Mayank Patel
Sep 15, 2025
5 min read
Last updated Sep 15, 2025
Every ecommerce team wants higher conversions. But too often, optimization eff orts rely on guesswork—redesigning a button here, changing copy there—without clear evidence of what actually moves the needle. This results in sporadic wins at best and wasted effort at worst.
The most effective CRO programs don’t chase random ideas; they follow a system. They start with careful observation of user behavior, enrich those signals with analytics and feedback, form structured hypotheses, and run disciplined experiments.
Heatmaps, session recordings, form analytics, and funnel data are powerful tools, but they’re only valuable when used to generate focused hypotheses you can test. This guide walks you through that end-to-end process: how to map user behavior, enrich signals, frame testable ideas, experiment without pitfalls, and scale what works.
The first step is observation. Mapping what users actually do on your site. This involves using tools to visualize and record user interactions. By capturing how visitors navigate pages, where they click, how far they scroll, and where they get stuck, you build a factual baseline of current UX performance. Key behavior-mapping tools include:
Visual overlays that show where users click, move their cursor, or spend time on a page. Hot colors indicate areas of high attention or clicks, while cool colors show low engagement. If a CTA or important link is in a “cool” zone with few clicks, it might be poorly placed or not visible enough.
A specialized heatmap showing how far down users scroll on a page. This reveals what proportion of visitors see each section of content. In practice, user attention drops sharply below the fold. If a scroll map shows that only 20% of users reach a critical product detail or signup form, that content is effectively unseen by the majority. This observation signals a potential layout or content hierarchy issue.
These capture real user sessions as videos. You can watch how visitors browse, where they hesitate, and what causes them to leave. Session replays are like a “virtual usability lab” at scale, for example, a user repeatedly clicking an image that isn’t clickable (a sign of confusion), or moving their mouse erratically before abandoning the cart (a sign of frustration).
By reviewing recordings, patterns emerge (e.g. many users rage-clicking a certain element or repeatedly hovering over an unclear icon). Establish a consistent process for reviewing replays (for instance, log “raw findings” in a spreadsheet with notes on each observed issue) so that subjective interpretation is minimized and recurring issues can be quantified.
Specialized tracking of form interactions (e.g. checkout or sign-up forms). Form analytics show where users drop off in a multi-step form, which fi elds cause errors or timeouts, and how long it takes to complete fi elds. For example, if many users abandon the “Shipping Address” step or take too long on “Credit Card Number,” those fi elds might be causing friction.
After gathering behavioral data, the next step is enriching that data. This is where we transition from what users are doing (quantitative data) to why they’re doing it (qualitative context).
Key enrichment methods include:
Quantitative analytics (from tools like Google Analytics or similar) help size the impact of observed behaviors. They answer questions like: How many users experience this issue? Where in the funnel do most users drop off ? For example, a heatmap might show a few clicks on a “Add to Cart” button but analytics can tell us that the page’s conversion rate is only 2%, and perhaps that 80% of users drop off before even seeing the button.
Analytics can also correlate behavior with outcomes: e.g. “Users who used the search bar converted 2X more often.” These metrics highlight which observed patterns are truly hurting performance. They also help prioritize a problem affecting 50% of visitors (e.g. a homepage issue) is more urgent than one affecting 5%.
Breaking down data by visitor segments (device, traffic source, new vs. returning customers, geography, etc.) enriches the signals by showing who is affected. Often, averages hide divergent behaviors. For instance, segmentation might reveal that the conversion rate on desktop is 3.2% but on mobile only 1.8%, implying mobile users face more friction (common causes: smaller screens, slower load times, less convenient input).
Or perhaps new visitors click certain homepage elements far more than returning users do. By segmenting heatmaps or funnels, patterns emerge. For example, mobile visitors might scroll less and miss content due to screen length, or international users might struggle with a location-specific element. These insights guide more targeted hypotheses (maybe the issue is primarily on mobile, so test a mobile-specific change).
Sometimes the best way to learn “why” users behaved a certain way is to ask them. Targeted surveys and feedback polls can be deployed at strategic points. For example, an exit-intent survey when a user drops out of checkout (“What prevented you from completing your purchase today?”). Or an on-page poll after a user scrolls through a product page without adding to cart (“Did you find the information you were looking for?”).
Survey responses often highlight frictions or doubts. For example, “The shipping cost was shown too late” or “I couldn’t find reviews.” These qualitative signals explain the observed behavior (e.g. “why did 60% abandon on shipping step?”). Even language from customers can be valuable; if multiple users say “the form is too long,” that’s a clear direction for hypothesis. User reviews and customer service inquiries are another VoC source.
In addition to direct user feedback, an expert UX/CRO audit can enrich signals by identifying known usability issues that might explain user behavior. For example, if session replays show users repeatedly clicking an image, a UX heuristic would note that the image isn’t clickable but looks like it should be (violating the principle of affordance). While this is more expert-driven than data-driven, it helps generate potential causes for the observed friction which can then be tested.
The result of signal enrichment is a more complete problem diagnosis. We combine the quantitative (“how many, how often, where”) with the qualitative (“why, in what way, what’s the user sentiment”) to turn raw observations into actionable insights. Quant data may tell us that users are struggling, but it doesn’t tell us what specific problems they encountered or how to fi x them, for that, qualitative insights are needed.
Likewise, qualitative anecdotes alone can be misleading if not quantified. Thus, a core LinearCommerce strategy is to triangulate data. Every hypothesis should ideally be backed by multiple evidence sources (e.x. “Analytics show a 70% drop-off on Step 2 and session recordings show confusion and survey feedback cites ‘form is too long’”). When multiple signals point to the same issue, you’ve found a high-confidence target for optimization.
Also Read: How to Engineer Cloud Cost Savings with Kubernetes
With a clear problem insight in hand, we move to forming an hypothesis. A hypothesis is a testable proposition for how changing something on the site will affect user behavior and metrics. Crafting a strong hypothesis helps you run experiments that are grounded in rationale, focused on a single change, and tied to measurable outcomes.
In the LinearCommerce framework, a good hypothesis has several key characteristics:
The hypothesis must directly address the observed problem with a cause-and-effect idea. We don’t test random ideas or “flashy” redesigns in isolation. We propose a change because of specific evidence.
For example: “Because heatmaps show the CTA is barely seen by users (only 20% scroll far enough) and many users abandon mid-page, we believe that moving the CTA higher on the page will increase click-through to the next step.” This draws a clear line from observation to proposed solution.
Defi ne exactly what you will change and where. Vague hypotheses (“improve the checkout experience”) are not actionable. Instead: “Adding a progress indicator at the top of the checkout page” or “Changing the ‘Buy Now’ button color from green to orange on the product page” are concrete changes.
Being specific is important both for designing the test and for interpreting results. Each hypothesis should generally test one primary change at a time, so that a positive or negative result can be attributed to that change. (Multivariate tests are an advanced method to test multiple changes simultaneously, but even then each factor is explicitly defined.)
A hypothesis should state the expected outcome in terms of user behavior and the metric you’ll use to measure it. In other words, what KPI will move if the hypothesis is correct? For example: “…will result in an increase in checkout completion rate” or “…will reduce form error submissions by 20%”. It’s important for you to pick a primary metric aligned with your overall goal.
If your goal is more purchases, the primary metric might be conversion rate or revenue per visitor; not just clicks or time on page, which are secondary. Defining the metric in the hypothesis keeps the team focused on what success looks like.
Pitfall to avoid: choosing a metric that doesn’t truly reflect business value (e.g. click rate on a button might go up, but if it doesn’t lead to more sales, was it a meaningful improvement?). Teams must agree on what they are optimizing for and use a metric that predicts long-term value. For instance, optimizing for short-term clicks at the expense of user frustration is not a win.
A helpful format for writing hypotheses is:
“Because we see (data/insight A), we believe that changing (element B) will result in (desired effect C), which we will measure by (metric D).”
For example: “Because 18% of users abandon at the shipping form (data), we believe that simplifying the checkout to one page (change) will increase completion rate (effect), as measured by checkout conversion% (metric).”
After writing hypotheses, prioritize them.
You’ll generate many hypothesis ideas (often added to a backlog or experimentation roadmap). Not all can be tested at once, so rank them by factors like impact (how much improvement you expect, how many users affected), confidence (how strong the evidence is), and eff ort (development and design complexity).
A popular prioritization framework is ICE: Impact, Confidence, Ease. For instance, a hypothesis addressing a major dropout point with strong supporting data and a simple UI tweak would score high (and likely be tested before a hypothesis about a minor cosmetic change) rather than falling for the HIPPO effect (Highest Paid Person’s Opinion) or pet projects without data.
With hypotheses defined, we proceed to experimentation, where we run controlled tests to validate (or refute) our hypotheses. A disciplined experimentation process is crucial: it’s how we separate ideas that actually improve conversion from those that don’t. Below are best practices for running experiments, as well as common pitfalls to avoid.
The most common approach is an A/B test; splitting traffic between Version A (control, the current experience) and Version B (variant with the change) to measure differences in user behavior. A/B tests are powerful because they isolate the effect of the change by randomizing users into groups.
For more complex scenarios, you might use A/B/n (multiple variants) or multivariate tests (testing combinations of multiple changes simultaneously), but these require larger traffic to reach significance. If traffic is limited, sequential testing (rolling out a change and comparing before/after, carefully accounting for seasonality) could be considered, though it’s less rigorous.
In any case, the experiment design should align with the hypothesis: test on the specified audience (e.g. mobile users if hypothesis was mobile-focused), run for the planned duration, and make sure you’re capturing the defined metrics (set up event tracking or goals if needed).
Perhaps the biggest testing pitfalls are statistical in nature. It’s essential to let the test run long enough to gather sufficient sample size and reach statistical significance for your primary metric. Ending a test too early, for example, stopping as soon as you see a positive uptick can lead to false positives (noise being mistaken for a real win). This is known as the “peeking” problem.
To avoid this, determine in advance the needed sample or test duration based on baseline conversion rates and the minimal detectable lift you care about. Use statistical calculators or tools that enforce significance thresholds. Remember that randomness is always at play; a standard threshold is 95% confidence to call a winner.
Before trusting the outcome, verify the experiment was implemented correctly. Check for SRM (Sample Ratio Mismatch) if you intended a 50/50 traffic split but one variant got significantly more/less traffic, that’s a red flag that something is technically wrong (e.g. bucketing issue or flicker causing users to drop out).
Also monitor for tracking errors. If conversion events didn’t fi re correctly, the results could be invalid. It’s wise to run an A/A test on your platform occasionally or use built-in checks to ensure the system isn’t skewing data. Quality checks include looking at engagement metrics in each group (they should be similar if only one change was made) and ensuring no external factors (marketing campaigns, outages) coincided only with one variant.
Robust experimentation culture invests in detecting these issues, for example, capping extremely large outlier purchases that can skew revenue metrics or filtering bot traffic (which can be surprisingly high). Garbage in, garbage out; a CRO test is only as good as the integrity of its data.
When the test period ends (or you’ve hit the required sample size), analyze the outcome with an open and scientific mind. Did the variant achieve the expected lift on the primary metric? How about secondary metrics or any guardrail metrics (e.g. it increased conversion but did it impact average order value or customer satisfaction)? It’s possible a change “wins” for the primary KPI but has unintended side effects (for example, a UX change increases sign-ups but also spikes customer support tickets).
Always segment the results as well. A variant might perform differently for different segments. Perhaps the new design improved conversions for new users but had no effect on returning users. Or it helped mobile but not desktop. These nuances can generate new hypotheses or tell you to deploy a change only for a certain segment. Avoid confirmation bias: don’t only look for data that confirms your hypothesis; also ask “what does the evidence truly say?” If the test showed no significant change, that's learning too.
A quick summary:
A single A/B test can yield a nice lift; a CRO system yields compound gains over time by constantly learning and iterating. This stage involves institutionalizing the practices from the first four steps, managing a pipeline of experiments, feeding lessons back into the strategy, and ensuring your CRO eff orts mesh with the broader e-commerce stack.
Think of the CRO process as a loop: Observe → Hypothesize → Experiment → Learn → (back to) Observe…. After an experiment concludes, you gather learnings which often lead to new observations or questions. For example, a test result might reveal a new user behavior to investigate (“Variant B won, suggesting users prefer the simpler form but we noticed mobile users still lagged, let’s observe their sessions more”).
Successful optimization programs embrace this loop. After implementing a winning change, immediately consider what the next step is, perhaps that win opens up another bottleneck to address. Conversely, if a test was inconclusive, dig into qualitative insights to guide the next hypothesis. By closing the loop, you create a cycle of continuous improvement.
As you conduct observations and brainstorm hypotheses, maintain a CRO backlog (or experiment roadmap). This is a living list of all identified issues, ideas for improvement, and hypotheses, each tagged with priority, status, and supporting data. Treat this backlog similar to a product backlog.
Regularly update priorities based on recent test results or new business goals. For instance, if a recent test revealed a big opportunity in site search, hypotheses related to search might move up in priority. A well-managed backlog also prevents “idea loss,” good ideas that aren’t tested immediately are not forgotten; they remain queued with their rationale noted.
Scale Up What Works
When a test is successful, consider how to scale that improvement. Deploy the change in production (making sure it’s implemented cleanly and consistently). Then ask: can this insight be applied elsewhere? For instance, if simplifying the checkout boosted conversion, can similar simplification help on the account signup flow? Or if a new product page layout worked for one category, should we extend it to other categories (with caution to test if contexts diff er)?
This is where CRO intersects with broader UX and product development. Good ideas found via testing can inform the global design system and UX guidelines. Integrate the winning elements into your design templates, style guides, and development sprints so that other projects naturally use those proven best practices.
Modernize Your Ecommerce Product Listing for AI-Powered Search
AI-powered search is redefining how customers discover products online but many ecommerce brands are still held back by outdated Product Information Management (PIM) systems. Traditional PIMs were never built for the demands of modern, semantic, and vector-based search. They store data, but they don’t understand it.
This article explores why legacy systems fail to deliver accurate, AI-driven search experiences and outlines a clear roadmap to modernize your product listings. From cleaning up inconsistent metadata and normalizing taxonomies to integrating with vector databases and semantic search engines, you’ll learn how to transform your PIM into an intelligent foundation for next-generation ecommerce discovery.
Legacy PIM systems were not designed with AI search in mind. Many B2B organizations still rely on older PIM or even ERP/catalog systems that manage product data in a rudimentary way. While these systems may serve as a basic “single source of truth” for product specs, they often lack the structure and richness needed for AI to truly understand and use the data.
In older or poorly governed PIMs, product attributes are often spotty and non-standardized. Different product lines might use varying names for the same attribute (“Size” vs “Dimensions”), values may be formatted inconsistently (e.g. some lengths in inches, others in mm), and many fields may be left blank.
Missing or non-uniform attributes lead to poor search recall and irrelevant results, for instance, if half your catalog omits a “material” attribute, an AI search for “stainless steel bolts” might miss many relevant items. Without complete, clean data, AI cannot infer product relevance or answer detailed queries.
A well-defined product taxonomy (categories, subcategories, attribute schema) is important for both humans and AI to navigate a catalog. Yet many PIMs either have outdated, fragmented taxonomies or none at all beyond basic category groupings.
In B2B, this is especially problematic. Products might be categorized differently by different teams or channels, leading to a disjointed structure. For example, the same item could be classified as “Protective Gear > Safety Glasses” in one system, but just lumped under “Accessories” in another.
This fragmentation creates confusion and poor search results, since semantic engines can’t easily learn relationships or compare products across inconsistent groups. Traditional PIMs also struggle to accommodate new attributes or categories without heavy manual rework.
In many traditional PIM workflows, any improvement to product data (like adding synonyms or updating categorizations) is a manual, rule-based process. If a new synonym or industry term emerges, someone has to manually update a mapping or keyword list in the system.
These manual processes break down at scale and speed as catalogs grow to thousands of SKUs and user behavior shifts rapidly, rule-based systems can’t keep up. For example, if buyers start searching using a new abbreviation for a product spec, a conventional PIM won’t capture that unless an admin notices and adds it.
AI-powered search, on the other hand, thrives on dynamic learning. Traditional PIMs typically aren’t integrated with AI or machine learning that could adapt taxonomies or attribute mappings based on real customer behavior. The result is stagnant data: the PIM’s view of your products doesn’t evolve with how customers talk about or search for them.
This is one reason keyword-only search fails for B2B buyers who use jargon and synonyms that a static system doesn’t understand. Traditional PIMs also often operate in silos, separate from analytics or search systems, so they don’t “learn” from search queries at all. In contrast, an AI-ready approach might mine search logs and feedback to continually refine product data (e.g. adding a new synonym or adjusting a category).
Also Read: How Horizontal Integration Helps B2B Marketplaces Grow Faster and Smarter
Upgrading your PIM for AI-powered search is not a single flip of a switch, but a multi-step journey. It involves re-architecting how your product data is structured, enriched, and governed. Below is a step-by-step roadmap covering the key facets you should focus on:
To lay the groundwork, start by overhauling your product data schema. The goal is to impose consistency and structure where it may have been lacking. This involves creating standardized attribute definitions, cleaning up values, and organizing products into a coherent taxonomy.
Begin with a data quality audit. Identify inconsistencies, duplicates, or ambiguities in your current product attributes. For example, you might find that “Width” and “W” are both used as fields for essentially the same data, or that one brand’s products list “Material” while another uses “Primary Composition”.
Decide on master attribute names and formats and apply them universally. If units of measure vary, choose a canonical unit for each measurement type (e.g. always use millimeters for dimensions internally) and convert or store equivalents.
Consistent attribute formatting is super important. Normalizing these attributes not only helps with search filters (faceted search) but also ensures that semantic algorithms know two values are the same (e.g. “2 inch” and “50.8 mm” should map together).
Define value ranges and allowable formats to prevent future drift. Essentially, you want a single source of truth for each attribute; one name, one format, and ideally no empty values on important fields. This master schema might be captured in a data dictionary or directly enforced in your PIM via validation rules.
Next, tackle your product categorization. If your categories are outdated or inconsistent across channels, now is the time to normalize them. Develop a hierarchical taxonomy that makes sense for your catalog size and product types. A best practice is to start with customer intent and logical groupings.
Analyze how customers naturally segment your products through search query clustering, analytics on frequently used filters, or industry standards. For instance, in a hardware B2B context, buyers might think first in terms of use-case (“Electrical > Wiring > Connectors”) rather than internal divisions (“Product Line A vs B”).
Align your taxonomy to how users conceptualize the domain. Ensure each level of the hierarchy is unambiguous and that products are not straddling multiple categories in confusing ways. Also, search engines like Google gain clarity on your site structure, helping SEO.
Modern AI can even assist here: machine learning can analyze product attributes and customer behavior to suggest optimal category groupings, a process known as AI-driven taxonomy standardization. The benefit is a taxonomy that’s informed by how customers actually browse and search, rather than just internal opinion. Once defined, enforce the taxonomy in your PIM (no rogue categories) and map legacy categories to the new scheme.
Also Read: When Your B2B Ecommerce Site Doesn’t Talk to Your ERP
With a structured dataset in place, the next step is to ensure your PIM bridges the gap between how you describe products and how customers search for them. This is where mapping synonyms, slang, industry jargon, and user queries to your official product data becomes critical.
Even the best product content might use different wording than a customer’s query. For example, your PIM might list an item as “polycarbonate safety glasses”, but a buyer searches for “shatterproof goggles”. AI-powered search can handle such discrepancies if it has a knowledge of synonyms and intent mapping. Here’s how to upgrade your PIM (and search integration) to align with customer language:
In traditional search, teams often maintain a synonym list manually (e.g. telling the search engine that “PTFE” is equivalent to “Teflon”). For AI search, you want this to be more expansive and dynamic. Start by aggregating all the term variations that customers might use for your products.
Sources include search query logs, website analytics (e.g. internal searches that returned zero results), customer reviews (for colloquial terms), and input from sales/support who hear customer lingo. Mine these data sources to find patterns, perhaps you discover that “SS” often appears meaning “stainless steel” in queries, or that users type “grinder” for what your catalog calls “milling machine”.
Create a synonym dictionary that maps these to your canonical terms. Modern AI can assist by clustering similar terms from large text corpora; some PIM systems allow uploading synonym lists or even auto-detecting them from usage data.
For instance, if your logs show “O-ring” and “seal” often lead to the same clicks, that’s a relationship to formalize. Feed these synonyms into both your PIM (as metadata) and search engine configuration. Your PIM could have a field for “synonyms” on each product or attribute, or you maintain a separate thesaurus file that the search index references. Without a synonym map, a search engine might return “no results” even when the right product exists.
Go beyond one-to-one synonyms and consider the intent behind queries. Semantic search engines attempt to parse user intent (e.g., “best for outdoor use” implies looking for weather-resistant features). Your PIM can support this by having attributes or tags that align with intents.
For example, have a boolean attribute “Outdoor Rated” which you set to true if a product is weatherproof; then map likely intent phrases (“for outdoor”, “weatherproof”, “rugged”) to trigger filtering or boosting on that attribute. Some of this happens on the search engine side (where AI models determine that “outdoor” = requires Outdoor Rated true), but it’s up to your data to have the structure to respond.
If you enriched content, you likely added many of these phrases into descriptions. Now ensure that if a user asks a question, the answer lives in the data. Integrating your PIM with a vector search or AI Q&A system will mean user queries can be answered from the product data.
So, if customers often ask “What’s the difference between X and Y?”, consider adding a comparative note or a dedicated field in PIM capturing that (even linking related products). Another example: map units and standards to what customers use. If European customers search by metric and Americans by imperial, make sure your PIM lists both, or that your search layer can convert on the fly (which it can if your attributes are normalized and synonyms set up for “inch” vs “mm” as done in Step 1).
A concrete tactic is to use customer query analytics: take the top N search queries on your site (and maybe on external search leading to your site), and systematically verify that your PIM has data to satisfy those queries. If “high-temperature gasket” is a frequent query, do you have “Max Temperature” recorded for all gaskets? If not, add it (enrichment again) and ensure “high-temperature” maps to that attribute range.
Traditional keyword synonym lists are useful, but AI offers a more robust way: vector embeddings can naturally encode similarity between words and phrases without explicit rules. For instance, an AI model might learn that “weatherproof” is close to “water-resistant” in vector space. Ensure your upgraded search stack takes advantage of this.
In practice, this means when you feed product data to a vector search engine, include all the rich textual info you’ve built: descriptions, attributes, etc. The model will then inherently connect many synonyms. For example, OpenAI or ELSER embeddings might place “SS” and “stainless” close together if your data has examples of both referencing steel.
To maximize this, consider adding common alternate terms into your content (which you may have done in enrichment). A tip from B2B search implementations is to use field-aware embeddings, e.x., have one vector for material attributes, one for product names, etc., to avoid noise.
That might be a technical detail, but the idea is to ensure the context of terms is captured. The benefit: embeddings can decode jargon on the fly; they map terms like “PTFE” and “Teflon” close to each other, meaning the system can retrieve relevant items even without an explicit synonym rule.
Many modern search platforms (like Algolia Neural, Elasticsearch dense vectors, or custom setups with Python and Pinecone) allow hybrid retrieval: using both traditional synonyms and vector similarity. Aim to configure your system this way: synonyms handle the critical exact mappings (ensuring no zero-result for simple cases), while vectors handle the fuzzy intent matches (“red running shoes” ≈ “scarlet sneakers”).
Up to now, our focus has been on getting the PIM data itself into shape. The next step is more systems-focused: making sure that your PIM can feed and work in tandem with modern AI-powered search infrastructure. Semantic and vector search engines are fundamentally different from traditional SQL or simple text indexing, they often require additional components like embedding generation, vector storage, and hybrid query handling. Here’s how to ensure a smooth connection between your PIM and an AI-centric search stack:
Traditional search indexes (like Solr or the classic Elastic index) can’t handle the high-dimensional vectors used in AI similarity search. You’ll need a vector database (e.g. Pinecone, Weaviate, Vespa) or an updated search engine that supports vectors (Elastic 8.x with KNN, or OpenSearch, etc.). The first step is to choose your solution and ensure your PIM can export data to it.
Many ecommerce teams are now using a hybrid approach: for example, keep using Elasticsearch for keyword search and faceted filtering, but layer in a vector engine for semantic matches. Make sure your PIM can supply a complete feed of product content to whichever engine you use. This often means developing an ETL (extract/transform/load) pipeline or real-time sync.
If your PIM is modern, it likely has APIs or connectors. For instance, Algolia’s AI Search or Azure Cognitive Search can connect to PIM feeds and automatically generate embeddings for each record. If you roll your own (say, using Python to generate embeddings via an LLM model and store in Pinecone), make sure you have a mechanism to keep it updated as products change.
The goal is that for every product in PIM, there’s a corresponding vector representation in the search index that encapsulates its meaning. Compatibility here also means handling data types: the PIM might store text, images, attributes separately, whereas a vector search might expect a single blob of text.
You may need to concatenate fields (like “title + description + attributes”) when generating embeddings, or create multiple embeddings per product (one for text, one for image). Plan this out and test different approaches for search relevance. The investment is worth it.
For most B2B scenarios, the best practice is not to rely solely on vector search but to combine it with traditional search. Ensure your search architecture supports this. This might mean running two queries in parallel: one against the keyword index (e.g. Elastic) and one against the vector index, then merging results.
Techniques like reciprocal rank fusion (RRF) are commonly used to blend the two result sets. Implementing RRF or a custom reranker is an added complexity but yields big gains in relevance. It ensures the “best of both worlds” (exact matches for those who know part numbers, plus semantic matches for natural language queries).
Many search platforms are starting to add this blending. For example, Elasticsearch’s Semantic Search capabilities allow a query to include both a vector similarity clause and a traditional BM25 clause. Algolia also offers a hybrid retrieval where their AI re-ranks keyword results using vectors. When upgrading your PIM, confirm that your data exports include all the fields needed for both types of search.
For instance, you may need to maintain a separate keyword index of certain fields (like SKU, part number, short name) for exact matching. Don’t throw away the structured nature of your data, facets and filters (e.g. filter by brand, by price) should still work alongside AI search.
A well-prepared PIM will have those facets clean (from Step 1’s normalization), and your search engine can use them to filter vector search results (many vector DBs support returning an ID list that you then intersect with a filtered set from a traditional DB). Setting up this integration might require help from a search relevancy engineer or using a service like Algolia Neural or Azure Vector Search if you want less custom coding.
Another compatibility aspect is hooking up a re-ranking model on your search results. Often, after retrieving a set of candidates via keyword/vector, an AI cross-encoder re-ranker can be used to fine-tune the ordering (e.g., BERT or GPT style model that looks at query-product pairs to score relevance).
This may be advanced, but many off-the-shelf solutions exist (Google’s Semantic Kernel, AWS Kendra’s ML ranking, etc.). The PIM’s role is indirect here, but ensure that product data fed to the re-ranker is sufficient. That means consistent titles, relevant attributes available as context, etc., which we’ve addressed in prior steps.
If going this route, you might have to deploy a model or use a SaaS API for re-ranking. The benefit observed in industry is significant. Just remember to cache and monitor performance, since these models can add latency. It’s a decision of where to get this re-ranking model (build vs buy) and making sure architecture can handle it. For the businessman in you, the takeaway is that AI can not only retrieve more stuff, but also sort it in a smarter way often yielding a better experience than the old “most relevant by text match” sorting.
Mayank Patel
Nov 4, 20256 min read
How Horizontal Integration Helps B2B Marketplaces Grow Faster and Smarter
What began as specialized, single-category platforms is now evolving into interconnected ecosystems where buyers can source everything they need in one place. Growth is about expanding smarter, creating value across categories, and leveraging data to power scale.
That’s where horizontal integration comes in. By expanding sideways into complementary markets or merging with peers operating at the same stage, B2B marketplaces can multiply their reach, accelerate growth, and create a richer experience for both buyers and sellers.
But achieving that kind of expansion doesn’t have to mean years of development, integrations, and tech complexity. With modern platforms like LinearCommerce, B2B businesses can launch, integrate, and scale horizontally faster than ever using prebuilt, plug-and-play systems designed for flexibility, and data intelligence.
Horizontal integration is a growth strategy where a company expands “sideways” by combining with another company or adding new lines that operate at the same stage of the business. In other words, a business merges with or acquires a peer (or expands internally) to offer more of what it already sells.
Unlike vertical integration (going up or down the supply chain into different stages, like a manufacturer buying a supplier), horizontal integration stays at the same level. For a B2B marketplace, this often means adding more product categories or services, or even merging with other marketplaces to widen the platform’s scope.
For example, Amazon began as a niche online bookstore but later expanded into selling everything, eventually launching Amazon Business to serve office supplies, industrial equipment, and beyond. Amazon’s evolution from a single-category store to an “everything marketplace” is a classic case of horizontal expansion.
Also Read: When Your B2B Ecommerce Site Doesn’t Talk to Your ERP
Horizontal integration can make a B2B marketplace grow faster by rapidly increasing its reach and market share. By expanding into new categories or joining forces with similar platforms, a marketplace can access more customers and markets overnight. This broader reach accelerates growth in several ways:
A horizontal B2B marketplace offers a diverse product selection. Buyers, in turn, draw even more sellers to the platform. It becomes a virtuous cycle: “More product offerings attract more customers, and a wider customer base attracts more sellers”. The result is exponential growth. A platform that started in, say, office supplies can grow much faster by also hosting sellers of machinery, chemicals, or food ingredients without starting from scratch.
By integrating horizontally, marketplaces reduce competition and capture a larger share of the B2B market. For instance, if two B2B platforms merge, the combined entity instantly gains all the buyers and sellers from both. This larger presence strengthens the marketplace’s brand and visibility. In practical terms, a bigger marketplace means any given seller has a bigger pool of potential buyers to sell to, which can translate to higher sales volume faster than if the market were fragmented.
A broader platform can operate more efficiently due to economies of scale. With more transactions and users, the marketplace can invest in better infrastructure and spread costs over larger volumes. This often enables faster innovation and rollout of new features. It also means the platform can scale up operations (adding new categories or geographies) more swiftly than a smaller, niche platform. For example, a horizontally integrated marketplace might consolidate warehouses or logistics for multiple product lines, speeding up delivery times for all. The net effect is faster growth not just in size, but in the quality of services.
Also Read: How Modern B2B Marketplaces Drive Sales Without Adding Complexity
Beyond just getting bigger, horizontal integration helps marketplaces grow smarter. With a broader base of operations, a marketplace can use more data, technology, and operational efficiencies which ultimately benefit both the platform and its sellers. Here’s how a horizontally integrated B2B marketplace becomes “smarter” and why that’s good news for you as a seller:
Shared Data & Insights
A large, multi-category platform generates a wealth of data on buyer behavior, market trends, and product performance across industries. When this data is pooled together, the marketplace can uncover insights that help everyone. In practice, this could mean the marketplace identifies a rising demand for a certain product and shares that trend with relevant sellers, or uses algorithms to match your products with the most likely buyers.
A broader marketplace may integrate services like logistics, warehousing, or payments across all its categories, often negotiating bulk rates. This can reduce the cost of fulfillment and platform fees per unit sold. For sellers, this might translate to lower customer acquisition costs (since the marketplace brings you more traffic organically), and potentially lower fees or better services as the platform optimizes its operations.
A horizontally diversified marketplace is often more resilient to market changes. Because it serves many industries, a slowdown in one sector can be balanced by demand in another. This stability allows the marketplace to continuously invest and innovate. Moreover, large marketplaces often pilot new programs (like financing options, insurance, or marketing programs) that you can take advantage of. For instance, a platform might use its data to offer a financing program for small sellers or introduce an AI-powered matchmaking service for business leads.
In B2B commerce, timing is everything and few moments have been as ripe for horizontal integration as today. The forces shaping digital trade aren’t just about growth anymore; they’re about adaptability, intelligence, and ecosystem control. Horizontal integration enables all three.
For decades, B2B buying has been fragmented. Buyers used multiple suppliers, portals, and distributors to meet their needs. But procurement is shifting from tactical purchasing to strategic sourcing, where efficiency, transparency, and predictability matter more than price alone. Horizontal integration allows marketplaces to become procurement ecosystems instead of product directories where buyers can source, compare, pay, and manage suppliers in one workflow.
Many B2B marketplaces have already dominated their initial verticals whether it’s office supplies, industrial tools, or packaging materials. But the marginal returns in a mature vertical diminish quickly. Expanding horizontally into adjacent categories isn’t just opportunistic; it’s strategic diversification that keeps growth compounding without diluting brand authority. The smartest players aren’t abandoning their niches; they’re building horizontally around them, creating adjacent offerings that turn transactional buyers into recurring visitors.
Scale is no longer measured only by GMV, it’s measured by data breadth and contextual intelligence. A horizontally integrated platform connects patterns across industries and buyer segments, allowing predictive insights (e.g., demand shifts, price elasticity, cross-sell opportunities). The more a marketplace integrates horizontally, the richer and more defensible its data becomes.
Consolidation is happening faster than innovation. Mergers, platform alliances, and category expansions are redefining who controls the buyer relationship. For smaller or specialized marketplaces, staying siloed means being at risk of becoming an integration partner rather than a market leader. Horizontal integration gives you the leverage not just through size, but through platform adjacency: owning multiple nodes of buyer attention and being able to connect them intelligently.
Global volatility has made resilience a boardroom metric. A horizontally integrated marketplace cushions itself against downturns in any single industry, supplier type, or demand cycle. It spreads operational risk while deepening overall platform stickiness.
Also Read: Do AI-Generated Product Descriptions Convert Better Than Humans?
By widening the scope of products, services, and partnerships, marketplaces create a richer ecosystem where everyone wins: the platform grows in size and intelligence, buyers get convenience and choice, and sellers (like you) get more opportunities with less friction.
In a space where timing and adaptability define success, LinearCommerce gives you the speed, scalability, and intelligence to build a marketplace that grows like the leaders without the multi-year tech investment.
Forget long development cycles. The team at LinearCommerce offers prebuilt marketplace themes from catalog management to payments, logistics, and analytics. You simply plug into our modular framework and launch in weeks, not months. This accelerates your go-to-market timeline while giving you the flexibility to expand horizontally later whether that means adding new categories, partners, or geographies.
Every module (catalog, pricing, logistics, CRM, analytics) is designed to scale across categories, so you can grow from a single vertical into a full-fledged multi-industry platform seamlessly. Our architecture enables you to integrate new suppliers, industries, and customer segments without re-engineering your core systems.
With LinearCommerce’s team, you don’t just get software; you get a growth partner. Our success team helps you design go-to-market strategies, onboard sellers efficiently, and use best practices from across industries. Whether you’re launching your first marketplace or expanding into your fifth category, we guide you through each stage of scale.
Mayank Patel
Oct 31, 20256 min read
