. Staying on top of these trends not only puts you in front of your competition, but also increases shopper satisfaction.

How to Get Started

1. Make sure that Headless Commerce is a good fit for your business. ✔
2. Understand the main points in which headless architecture differs from the traditional store set-up.
3. Examine what Front-End storefront options you can choose from.

A few of these options are:

Frontend-as-a-Service

If you choose this option, you will work with a third-party agency to carry out the front-end of your commerce solution.

With this technology, you can effortlessly construct and design various features and functionalities for your products, including grid layouts and carousels, with minimal coding required.

FEaaS can be a good fit for businesses that have 1 or 2 stores to manage, want to innovate quickly, and deploy experiences across multiple channels, with minimal development and technical resources to manage the front-end.

Digital Experience Platform

It is a comprehensive solution for creating dynamic and engaging storefronts that cater to diverse customer needs across multiple regions and channels.

DXP can suit your business if you:

1. are in favor of rapid innovations, frequent changes
2. would like to build an omnichannel experience
3. have multiple stores, operate in multiple regions
4. are willing to invest in a team/agency to manage the front-end

The benefits of enhanced agility, omnichannel integration, and accelerated time-to-market make DXP a compelling option for ambitious businesses seeking to stay ahead of the competition.

Custom Build

This approach means developing the front-end part of your store in-house, which, quite clearly, requires you to set up a responsible team or hire an agency. Doing so, you can tailor every aspect of your storefront to align with your brand identity and customer expectations.

This is a good choice for businesses with unique requirements or a preference for complete control over the front-end development process.

For example, Salesforce supports headless commerce architectures. It offers a full suite of tools to choose from to expand capabilities and enhance user experience.

Wrap up

The rise of headless technology offers retailers unprecedented flexibility, scalability, and customization.

Embracing this innovative approach that is headless commerce not only future-proofs businesses but also fosters innovation and gives a competitive advantage in an increasingly dynamic marketplace.

Prefixbox’s solution optimizes the search experience on all headless storefronts. What’s more, we are deeply integrated into many of these platforms, including native integration with Salesforce Commerce Cloud.

Importance of E-commerce site search

Site search serves as the way through which consumers navigate vast product catalogs, seeking the perfect item to meet their needs on Ecommerce sites. A great search experience can be the difference between a satisfied customer swiftly finding what they seek and a frustrated user abandoning their shopping journey.

With Jon MacDonald’s famous words from The Good:

“If you’ve been treating on-site search as a necessary function, rather than as one of the most powerful revenue-generating tools in your arsenal, I have three words for you: Please stop that.“

In a previous article, we delved into the essential features necessary for an E-commerce search engine to provide your shoppers with the best experience possible. From intuitive autocomplete and typo correction to advanced filtering options and personalized recommendations, these features are paramount in meeting the evolving expectations of today’s online consumers. You can find this comprehensive guide here.

Why would e-tailers choose third-party site search tools?

Many online stores now opt for search solutions developed by third-party providers rather than investing in developing their own. The main advantages of this approach are the following:

Less effort, more expertise

Specialized search companies are typically more knowledgeable about the latest technologies and can leverage their expertise to offer enhanced search functionality for their partners’ stores. They typically offer pre-built solutions with latest tech or easy-to-implement APIs that can be integrated seamlessly with existing platforms, minimizing development time and effort.

Outsourcing site search to a third-party provider this way allows online retailers to focus on their core competencies, such as product development, marketing, and customer service, rather than diverting resources to non-core activities like search engine development.

Advanced technologies

Third-party providers continuously innovate and integrate advanced features and technologies into their site search solutions, such as

• AI-powered search algorithms
• Natural Language Processing
• Personalization, and
• Voice search capabilities.

These features enhance the user experience and drive better conversion rates. Vector search, for example, is the latest technology in search that goes beyond looking for matches in keywords, and understands context. 

Machine learning-based solutions can offer users a whole another level of personalization, be it by recommendations or smart search results (e.g. returning women’s shorts if the user had been browsing women’s clothing previously).

Developing and maintaining such advanced capabilities in-house requires significant investment in research and development.

Merchandizing

As Shopify found, many e-tailers look for a third-party search solution because their own setup does not offer advanced merchandizing features. Having the ability to re-rank search results and promote the products with the highest margin is an essential capability for retailers.

Most third-party providers bring a wealth of specialized expertise, advanced technology, and industry insights to the table, making them valuable partners for retailers looking to elevate their merchandizing strategies and drive revenue growth. 

In 2024, amongst the most known E-commerce search providers are Algolia, Klevu, DooFinder, Constructor and Luigi’s Box. 

While third-party providers undoubtedly offer compelling advantages, it’s crucial for retailers to conduct a comprehensive evaluation before integrating these solutions into their E-commerce platforms in order to make sure their criteria will be met.

Challenges with third-party E-commerce Site Search Tools According to Public User Reviews

Without further ado, let’s delve into the intricacies that retailers encounter while navigating the quest for the perfect search solution.

User Interface / User Experience (UI/UX)

While third-party search solutions offer a range of features, they may not fully align with the specific needs or branding of every online retailer. Customization options might be limited, which could impact the seamless integration of the search function with the overall website design and user experience. For example, retailers may be unable to fine-tune search rankings or adjust filtering options to reflect specific product attributes or customer preferences.

It’s also common that some customization options are available, but require developer effort from the retailer’s side. This goes against the idea that search providers should take weight off retailers’ shoulders, so they can focus their efforts into other areas. 

So, if you don’t have a large development team with capacity to work on your search customization, be sure to avoid choosing a third party solution that comes with these possible limitations.

Several users of the biggest Finnish site search provider, for example, consider the limited customization options for the search results layout, and the time needed for styling their store to match the search pages with the rest, as a disadvantage.

Usability

It’s imperative for third-party search providers to prioritize user-centric design principles, streamline on-boarding processes, and offer intuitive interfaces that empower retailers to efficiently configure and manage their search functionalities.

Retailers often face a learning curve when integrating and configuring third-party search solutions, compounded by the lack of comprehensive documentation or user-friendly interfaces.

Complicating matters further, certain settings may only be adjustable with the assistance of the provider, leading to potential delays and frustrations in fine-tuning the search experience.

Accuracy and Relevance Problems

While returning the most relevant results for a search query is a basic functionality of site search engines, achieving this consistently can be challenging.

Third-party site search solutions often come with advanced search algorithms and functionalities with features like autocomplete, typo correction, and faceted search, which help users find products more easily and quickly. However, some of them may struggle with accuracy and relevance due to limitations in their algorithms or indexing methods.

Without advanced technologies like Natural Language Processing (NLP) and vector search, these solutions may rely solely on keyword matching, leading to inaccurate or irrelevant results, especially for complex or ambiguous queries.

E.g. some of the biggest providers’ solutions still return inaccurate results sometimes, based on customer reviews.

To make sure your search always returns relevant results, choose a third-party provider that stays on top of technological advancements and leverages the latest search technology.

Issues with Integration and Technical Requirements

Ensuring seamless integration of third-party search with the existing systems and platforms of the retailer is crucial for maintaining operational efficiency and providing a consistent user experience.

Sometimes however, compatibility issues, API documentation clarity, and ongoing maintenance requirements may pose challenges for the retailer’s development team. 

Some of the users of a globally known enterprise search provider, for example, find that the complexity of their features, and the need for developers in the integration phase takes away from the user experience (“It’s hard to know what you can/can’t do. No applied examples of how to use features, makes it hard to test and implement.”)

Similarly, a Finland-based provider’s reviewers mention that the non-seamless integration of the search solution may make it feel like the landing pages come from a connected system and stand out from the rest. 

These kinds of issues, partnered with limited technical documentation and lack of compatibility with the retailer’s existing platforms can be quite a let-down in the retailer’s user experience, which is why these are factors to be aware of before making a decision about a search provider.

On the positive side, many providers offer comprehensive support and resources to facilitate seamless integration processes.

Insufficient Speed

Delays in indexing frequency and slow updates to product databases can negatively impact the speed and accuracy of search results. This can result in customer frustration for and lost sales opportunities for retailers. Improving indexing frequency and optimizing search result speed should be a priority for third-party search providers to enhance overall performance.

Weak Analytics Functionality

Access to user-friendly dashboards and robust search analytics are essential for retailers to gain valuable insights into customer behavior and preferences. Monitoring user search behavior, including autocomplete queries, can offer valuable insights into undiscovered content or tasks on your website.

However, some third-party search providers may fall short in providing predictive analytics and actionable insights, leaving retailers without the tools necessary to make informed decisions and drive business growth.

Some of the retailers using a known American search provider’s solution, e.g., mention having to work with a less than user-friendly analytics dashboard, but also give credit to their helpful support team, and their readiness to get the best out of every situation as a mean of compensation.

Customer Support

Effective and responsive consumer support is critical for retailers relying on third-party search providers to address issues and optimize performance. However, it’s not uncommon for some providers to struggle in delivering timely assistance, which can result in frustration and dissatisfaction among retailers.

While providing extensive technical documentation is undoubtedly valuable, it alone is not a sufficient form of support. Retailers often require personalized guidance, troubleshooting assistance, and proactive communication to resolve issues efficiently and ensure optimal performance of the search solution.

Wrap up

All in all, choosing a third-party E-commerce site search tool allows retailers to leverage specialized expertise, advanced features, scalability, and cost-effectiveness to enhance the search experience for their customers and drive business growth.

However, it’s worth noting that challenges may arise depending on the chosen provider, such as usability and customization issues, limited search analytics, and integration and support concerns. Given the framework of your team and the objectives of your company, it’s crucial to assess providers with the potential issues discussed above. This evaluation will enable you to identify the search solution that aligns most effectively with your needs and company goals.

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.

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.

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 2025

1. Conversational Commerce and AI Agents

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 (AI agent) 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 agent 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 agents 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.

Heard about AI agents, 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 an AI agent 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 AI agents

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 AI Agent: 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 AI agents that stand out from the rest: 

1. Support

Through the integration of AI agents, 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 AI Agents (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 Agent platform solutions

These messaging platforms enable you to build your own AI-powered chatbot (AI agent) to leverage the power of AI in customer support and engagement. The communication channels are up to your (and your customers’) preferences.

Instant agent 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 AI agents.

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 agents 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 AI agent 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.

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. 

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

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. 

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, an AI agent, 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 agents, 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 (AI agents) 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.

George El-Hage, CEO of Wave Connect, says, “Chat commerce isn’t just about automation—it’s about creating real connections with customers. At Wave Connect, we’ve seen how AI-driven messaging can transform the buying experience, making it more intuitive and engaging. By implementing personalized chat solutions, we reduced response times by 60%, improved customer satisfaction scores by 45%, and saw a 35% increase in repeat purchases. When done right, chat commerce doesn’t just answer questions—it builds trust and loyalty.“

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 agents.

Thanks to AI agents 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 agents 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?

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 agent
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 agent

For this to work, you’ll need to choose a conversational commerce platform that offers AI agent capabilities. These platforms often provide easy-to-use tools and integrations to build and deploy your AI agents effectively.

Once you have selected a platform, you can start training your AI agent using natural language processing (NLP) and machine learning (ML) techniques. NLP allows the AI agent to understand and interpret user messages, while ML helps with learning from past interactions to improve its responses over time.

Additionally, sentiment analysis can be incorporated into AI agents to understand the emotional tone of customer messages. This capability helps AI agents provide more empathetic and personalized responses, ultimately leading to enhanced customer service experiences.

These AI agents act as virtual shopping assistants, and with sufficient training, they can replicate the feel of interacting with a store associate. AI agents are available 24/7 to answer questions, suggest options, 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 AI agents 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.