You invest in media. You generate traffic. Retail teams talk to you about sell out. And yet between the two, there is still a blur. Who clicks? Where? For which retailer? At what price level or stock level? If you manage a brand in indirect distribution, you know this black box well.

The answer is not one more dashboard. It is the connection between digital shelf analytics and where to buy. In other words, linking what the consumer sees such as price, stock, and visibility to what they do such as click, redirect, and retailer choice. That is when we start talking about purchase intent.

The reality on the ground data everywhere but not connected

On the e retail side, you already have distributor price and stock monitoring. You track stockouts, pricing gaps, and sometimes even MSRP compliance. On the marketing side, you have clicks, campaigns, and sessions.

The problem is that these data points live in silos. Multi retailer price visibility is not connected to redirects. Price gap alerts are not cross checked with traffic spikes. The result is simple: you see symptoms, never the full journey.

In most cases, a conversion drop does not come from weak creative. It comes from a stockout, a price that is too high at the most clicked retailer, or poor digital coverage. But without connecting the data, you blame the wrong lever.

Why digital shelf on its own is not enough

A digital shelf platform for mid sized businesses gives you an accurate snapshot: availability rate, pricing compliance across the distributor network, and online stockout tracking. That is essential.

But it does not tell you whether the consumer tried to buy. It does not tell you which retailer they moved toward. And it does not tell you whether a pricing gap actually reduced intent.

In other words, digital shelf measures the environment, not the movement.

What changes when digital shelf is connected to where to buy

When you connect your retail monitoring to a where to buy solution, you finally link exposure and action.

In practical terms:

• You identify which retailers actually capture intent through clicks.
• You compare those clicks with the observed price level.
• You detect whether an online stockout diverted traffic to a competitor.
• You measure the real performance of each campaign all the way to the digital or physical point of sale.

That is when you move from surface level reporting to omnichannel distribution management. You are no longer just looking at product presence. You are analyzing buying dynamics.

Measuring online purchase intent the right way

Intent is not something you declare. It is read through weak signals such as a click to a retailer, repeated consultation of a point of sale, or a redirect after price comparison.

Well used real time retail data for brands helps you understand:

• Which marketing channel generates the most qualified redirects.
• Which retailer is preferred at the same price level.
• At what pricing gap intent starts to fall.
• How digital and physical retail coverage influences the final choice.

That is exactly what a distribution analysis tool for brands connected to partner redirects makes possible. You are no longer guessing. You are observing.

From showcase site to transactional entry point

Many brand websites still act as catalogs. With an intelligent redirect module, they become conversion accelerators. Each product page can include dynamic logic: show available distributors, redirect to an alternative in case of stockout, or prioritize a strategic partner.

Combined with a landing page editor, you can adapt the experience by campaign, country, or product range. It is no longer just a button. It is a business lever.

Connecting marketing performance and retail reality

CMOs want to justify budgets. Brand Managers want stronger negotiation arguments. Retail Managers want a clear view of the network.

By connecting indirect distribution analytics for brands with redirects, you get a usable reading:

This type of analysis changes the internal conversation. You talk about facts, not impressions.

And if you want to go further on measuring in store impact, you can read more here. It is the same logic: connect intent and action.

We see it in the field: brands that connect digital shelf and where to buy make better decisions. They arbitrate media budgets with a retail view. They detect competitive gaps faster. They manage their network using concrete data, not assumptions.

This is not about one more tool. It is about data architecture. Connect visibility, price, stock, and retailer clicks. The rest follows.

You can also explore related topics here and here.

20 reviews

 

Maxence Antao, Communications Manager at Click2Buy

“Our role at Click2Buy is to guide our clients throughout the buying journey and optimize their marketing ROI thanks to real time retailer stock data.”

The post Digital shelf and where to buy manage the omnichannel buying journey and online intent appeared first on Click2Buy.

What price monitoring tools have in common and why that is not enough

Most competitive intelligence tools rely on the same basic mechanism: automated price scraping, a visualization interface, and alerts when price gaps appear. That is useful. But this common foundation hides major differences in collection frequency, geographic coverage, promotion and bundle handling, and the reliability of the data collected.

A tool that scrapes once a day will not help you react to a price war unfolding within a few hours. A tool that covers 50 sites but not your 3 main retailers is useless. Before comparing solution prices, compare what they actually do in your context. That is the first filter.

The main tools on the market in 2026

The market has consolidated around a few well positioned players, with different approaches depending on the target customer and the level of sophistication required. Here is an overview of the solutions most commonly used by brands selling through indirect distribution.

• Prisync: an accessible solution, well suited to small and mid sized businesses. Clear interface, fast setup, pricing based on the number of tracked products. Limitation: international coverage can be improved and advanced integration options are limited.
• Price2Spy: a similar positioning to Prisync, with solid retailer coverage in e-commerce. Mostly used for competitor price benchmarking on large catalogs.
• Minderest: a more robust solution designed for pricing teams that need frequent data updates and deeper analysis. Strong international coverage, but longer onboarding.
• Netrivals: strong in pure e-commerce, with interesting automatic repricing features for businesses selling direct.
• Click2Buy: a different approach. Price monitoring is part of a broader vision that also includes product availability, distributor presence, and campaign impact measurement. Relevant for brands that want to connect price data with retail performance data.

For brands hesitating between Prisync and a more suitable alternative for their structure, you can read more here.

Tool comparison what really differentiates the solutions

What standard comparisons do not tell you

Most comparisons stop at features. What they do not evaluate is the real quality of scraped data, the error rate on collected prices, or how the tools handle complex cases such as strikethrough pricing, bundles, or location based prices. That is where the tools truly differ.

There is also a significant difference in how tools handle out of stock situations. Some continue to report a price even when the product is unavailable, which distorts the analysis. A good price intelligence tool must connect availability status and price level to provide an accurate market view. This is also why this topic matters so much.

The selection criteria that really matter

Beyond the features listed in brochures, here is what you should actually assess before signing:

• The real coverage of your priority retailers. Ask for a demo on your own products, not generic examples.
• The update frequency that fits your sector. If your competitors change prices several times a day, daily collection is not enough.
• The ability to detect promotions and strikethrough prices. A displayed price is not always the real paid price.
• Alert options and integration with your existing management tools.
• The level of support and onboarding, especially if you do not have a dedicated pricing team.

For brands already using a where to buy solution and wanting to connect price tracking with broader performance measurement, this article gives a useful framework so you do not manage in silos.

19 reviews

 

Maxence Antao, Communications Manager at Click2Buy

“Our role at Click2Buy is to guide our clients throughout the buying journey and optimize their marketing ROI thanks to real time retailer stock data.”

The post 2026 comparison: the price monitoring tools we actually tested (and what we think of them) appeared first on Click2Buy.

The real problem: last click hides everything

Most brands still attribute conversion to the last measured touchpoint. It is simple. It is reassuring. It is also wrong in most cases. A consumer sees your display ad, searches for your product on Google, visits your website, clicks on a where to buy link, and ends up purchasing in a supermarket three days later. Which channel “converted”? According to traditional analytics: none.

That is where the multi-touch attribution model changes everything. It does not look for one single culprit. It distributes impact across all the touchpoints that contributed to the purchase journey, from the first exposure to the final act of purchase, even if it happens offline.

What an omnichannel attribution model really measures

A good model does not just count clicks. It reconstructs the full omnichannel customer journey: which channels exposed the consumer, in what order, at what frequency, and above all, which one triggered real purchase intent.

Here is what marketing teams are actually trying to measure:

• The impact of each channel on indirect sales, including physical sales
• The role of upper-funnel campaigns in lower-funnel conversions
• The real contribution of search, social, and display to retailer sales
• The touchpoints that create intent without generating a measurable click
• Performance gaps between retailers for the same volume of traffic sent

Without this data, you are flying blind. And you are probably cutting budgets that are working without you realizing it. If you want to better understand how availability impacts performance, you can take a look at this detailed breakdown.

Attribution models: which one should you choose?

There is no universal model. The right choice depends on your purchase cycle, the maturity of your data, and the complexity of your distribution network. Here is a quick comparison to make things clearer:

For brands that sell through retailers, the weighted multi-touch model is often the most relevant. It makes it possible to connect marketing actions to indirect sales without requiring direct access to checkout data, which is difficult to obtain in most retail networks. If you want to go deeper into measurement logic, this article explains it well.

Why retailer data is the missing link

Omnichannel attribution often runs into the same obstacle: sales data sits with the retailer, not with the brand. You know you sent qualified traffic to Darty or Fnac. But you do not know what happened after the click.

This is exactly what marketing performance measurement solutions built for indirect distribution are designed to solve. By tracking every click to a retailer from any digital touchpoint and combining it with product availability and pricing data, it becomes possible to rebuild a consistent dashboard. You can then see which channels generated intent, which retailers converted, and where the traffic was lost. You can also explore how this works in practice depending on your setup.

What this changes in practice for marketing teams

Once attribution is properly set up, the nature of decisions changes. You no longer cut a channel because it generates few direct conversions. You look at whether it contributes to purchase intent on other channels. You no longer measure only the ROI of the last lever activated. You manage the entire customer journey with a broader view.

For a CMO in indirect distribution, this is also a decisive argument in budget negotiations: proving that a campaign generated qualified traffic to retailers, that this traffic converted above average, and that indirect revenue increased during the period. This is measurable impact-driven marketing, not visibility for the sake of visibility.

Cross-channel attribution does not solve every problem. It does not replace a strong strategy, a strong product, or a solid distribution network. But it finally gives you the ability to see what you were already doing without realizing it: generating sales you could not measure.

18 reviews

 

Maxence Antao, Communications Officer at Click2Buy

“Our role at Click2Buy is to guide our clients throughout the buying journey and optimize their marketing ROI using real-time retailer stock data.”

The post How I finally proved that my marketing was generating indirect sales appeared first on Click2Buy.

Why retailer data is structurally imperfect

Each retailer has its own inventory management logic. Some provide near real-time updates, others rely on daily or weekly batch processing. Some distinguish between warehouse stock and sellable stock, others don’t. And when a product goes out of stock, how it’s flagged—or not—varies widely across distributors.

This is where e-commerce product availability tracking becomes complex: you’re not comparing apples to apples. You’re comparing heterogeneous data streams, produced at different frequencies, with different definitions of what “available” actually means. Before even thinking about a score, you need normalization. Without it, you’re just aggregating noise.

Retailer data quality has become a strategic issue precisely because it drives every downstream decision: pricing, campaign activation, and distributor prioritization.

The components of a robust availability score

A strong retailer availability score is not just a percentage of time “in stock.” It combines multiple signals, weighted based on reliability and business relevance. Here’s what to include:

• Declared stock status from the retailer — primary source, but not sufficient
• Out-of-stock signals detected via scraping — product page unavailable, disabled buy button, “out of stock” labels
• Sell-out data — a sudden drop in sales can indicate unreported stockouts
• Displayed delivery time — unusually long delays often signal stock pressure
• Retailer historical reliability — some distributors consistently report late or inaccurate data

This triangulation is what separates a decorative retail KPI from a real operational tool. The more sources you cross-reference, the more actionable your score becomes.

How to weight sources to avoid bias

Not all data sources are equal. A retailer updating stock every 4 hours should not carry the same weight as one updating weekly. Weighting must account for two dimensions: data freshness and historical reliability.

In practice, you can build a dynamic confidence coefficient per source. If a retailer consistently fails to report stockouts that are clearly visible via scraping, its weight in the overall score should decrease. This is applied retail data analytics: continuously learning from data quality to refine your model.

From measurement to action: what the score should enable

An availability score only has value if it drives concrete action. Too often, it’s built for reporting—and ends up sitting in a dashboard no one really uses. The right approach is the opposite: the score triggers alerts, and teams act.

In practice, a score below a defined threshold should trigger an alert to the relevant retail manager, a manual check for strategic retailers, and potentially a redirection of marketing traffic to a better-stocked distributor. When out-of-stock management is connected to where-to-buy flows, you can automatically redirect consumers to available retailers—without losing the sale.

Common mistakes to avoid when building the score

The same mistakes come up again and again. The first: using a single data source and calling it a score. That’s not a score—it’s just rebranded raw data. The second: failing to differentiate retailers based on business importance. An aggregated availability rate that hides a key retailer among smaller ones is useless.

The third—and most costly—mistake is not updating the model. Retailer behaviors evolve, systems change, update frequencies shift. A static model becomes inaccurate within months. Effective product availability tracking is a living system, not a fixed Excel formula.

21 reviews

 

Maxence Antao, Communications Manager at Click2Buy

“Our role at Click2Buy: guide our clients throughout the buying journey and optimize their marketing ROI using real-time retailer stock data.”

The post Measuring online product availability, the real challenge no one talks about appeared first on Click2Buy.

What the acquisition actually changes for you

An acquisition in the SaaS world almost always brings adjustments. Sometimes minor. Sometimes structural. Product priorities change, teams move, and roadmaps get revised. This is not a judgment on MikMak. It is simply the reality of any acquisition.

What should concern you is not the acquisition itself. It is being dependent on a tool without having reviewed alternatives in a long time. If you have not done that work in more than two years, now is the time. Not six months from now.

The real criteria for choosing a where to buy solution

Many where to buy platforms look similar on the surface. They display retailer logos, redirect to product pages, and promise a smooth experience. The real difference lies elsewhere.

• Retailer coverage: does your solution actually cover all your retail partners, online and offline? Redirecting to a retailer that is out of stock means a lost opportunity.
• Data quality: do you know exactly where traffic comes from, which products perform, and which channels trigger purchase intent? A simple click count is no longer enough.
• Integration across your touchpoints: brand site, campaigns, newsletters, social media. Your where to buy solution must integrate everywhere without technical friction.
• Pricing flexibility: pricing models vary widely. Fixed fee, pay per click, hybrid. Make sure the model fits your volume and your budget.
• Real support: a tool without follow-up is a tool that will be underused. Ask what the Customer Success team actually does, not what it says it will do.

Quick comparison of market approaches

The market for drive-to-store and where to buy solutions is not uniform. Here is how the main categories of tools compare, to help you get a quick overview.

What we see in the field

The brands that get the most out of their where to buy tool all have one thing in common: they do not use it as a simple redirection widget. They manage it as a performance lever, with clear objectives, regularly monitored KPIs, and a mindset of continuous optimization.

On the other hand, disappointments almost always come from the same place: a tool deployed once, never reviewed, never optimized. The MikMak acquisition is an opportunity to revisit that logic. Not to switch tools on principle, but to make sure what you use is truly aligned with what you expect. For a broader view of performance indicators, this article is worth a look.

If you are working on product landing pages or digital campaigns, the question of redirecting users to the right retailer at the right moment becomes even more critical. A poor tool choice means media budget spent without measurable conversion. In that context, this example can help frame the issue.

How to organize your evaluation

Do not compare solutions based on commercial demos. Compare them using concrete cases from your own business. Take one product, one recent campaign, and ask each solution how it would have tracked, redirected, and measured that traffic. The answer will tell you everything.

Also ask to speak with brands that have been using the solution for more than a year. Not handpicked references, but real users in a sector close to yours. That is the only way to know whether the promised support actually exists over time. And if retailer data is part of your selection criteria, this piece gives useful context.

The MikMak acquisition is not a disaster. It is a signal. Take advantage of it to do the evaluation work you have been postponing for too long.

14 reviews

 

Maxence Antao, Communications Officer at Click2Buy

“Our role at Click2Buy is to guide our clients throughout the buying journey and optimize their marketing ROI using real-time retailer stock data.”

The post MikMak acquisition which where to buy solution should you choose appeared first on Click2Buy.

Why drive-to-store is more fragile than it looks

On paper, it’s simple: run an ad, bring shoppers to the store. In reality, the journey is full of micro-frictions. Targeting that’s too broad, an unclear offer, no stock on shelf, and the entire campaign goes up in smoke. The worst part is that surface metrics like impressions, clicks, and reach can look great while drive-to-store conversion silently collapses.

Here are the 12 most common causes, the ones we keep seeing again and again in the field.

The 12 causes that block conversion

• Geographic targeting that’s too broad – Reaching people 40 km away from a store is wasted budget. The right radius depends on the product and category, but in most cases, beyond 15 km, willingness to travel drops sharply.
• A message disconnected from the local context – A national creative with no local adaptation generates limited engagement. Shoppers want to know whether the product is available near them, not just that the brand exists.
• No mention of the point of sale – Not naming the retailer in the ad forces shoppers to make the connection themselves. Many won’t.
• A promotional offer that isn’t distinctive enough – “Minus 5 percent” on a 12 euro product won’t move people. The offer must justify the trip. If it doesn’t, the click stays a click.
• Poor timing – Running ads on weekdays for purchases that happen on weekends, or promoting when stores are closed, destroys real-time relevance.
• Unanticipated out-of-stocks – This is the most underestimated cause. The shopper is convinced, makes the trip, and the product isn’t there. Not only is the visit lost, trust is lost too.
• A landing page that doesn’t send users to the right retailer – Sending shoppers to the brand homepage instead of a clear where-to-buy destination adds unnecessary friction right before conversion.
• No actionable performance data – If you don’t know which channels actually drive visits, you optimize blindly. You cut what works and keep what doesn’t.
• A gap between the digital promise and the in-store experience – The ad promises an exclusive deal, but the shelf doesn’t highlight it. The disconnect between digital and in-store reality is often underestimated.
• Retail partners not being valued on brand touchpoints – If retailers don’t see qualified traffic coming from the brand, they invest less in featuring the product. It becomes a vicious circle.
• Lack of consistency across channels – Social ads say one thing, a newsletter says another, and the brand site says nothing. Shoppers get lost or give up.
• Inability to measure real impact – Without clear attribution between campaign and visit, it’s impossible to measure real impact and justify budgets internally. Local marketing becomes a cost line, not a performance lever.

What changes when you fix these issues

Fixing these 12 causes doesn’t require rebuilding everything. In most cases, three or four targeted adjustments are enough to significantly increase the in-store conversion rate. The key is knowing which ones to tackle first, and for that, you need data.

A table to quickly map each blocker to its correction lever:

The most expensive mistake: optimizing without data

You can tighten targeting, improve creatives, refine the offer and still miss if you don’t know what truly triggered the visit. The real drive-to-store problem is often less about the campaign and more about the inability to read what it produces. Without reliable attribution, you go in circles.

The brands that improve fastest treat every digital touchpoint site, campaigns, social, newsletters as a measurable lever, not a storefront. Every click toward a retailer should be trackable, qualifiable, and analyzable.

What we recommend in practice

There’s no universal recipe. But one principle holds in most cases: audit your conversion chain before increasing budgets. Double down on a campaign that breaks at the landing step, and you double the waste.

Next, treat your relationship with retail partners as a lever in its own right. A retailer receiving qualified traffic from your brand touchpoints has every reason to feature your product better. That’s not diplomacy, it’s commercial mechanics.

Finally, stop separating digital performance from in-store performance. They are two sides of the same outcome. Shoppers don’t make that distinction.

12 reviews

 

Maxence Antao, Communications Officer at Click2Buy

“Our role at Click2Buy: guide our customers throughout the purchase journey and optimize their marketing ROI with real-time retailer stock data.”

The post Drive to store: why your campaigns do not convert and how to fix it appeared first on Click2Buy.

Retailer data, the forgotten piece of marketing strategy

For a long time, managing product data with retailers was treated as an administrative task. You send a file, check the box, move on. The issue is that the file ends up in heterogeneous systems, poorly interpreted, rarely updated. And on the brand side, you have no visibility into what is actually displayed by the retailer.

The result: incomplete product pages, pricing drift, and stock shown as available even though the product hasn’t been on shelf for three weeks. Consumers see this in real time, and they switch to a competitor whose product page is clean and reliable.

This isn’t a marginal issue. It’s the day-to-day reality for most brands in indirect distribution.

Why data degrades and why it’s hard to fix

The chain is long. Between the brand, logistics teams, e-commerce platforms, and retailers, every link can introduce an error: a mistyped EAN, a truncated description because a field is limited to 150 characters, an image rejected because it doesn’t meet a retailer’s technical specs, a promo price that was never turned off.

These errors pile up. And because no one has a consolidated view of what happens outside their own systems, they persist. In most cases, teams only discover inconsistencies when a customer complaint comes in or during a commercial audit, too late to limit the impact.

Product data governance is often fragmented: marketing owns content, supply chain owns stock, sales owns retailer relationships. No one owns the full picture. And that’s exactly where problems start.

What it costs in real terms

Here are the most common impacts when data quality isn’t under control:

• Products delisted or deprioritized by the retailer because the data doesn’t meet their requirements
• Ranking algorithms penalizing incomplete product pages, reducing organic visibility on e-commerce platforms
• Undetected out-of-stocks leading to a direct loss of sales without the brand being informed
• Avoidable product returns caused by incorrect descriptions or misleading visuals
• Pricing decisions made on outdated data, creating uncontrolled price gaps across the retail network
• Significant internal time spent fixing recurring errors manually instead of managing performance

Each of these points carries a real cost in revenue, in people-hours, and in the commercial relationship with the retailer.

What the most advanced brands do differently

Brands that take this seriously don’t just fill product files more carefully. They implement continuous monitoring of their presence across retailers: which product is correctly listed, at what price, with what availability, and on which channel. That’s what allows them to react quickly when something goes wrong.

They also stop separating product data from marketing performance. A well-configured where-to-buy setup, for example, doesn’t just redirect a shopper to a point of sale. It surfaces signals about which retailers convert, which products generate purchase intent, and where invisible out-of-stocks may be happening. That’s real-world data, in real time, actionable immediately.

It also changes the nature of commercial negotiations with retail partners. When you show up with reliable data on the traffic you drive, product availability, and price gaps in their network, the conversation is not the same.

Retailer data: what to monitor first

From file-based thinking to performance management

The real shift isn’t filling out an Excel file more carefully before sending it to a retailer. It’s building the capability to see, continuously, what’s actually happening in the market and to fix issues fast when things go off track.

That means connecting your tools: your PIM, your marketing campaigns, your indirect sales data, and the information coming back from retailers. When these flows are connected, you can finally turn your digital touchpoints into measurable performance levers, not just storefronts with no feedback loop.

Retailer data quality is no longer a technical topic reserved for data teams. It’s a growth lever directly tied to a brand’s ability to sell, position products correctly, and manage retailer relationships with solid evidence. Brands that understand this are ahead. The rest catch up through manual fixes and quietly lost revenue.

25 reviews

 

Maxence Antao, Communications Officer at Click2Buy

“Our role at Click2Buy: guide our customers throughout the purchase journey and optimize their marketing ROI with real-time retailer stock data.”

The post Retailer data: why brands can no longer afford to ignore it appeared first on Click2Buy.

From Click to Purchase Intent: The Where To Buy KPIs That Reveal Real Sales

The click-volume trap

Here’s the on-the-ground reality: a media campaign drives users to a where-to-buy module. Clicks spike. Everyone’s happy.

So why does it still fall apart? Because retailer click tracking is only the starting point. A click can reflect curiosity, a quick comparison, or even a misclick. It says nothing about real conversion.

What we recommend: look at the retail redirection rate in detail. Which retailers capture the traffic? Which products generate the most interest? Which channels show the strongest signals?

With a where-to-buy setup, you’re not just displaying retailer logos. You know exactly where traffic goes — and, more importantly, what it reveals in terms of intent.

What truly reveals purchase intent

Second reality: marketing teams talk traffic, sales teams talk sell-out. Different languages. Same objective. The result is often misunderstanding.

Why it gets stuck: digital-to-retail conversion isn’t always visible from the brand side, especially in indirect distribution.

What we measure in practice:

• Engagement depth after redirection (specific product page vs generic landing page).
• Repeat clicks on the same product.
• Consistency between media pressure and redirection spikes.
• Variations by retailer, area, or channel.

By combining these purchase-intent data points, you get closer to business reality. This isn’t wishful thinking — it’s behavioral reading. You may not see the final transaction, but you do see the signals that come right before it.

From marketing to retail performance

Third reality: a CMO must justify budgets. A Brand Manager must defend product presence. A Retail Manager must coordinate the ecosystem.

If your where to buy performance isn’t connected to what’s happening on the ground, you’re steering blind.

What we recommend: integrate where-to-buy indicators into your retail performance reporting. Not on the side. Not as an appendix. At the core.

This table is simple. But used well, it changes the conversation in the executive room.

Connecting where to buy, pricing, and product availability

Another field reality: you invest media dollars behind a product… that isn’t available or is poorly positioned at certain retailers.

It happens more often than you’d think.

A strong approach doesn’t stop at where-to-buy analytics. It should also monitor product availability, price gaps, and overall ecosystem consistency.

When you combine retailer performance management with pricing data, friction points become obvious fast: out-of-stocks, competitive price drops, pricing inconsistency.

Data quality on the retailer side is far from a theoretical issue. It directly determines how reliable your analysis is.

What an intent-led approach really changes

When you move from a click mindset to real retail-sales indicators, a few things happen:

• Media budgets are allocated based on real impact, not volume.
• Discussions with retailers become fact-based.
• Omnichannel purchase-journey optimization becomes continuous.
• Indirect sell-out measurement is built on tangible signals.

We’re not claiming to see every receipt. We don’t know everything. But we know far more than what most brands leverage today.

And that’s where the difference is made: between a brand site that generates traffic… and a digital ecosystem that reveals, channel by channel and product by product, where value is actually created.

16 reviews

 

Maxence Antao, Communications Officer at Click2Buy

“Our role at Click2Buy: guide our customers throughout the purchase journey and optimize their marketing ROI with real-time retailer stock data.”

The post Where to buy KPIs what they really reveal about your retail sales appeared first on Click2Buy.

The core issue: you’re managing with sell-in

Most brands without a direct channel measure what they ship to retailers — sell-in — and assume it sells through. That’s a management mistake. What you ship to a wholesaler or a retail chain says nothing about what the end consumer actually buys at shelf. In between, there’s slow-moving stock, silent out-of-stocks, and promotions that never really get executed.

True in-store sell-out happens without you. And without data, you’re flying blind.

What your retailers have (and don’t always share)

Your retail partners have access to detailed data: units sold by SKU, purchase frequency, sell-through rate, stock levels. Some share it proactively via dedicated portals or automated exports. Others send it only on request, in a raw format, in a hard-to-read Excel file. Others don’t share it at all unless you negotiate it contractually.

So collecting retailer data is first a relationship and contractual challenge before it’s a technical one. If you don’t have a sell-out reporting clause in your commercial agreements, start there.

Available data sources

In practice, the data you can access to measure your in-store sales comes from several channels:

• Retailer portals (Carrefour, Leclerc, Système U…) offering reporting interfaces to listed suppliers
• EDI feeds (Electronic Data Interchange) automated between your systems and your partners’ systems
• Manual exports sent by your retail contacts
• Retail panel data purchased from institutes like Nielsen or Circana (formerly IRI), aggregating checkout data across many chains
• Where to buy tools that track purchase-intent clicks toward retailers and help reconstruct part of real demand

Each source has limitations. Portals are often fragmented. EDI requires technical integration. Panels are expensive and remain declarative. Ideally, combine at least two sources to cross-check signals.

How to structure your sell-out reporting

Once you’ve collected the data, you still need to make it usable. Most files come in different formats, with different naming conventions depending on the retailer. Before any analysis, you need to normalize: align product IDs, harmonize time units, remove duplicates.

Here are the priority metrics to track in your retailer performance dashboard:

Crossing sell-out with marketing actions: the real challenge

Having in-store sales data is good. Knowing whether last month’s digital campaign moved those sales is much better. That’s where retail sell-out measurement becomes a true marketing management lever.

In practice: if you run a drive-to-store campaign in October and your retailer data shows a sell-out lift over the same period in the targeted geographic areas, you have a strong signal. It’s not absolute proof of causality — but it’s an actionable correlation to optimize your next budgets.

What blocks you in real life

Even with the right method, a few obstacles show up again and again:

• Not all retailers share the same data at the same pace
• Formats vary by chain and sometimes by contact
• Data arrives late — sometimes two to three weeks after the period concerned
• Some smaller retailers simply don’t have reliable reporting tools
• Consolidation takes time if done manually

That’s not a reason not to do it. It’s a reason to do it right from the start: automate what can be automated, and accept that data will never be perfect — but it will always be better than a rough guess.

20 reviews

 

Maxence Antao, Communications Officer at Click2Buy

“Our role at Click2Buy: guide our customers throughout the purchase journey and optimize their marketing ROI with real-time retailer stock data.”

The post Without e-commerce, here is how I really measure my in-store sales appeared first on Click2Buy.

The real-world problem: you manage media, but the sale happens elsewhere

CMO, Brand Manager, Retail Manager: same frustration. You fund local—sometimes national—campaigns meant to drive store traffic. Yet in-store traffic attribution remains unclear. Retailers keep transactional data. You have little or no access to receipts. And your dashboards mostly talk about CPC.

What’s blocking you? The lack of a closed loop between media exposure and a physical purchase. Until you structure your drive-to-store strategy without e-commerce, you remain dependent on intermediate indicators.

In practice, start by turning every digital touchpoint into a measurable entry point to your retailers. A well-integrated Where to Buy module makes it possible to understand where traffic goes, to which products, and to which retailers. It’s no longer just an outbound link. It’s actionable data.

What you can realistically measure (and what you’ll never know)

Let’s be clear: perfect offline conversion attribution doesn’t exist. You won’t connect every Facebook impression to a checkout moment. But you can measure:

• Clicks to retailers and their split by retailer.
• In-store visit measurement via aggregated location data.
• Changes in store traffic during and after the campaign.
• Performance gaps by geographic area.
• Correlations between media pressure and sell-out (when you have access to it).

Location marketing data helps estimate incremental visits. It’s not individual-level. It’s not perfect. But it’s enough to analyze trends and reallocate budgets.

The right indicators to manage local marketing performance

Stop multiplying KPIs without prioritizing them. In a local marketing performance approach, everything should answer one question: does it generate qualified traffic to my retail partners?

This table should live in your back office. At Click2Buy, these indicators are consolidated to connect in-store retail performance analysis and media activation. Not for show—so you can decide.

Why many drive-to-store campaigns underperform

Simple reality: brands invest in media without verifying the network’s actual ability to convert. Out-of-stocks in-store. Inconsistent pricing across retailers. Poorly referenced product pages.

Result: you generate traffic… that doesn’t buy.

Real in-store traffic campaign optimization starts with field data: monitoring product availability, comparing prices, identifying competitive gaps. The platform helps track these elements continuously and adjust activations based on real network conditions.

Another commonly overlooked point: the landing page. Sending users to a generic page dilutes intent. Using a dedicated editor to contextualize the offer by product, area, or retailer mechanically improves redirect rates—and therefore your in-store traffic tracking potential.

Building a coherent offline customer journey analysis

We talk a lot about omnichannel journeys. In reality, the offline journey remains fragmented. A user sees a geotargeted local ad. They click. They compare retailers. They go to a store later—sometimes the same day, sometimes the following week.

The goal is to connect these steps:

• Media exposure → click to retailer.
• Click → targeted geographic area.
• Area → estimated incremental visits.
• Visits → sales uplift when data is available.

This chaining enables a true offline customer journey analysis. Not perfect, but actionable.

Clear stance: if you don’t link your campaigns to a measurable redirection point, you’re doing awareness—not business. Conversely, once you connect activations to retail partners with precise flow tracking, you move into a defensible local campaign ROI logic in executive reviews.

Measuring drive-to-store campaign impact isn’t about chasing absolute truth. It’s about accepting imperfection, structuring the right indicators, and managing with consistent signals. It’s also about strengthening brand–retailer relationships with shared, factual data that goes beyond media narratives.

Above all, it’s about moving from presence marketing to marketing that embraces business accountability. Without e-commerce. But with method.

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Maxence Antao, Communications Officer at Click2Buy

“Our role at Click2Buy: to guide our clients throughout the buying journey and optimize their marketing ROI with real-time retailer stock data.”

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