When online visitors can’t quickly find what they need, the impact is immediate. Traffic is wasted. Confidence drops. Revenue disappears and rarely comes back.  

At the same time, internal teams lose valuable time when every merchandising change or search optimization depends on technical resources in a market that keeps accelerating. 

That’s why more eCommerce teams are no longer buying product discovery solutions based on promises, lengthy feature lists or AI claims. Instead, they’re turning to proof from peers who use these solutions every day. Customer feedback offers a clearer picture of how platforms perform across implementation, daily operations and long-term partnership. 

We see this shift is reflected in the Gartner Peer Insights Voice of the Customer for Search and Product Discovery report (available to Gartner clients), where FactFinder is recognized as a Strong Performer. For us, this recognition marks an important milestone. It is the first time FactFinder has been placed in this quadrant, which we believe reflects years of close collaboration with customers who depend on product discovery as a core part of their eCommerce operations. 

Why the Voice of the Customer report matters to us 

Gartner Peer Insights is a public platform that offers verified, first-hand reviews and ratings of enterprise technology solutions by end-user professionals for end-user professionals. The Voice of the Customer report is a document that applies a methodology to aggregated Gartner Peer Insights’ reviews in a market to provide an overall perspective for IT decision makers.*  

To us, this inclusion reflects how a solution performs in real-world use over time from the customer’s perspective, as compared to analyst opinion or feature evaluation. This customer-led perspective is what gives the “Strong Performer” recognition its real meaning for us. As FactFinder’s CEO, Istvan Laszloffy, puts it:  

What customers experience with FactFinder in practice

We’re extremely grateful to the customers who took the time to share their experiences. Across verified reviews and ongoing conversations from the last year, a few themes consistently stand out. From our perspective, the following reflect the areas where FactFinder delivers the most value in real-world eCommerce environments and why customers continue to trust us as a long-term product discovery partner. 

• Helping online visitors find what they need faster, even in complex assortments: At the core of product discovery is one simple expectation, that online visitors should be able to find relevant products quickly and with confidence. We’ve consistently invested in relevance and result quality to reduce friction, especially for businesses with large, seasonal or highly structured assortments. Customers frequently point to clearer results, fewer dead ends and more confident navigation as outcomes of this focus, even with 500,000+ SKUs, supporting stronger conversion and a better overall purchasing experience. 

• Giving business users control without technical dependency: We believe business users should be able to adapt search, navigation and merchandising without relying on technical resources for every change. Customers highlight the balance FactFinder strikes between automation and control, enabling teams to respond faster to commercial needs while maintaining consistency and governance. 

• A partnership that goes beyond the product: Customer experience doesn’t stop at go-live. From onboarding through ongoing collaboration, we place a strong emphasis on support, responsiveness and partnership. Customers often reference the value of working with teams who understand eCommerce challenges and adapt the platform to business goals rather than forcing businesses to adapt to the software. This long-term, relationship-driven approach is a key part of how we work. 

Built on real-world experience, designed to evolve 

For eCommerce teams evaluating product discovery today, peer experience offers something that product claims and feature lists alone cannot. It shows how a solution performs once it’s live, adopted by teams and embedded into daily operations. That perspective helps separate short-term promises from platforms built to deliver lasting value. 

We believe being recognized in the Gartner® Voice of the Customer reflects a deliberate balance we strive for. We focus on getting the fundamentals of product discovery right – relevance, control and usability – because they directly shape how shoppers find products and how teams work every day. At the same time, we continuously invest in innovation to ensure our customers benefit from new technologies in ways that support real-world use cases, not abstract ideas. 

Customer feedback plays a central role in maintaining this balance. It helps shape how the platform evolves today while informing where we go next. Many customers trust FactFinder not only because it delivers reliable results now, but because they see a partner that stays at the forefront of product discovery – grounded in real-world needs, not market hype. 

Staying close to customers, learning from how discovery is used in practice and pairing proven fundamentals with forward-thinking innovation will continue to define how we build product discovery for the future. 

Gartner, Voice of the Customer for Search and Product Discovery, 24 December 2025 

*Gartner Peer Insights Content Compliance Policy, Gartner Peer Insights, 15 January 2025 

Gartner and Peer Insights are trademarks of Gartner, Inc. and/or its affiliates. All rights reserved. Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences, and should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose. 

Anyone can say they’re the best. Walk down a street and you’ll likely see a burger joint boasting the “world’s best burger.” Do you believe them? Probably not. What makes you stop and pay attention is when the recognition comes from someone credible, for example a 4.9 star rating with thousands of reviews or a Michelin star. 

The same is true in business technology. Vendors can all claim they’re doing great things. What carries weight is when an independent, respected source does a thorough review and confirms your capabilities. 

That’s why we’re excited to share that FactFinder has been recognized by Forrester as one of the most significant commerce search and product discovery vendors in their recent report, The Forrester Wave: Commerce Search And Product Discovery Solutions, Q3 2025.*  

We also scored among the top 5 in the ‘current offering’ category. From our perspective, this highlights how we’re delivering real value for customers right now while continuing to innovate. 

*Note: the report is only available to Forrester clients. A publicly available blog that mentions some of the highlights can be found here.  

Who is Forrester? 

Forrester is one of the most influential research and advisory firms in the world, advising companies across North America, Europe, and APAC, including 61% of the Fortune 100. Their analysts dig deep, combining extensive market knowledge with rigorous evaluation methods. 

Emily Pfeiffer, Principal Analyst at Forrester, authored this report. She is a leading voice in commerce technology, brings hands-on experience as a digital and marketing lead and is known for her clear FIRE vision for technology: flexible, inexpensive, rapid and easy. 

How the Forrester Wave works 

Forrester only evaluates the most significant vendors in a category. To even be considered for this category’s evaluation, a provider must support all core functions of commerce search and product discovery, reach a revenue threshold and have proven adoption and mindshare among Forrester’s enterprise clients. 

From there, the process is rigorous: a detailed questionnaire, 2.5 hours of live demos, a strategy briefing and interviews with customer references. It’s designed to cut through marketing claims and assess real capabilities. 

The market landscape 

As the report notes, “Most of the market is still using the commerce search functionality embedded in their commerce solution, or even a legacy (and likely highly customized) system such as Endeca. However, modern, standalone solutions like those in this evaluation are gaining visibility. We expect massive first-time adoption of them in the next couple of years.”  

From our perspective, this tech has been crucial for decades. But amidst all of these other changes in eCommerce and the expansion of AI, this prediction underscores just how crucial modern, standalone search solutions will become – and we’re ready to help businesses modernize now. 

What Forrester said about FactFinder 

1. “It is a best fit for businesses that prioritize strong visual editing for nontechnical users in a product that is on the rise.” 

2. “FactFinder is superior in visual merchandising and guided selling, as Next Generation provides improved interfaces for nontechnical users to configure digital experiences.” 

3. FactFinder had the highest score possible in the “known user” personalization, visual merchandising, attributes and facets, guided selling and basic search configuration criteria.  

Expanding on our strengths from our perspective 

Here’s how we see it: 

• Search & merchandising: We enable merchandisers to create compelling experiences without code. Our platform provides full visibility (no black box) while also offering smart automation. For example, our AI Guided Selling module lets teams build guided advisors in minutes, not days. 

• Roadmap: Our roadmap is packed with AI innovations. Recent releases include vector search, built on our own offline language model, and data enrichment, all designed to give nontechnical practitioners more powerful tools.  

• Personalization at scale: We help retailers deliver truly individual experiences. Our Atlas AI enables real-time 1:1 personalization after a single click, while our Customer-Specific Information module ensures every shopper sees results, prices and information tuned to their unique agreement. This combination lets businesses serve each customer as if they were the only one. 

What’s next 

We believe being included in this Wave is a team achievement. Thank you to our colleagues, partners, and customers – your collaboration and feedback drive us forward. 

We’re excited to keep building the future of eCommerce, fueled by AI and powered by a modern platform that’s designed for business users. If you’d like to explore more about our platform and roadmap, get in touch here. 

Source & objectivity: Source: The Forrester Wave: Commerce Search and Product Discovery Solutions, Q3 2025, Emily Pfeiffer. Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here. 

Product data has always been the backbone of the eCommerce experience, yet most teams are stuck dealing with inconsistent formats, missing attributes and unreliable information. The result is poor discoverability, irrelevant search results, frustrated customers and missed sales. Each year, companies lose an average of $9.7 million due to inaccurate or incomplete product information. That’s revenue disappearing simply because customers can’t find the products they want. 

Until now, solving these issues meant manual cleanup, long-running data projects or accepting underperforming product discovery. With AI-powered data enrichment, there’s finally a faster and more intelligent way to fix your product data. 

Better data leads to better product discovery, and that means higher revenue and happier customers. 

Why bad product data still holds eCommerce back

Most product discovery issues can be traced back to one root cause: bad data. Across industries, eCommerce teams face the same core challenges: 

• Missing or incomplete attributes such as material, dimensions, color or use case 
• Non-standard units that make comparisons difficult 
• Inconsistent naming conventions across suppliers and systems 

In many cases, manually fixing this at scale is simply not feasible. There aren’t enough hours in the day or people on the team to get through it all. 

The impact is immediate and far-reaching. Poor product data leads to poor search and navigation. Shoppers can’t find what they’re looking for, results feel irrelevant and the overall experience breaks down. That directly affects conversion, customer satisfaction and revenue. 

Introducing data enrichment by FactFinder

FactFinder’s new data enrichment solution gives eCommerce teams a faster, smarter way to take control of their product data. As a standalone tool, it works with any eCommerce tech stack (even if you already use another product discovery solution), so you can improve your data without reworking your entire setup. 

For FactFinder Next Generation customers, it’s available right inside the familiar interface, with direct integration to your existing systems for an even smoother workflow.  

Powered by large language models (LLMs), it analyzes existing product information and generates high-quality, structured data tags and attributes. Whether your catalog is missing key fields or just inconsistent, data enrichment helps fill the gaps with enriched tags automatically and at scale. 

For every suggested tag, the system calculates a confidence score. Tags that exceed your defined confidence threshold are automatically approved, reducing manual effort. Everything else can be reviewed in an intuitive interface where business users can accept, edit or reject suggestions individually or in bulk. No technical skills are required, and manual data entry is also possible whenever needed. The system does the thinking, but you stay in control. 

It’s the fastest, easiest way to go from incomplete or inconsistent data to a foundation that supports powerful, high-converting product discovery. 

Benefits for eCommerce teams

With high-quality product data in place, eCommerce teams can shift their focus from cleaning up the basics to driving meaningful business outcomes: 

• Increase conversion rates with more relevant search results and smoother shopping experiences. 
• Free up resources by letting AI optimize your data for you, giving teams more time for strategic projects. 
• Boost the impact of your entire tech stack. Better data powers every AI-driven feature from guided selling to personalization and vector search. 

What makes us different?

Most data enrichment tools rely on long onboarding cycles, manual input or expensive service layers. FactFinder takes a different approach. 

This solution is AI-powered with confidence-based auto-approval, making it faster and more scalable than traditional methods. It enhances your existing data, creating entirely new context from minimal inputs like an image and a short product title. 

There’s no need for lengthy collaboration with data teams or ongoing manual reviews. It’s quick to implement and delivers value from day one. 

Data enrichment also works hand in hand with other innovations in your stack. For example, when combined with vector search, the enriched data provides essential context that helps increase result accuracy and relevance even further.  

What’s next

This first release of data enrichment is just the beginning. While today’s version already automates key steps, future updates will reduce the need for manual input and make the experience even more hands-off for busy teams. 

Planned enhancements will make data enrichment even more powerful: 

• Unit standardization, so your technical specifications are always consistent and comparable across suppliers. 
• Improved tag-based filtering, so shoppers can narrow results more accurately and find the right product faster. 
• Use-case attributes, so customers can search and browse by the specific scenarios or needs your products address. 
• Fully automatic enrichment at import, so new products are ready for discovery the moment they’re added to your catalog. 

The goal is simple: to support a richer, more intuitive product discovery experience at every stage of the customer journey. 

Ready to improve your product data?

Bad data shouldn’t hold your eCommerce experience back. 

FactFinder’s data enrichment makes high-quality product information accessible, fast and automatic regardless of the platform or systems you use. It’s a smarter way to fix your data and unlock better product discovery across all your sales channels. 

Book a demo today and see how quickly your product data can start driving more conversions. 

What this recognition means to us

We’re thrilled to announce that we’ve been named in Gartner’s latest Magic Quadrant for search and product discovery. To us, this reinforces our belief in the strength of our product and customer relationships.

The report maps players in a given space based on two key axes: Completeness of Vision and Ability to Execute. We believe these factors reflect not just the quality of a solution, but a deep understanding of what businesses truly need and their ability to deliver on it. This year, for example, Gartner was looking out for conversational search:

“Hybrid search (keyword and semantic search combined) is now a differentiating factor, and vector search is commonly used to improve query understanding. Vendors are building semantic search capabilities using third-party vector databases or integrating with other commercial platforms.” – (Gartner Critical Capabilities 2025)

This reflects a broader shift in how people shop online and how eCommerce businesses are adapting to meet their needs. At FactFinder, we’ve seen firsthand just how important conversational search has become, as more people use natural language to find what they want.

We see our recognition in the Magic Quadrant as a sign we’re on the right track. Staying true to our mission, we combine sophisticated AI with human expertise to make search and product discovery reliable, tailored, and results-driven. We don’t chase trends but innovate with solutions designed specifically for eCommerce. And because real innovation means flexibility, we’ve made our platform transparent and fully customizable for every B2B or B2C use case. While the report covers some of our capabilities, we’d like to highlight the features we believe truly set our product apart.

A good fit for all industries

FactFinder is designed for eCommerce businesses across all industries, with the speed and flexibility to manage even the most complex catalogs. We’ve shaped the intuitive UI and AI-enriched modules around real-world use cases and customer input to ensure they suit all businesses.

Whether you’re selling electronics, groceries, apparel or parts, our product discovery and search core makes purchasing online quick and easy for every customer. They can search with keywords and unit codes or vague, conversational queries. Even if they spell terms phonetically, with typos or in multiple languages, your customers will still find the items or content they need on the first try. No more zero results or abandoned purchases.

Extra features for B2B

Most search and product discovery solutions are built for more general eCommerce and forget the unique requirements and challenges B2B companies face. For example, B2B customers often search for specific items, and their queries, ranging from complex terms to exact SKU numbers, confuse most engines.

FactFinder’s AI-driven search, on the other hand, delivers the right results every time. When used with Customer-Specific Info to show personalized pricing for each customer and the Predictive Basket, which speeds up shopping by 66%, it’s a game changer for B2B eCommerce.

Customer success

We take customer success very seriously, ensuring every customer has a dedicated Customer Success Manager to help them get the most out of the platform. Through open dialogue, we shape the solution to each business’s unique needs and goals. Our enablement program includes a library of training materials and hands-on support with active business monitoring, one-to-one guidance and quarterly business reviews.

FactFinder has been recognized for customer satisfaction based on a 4.9/5-star Gartner Peer Insights rating for the last 12 months (as of 9 July 2025) and consistently positive feedback on both the platform and our customer support. It means a lot to us that so many customers took the time to share their experience, and we remain dedicated to their success.

GenAI expertise

FactFinder started using AI long before the hype began, so we know how to apply it in ways that deliver real value, from smart product ranking and recommendations to 1:1 personalization and our trademarked Predictive Basket. We enable sophisticated automation wherever it makes sense, but business users can easily take control at any time through the user-friendly backend.

Recently, we’ve been innovating with in-house LLM-based features, including AI Guided Selling and GPT Synonyms. AI Guided Selling automates guided selling campaigns to improve the customer experience and boost sales while saving tons of time for eCommerce teams. GPT synonyms automatically generates relevant synonyms for any search term, so customers get the best results, whether they search for a “battery pack”, “portable charger” or “power bank” without the manual effort.

Exciting times ahead

The Magic Quadrant was published at an especially exciting time for FactFinder, just as we announced the official launch of our vector search, which handles all the natural and conversational queries that keyword search struggles with. Unlike other solutions, our vector search is built specially for eCommerce on our own offline language model, meaning it’s 100% private and finely tuned to the way your customers shop.

But this is just the beginning. As our recent session on LLMs in eCommerce highlighted, FactFinder is making strides in AI not just for the sake of innovating but to solve everyday real-world challenges in eCommerce.

Along with vector search and AI Guided Selling, we’re bringing to market a way to automate and scale data enrichment. Our AI will improve the quality, consistency and completeness of product data by generating helpful tags, standardizing values across fields, fixing typos and filling in missing details.

Better data means better product discoverability for buyers. They can find what they’re looking for faster, resulting in fewer bounces, happier customers and higher conversion rates. Like a chain reaction, improved data boosts every AI feature that relies on it, from smarter search to more relevant product recommendations.

Want a more visual explanation?

Hit play on our discussion to hear all about LLMs in eCommerce, changing online shopping behavior and our latest cutting-edge AI releases.

Ready to redefine product discovery

We’re thrilled to be recognized in Gartner’s Magic Quadrant and can’t wait for what’s next. With continued investment in AI, product data quality, operational efficiencies and the customer experience, we’re shaping the future of eCommerce. Will you join us? If you’re a current partner or a business considering product discovery options, we’d love to hear from you. Let’s explore how our solutions can meet your needs and drive your business forward.

Source: Gartner, Magic Quadrant for Search and Product Discovery, Aditya Vasudevan, Mike Lowndes, Noam Dorros and Sandy Shen, 24 June 2025

Gartner, Critical Capabilities for Search and Product Discovery, Aditya Vasudevan, Mike Lowndes, Noam Dorros and Sandy Shen, 24 June 2025

Gartner disclaimer

GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

For over two decades, we’ve conditioned ourselves to shop online using keywords, because that’s all search could understand. It works, and with the right engine behind it, it converts. But large language models (LLMs) like ChatGPT, Perplexity and Gemini, are influencing how people search. More and more, buyers expect the right results from natural language and open-ended queries. eCommerce needs to keep up.

This is where vector search comes in. Our semantic search technology understands the context and meaning behind queries where pure keyword matching reaches its limits — for example, a descriptive search like “high-performance laptop with plenty of storage for gaming”. While a good keyword-based search can pick out terms like “laptop,” “gaming” and “storage,” it often returns a mixed bag of devices and accessories — or even zero results — leaving the shopper without the gaming notebook they actually wanted.

Vector search, on the other hand, recognizes the meaning and intent behind conversational, natural language queries and instantly shows the right products — even when there are no keyword matches. This cuts bounce rates, speeds up shopping time, improves the customer experience and wins you sales and brand loyalty. Read on to uncover the magic behind FactFinder’s vector search — or hit play to watch the video walkthrough from our Vice President of Product and Technology, Holly Murdoch.

What is vector search?

Put simply, vector search works like a navigation system on a map. In eCommerce, it turns search queries and products into numeric vectors, which act like coordinates. It then measures the distance between them using “nearest neighbor search”.

The closer the product vector is to the query vector, the more relevant it is and the higher it ranks in the search results — even if the two don’t share matching keywords in the product data.

Benefits of vector search

No more zero results — vector search brings semantic understanding to every query, helping customers find what they need even from vague and conversational language. No dead ends like with traditional search, and no manual optimizations required.

More sales — thanks to customers finding the right products faster.

Fewer bounces — customers stay engaged and inspired with relevant results from their first search.

Smarter processing of unstructured data — less manual effort, huge time and cost savings and faster return on investment (ROI).

Happier customers and higher lifetime value (LTV) — buyers are more likely to make repeat purchases and recommend you to other people.

What makes our vector search different?

Vector search has been a trending topic over the last year. So, what’s different about FactFinder’s approach?

Based on our own in-house language model

Most other vector search models use online third party LLMs and language models. We use our own. That means we can keep our vector search database offline and in-house here in the EU, ensuring all data is kept 100% secure and private. No dodgy online or overseas transfers.

Plus, we train the language model ourselves. With 20+ years of eCommerce site search expertise behind us, we make sure it delivers across real-world use cases. While other vector models are built for generic tasks, ours is made for online purchases. We can even train it for specific industries, so it picks up on specialized language and understands searches in context.

Special multilingual support

Most vector search models are only tested in English, showing questionable performance in other languages, which slows your global expansion. Ours breaks through that barrier, with proven success handling complex languages like German and even multilingual queries like “rote handbag.” This enables you to deliver a flawless search experience everywhere.

Backed with advanced keyword search

Vector search is great, but it’s not always the best tool for the job. For exact matches, like product IDs or brand-name searches, keyword search often outperforms it. While keyword search shows only the specified item, vector search can return a mix of similar products that distracts from what the customer actually wants.

That’s why FactFinder blends advanced keyword search with vector capabilities — so you get exact matches when needed, and smart results when keyword search fails.

Flexible to fit your eCommerce strategy

No two eCommerce businesses are the same. Depending on your assortment, audience and strategy, every shop has its own unique requirements. We give you the flexibility to tailor this new technology to your use case, combining the strengths of vector and keyword search for the best possible customer experience. In the intuitive backend, you can choose between two modes:

1. Hybrid mode delivers a smart product list made up of both keyword-based and vector-based results. You can fine-tune the weighting between the two to match your strategy. This mode is ideal for audiences who often browse for inspiration, have limited product knowledge or shop for specific occasions, such as in fashion, DIY or beauty.
2. Fallback mode steps in only when keyword matching would return no results. Instead of landing on a zero-results page and potentially leaving, customers see all products that best match the meaning of their search. Perfect for technical B2B assortments where shoppers typically search for specific product variants or SKUs, and where keyword search should take clear priority over semantic search.

Vector search in action: applying vector to your eCommerce site search

Vector search can be powerful, especially when backed by keyword search. But will it work for your business? Let’s take a look. Here are a few examples of how it performs across different industries.

Fashion

Fashion shoppers increasingly search with a feeling in mind instead of a product name. Take “outfit for a first date” — vague, emotional, and open to interpretation. Keyword search would likely miss the mark, but vector search understands the intent and maps the context to real products. The result? Higher engagement and more confident buying decisions.

Sporting goods

Vector search comes in very handy for online shoppers taking up a new sport. Generally, they have an idea of what they need but not specific features. If someone searches for “cute sneakers for a 5km park run,” vector search catches the meaning and delivers breathable, supportive sneakers for cross-country running that look good.

Food & beverage

Many grocery shoppers know their shopping list by heart, but when faced with a special occasion or looking for inspiration, they turn to the search bar for help. A query like “vegan food for BBQ” won’t match product names directly, but vector search captures the implied requirements — plant-based products suitable for a barbecue — and suggests the perfect items.

B2B eCommerce

If a B2B buyer knows their needs but not which product, vector search can find the perfect match for their specific use cases. For example, queries like “thermal insulation sleeve for electrical cables” or “roller bearing 6205 with heat resistance,” are mapped to relevant product features and categories, helping customers self-serve with confidence.

Never miss a sale again with future-proof eCommerce vector search technology

Most people still search like keyword users. But that’s changing fast. Customers are moving toward smarter tech where apps understand natural language. They don’t need to use keywords with Google anymore. So why should they have to on your site?

With our vector search, you’re already aligned with the next generation of shopping — making it more intuitive and exceeding higher expectations. Smarter eCommerce site search means fewer missed moments and more confident purchases.

Try our new vector search now. Hit the banner below to book a personalized demo and see what FactFinder can do for your business.

Already a FactFinder customer? Just get in touch with your CSM or contact us here to try out vector search on your product data.

Ever had that moment when an online shop just gets you, showing you exactly what you want? More likely, you’ve had the opposite: endless irrelevant results. The reality is most online stores still struggle with so-called “personalization.” It frustrates shoppers and costs businesses money. And as privacy concerns grow, the third-party data companies have relied on won’t be as accessible going forward. Smarter, privacy-friendly solutions are a must – not only to make shoppers happy but to drive sales and build long-term loyalty, too.

That’s where 1:1 personalization comes in, using real-time signals to tailor each customer’s experience from their first session on your site.

71% of consumers expect companies to deliver personalized interactions, and 76% get frustrated when it doesn’t happen.

Personalization key terms defined

The world of data and personalization is filled with buzzwords, but what do they all mean and which ones do eCommerce businesses need to know about?

• Micro-segmentation splits your customers into tiny groups based on demographics (e.g. age, income level, geography, etc.) and psychographics (favorite colors, brands, pets, etc.). This helps with targeting, but it lacks precision because it can’t always determine what the individuals in each segment like, often resulting in irrelevant suggestions and stereotyping.

• Personalization drills down to the individual level, wielding user attributes and historical data to tailor the shopping experience to each customer. Unlike 1:1 personalization, standard personalization doesn’t recognize contextual information, like the user’s immediate intent.

• 1:1 personalization (aka one to one personalization) takes standard personalization further by combining historical and real-time user data to customize the experience to the individual at that moment in time. It also recognizes when a shopper’s search intent switches mid-session, dynamically adapting to reflect what they’re looking for next.

• First-party data is gathered directly from your website visitors. You can collect this explicitly through forms, live chats, etc. or implicitly by tracking their interactions on your site. FactFinder’s 1:1 personalization taps into this implicit data via Atlas AI, while drawing insights from their purchases. Because no other sources are involved, first-party data is generally more reliable and helps you stay in line with privacy regulations.

• Third-party data is bought or sourced from providers that aren’t connected to your company or audience. It has a much broader scope but can lead to inaccuracies since it rarely aligns with your actual customers. That’s why FactFinder doesn’t use third-party data: it gets in the way of delivering a truly 1:1 personalized customer experience. Plus, it’s often unclear where it’s from and whether it has been collected according to privacy regulations.

Converting first-time customers with 1:1 personalization

Imagine a new customer visits your online shop for the first time. Let’s call her Anna. Depending on her cookie settings, you might know her device, location and how she landed on your site, but little else. Your system learns more about her as she navigates your site, whether she’s applying filters, engaging with content or making a purchase.

Anna searches for “running leggings”. With no previous customer history to go on, your engine shows her a mix of running leggings across brands, sizes, colors, etc. She clicks on a pair, selects size and color, but doesn’t add them to her cart.

Anna then runs a search for “Gymshark”. Instead of general results, the engine registers her intent and ranks Gymshark running leggings first, in her preferred size and color.

This is 1:1 personalization at work. Even before Anna makes a purchase or shares any details explicitly, she benefits from search results tailored to both her preferences and immediate needs.

Want a more visual explanation?

Hit play on this dynamic demo experience to see how 1:1 personalization works for B2B and B2C stores.

Raising AOV with personalized recommendations

1:1 personalization can boost your average order value (AOV) by showing visitors ultra-relevant recommendations. Most online shops suggest products based on what other customers have bought. But with 1:1 personalization, recommendations evolve as the shopper moves through the site. They adapt to shifting intents to ensure shoppers always see the most relevant upsells and add-ons.

Let’s say Anna adds black Gymshark running leggings to her cart. At the check-out page, the AI recommends her running shoes and tank tops to complete the outfit. If she searched for gym leggings, the engine would recommend hybrid and indoor sneakers.

In this way, 1:1 personalization goes beyond helping customers find what they need, inspiring them to discover ideal add-ons they hadn’t thought to look for. It even considers new and niche items other engines typically ignore.

Turning first-time customers into lifelong fans

41% of an ecommerce store’s revenue is created by 8% of its customers, and your top 5% of customers generate 35% of revenue.

You’ve converted Anna from a first-time visitor into a paying customer and raised her AOV. But how do you win her long-term loyalty?

Having helped her find what she was looking for quickly and easily with 1:1 personalization, you’ve already done a lot of the work. According to McKinsey, 78% of consumers are more likely to make repeat purchases from companies that personalize and recommend them to friends and family.

However, a fantastic eCommerce customer experience isn’t always enough. Some people need reminders to choose your brand for new purchases – or to make that purchase in the first place.

With 1:1 personalization, you can embed AI-powered recommendations into emails, ads and texts, suggesting products that complement each contact’s purchases and browsing history.

You might even provide personalized pricing, offering exclusive discounts for returning customers and larger rewards for those who spend more. Personalized pricing really shines in a B2B context, where you can show negotiated rates, tiered models and VAT-inclusive or exclusive options.

How 1:1 personalization works for returning customers

A month later, Anna returns to your online shop. Your system has learned from her purchase history. But it also knows her intent has shifted. So instead of just showing leggings, it greets her with a fresh mix of items that match her preferences.

This time, Anna searches for a hoodie. With 1:1 personalization in place, your system shows hoodies in her size and preferred colors, with Gymshark at the top. It balances context with past purchases to help her find what she needs faster, preventing decision fatigue and making the eCommerce customer experience more intuitive.

Likewise, a returning B2B customer will see products aligning with their purchase history, such as screen protectors and hard drives that fit their company laptops.

Adapting to customers’ contradictive nature

1:1 personalization stands out for its ability to recognize each customer’s contradictions and complexities.

Anna might prefer Gymshark leggings but Superdry hoodies and simply buy the cheapest socks available, regardless of brand. Standard personalization techniques often struggle to address these nuances, leading to missed opportunities.

FactFinder’s 1:1 personalization, on the other hand, applies customers’ preferences in different levels of detail, automatically adjusting to individuals’ eclectic tastes. This saves you development time and effort while improving the relevance of search results and recommendations.

Shortly after switching to FactFinder’s AI-driven personalization solution, Tabakguru increased its repeat buyer rate to 60%. See the full impact it had on their business.

The future of personalization in eCommerce, featuring insights from Forrester

Tools like DALL-E 2 and ChatGPT have impressively shown us what is already possible with AI. Especially for eCommerce, there are exciting opportunities around AI and personalization. That’s why we sat down with Brendan Witcher, VP & Principal Analyst at Forrester, to explore the current state of the market, best practices for personalization, and insights into what the future holds. Check out the video below.

1:1 personalization in search and product discovery

While personalization has helped increase some business’ revenue, it can’t operate alone. After all, how can your online store personalize the shopping journey if it doesn’t recognize the search term?

An exceptional eCommerce customer experience starts with sophisticated search and product discovery. One that’s smart enough to handle misspellings, synonyms, even intent-based queries and still get it right, every time.

Kübler Sport, a leading B2B and B2C sports and fitness retailer, switched to FactFinder when their site search solution couldn’t find what shoppers wanted. Since then, the retailer has seen a 35% increase in conversions and an influx of positive feedback. Now, they’re ready to take the customer experience further by integrating 1:1 personalization technology. Discover the secret to their success.

How to achieve 1:1 personalization

A search and product discovery platform using 1:1 personalization should continuously self-optimize to keep up with customers’ changing needs and how they relate to your product range. This is only possible (and scalable) with AI.

Every online shopper searches differently. Two people may use the same search query but mean wildly varying things. Atlas AI gets around this by tracking how customers behave outside the search, including the categories, products and filters they click on. Your store can then adapt in real time to provide each customer with personalized, contextually relevant suggestions. No need for explicit first- or third-party data.

Beyond this, our 1:1 personalization uses wisdom of the crowd to self-optimize for general relevance, building a deep understanding of the relationship between your products and how they relate to different search queries and interactions.

Unlike other AI models, FactFinder needs minimal learning data, enabling it to scale up fast and deliver true 1:1 personalization from the get-go. Automation covers everything that would never be possible with manual segmentation rules. That said, you have the option to manually adjust ranking rules to your liking. For example, you can pin products from the latest merchandising campaigns at the top of the results and downrank out of stock items. You might also choose to manually select cross- and up-sell products to match your sales strategy. It’s the perfect balance of automation and (optional) control.

Future-proof your business with 1:1 personalization

As consumers grow frustrated with eCommerce customer experiences based on standard segmentation and personalization techniques, 1:1 personalization can set your brand apart. Using minimal data, this AI-powered technology delivers tailored results and recommendations using real-time signals. It not only saves shoppers from sharing their personal details but also picks up on their immediate search intent to show them exactly what they want at that moment in time.

Shoppers want intuitive, helpful interactions, not brands that feel like they’re watching their every move. FactFinder’s solution delivers just that, boosting conversions, AOV and customer loyalty with no need for sensitive information.

FAQs

41% of online shops have problems with their search function.¹ Is vector search the key to higher relevance and fewer drop-offs? Here’s a crash course from an eCommerce perspective.

Definition: what is vector search?

Vector search uses AI to translate complex data, like products, texts and images, into numbers (vectors). It stores this data in a vector database so that when someone searches with an image or another type of complex data, it can quickly find and show similar content based on mathematical distances.

In eCommerce, this technology is used to maximize the relevance of product listings for search results, category pages, recommendations, etc. AI models, like machine learning and natural language processing (NLP), understand the semantic connection between products and search queries, allowing them to deliver relevant results even when there’s no exact match. So, your customers will still see sneakers, whether they search for “kicks” or “trainers”.

Key terms in vector search

• Semantic search is an umbrella term for technologies that understand the context and meaning of search queries to find matching results, often using NLP. Vector search is a type of semantic search, specifically based on calculating mathematical distances between vectors.

• Vector search is a specific method of semantic search that understands context and intent behind queries, even without any matching keywords. It compares shopper input with product data in a multidimensional space using “nearest neighbor search.” While the graphic shows three dimensions, AI models typically use hundreds or even thousands.
  
• Keyword search in eCommerce looks for similarities between search requests and product data. It’s ideal for query types like brand names, product names and even product IDs, where precision matters. The quality of results depends on the search engine. Basic ones can stumble when a query doesn’t exactly match what’s in the catalog. Smarter systems, like FactFinder’s, use more advanced algorithms to detect not only obvious typos like “sheos” vs. “shoes” but also phonetic similarities like “fone” vs. “phone”. However, even the best keyword search doesn’t actually understand what the user means.
• Hybrid search combines the strengths of keyword search and vector search. The goal is to return relevant hits in every situation, no matter what the customer types into the search bar: SKUs, keywords, mixed language and conversational queries or any mix of these. By intelligently weighting and ranking results, it creates a varied yet highly relevant search experience that meets a wide range of user intents. Resulting in fewer drop-offs, happier customers and a direct boost in revenue per customer.

How does vector search work?

We’ll walk through the workflow with a typical example: intent-based search queries. These queries indicate the reason behind the search. For example, if an online shopper types in “comfortable shoes for a city trip in July,” we can assume they’re looking for shoes suitable for long walks on hard surfaces during summer temperatures. But how does vector search recognize this purchase intent and deliver fitting results? This process consists of three steps:

1. Vector embedding – An AI model translates the words into numeric expressions to capture the meaning behind the query. Now, it can understand that “comfortable” in the context of shoes refers to ergonomically designed, cushioned, lightweight, etc. And from “city trip” and “July,” it extracts, among other things, attributes related to the ground surface and season.

2. Nearest neighbor search – Next, the AI checks these numeric expressions with the product data to semantically similar items based on the mathematical distances in the vector space.

3. Response – The closest products are then returned to the shop as search results. For this search query, these might be breathable, low-cut hiking shoes with arch support.

Use cases: vector search vs. keyword search

Let’s explore different types of search queries to compare the hypothetical performance of a vector search with keyword-based search methods.

In some cases, vector search yields better results; in others, keyword search is more efficient. Our analysis of search queries from over 2,000 shops shows that online customers still mostly do not use natural language inputs — they don’t ask questions like they do in Google, but search with short, precise terms. However, user behavior is changing as we speak, and in the coming years, a reliably functioning vector search could become vital for online retailers.

Hybrid mode keeps the customer experience in focus

Vector search isn’t always enough on its own, especially when it comes to exact product names, brands or item numbers. Many online shoppers still expect reliable keyword search. That’s why FactFinder blends advanced keyword search with vector capabilities — so you get exact matches when needed, and smart results when keyword search fails.

1. Linguistic matching – The core of our solution is the Worldmatch® search algorithm, which we’ve been refining for over 20 years. The technology detects phonetic similarities between terms and ensures incorrect or imprecise inputs, like “naiki” vs. “Nike,” lead to the correct result.

2. Semantic relevance – Every industry, audience and product range comes with its own unique requirements. That’s why FactFinder’s vector search offers two modes:

• Hybrid mode delivers a smart product list combining keyword-based and vector-based results. You decide which type of result should be weighted higher or lower based on your specific business needs. This mode is ideal for audiences who often browse for inspiration, have limited product knowledge or shop based on specific occasions.
• Fallback mode kicks in only when keyword matching would return no results. Instead of landing on a zero-results page and potentially bouncing, customers see a selection of products that best match the intent and context of their search query.

3. Human expertise – Unlike some search and product discovery platforms, FactFinder is not a “black box.” You can easily track the sorting of your product and recommendation lists in an intuitive backend editor. It’s the perfect blend of automation and control: you can let the AI do the work for you, while having the flexibility to manually adjust the configurations in line with your business goals, e.g., through ranking rules that push selected brands or sale items.

Instead of blindly following the hype around vector search, FactFinder focuses on thoughtful, step-by-step integration. We don’t view new technologies as an end in themselves but concentrate on the experience they bring to users and shoppers. Our solution applies AI to the areas where it creates real value, striking the best balance between keyword precision, semantic context and human expertise.

Conclusion: get the best of both worlds

Vector search offers huge potential to increase conversion rates and AOVs, especially for intent-based search queries, synonyms and natural language inputs. However, it’s not a cure-all. The future lies in hybrid search, a combined approach that blends semantic understanding, keyword precision and AI-powered personalization to show the best results every time.

We’re here to help you address your unique eCommerce challenges. Contact us today to learn more about how our vector search’s hybrid mode can drive sustainable revenue growth.

Interactive demo video

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1. Deconstructing E-Commerce Search UX: The 8 Most Common Search Query Types (41% of Sites Have Issues) – Baymard ︎

Choosing the right search and product discovery platform 

A powerful search and product discovery is key to any online shop’s success. Today’s customers expect fast, personalized, and relevant experiences that go far beyond the basic search of the past. Yet, creating these experiences is easier said than done.  

Some businesses rely on the built-in search function of their online shop platforms, which typically use open source and offer only basic features, leading to irrelevant search results and lower conversion rates. Others opt for alternative open source tools that offer customization but require a tech-savvy team. Then there’s the third group, which embraces out of the box solutions with varying levels of eCommerce-specific features, flexibility and ease of use. 

But which is best for your business? 

In this blog post, we’ll compare open source and out of the box solutions, like FactFinder, exploring the strengths and limitations of each to help you make an informed decision.

The challenge of open source site search in eCommerce

Countless businesses use open source for their search and product discovery, drawn by the promise of supposed low price and complete control over the configurations. In theory, open source should be the easiest, most customizable option, but don’t be fooled. Due to its code-based nature, it can leave you dependent on your development team or a partner, who charges by the hour. Even small changes can require heavy development work, additional charges and valuable time, leading to bottlenecks and missed opportunities.

Debunking the myths around ready-made eCommerce site search solutions 

A ready-made search and product discovery platform might be the answer — but persistent myths push online shops toward open source, only to hit unexpected costs and obstacles. It’s time to separate the facts from the fiction. 

• “Out of the box platforms are expensive” — Fiction: Ready-made eCommerce site search solutions might appear more expensive at the start, but in the long run, they can save you a great deal of time and money in ongoing development. One retailer has been saving €1,000 per month in server fees since they replaced open source search with FactFinder’s AI-powered ready-made search and product discovery solution.

• “Good enough” is good enough when it comes to search — Fiction: All search is not the same. The search and product discovery features of your online shop can make or break the customer experience. Hence, providing a system that is just about “good enough” will result in lost opportunities, while your customers miss out on a great experience. Remember, what’s deemed “good enough” now will soon be “poor” and “outdated”. 

• “Ready-made site search solutions don’t provide enough insight into the search results” — Fiction: Many businesses assume that out of the box solutions have “black box” backends, with complex configurations that make it impossible to understand the rankings. But this isn’t always the case. FactFinder, for example, empowers you to see the impact of the algorithms shaping your site search results and adjust them to your heart’s content. 

• “Out of the box solutions are restrictive and inflexible” — Fiction: FactFinder gives you a unique blend of automation and control. Offering a host of product discovery features designed for eCommerce businesses, both in the B2C and B2B space, it integrates seamlessly with your existing software and can adapt to your business needs. Any additional modules will fit easily with the core out of the box search solution — worlds apart from the cumbersome, time-intensive process of integrating new tools with an open source platform

Since switching from an open source site search engine, B2B wholesaler Gustav Ehlert GmbH & Co spends less time dealing with search-related enquiries. They can now fine-tune their online storefront to reflect the latest sales strategies while relying on AI to dynamically prioritize new products. What’s more, their conversion rate stands at 12%, almost four times higher than the industry standard. 

Quick comparison: Open source search vs FactFinder 

Ready-made solutions offer a powerful alternative to open source search in eCommerce. While not all options offer the same benefits, we’ll focus on FactFinder in this post.  

This comparison chart breaks down the resource requirements, user friendliness and eCommerce features of open source search versus FactFinder as a ready-made solution. We’ve summarized the key details to help you make the best decision for your B2C or B2B business. 

Resource requirements 

How much money, time and in-house skills do you need to invest upfront and long-term?  

Ease of use

Managing an eCommerce business means constantly fine-tuning your tools to keep up with trends. Whether you’re using FactFinder or an open source platform, occasional updates are inevitable for special promotions, product changes or search optimizations. The easier these adjustments are, the more time you’ll have to focus on what really matters: your customers and your business.

The Popken Fashion Group switched 12 online shops to FactFinder when they reached a dead end with their old site search solution and needed a platform that could support, rather than inhibit, their rapid growth.

eCommerce-specific features

FactFinder was built specifically for eCommerce, while most open source search solutions are not. But does that really matter if you can customize an open source platform to include all the features you need? Let’s see how using an eCommerce-focused platform can benefit your business in comparison to a more generic solution.

By transitioning from an open source search solution to Factfinder, GLOBUS Markthallen, one of Germany’s leading supermarket chains, revolutionized their search functionality, ensuring smooth product discovery.

Why FactFinder is the ideal alternative to open source search in eCommerce

• AI automation & control — FactFinder gives you much more transparency, providing visibility into what is contributing to a product’s position and the power to influence that.

• Extensive functionality — The most advanced product discovery and search solution on the market, with 8 core modules and the (optional) ability to fine-tune each feature to suit business and resource requirements. See it at work.

• Our people — With an award-winning team of eCommerce search and product discovery experts, FactFinder offers dedicated and proactive support to thousands of brands worldwide. Not only does FactFinder have over 20 years of experience and product innovation, but we also provide outstanding support, priding ourselves on the personal touch.

FactFinder can save you frustration, time and money

In conclusion, while open source solutions may seem like the lower-cost option at first, they usually require more investment over time. They come with hidden expenses, surprising you with charges for everything from selling licenses to plug-ins and software updates. Add to that the expense of hiring specialist developers to implement the software, fix bugs and fine-tune the search engine in line with industry standards, and you’re looking at parting with tens of thousands of Euros over the foreseeable future.

Meanwhile, as a ready-made alternative, FactFinder presents upfront costs, but these are always transparent, predictable and customized to your business — no nasty surprises! What’s more, FactFinder’s industry-leading AI-powered eCommerce solution updates itself in real time, preventing the need for manual adjustments. With out of the box smart search and product discovery features, including personalization, recommendations, Customer-Specific Info, Geo and Predictive Basket, your business will benefit from higher customer satisfaction and maximized conversion rates — plus a reliable support team on hand whenever you need them.

Interactive demo video

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Definition: What is product discovery in eCommerce?

Product discovery is the process where online shoppers find, compare and choose products to purchase.

It goes far beyond search. Product discovery shapes the entire shopping journey, guiding shoppers from the moment they enter your online store to ensure they find the most relevant products and checkout without abandoning their cart. Sophisticated product discovery technology should also include post-sale services, personalized newsletters and repeat purchases.

Personalization, merchandising and recommendations are just a few of the powerful functionalities involved in product discovery. These capabilities not only help customers find what they are looking for fast but inspire additional purchases, recommending ideal complementary items and alternatives, resulting in a higher average order value (AOV).

Many people see search and product discovery as separate: search is a precise tool that allows customers to state their needs, while capabilities like recommendations provide passive inspiration. At FactFinder, we don’t make this distinction, as in practice, active search and passive inspiration merge together.

“77% of online retailers say their customers don’t know exactly what product they want until they see it.” – Salesforce²

Pro tip: Product discovery in eCommerce is not the same as in project management

The term product discovery might make you think of project management, but don’t be misled. In product management, it refers to evaluating and developing new products to test their market fit — a process within a company rather than a customer-focused process.

Why is product discovery vital to your eCommerce success?

Simply put: because your sales success depends on it. The faster and more reliably you connect online shoppers with the products that matter to them, the more revenue your store will generate. This applies to both B2C and B2B shops. Many businesses focus heavily on driving traffic but often neglect crucial steps to increasing eCommerce sales, conversion rates and revenue generated by visitors.

A quick thought experiment: Let’s say an online store generates €20 million per year with a 1.9% conversion rate. A small conversion uplift of just 0.05% would bring in over €500,000 in additional revenue. And improving conversion rate is only one of many goals that can be achieved by optimizing product discovery.

eCommerce goals you can achieve with smart product discovery features

• Increase your conversion rate: Kübler Sport, a sporting goods retailer, saw a 35% increase in conversions by combining smart search with inspiring merchandising. See Case Study

• Boost your AOV: KASTNER, Austria’s leading food wholesaler, saw shoppers add 2.3 more products to their carts, due to AI reminding them of forgotten items. See Case Study

• Elevate revenue per session: Bergfreunde, an outdoor retailer, increased revenue per session by up to 70% in certain categories with automated, interactive shopping advice. See Case Study

• Reduce bounce rate: Globus Baumarkt, a DIY chain, reduced its bounce rate by 50% in sessions with guided selling. See Case Study

• Make your brand stand out: The quality of your product discovery directly impacts your company’s image. By implementing a more advanced product discovery solution, B2B giant Berner has improved its eCommerce experience—turning misinterpreted search queries and irrelevant results into a smooth, intuitive journey that boosts conversions and delights customers. See Case Study

• Improve customer satisfaction and loyalty: RE-IN’s regular customer satisfaction surveys have revealed more positive feedback and fewer support requests, thanks to precise and efficient search. See Case Study

• Free up internal resources: Since introducing an AI-based product discovery solution, Flaschenpost, a food delivery service, has saved valuable time and effort for other key projects. See Case Study

The product discovery matrix

You should approach product discovery holistically in your online shop. Search is just one of many key features that work together to provide the best possible shopping experience and increase eCommerce sales:

Checklist: Does your product discovery live up to online shoppers’ expectations?

Let’s quickly go through the stages of product discovery in your online shop. Drawing from our work with over 2,000 stores, we’ve identified key areas at each touchpoint that are essential yet often overlooked. Use this checklist to prioritize your eCommerce optimizations and take your online store to the next level.

1. Awareness – Win over shoppers the moment they enter your store

• Search: Does your search work with error tolerance, understanding single-word terms, complex multi-word inputs and numerical queries?

“68% of customers believe that the search function in online stores should be improved.” – PR Newswire³

• Suggest: What does your suggestion menu show after entering the first few letters? Help shoppers find what they are looking for faster by not only showing search terms but also categories, products with images and helpful content.

• Navigation: Does your store’s navigation adapt to inventory changes automatically without manual effort?

2. Consideration – Cut bounce rates and smooth the path to purchase

• Performance: Does your content load quickly enough? Shoppers don’t have time – or patience! – to wait around. Make sure search results and category pages load within milliseconds, including product listings, images and other rich content. Your eCommerce conversion rate will decrease by an average of 0.3% with every additional second your site takes to load.

• Faceted navigation: Is your faceted navigation clearly structured and does it show all relevant filter options?

• Personalization: Does your product sorting self-learn to align with each customer’s immediate requirements? 1:1 AI personalization enables your website to keep up with customers’ unique, changing needs from their first session.

“71% of customers expect personalized shopping experiences—and 76% become frustrated without personalization.” – McKinsey⁴

• Ranking: Can you refine sorting according to your own rules, such as ranking by brand, margin or sale?

• Guided Selling: Do you offer automated, interactive shopping advice to help customers make decisions?

• Merchandising: Can you easily enrich product lists and zero results pages with inspiring content, like banners, best-selling products and guide videos? Turn low-converting queries into high converting recommendations.

• Analytics: Can you track what’s being found and not found in the backend, so you can optimize accordingly?

5-minute checklist: Evaluate your search and discover untapped revenue potential

3. Conversion – Provide assurance and drive AOV

• Cross-selling and upselling: Do you show relevant recommendations for selected products to increase revenue per customer, such as add-ons, accessories and higher-priced alternatives?

• Personalized recommendations: Do you use AI to tailor your recommendations in real-time based on the buying signals and preferences of each customer?

“64% of customers want personalized shopping experiences tailored to their individual preferences.” – Adobe⁵

• Predictive Basket: Do you remind customers in the cart or checkout about potentially forgotten items based on their purchasing patterns and seasonal trends?

4. Retention – Inspire and encourage repeat purchases

• Merchandising: Can you easily integrate trending product campaigns from your store into your newsletter?

• Recommendations: Do you inform cart abandoners about forgotten products and related recommendations?

• Predictive Basket: Do you remind customers to reorder consumables, refills or replacement parts before they run out?

Single solution vs a patchwork of tools

While using multiple solutions might seem more flexible, it usually comes with high integration costs, system breakdowns and greater maintenance efforts.

An AI-based product discovery platform like Factfinder, on the other hand, connects all the key functionalities in a single solution. For example, search queries can instantly improve personalization, or user behavior in categories can refine recommendations without manual intervention. It’s an intelligent, self-optimizing system that not only increases revenue but also saves long-term costs, time and internal effort.

For businesses that require the option of automation and control, FactFinder provides unmatched transparency. The intuitive backend makes it easy to align the AI with individual business strategies — such as through ranking rules or product campaigns — to prioritize specific items or categories.

So, if you’re looking for a scalable, efficient and future-proof solution, an AI-based all-in-one platform far surpasses a mix of tools.

Customer testimonials on working with FactFinder

“FactFinder is one of the few service providers who supported us quickly and competently from the very beginning, even during the project phase. And the speed with which the Web Components integration for Shopware was implemented is something we haven’t experienced before. Thanks for the excellent collaboration!“
– Kevin Hansen, Managing Director

“22 days until go-live: The project went very well to be honest, everyone pulled together. We reached our goal quickly and my technical expectations were also met.”
– Aron Spohr, CTO

Read more about their experiences and results with our platform.

Conclusion: Optimize your product discovery to drive customer satisfaction and increase eCommerce sales

An intuitive, personalized product discovery is more than just a technical measure — it’s the key to an outstanding shopping experience and achieving your goals. It ensures that online shoppers quickly and easily find the products that meet their expectations, thereby increasing eCommerce sales, conversion rates, revenue and customer loyalty. Additionally, product discovery plays a crucial role in strengthening your brand image. But it’s not just your business that benefits. Those who drive better product discovery projects see visible results and enhance their reputation as successful innovators.

Need support with product discovery for your store? Contact us today — we’ll help you unlock the full potential of your eCommerce business!

Sources:

1. https://techjury.net/blog/user-experience-stats/ ︎
2. https://appexchange.salesforce.com/image_host/b3595d2d-c956-43d8-88cb-06c91f2667a4.pdf ︎
3. https://www.prnewswire.com/news-releases/4-in-10-online-shoppers-give-product-discovery-experiences-a-c-grade-or-below-according-to-new-study-302248880.html ︎
4. https://www.e-commerce-magazin.de/kaufentscheidung-werte-der-verbraucher-beeinflussen-diese-massgeblich-a-ec7cd0fe8f68daf6a43cb096bb730763/ ︎

Online shoppers are impatient, especially in pharmacies where they need quick relief for urgent health concerns. These shoppers face two big challenges. First, a huge range of products, from prescription medicines to supplements and cosmetics. Second, the variety of search queries, from general terms like “child cough” to specific (and often misspelled) terms such as “zetterizin 50.”

Your shop’s success depends on how well it delivers the right search results to each shopper. It results in a satisfied purchase or a frustrated bounce. A key metric to monitor is your search bounce rate, which tracks how many people leave after searching without selecting a product.

How can you lower your bounce rate? This article shows how AI-powered tools can make searches easier and more personalized, creating a smooth shopping experience that works.

Reduce bounce rates with personalized search

Every search is different because each shopper has their own needs. AI-powered personalized search adjusts results based on what each shopper wants. It looks at their past purchases, preferences and even what they’re doing on your site right now.

For example, if someone searches for “cough syrup” and usually buys sugar-free products, their results will highlight sugar-free options. Another shopper who buys products for kids will see child-safe syrups, like alcohol-free ones, at the top of their results for the same search.

Personalized search helps customers find what they need faster, which reduces bounce rates, increases sales and builds trust in your brand. Research shows 60% of customers are more likely to return after a personalized shopping experience (Segment, 2021).

1:1 personalization with Atlas AI

Personalized search sounds simple, but doing it right is key. Many systems depend on big data or customer groups, but these don’t always work well. Most pharmacies don’t have enough clicks or purchase data to make these systems accurate. And grouping customers by age or location doesn’t reveal what they actually want to buy.

That’s why we built Atlas AI. It doesn’t need lots of data or extra customer info. Instead, it focuses on understanding your products and matching them to what each shopper wants, much like a helpful pharmacist would. Atlas AI uses just one interaction to personalize results in real time.

How it works
Atlas AI maps out all your products and their details, like ingredients or uses, creating a smart system of connections. Think of it as a map where related products are close together. When a customer interacts with your site, Atlas AI uses this map to understand what they’re looking for. Even with just one click, it delivers highly accurate results, solving the problem of limited data.

Key benefits of Atlas AI for reducing bounce rates

• General relevance: Atlas AI understands products deeply, so it shows relevant results without needing manual adjustments. For example, a search for “ibus” will prioritize ibuprofen over unrelated items like rooibos tea.

• Real-time relevance: Atlas AI adjusts instantly based on what the customer does. If they search for “vitamin D” and then “Ratiopharm,” it shows vitamin D from Ratiopharm instead of random products from the brand.

• Preference-based relevance: Atlas AI remembers preferences. A shopper who usually buys Verla vitamins will see Verla products ranked higher in searches like “winter vitamins.”

• A blend of artificial and human intelligence: An intuitive interface allows you to align the AI with your business goals while automating routine tasks like search optimization.

Largest online pharmacy in the Netherlands achieves +12% conversion

DeOnlineDrogist.nl is a compelling example of how personalized search can transform eCommerce KPIs. As the leading online pharmacy in the Netherlands, it faced problems with its old search system, which couldn’t handle its large product catalog. After switching to our AI-powered system, its results improved dramatically.

In just three months, they saw a 12% increase in conversions from searches. They also saved time:

“Previously, we spent about 30 hours a month maintaining our search system. Now, we spend no more than four hours a month. This is a major improvement for us, freeing up time and resources for other areas of the business.” — Joachim de Boer, Co-founder of DeOnlineDrogist.nl

Continue reading the case study.

Why personalized search is essential for online pharmacies

Personalized product search isn’t just nice to have — it’s a must for online pharmacies. AI tools like Atlas AI lower bounce rates, boost search conversions and help you stand out from competitors. And they do this without needing big data.

With customer expectations higher than ever, combining technology with personalization creates a shopping experience that exceeds expectations. It makes finding the right products faster, easier and more tailored to each shopper.