Mayank Patel
Sep 24, 2025
4 min read
Last updated Sep 24, 2025
The way businesses buy and sell has changed dramatically in the last decade. Today’s B2B buyers expect the same seamless, convenient, and transparent experiences they enjoy on consumer platforms like Amazon or Alibaba.
This is where modern B2B marketplaces step in.
In this blog, we’ll explore how B2B marketplaces differ from traditional e-commerce, why they’re rapidly gaining popularity, and—most importantly—how they help businesses drive sales while keeping operations lean and manageable.
A B2B marketplace is like a digital mall for businesses. Instead of a single company selling its goods (as in a typical e-commerce website), a marketplace hosts multiple vendors under one roof. Each vendor can list products or services, and business customers can shop across all these offerings in one convenient site.
For the buyer, the experience is similar to any online store: browse products, compare options, add to cart, and checkout, except they benefit from a much wider selection in one place. In fact, buyers often prefer marketplaces because they can easily find and compare similar products from different sellers side by side.
From the business owner’s perspective, however, the difference is significant. In a traditional B2B e-commerce setup, your company is responsible for everything. You stock the inventory, set prices, fulfill orders, handle logistics, etc. Your online store only offers what you can supply.
In a marketplace model, by contrast, you open your platform to third-party sellers who list and sell their products. You, as the marketplace operator, focus on running the platform (and perhaps selling your own core products too), but you don’t have to own or manage the entire inventory being sold. The site’s owner and various providers all display their products for sale, making the marketplace a shared channel to reach more customers.
B2B marketplaces have been rapidly gaining traction across industries and for good reason. As business buying moves online, companies see marketplaces as a way to meet evolving customer expectations and tap into new revenue streams.
A recent analysis by Boston Consulting Group noted that e-commerce marketplaces are reshaping B2B procurement, as more buyers demand streamlined, digital purchasing options. Today’s B2B buyers (whether retailers sourcing products or factories buying parts) want the same ease of shopping they experience on consumer platforms.
Companies themselves are also embracing the marketplace model to strengthen their market position. Because trying to be a broad one-stop supplier on your own is challenging (it’s hard to stock “everything”), businesses are pivoting to an “ecosystem” strategy: they focus on their specialty products, and for everything else their customers might need, they bring in third-party sellers.
This is happening especially in large sectors like food and hospitality, healthcare supplies, industrial equipment, and automotive parts. Instead of a generic, one-size-fits-all site, these businesses launch specialized marketplaces tailored to their niche. They carefully select high-quality third-party vendors (a “curated” seller base) so that buyers trust the offerings, and they customize the user experience for industry-specific workflows.
The core promise of a B2B marketplace is greater sales potential for both the marketplace operator and the participating sellers. Here are several ways modern B2B marketplaces can turbocharge sales:
By hosting multiple vendors, a marketplace can off er an extensive catalog of products or services far beyond what one company could manage alone. This attracts more buyers because they’re likely to find “everything they need” in one place.
Joining or running a marketplace can open the door to new markets and customer segments that you might not reach on your own. As the marketplace owner, you can invite sellers that complement your core business. Conversely, if you are a seller on someone else’s marketplace, you gain exposure to that marketplace’s audience.
Buyers on marketplaces enjoy easier access to a wide range of quality products, better visibility into availability, flexible delivery options, and even personalized pricing or volume discounts for their business.
In traditional B2B purchasing, finding the right product at the right price could mean contacting multiple vendors, requesting quotes, and dealing with lots of back-and-forth. On a well-designed marketplace, the buyer can do all of this on a single platform: quickly search and compare options, see transparent pricing (or request quotes) and stock levels, and complete orders in a standardized, efficient checkout.
Successful B2B marketplaces often curate their seller base and enforce quality standards. By doing so, the marketplace builds trust with buyers. Buyers are more willing to try a new supplier’s product if it’s on a trusted marketplace they already use. They feel, “If it’s available on XYZ Marketplace, it must be vetted and reliable.”
Beyond just selling more products, a marketplace model introduces entirely new revenue lines. You earn commission on third-party sales without investing in those products. Many marketplace operators also charge subscription or listing fees to sellers for the privilege of accessing the platform’s customer base. These mechanisms can be quite lucrative.
In fact, B2B marketplaces often enjoy steady profits from these fees. Commissions can range from around 8% in some industries up to 20% in others, and overall a well-run marketplace can add an extra 5–7% to the operator’s EBITDA profit margins. That’s on top of whatever profit you make on your own direct sales.
A common concern when considering a B2B marketplace is whether it will introduce extra complexity into your business. After all, more sellers and more products could mean more operational headaches, right? The good news is that modern marketplace platforms are specifically designed to handle complexity behind the scenes, so that you as the operator (and your buyers) experience less friction, not more. Here’s how:
Perhaps the biggest operational advantage of a marketplace is that you can dramatically expand your catalog without taking on inventory management for every item. The third-party sellers take care of storing their products, maintaining inventory, and often even drop-shipping orders to the customer.
You off er more products and reach more markets “without having to provide storage, management and fulfillment” for those added items. This is crucial. Say you currently off er 1,000 SKUs on your own. By bringing in outside vendors, you could off er 10,000+ SKUs on your marketplace but you’re not the one housing or handling those extra 9,000.
Your responsibility is to ensure the platform runs smoothly and that sellers meet your standards, but you’re not packing 10,000 different products in your warehouse.
Along similar lines, a lot of the detailed work that comes with selling products: uploading product information, updating stock levels, setting prices, picking/packing orders, managing return is handled by each individual seller through their own dashboard on the marketplace.
As the marketplace owner, you provide the infrastructure and guidelines, but you are not micromanaging each SKU. This decentralizes the workload. We build tools for automating vendor onboarding, product catalog submissions, and compliance checks. For example, you might set rules for product data quality or pricing, and the system can auto-approve (or flag) listings that meet those criteria.
In a traditional model, when you add more products or more customers, your fulfillment operations (warehousing, shipping, etc.) become more complex. In a marketplace, however, much of the fulfillment can be distributed among your sellers. Each seller might ship their own orders directly to the buyer, or you might have a dropship arrangement.
Either way, you’re not handling every single shipment yourself. You might still operate a central logistics for your own products, but third-party items flow through other channels. This can actually simplify logistics, for instance, you’re not receiving large POs, breaking bulk, and redistributing; instead, sellers ship directly.
A well-implemented marketplace automates a lot of the previously manual tasks in B2B transactions. For example, rather than buyers and sellers negotiating via phone or email for each order, a marketplace can provide an interface for online quotes, bulk pricing negotiations, and purchase approvals.
The entire procurement process becomes more efficient. Buyers can self-serve much of the ordering process (which reduces the need for as many sales reps taking orders), and sellers can manage their accounts through self-service portals. Payment processing is centralized through the platform (even if multiple sellers are involved, the platform can split payments or handle invoicing workflows).
Being a marketplace operator actually gives you more control in some respects. You set the rules for who can join as a seller and what products can be listed. You can enforce quality standards, pricing policies, and service level agreements. The technology can support this by, for instance, allowing you to easily approve or reject products before they go live, and to monitor seller performance metrics (like fulfillment times, defect rates) in real time.
Transitioning from a traditional model to a marketplace model can seem daunting, especially for businesses that aren’t tech-focused. However, you don’t have to go it alone. This is where you should partner with the right commerce technology experts.
LinearCommerce is one such partner that specializes in helping businesses design, build, and scale custom e-commerce and marketplace solutions. We understand that many retailers and distributors have built successful offline businesses and now want to go online the right way.
In practical terms, if you decide to launch a B2B marketplace, LinearCommerce can assist by:
We build the core infrastructure that supports multi-vendor operations including vendor onboarding modules, product catalog management, order routing to the right seller, and commission tracking. Our solutions are built to be fast, scalable, and reliable. We ensure that features crucial for B2B are baked in: for example, the ability to handle negotiated pricing, volume discounts, custom catalogs for different buyer companies, and even things like credit terms or invoicing if needed.
We tailor the marketplace system to reflect how your business and industry operate. Do you need a specialized search function for technical parts? Or a workflow for buyers to get approval from their manager before ordering? We can deliver on all these requirements. The goal is to have a marketplace that feels like a natural extension of your current business, rather than a bolt-on.
We know that adoption is key. We can create a modern, intuitive interface (web and even mobile app if needed) where buyers can quickly navigate the large catalog, and sellers have clear tools to manage their storefronts. Speed is also critical to keep B2B users (who are often in a hurry) happy. Essentially, we build the marketplace for conversion.
The most successful B2B marketplaces are not the biggest, they’re the smartest. They focus on trust, curation, and industry specialization rather than trying to be “everything for everyone.” By narrowing in on specific verticals and curating seller quality, you can build ecosystems where buyers feel confident and vendors feel valued.
If you’re considering launching or joining a marketplace, the real question isn’t whether the model works—it clearly does. The question is: how will you differentiate your marketplace so that it becomes indispensable in your industry? Answer that well, and your marketplace will drive sales.
The Hidden Cost of Delayed Digital Adoption: It’s Not Just Sales, It’s Margins
Most business leaders think of digital adoption as a sales enabler, something that matters only if you’re building an online storefront or e-commerce platform. But the reality is much broader, and far more urgent. Staying manual or offline doesn’t just slow down growth; it quietly eats away at your margins every single day.
Errors in data entry, delayed invoicing, slow cash flow, and time wasted on reconciliation don’t appear as line items on your profit and loss statement, but they’re there, draining profitability in ways that compound over time. What seems like “saving money” by holding off on digital tools often ends up costing far more in hidden inefficiencies, lost opportunities, and operational friction.
In this article, we’ll break down how delaying digital adoption hurts your bottom line—beyond lost sales—and reveal the hidden cost centers that many traditional businesses overlook. More importantly, we’ll explore how even small, incremental steps toward digitization can protect margins, improve efficiency, and free up resources for real growth.
Many business owners assume that “going digital” only matters if you plan to sell online. In truth, staying offline or manual has a direct hit on your bottom line through higher costs and lower productivity. When processes are not digitized, they tend to be slower, labor-intensive, and error-prone. This shows up in many ways:
A typo in an order or an invoice can ripple through with returns, credits, or lost customers. We have internally observed that data entry error rates can be as high as 4%, which in a business processing 10,000 orders a month could mean 400 messed-up orders. If each mistake costs ~$50 to fi x, that’s $20,000 per month eaten up in corrections, nearly a quarter million dollars a year just on avoidable errors.
A purely manual, paper-based billing and payment process often means slower invoicing and collections. Without digital payment options or automated reminders, customers take longer to pay, hurting your cash flow and possibly incurring financing costs.
In a non-digital setup, information lives in silos (ledgers, spreadsheets, individual employees’ heads). You don’t have real-time visibility into inventory, sales, or financial metrics. Decisions end up being reactive and based on outdated data, which can cause overstocking, stockouts, or misallocation of resources.
Also read: How Modern B2B Marketplaces Drive Sales Without Adding Complexity
To make it concrete, here are some common “hidden” cost centers in traditional B2B setups that often go unnoticed until you quantify the impact:
Without digital inventory management, stock levels are often guessed or reconciled infrequently. This can lead to stockouts (missed sales when you underestimate demand) or overstock (tying up capital in excess inventory). Both scenarios hurt margins.
If your billing is not automated, say, invoices are generated slowly or mistakes slip in, you may experience late payments or disputes. Slow payment processing extends your Days Sales Outstanding (DSO), meaning you wait longer to turn sales into cash.
Traditional setups often involve multiple systems (or spreadsheets) that don’t talk to each other. As a result, employees spend a lot of time on reconciliation: matching orders to invoices, delivery notes to inventory records, bank statements to ledger entries, and so on.
This is tedious work that adds no value for the customer. Duplicate data entry between systems is another silent margin killer: not only do you pay people to do the same job twice, but every handoff is another chance for an error that will need fixing.
In traditional setups, your sales team might spend time writing down orders, manually checking stock or past orders, and following up on quotes, all through phone or email. This slows down the sales cycle and often results in missed reorders or delayed responses.
If your competitors off er an easy online ordering platform and you don’t, you might be losing business not because of your product or price, but because of convenience. Remember, today a chunk of B2B buyers prefer ordering online whenever possible.
If you’re convinced that it’s time to start chipping away at those inefficiencies, you might wonder where to begin. Digital adoption isn’t a single monolithic project. It’s a series of improvements in different parts of your business. Here are some high-impact areas where going digital can directly improve your margins:
By moving from phone/email orders to an online order management system (or at least an internal CRM/ordering tool for your sales reps), you eliminate re-entry of data and reduce mistakes. Automated order workflows can cut order processing costs dramatically (less labor per order) and shorten order-to-cash time.
For example, introducing a self-service ordering portal or a mobile sales app means salespeople spend less time on paperwork and more on selling; it also means orders go straight into your system with fewer touches. Fewer touches = fewer costs. Some companies have seen their cost per order drop up to 10 to 50% after automating.
Implementing a digital inventory management system (often part of an ERP or a specialized software) with real-time tracking can prevent costly stock discrepancies. Real-time inventory sync across your sales channels and warehouses ensures you’re not selling what you don’t have, and you’re not over-ordering stock.
Better demand forecasting using digital tools (even simple analytics, or advanced AI for larger fi rms) means you carry optimal stock levels. The result is improved inventory turnover (freeing up cash) and fewer emergency shipments or production overtime.
This includes B2B e-commerce websites or customer portals where clients can place orders, check status, download invoices, etc., on their own schedule. By offering this, you reduce the burden on your sales and support team. It also typically increases customer satisfaction (they get what they need faster) and can boost sales (customers tend to order more when it’s easy and transparent).
One of the quickest wins is moving to digital billing and payment solutions. Sending invoices through email (or better yet, through an integrated system) as soon as an order is fulfilled speeds up the process. Even better, enabling online payments (credit cards, digital wallets, bank transfers with automated reconciliation) can shorten the payment cycle.
For example, if you implement an online payment link in invoices or off er an online bill-pay portal, customers are more likely to pay faster than if they have to issue cheques. Faster payments improve cash flow and reduce financing costs or write-offs, directly affecting net margin.
Plus, automated payment reminders and tracking mean fewer AR staff hours chasing down payments. Many B2B fi rms also find that accepting digital payments reduces errors in matching payments to invoices, again saving labor and avoiding costly mistakes (like misapplied payments or incorrect credit holds).
This might not be glamorous, but automating back-office processes (accounts reconciliation, expense approvals, procurement workflows) can save a lot in overhead. Technologies like robotic process automation (RPA) or even simpler macros can handle repetitive tasks far faster and without errors.
For example, automating invoice matching or report generation can cut the time your finance team spends on closing books by days. Companies have reported 30%+ cost savings in processes like finance and IT by leveraging automation over a few years. Fewer back-office errors also means fewer costly corrections or compliance issues.
Also Read: How to Use Heatmaps, Data, and Hypotheses to Continuously Improve Conversions
A common fear among traditional business owners is that “going digital” means
a) spending a fortune on IT, and
b) replacing their tried-and-true staff or systems wholesale.
The reality is much friendlier.
Digital adoption does not mean you throw away all your old systems overnight or make your loyal employees redundant. In fact, the best approach is usually incremental and augmentative.
You can begin by linking some of your currently disconnected processes or automating one or two manual tasks, and build from there. For example, keep your legacy accounting software but add a tool that automatically syncs your Excel inventory sheet with that system daily.
Or give your sales reps a mobile app that feeds orders into your office system; reducing the retyping work back at HQ. These are not massive upheavals; they’re targeted fixes that yield quick wins.
Moreover, digital tools are often aimed at empowering your employees, not replacing them. When routine tasks are automated, your staff is free to focus on more important work (like building client relationships, improving operations, analyzing data for insights, etc.).
On the cost front, technology solutions have become more affordable and scalable. Cloud-based software, software-as-a-service (SaaS) models, and modular tools mean you can start small without a huge upfront investment.
Many providers offer pay-as-you-go pricing or subscriptions, so you don’t need to invest in expensive infrastructure. In fact, sticking with inefficient processes has an opportunity cost that often outweighs the price of a good digital tool.
Remember the earlier scenario: losing hundreds of thousands due to errors and inefficiency. Even a fraction of that amount invested in the right software or integration can eliminate those losses.
Digital adoption is one of the rare levers that improves both top line and bottom line simultaneously. When you digitize intelligently, you don’t just sell better, you operate leaner, faster, and with fewer hidden costs. This means higher margins, greater agility, and the ability to reinvest in growth instead of patching inefficiencies.
Mayank Patel
Sep 26, 20255 min read
How to Use Heatmaps, Data, and Hypotheses to Continuously Improve Conversions
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.
Mayank Patel
Sep 15, 20255 min read
