Insights

When “New” Doesn’t Mean Innovation

Mengshan Chen — Wed, 06 May 2026 22:00:00 GMT

Innovation is often seen as the engine of growth in FMCG. New products bring excitement, attract shoppers and create opportunities to transform product portfolios. According to Deloitte’s 2026 Consumer Products Industry Global Outlook, companies put efforts into establishing category-killer portfolios, and 67% of surveyed companies focus on a faster innovation cycle. On the surface, measuring innovation might seem straightforward: track what’s new, see how it performs and understand its impact.

In reality, it’s far more complicated.

The definition of “innovation” isn’t clear

The first challenge is surprisingly simple: what does actually count as innovation?

A new product could be a completely new idea, like a different flavour or format that hasn’t existed before. But it could also be a smaller change, such as a new pack size, a limited-edition version or a refreshed product that has been relaunched.

All of these can appear as “new” in the market. But they don’t all represent the same level of innovation. Without a clear and consistent definition, what looks like a strong innovation might just be a minor change.

What makes a product an innovation?

From an analytics perspective, the distinctions between core, introduction or innovation matter a great deal, and they are not always easy to make. Let’s look at a few scenarios of “new” products in analytics.

Some products are classified as in & out, meaning they appear briefly in the market but don’t stay long enough. They may create short spikes in data, but don’t represent an innovation.

Others are relaunches, where a product is reintroduced with the same characteristics as before. As shown by BCG analysis, 65% of new product launches are renovations rather than innovations. A pack-size change or a new packaging design, may look new to consumers and appear as a new item in the data. However, they are relaunches with minor adjustments, not true innovation.

Then there is what can be considered true innovation: products that are newly launched to the market or meaningful variations of an existing product line, with no sales recorded in the last two years. This could include a genuinely new flavour, a new type of product or a different form of packaging that changes the offer.

If these classifications are not made before the data reaches dashboards or reports, the analysis is already flawed from the start.

Then, not all “new” products behave the same way

Even once something is considered innovation, performance can vary widely.

Some products are designed to stay long term and build the category. Others are short-lived, created for a specific promotion or season. Some are launched with strong support and distribution, while others remain niche.

This mix makes it hard to assess performance fairly. A short-term product might generate a quick spike in sales, while a truly innovative product may take longer to build momentum. Looking at them side by side without context can lead to misleading conclusions.

The complexity increases even further when innovation is analysed alongside seasonality. Many new products are launched around specific moments of the year like Easter editions, summer flavours or Christmas promotions. This raises important questions. Is a product successful because it is truly innovative or because it benefited from a seasonal peak?

Given all this complexity, how products are identified becomes critical.

Why getting innovation right matters

Innovation is not just about launching something new. It plays a critical role in shaping shopper behaviour and driving revenue.

The line between success and noise is thin. Short-lived items, low-distribution launches or products with very small sales can all appear in the numbers. While they technically count as new, they don’t always contribute meaningfully to innovation strategy or growth. This is especially true in the fast-paced FMCG world, where over 80% of product launches fail.

Leveraging innovation for growth only works if it is understood correctly.

This is where having consistent and reliably coded innovation products becomes essential. It provides the foundation needed to identify real opportunities, rather than reacting to what simply appears new. By looking at past launches through a consistent lens, brands can learn what has worked, and what hasn’t. Over time, this creates meaningful benchmarks for successful product launches on speed of sales and distribution, making it easier to assess performance early on and take action when needed.

Data, with smartly coded innovation products, is what turns innovation from trial and error into a measurable growth driver. The right innovation can attract new shoppers, justify premium pricing, strengthen brand perception and create long-term growth.

From complexity to smarter innovation strategies and execution

To make innovation analytics work in practice, a few things need to come together.

There needs to be a shared definition of innovation that is applied consistently. Products need to be correctly identified before they enter reporting, ensuring that relaunches, in & out items and low distributed products are treated appropriately. Clear thresholds, such as minimum sales periods or performance levels, help to ensure that only relevant products are evaluated.

Most importantly, this approach needs to be applied consistently across markets, so that results can be compared.

Innovation will always involve uncertainty. But with the right structure behind the data, it becomes much easier to understand what is truly making a difference and where the next opportunity lies.

At Redslim, we work with clients to bring visibility into how innovation truly performs. Using Smart Coding methodology, we ensure that products are properly coded and identified in the data, distinguishing true innovation from relaunches, in&out items, low distribution or late entries. This creates a strong foundation for analysis from the very beginning.

On top of that, we design intuitive dashboards that allow teams to explore innovation performance across markets and categories over its own launch periods. This makes it easier to track how they are performing, identify opportunities and support innovation and growth strategies.

Offline Retail Media Measurement | Redslim

Reka Toth — Thu, 23 Apr 2026 22:00:00 GMT

Today, retail media is not only a rapidly growing channel in digital advertising. It’s actually predicted to be a $175 billion global industry in 2026. However, it’s also one of the most complex channels. In fact, at all the conferences we attended, retail media was always a hot topic, drawing strong interest from attendees and sparking interesting conversations not only about its potential but also about the challenges it comes with.

That ability to link media to outcomes is what makes retail media so powerful, and why investment continues to accelerate. But with that growth comes complexity.
— Tanya Sarakinis, Business Development Director at Redslim

But while everyone is talking about the digital world with sponsored products, display banners, onsite search, a significant share of retail sales still happens in physical stores. However, it’s also the point where most brands lose visibility.

Offline retail media, such as in-store screens, shelf-edge displays, point-of-sale signage or digital fixtures is where the moment of truth actually happens. And what makes it so powerful is the context.

You’re reaching shoppers in the store, at the shelf, in the exact moment they’re making a purchase decision, and it’s arguably the most valuable moment in the entire customer journey.
— Tanya Sarakinis

But without integrating online and offline behaviour, you can’t see the whole picture. And it’s also where ROI quietly goes missing.

In our recent webinar Tanya Sarakinis and Nils Thott shared their insights on the changing retail media landscape with a particular focus on offline retail media measurement.

The gap between what you paid for and what happened

Walk into many major European supermarkets and you’ll find in-store TV screens running multi product promotions. But sometimes the display next to them holds completely different products. There might also be some branded campaigns for new product launches, leaflets from different retailers with different pricing. But how do you know which ad space is driving revenue?

After the promotion is over, you receive the report from the retailers which includes different metrics from reach to awareness and sales uplift. But compared to what? The retailer sold you the space, controls the data and produces the measurement. But the question is:

How can we ensure that we can trust the reports and analysis from retailers? Because they might be biased to tell that your return on ad spend or your sales uplift was very positive, and this is why you should invest more in the retail media.
— Nils Thott, Global Commercial Director at Redslim

The data exists, it’s just fragmented

The information needed to answer these questions is already out there. Retailer POS data, retail media reports, field sales CRM, trade promotion management systems, loyalty card data, market research…they all exist. The problem is that most brands look at each source in isolation and end up with conclusions that don’t hold up when you see the full picture.

We need to take all of this and combine it into one place. Get hold of all that data, take ownership as suppliers, because the information is out there.
— Nils Thott

Getting granular data matters as well because aggregated KPIs hide the patterns. Store-level, SKU-level, daily data is what reveals why one campaign outperformed another, and whether it was the retail media, the field sales activation, the display, or the competitor promotion next door doing the heavy lifting.

Insight belongs in the hands of decision-makers

The goal isn’t getting super complex analytics that you don’t know what to do with. Instead, what matters to key account managers, brand managers and field teams is to make faster, clearer and reliable decisions.

Democratize insights out in the organization, because that’s where decisions happen. It’s not about making reports heavy in analytics. It’s about what does a brand manager need to know, what does a field sales manager need to know.
— Nils Thott

That means connected reporting that integrates online and offline data, your own internal data and retailer data, and surfaces what a good promotion looks like versus a bad one, depending on the KPIs that actually matter to your business.

Summary

Retailers are doubling down on retail media, and they will keep coming to brands for more investment. The question isn’t whether to participate. It’s whether you’re measuring it on your terms or theirs.

At Redslim, we help brands take ownership of their retail media analysis by connecting POS data, field execution, loyalty insights and both online and offline media into reporting built for business users. We’d love to hear where you are on that journey and explain how we can support you.

Data Management Challenges & How to Overcome Them | Redslim

Reka Toth — Tue, 14 Apr 2026 22:00:00 GMT

Data has become one of the most critical strategic assets for brands and retailers. But with the growing complexity of data management, they’re still struggling to leverage the data effectively.

From managing multiple fragmented data sources to ensuring consistent, accurate reporting across categories and markets, organizations are still facing the challenge of turning raw data into reliable and actionable insights.

This article explores the most common data management challenges that Consumer-Packaged Goods (CPG) and Consumer Healthcare (CHC) companies are facing and provides practical strategies for overcoming them.

Common data management challenges

1. Data volume and scale

One of the biggest challenges is the quantity of data that you have to manage. Where once a single dataset from a major data agency could provide you with a sufficient market view, today you must pull from multiple sources across multiple channels.

For example, if you’re a personal care manufacturer, you don’t have to track performance only in traditional grocery stores anymore. You have to include pharmacy, parapharmacy, and online data as well, each coming from different suppliers with different standards and inclusions.

Compared to CPG, in consumer healthcare the breadth of data sources is even greater, with many more local, country-level agencies operating alongside the global providers. To manage this expanded data ecosystem you need robust infrastructure, significant internal resources, and deep expertise in each individual source.

2. Data quality and inconsistencies

With more data sources, you also increase the risk of having data quality issues. Different agencies might define the same metric differently, apply different methodologies, or cover slightly different universes of outlets, which can lead to inconsistencies in the data. And if you don’t understand and manage these properly, you risk drawing misleading conclusions and poor business decisions.

This risk is especially acute in pharmacy and parapharmacy channels, where local data providers may have unique inclusions or exclusions that differ from the standards applied in grocery. Without a clear understanding of these nuances, you can easily find yourself drawing comparisons across non-comparable data sets.

3. Data heterogeneity

Related to quality, data heterogeneity refers to the structural and definitional differences between data sources. Different suppliers may use different product hierarchies, market definitions, time periods, or units of measurement.

Bringing these together into a single, coherent dataset requires careful mapping, transformation, and documentation which is both technically demanding and time-consuming.

4. Semantic interoperability

When you bring your data sources together, you must ensure that the harmonized data is still translatable back to the individual source data. This is very important, because internal teams, local market stakeholders and external retail partners all need to be able to use and verify figures that tie back to the original data sources.

If you manipulate or transform the data too aggressively in the process of building a global view, your teams could easily lose the ability to reconcile their internal numbers with what they see in a data provider’s own reporting tool or what a retailer presents in a joint business planning meeting.

So, maintaining traceability – the ability to link any aggregated figure back to the original local data point – is essential.

5. Time and cost

One of the data management challenges that still many companies underestimate is the time and cost required to manage the growing data complexity. What was once a straightforward process of onboarding a single dataset per country has expanded into a multi-supplier, multi-format effort in each market.

You have to invest considerable time to understand each new data source, from its coverage and methodology to the nuances and limitations before you can start using them reliably. And this effort compounds overtime as you add new channels or data sources, when your suppliers change their methodologies or there are changes in your teams.

The cost of getting this wrong — through poor decisions based on wrong data — can far exceed the investment required to manage data properly from the outset.

6. Data governance

Without clear policies and ownership around how data is collected, stored, accessed, and used, you can easily find your teams working from different versions of the truth. Data governance encompasses the rules, standards, and processes that ensure data integrity and consistency across the organization.

For CPG and CHC companies operating across multiple markets, this challenge is particularly significant. Each local team may have its own approach to data management, making it difficult to build a coherent global view without establishing overarching governance frameworks.

7. Legal and ethical considerations

With the proliferation of data sources comes increasing regulatory and ethical responsibility. You must ensure that you are using data in compliance with local laws, supplier contractual agreements, and broader data privacy regulations.

8. Domain-specific challenges

CHC companies face additional complexity as their data often involves a wider range of distribution channels, more complex regulatory environments, and a greater number of local data providers. While the fundamental data management challenges are similar, the scale and complexity are magnified, requiring even more rigorous approaches to data integration and governance.

9. Versioning and updates

Data is not static. Suppliers regularly update their methodologies, revise historical data, or change their coverage universes. Managing these changes — while maintaining comparability over time and communicating updates to stakeholders — adds another layer of complexity to already demanding data operations.

10. Stakeholder cooperation

To effectively manage data, you need buy-in and cooperation from multiple internal and external stakeholders. Local market teams, regional management, data suppliers, and retail partners all have roles to play. Aligning these groups around common data standards, definitions, and processes can be as challenging as the technical integration work itself.

Strategies for overcoming data management challenges

We illustrated the 10 biggest challenges that CPG and CHC companies usually face. And while they are real and can mean a significant threat to both brands and retailers, it doesn’t mean there is nothing you can do to overcome them.

If you implement the right strategies, tools, and ways of working, you can transform these challenges into a competitive advantage. So, what are the most effective approaches?

1. Clearly define your data management objectives

Before selecting any platform or process, you should be clear about what you want to get from your data. Are you looking to make better commercial decisions or improve your supply‑chain operations and the collaboration with retailers? Or do you want to accelerate product innovation?

Well‑defined objectives serve as a compass that helps you choose the sources you should prioritize, influences how reporting is structured, and determines the level of governance you need. Since you might operate in different markets, the goal‑setting process should reflect both a global need for comparable, aggregated insight and local demands for granular, market‑specific data.

2. Implement strong data governance

A well‑designed governance framework defines data ownership at a granular level, the policies that regulate the collection, storage, and access, and the standards which keep metrics consistent across regions.

By clarifying data ownership and recording the steps for resolving issues, you can guarantee that every team, from global insights or local analysts, has access to the same “single source of truth.” Without this foundation, even the most advanced technology will have a hard time producing reliable and trustworthy results.

3. Simplify data integration

Investing in an integration layer that pulls together various sources and creates a single view helps to avoid double‑counting, reduces discrepancies, and maintains the audit trail back to the original data points. Once you have your data harmonized and enriched, you can implement data visualization software tools (like Redslim SPRINT or Power BI) to see both the global picture and the local breakdowns and enable your users to effortlessly alternate between strategic and operational views without the need for manual reconciliation.

4. Make data security a top priority

With the expansion of data and an increasing number of users accessing it, security should be an integral part of the design rather than an afterthought. By implementing role‑based access controls and detailed audit trails, and complying with supplier‑mandated usage rights, you can protect your sensitive commercial and health‑related information. In this way, you can reduce the risk of breaches, regulatory penalties, and loss of partner trust.

5. Partner with a data management company

Manual data preparation is tedious, prone to errors, and heavily reliant on an individual’s expertise. What can you do instead?

You can choose collaborating with an external partner for data ingestion, cleaning, harmonization, and enrichment who can do the heavy lifting for you by delivering up-to-date, reliable data to allow your teams to concentrate on extracting value from the data.

6. Purchase data quality solutions

Inadequate data quality can be extremely costly and hinder forecasting, negotiations, and decision‐making. With real‐time quality‐management systems you can detect and fix errors, duplicates and gaps in your data automatically. On the other hand, detailed source documentation explains the extent of available data and suitable use cases, thereby helping to avoid costly misinterpretations.

7. Develop a data-driven culture

Having the right technology and partners will not bring about value on its own. You have to take care of promoting data quality and data usability at every level of the organization. With regular training, a strong leadership commitment and organized knowledge‐sharing, you can ensure that employees are well-equipped with the skills, responsibility, and organizational memory to treat data as a common, strategic resource.

Summary

The data management challenges that CPG and CHC companies are facing every day are growing and evolving together with the industry.

The good news is that the solutions exist. With the right systems, governance, and partners in place, overcoming today’s data management challenges is not just possible – it’s a direct path to stronger performance and sustainable competitive advantage.

At Redslim, we help CPG and CHC companies take control of their data landscape with a combination of advanced technology and deep industry expertise. Our harmonization, integration, and data management services are designed to simplify complexity, improve data quality, and enable confident decision‑making across markets, functions, and teams.

Learn more about our data harmonization services or contact our experts to discuss your particular needs and understand how we can help.

Structured data enrichment is critical for seasonality analysis

Mengshan Chen — Wed, 01 Apr 2026 22:00:00 GMT

Seasonality analysis is a critical factor in many categories. It supports planning, assortment and inventory allocation, promotional timing and revenue growth.

Yet many organizations underestimate one foundational requirement: strategically designed data enrichment. Without well-thought data enrichment, seasonality analysis can be misleading, especially when new product launches enter the picture. Peaks are misinterpreted. Forecasts become inflated.

For categories where seasonality plays a critical role in the sales calendar, such as Confectionary and Beverages, understanding true seasonal uplift coupled with innovation-driven growth is not just analytical work. It can be a structural data challenge.

The core problem: seasonality and innovation overlap

In theory, seasonality is predictable. Ice cream and sun cream sells more in summer. Chocolate peaks during festive periods. Certain beverages, rice and dairy products spike during celebrations such as Ramadan.

But what happens when a new product is launched during that same seasonal peak? If a new SKU is introduced in June and sales surge, is that because of strong consumer adoption, or because it coincided with peak seasonal demand?

Without well-defined data enrichment, the system cannot distinguish between organic seasonal uplift, innovation-driven incremental growth and promotional impact.

When these elements are not clearly coded, baseline projections and demand expectations could be distorted for the following year.

This is where meaningful data enrichment becomes essential.

Why this is more complicated than it seems

One may assume that AI models or IT systems can solve seasonality challenges automatically. While advanced algorithms can detect patterns, the real complexity lies in definitions.

What qualifies as a seasonal product? Is it limited-time availability? Or is it a specific flavour profile or format? At the same time, is a seasonal limited edition considered innovation? These distinctions are strategic decisions, not purely technical ones.

AI can apply rules, but it cannot automate without deep industry context. IT teams can build robust infrastructure, but they often lack category-specific knowledge about consumer behaviour and product definitions.

This defined versus undefined ambiguity complicates seasonality analysis.

Aligning seasonal periods introduces additional complexity

Season period alignment adds another layer of complexity.

Take Easter in the U.K. as an example. In 2025, Easter Sunday fell on April 20th, whilst in 2026, it’s on April 5th. For categories like chocolates and baked goods, sales peak around the holiday. If analysts compare the same calendar weeks across these years, the Easter spike appears to shift.

On top of that, the relative length of the Easter season can vary. When Easter moves, the length and timing of that demand window also change. In 2025, Easter season spanned from April 7th through to April 21st, creating a longer build-up period in April. This is particularly relevant for categories like Confectionary, where the Easter season may begin soon after the Christmas season ended.

Whilst 2026 Easter holiday season starts from March 28th through to April 13th, pushing demand forward into March. If analysts simply compare the same length of calendar weeks across both years, the seasonal peak appears inconsistent and the shorter season could be deemed less successful.

Because both the timing and the duration of the season shift year to year, this makes defining a consistent “Easter period” challenging. Without correctly aligning reporting periods with the festive seasons, as well as category knowledge, it’s impossible to tell whether a sales increase is due to Easter, a promotion, or a new product launch. By coding products as Easter-related and aligning sales relative to the holiday week, analysts can capture the true seasonal pattern year after year.

How to do data enrichment well for seasonality analysis

Good data enrichment goes beyond tagging for holidays. It requires governance, strategy alignment, and domain expertise.

Start with clear product attributes: brand, flavour, pack size, category, and whether it’s an innovation. Include info like limited editions to separate seasonal spikes from new product performance.

For seasonal products, it’s important to code them correctly for the seasonal event they belong to, such as Easter chocolates. This can be achieved by specifying the relevant information to the product, either adding it in its description or in the background coding. This way, the products are properly classified and can be easily filtered when analyzing data.

What’s more, harmonize categories across markets so products are defined in a consistent way, making it easier to compare performance globally. But at the same time, allow for local variations where consumer behaviour differs. Keep the coding flexible so it can adapt as growth strategies evolve.

Finally, combine automation with industry know-how. AI can scale coding, but domain expertise makes sure your enriched data tells the right story.

Smarter planning and forecasting with enriched data

Seasonality analysis is more than a best mapping exercise. Without well-structured data enrichment, even the most advanced forecasting models will produce misleading outputs. In reality, it is a data design challenge.

When the data is enriched properly, companies can see the true seasonal pattern instead of a distorted one.

First, baseline demand becomes clear. Analysts can separate normal year-round demand from seasonal uplift.

Second, innovation performance becomes easier to evaluate. If a new product launches close to a seasonal peak, enriched data helps determine whether the sales surge came from real consumer adoption or simply from the seasonal wave. Alternatively, companies can understand whether the seasonal peak can be used as a boost to support new product launches, trialling new product to refine future innovation strategies.

The end result is simple: better planning, better forecasting.

For organizations aiming to scale sales and innovation while controlling risk, data enrichment is not optional. It is the key to accurate planning and forecasting.

At Redslim, we provide data enrichment solutions making sure data is designed to fit for your seasonality analysis. Our approach combines smart coding, automation with deep category expertise, ensuring your data is structured to reflect the real demand patterns and innovation impact.

Discover how you can gain clarity in your seasonal sales trends for planning and forecasting, contact the Redslim Team.

Omnichannel Execution Powered by Data Harmonization |Redslim

Mark Bortacki — Wed, 18 Mar 2026 23:00:00 GMT

Omnichannel has moved from ambition to expectation. Pretty much all multinational consumer goods organisations now operate across physical retail, ecommerce, marketplaces, and retail media ecosystems simultaneously.

Commercial teams are expected to understand performance across these environments and make coordinated decisions that reflect how shoppers actually behave. However, while shopper journeys are cross-channel, the underlying data environments remain fragmented. Manufacturers need the right data foundations that connect retail, ecommerce, marketplace, and retail media signals into one coherent view.

Why omnichannel measurement breaks without data coherence

Shopper behaviour rarely follows channel boundaries. A shopper standing in a physical store may review product content online, compare pricing across retailers, check availability, or recall exposure to digital media before making a purchase decision.

The physical and digital experience therefore converge at the moment of conversion, even when the transaction itself occurs in-store. The shopper journey is increasingly omnichannel by default, while measurement and decision frameworks often remain channel-specific.

Despite this strategic clarity, execution remains uneven. The primary constraint is rarely lack of data or analytical capability. Instead, organisations are increasingly encountering a structural challenge: the difficulty of using multiple commercial datasets together in a consistent, scalable way.

Disconnected datasets

Across markets and functions, data ecosystems continue to expand. EPOS and syndicated sources sit alongside retailer-direct feeds, digital shelf monitoring platforms, marketplace analytics, loyalty datasets, and retail media reporting environments. Each source provides valuable perspective and is typically well understood within its domain. The complexity emerges when organisations attempt to connect these perspectives into a coherent view of performance.

Differences in product hierarchies, retailer definitions, time granularities, metric methodologies, and geographic scopes create barriers to comparability. As a result, analytical workflows often begin with reconciliation rather than insight generation.

Significant effort is spent aligning datasets before commercial questions can be addressed.

More visible fragmentation for digital commerce

Digital shelf and retail media capabilities have become central components of commercial strategy, yet the data they generate frequently remains structurally disconnected from broader commercial datasets.

Digital shelf signals describe availability, visibility, and content quality. Retail media signals capture exposure, spend, and platform performance metrics. Commercial datasets reflect sales, distribution, pricing, and promotion outcomes. Each provides a valid lens, but none independently explains performance.

The questions organisations increasingly seek to answer span these domains. Whether changes in search visibility influence sales outcomes, whether retail media investment drives incremental demand, or whether availability gaps constrain campaign effectiveness are inherently cross-dataset questions.

Addressing them consistently requires an analytical environment where datasets can be used together without compromising source fidelity.

From integration to usability

Many organisations have already invested heavily in data integration platforms, lakehouse environments, and reporting layers. While these capabilities are critical, they do not fully address the challenge of comparability. Moving data into a shared environment does not automatically resolve differences in definitions, structures, and business logic.

What is emerging instead is recognition of the need for a harmonized decision layer that sits between source systems and downstream analytics. This layer aligns product and retailer structures, standardises selected metrics where appropriate, preserves transparency around source definitions, and enables consistent use across markets and functions.

The objective is not to replace source systems or overwrite their methodologies. Rather, it is to enable them to coexist within a framework that supports shared interpretation and decision making.

How harmonized data unlocks omnichannel execution

The presence of a harmonized layer materially changes how organisations approach omnichannel execution. Commercial teams can more reliably connect retail media activity to sales outcomes, understand the relationship between availability and conversion, and evaluate digital shelf performance within broader retailer context. Also, cross-functional collaboration becomes easier when teams operate from aligned reference frameworks.

This also has implications for advanced analytics and AI initiatives.

As organisations expand modelling and automation capabilities, consistency of underlying definitions becomes increasingly important. Analytical sophistication cannot compensate for structural fragmentation; in many cases, it amplifies it.

Establishing a comparable data foundation therefore becomes a prerequisite for scalable advanced analytics.

A foundational but under-recognised capability

While omnichannel, retail media, and AI dominate strategic agendas, the connective work required to enable them often receives less visibility. Data harmonization is typically perceived as technical infrastructure rather than commercial capability.

However, its impact is fundamentally commercial. The ability to move from fragmented perspectives to shared understanding directly influences speed of insight, quality of decisions, and effectiveness of activation.

As data ecosystems continue to expand, organisations that invest in this connective layer position themselves to extract greater value from existing data assets while enabling future capabilities. Omnichannel maturity increasingly depends not only on breadth of data but on coherence of its use.

Redslim supports global organisations in establishing this harmonized decision foundation, enabling disparate commercial datasets to be aligned, governed, and activated consistently across markets, channels, and functions.

If your teams are aiming to improve omnichannel execution but are held back by fragmented, inconsistent data, a unified foundation is the fastest way to unlock progress. Redslim helps organisations build such a foundation by connecting retail, ecommerce, marketplace, and media signals into one coherent decision layer that fosters insight and action.

If you’re ready to move beyond fragmented views and create a data environment that genuinely supports omnichannel performance, let’s talk. Redslim can guide you from complexity to coherence so you can act with confidence and speed.

How to Check CPG Data Quality | Redslim

Reka Toth — Wed, 11 Mar 2026 23:00:00 GMT

In a highly competitive environment like the Consumer-Packaged Goods (CPG) industry, the quality of the data that brands have to work with can make or break the performance of the business.

Poor quality data leads to faulty analysis and inaccurate decisions that might cost companies millions in lost revenue.

The companies that get this right don’t just collect data, but they connect it, harmonize it, and validate it continuously. They make data quality a strategic priority, because they understand that accuracy drives better pricing, forecasting, and market execution. And this is where the right partner can make all the difference.

At Redslim, we process billions of data points across markets and categories every month, supporting global CPGs in turning siloed, raw inputs into reliable, decision‑ready insights. Our day-to-day work with the different sources and client use cases gives us in-depth visibility into the common patterns, pitfalls, and success factors that determine whether the data is trustworthy.

So, how can you check if your data is good enough?

Continue reading and we’ll guide you through 6 fundamental aspects you should pay attention to, to make sure your data reflects reality and takes you to reliable conclusions.

1. Check if you see a consistent story through data sources

As a first step when checking data quality, ask yourself if your different data sources tell the same story. Do they show the same trend? Then you can move to the next step. But what if your retail direct data source shows growth in the category, but your consumer panel data indicates a decline? That’s a signal.

In this case, you have to investigate to understand the reason behind it. The issue might still be valid, but you have to review trends across the key data sources on a monthly basis and understand why before making decisions on conflicting stories.

Said that, having people with technical expertise to ingest data is not enough. You need people with a deeper understanding of the different datasets. People who understand the sources’ limitations and where and when you should use them.

2. Verify alignment in product attribution and taxonomy

If you’re working with different data providers, across various countries and categories, product attributes are not coded in the same way. Each provider defines categories differently or uses a different hierarchy. Depending on the countries you’re operating in, there might be differences also in the prices and measures (€ vs. $ or kg vs. lb).

So, the next thing you have to check is if the taxonomies are consistent and your datasets have the right “building blocks” to create a consistent view of your performance. And this is exactly what most CPG companies are struggling with when integrating data from disparate sources.

Make sure that all data follows your global taxonomy and that each source provides the attributes (size, pack type, flavor etc.) needed for the mapping.

3. Validate if aggregation is done in the right way

The next thing on our checklist is about aggregation, the caveats of regrouping things together. From a data perspective, there are certain measures which can’t be simply added up, even if from a technical point of view, it seems an easy-to-do task.

Distribution is a great example of this. You can’t add up SKU distributions to get the brand distribution because you can’t see the overlap at store level. If you have “SKU A” and “SKU B” each with 5% distribution, those might be the same 5% of stores. Or there may be some overlap or there may even be no overlap at all.

So, instead of just checking if you can technically aggregate your data, you have to understand what that means in terms of the usability of the data and how to work around that to get a meaningful figure.

4. Check period and refresh cycle alignment to prevent historical drift

Usually, different data sources update with different frequency (weekly, bi-weekly, monthly etc.) and restate historical data at varying schedules. Sometimes more often than many teams would expect.

Simply adding the latest data without checking for historical changes results in variations between your back data and what the data providers show. Historical restatement frequency varies also by country, even within the same agency. For example, one provider’s Asia Pacific data might restate more often than Europe, requiring different ingestion approaches.

At Redslim, we’ve been working with the global data providers for years, so we can advise on the best way to work with the data received from them. And quite often it requires a different approach. Most importantly, it’s essential to understand things like restatement patterns – where and what those changes usually are.

5. Install pre- and post-harmonization quality checks

We recommend checking the quality of your data twice. First, you should assess input on its own as you get it from the data providers. Check the data sets for completeness, format consistency and outliers before even starting the data harmonization process.

Then, you have to validate the data after the harmonization as well. After applying mapping and aggregation, validate that the results actually make sense. Also, check if the agencies restated sales, changed attribution or shifted category definitions. Catching these before they hit dashboards prevents data-freeze moments.

6. Confirm the first line defense for data governance and democratization

According to a recent Gartner research, to achieve data quality, a fundamental thing is to have an effective data management and data governance in place.

Yet, many brands tend to think about it like an afterthought. Another McKinsey study shows that only 40% of consumer-goods companies that have made digital and analytics investments are achieving returns above the cost of capital. Most of them think that it’s because they still don’t have enough data. But the truth is that they simply lack data governance.

Data democratization amplifies quality issues even further. While once data was analyst-controlled who caught problems first, now, with dashboards everywhere, quality issues become instantly public.

To avoid that happening, your data must be clean before release. Once your business users see contradictory numbers, even if corrected, their trust in the data is already damaged. So, robust governance should include preventative validation as well.

What you can do is to build quality gatekeeping that validates data before it reaches users. This might seem like limiting access to end users. But in reality, it ensures that when people get access, they’re getting trustworthy data.

Build confidence in your data

When you’re preparing your data sets for analysis, the goal is to help business users to make confident decisions based on the harmonized data. When teams stop second-guessing numbers and start acting on them, you’ve crossed from “good enough” to genuinely useful data.

CPG companies that win aren’t those with the most data or sophisticated algorithms—they’re the ones whose data tells the true story that people can trust enough to bet the business on. Hopefully, these six checks will help you get there.

Feeling overwhelmed by the growing complexity of your data? Is maintaining high‑quality data taking time away from what really matters?

The Redslim is ready to support you. We specialise in data management, harmonisation, and integration, so your teams can focus on impact, not infrastructure.

If you’re ready to outsource the heavy lifting and unlock the true value of your data, let’s talk.

Winning at the Shelf in CPG | Redslim

Reka Toth — Thu, 26 Feb 2026 23:00:00 GMT

For most consumer-packaged goods (CPG) companies, visibility, placement, and product performance – both on the physical and on the digital shelves – are probably the strongest predictors of how they compare to the competition.

Yet, many of them struggle to cut through the noise of competing products, shifting consumer preferences, and fragmented retail ecosystems. The key to winning at the shelf lies in data. But not just any data. It’s about harmonized and actionable data that separates industry leaders from the average.

And especially in a continuously growing global CPG market, predicted to reach $10 trillion by 2028, this means a real growth opportunity for the market players.

At Redslim we know this very well and we are on a mission to help brands leverage retail data harmonization to dominate shelf space, drive sales, and differentiate themselves from their competitors.

Common challenges that brands face on the shelves

Most brands sell their products on different channels, including grocery stores, pharmacies, para pharmacies, online marketplaces or even directly to consumers. And each of these channels have different ways to optimize visibility, potential revenue and market share.

While on the physical shelf brands have to negotiate the eye-level space, on the digital shelf they lose the black and white control over where their product sits on the shelf. What matters instead are optimized product pages, keyword relevance, pricing, and reviews that heavily influence whether a product is seen or ignored. Most consumers discover products through category searches, not brand searches, so for brands failing to rank on page one can dramatically lower conversions.

And quite often, the customer journey isn’t that linear. Digital actions can also influence physical store behavior. Indeed, many shoppers often research online the product details or reviews to make more informed decisions when buying in store.

So, while positive reviews help to increase the likelihood of purchase, negative reviews – and on the digital shelf even low volumes – can hurt both search ranking and purchase confidence.

Why data is the new shelf real estate

As we illustrated, the modern retail landscape is a complex and dynamic environment. With thousands of SKUs fighting for customers’ attention, brands face increasing pressure to optimize shelf presence.

And guesswork or delayed reports don’t cut it anymore. What retailers and brands need today is real-time, accurate and aggregated data from the different sources to make quick decisions that maximize profitability and product visibility.

This is where retail data harmonization becomes critical.

Retail data harmonization is the process of unifying disparate data sources, like sales performance, in-store and online promotions, shelf imagery, and customer behavior analytics, into a single, coherent dataset. Without harmonization, brands are left juggling siloed data from multiple retailers and data providers, each with its own formats, metrics, and reporting standards, resulting in poor visibility, reactive decisions, and missed growth opportunities.

How Redslim helps

Redslim takes on this problem directly by bringing together data that is spread out across different channels and systems. Whether it’s sales numbers from a retailer like Tesco or ASDA, shelf imagery from a regional grocer, data from different e-commerce platforms like Amazon, or from qualitative research, Redslim harmonizes these data sources into one single database.

This allows you to reveal search visibility, PDP compliance, review health, availability, and pricing competitiveness across all major retailers, and to measure cross‑channel impact to understand how your online visibility affects your in‑store performance.

What this means for your company

Optimize product placement

At Redslim, we empower your analytics and commercial teams to bridge the gap between digital and physical shelf data and make sure your product is always in front of a customer across the different channels. By benchmarking competitor placement strategies, new product launches, and private label approaches, you can refine your own tactics to increase your market share.

We also help you identify which keywords, titles or product descriptions help you rank higher on the online marketplaces, and which in-store tactics – displaying your products at the end of the ailes or simply adding promotion labels in their regular position – lead to more sales.

Analyze product launches and attributes

We help you understand which packaging formats are the most or less popular and give you a consolidated view of your new product launches across the different channels. Whether it’s about new flavors or different pack sizes, with a harmonized dataset you can analyze these attributes and compare them with competitors.

In this way, you can understand the latest trends between consumers, and you can refine your packaging strategies to stand out, while aligning with evolving consumer expectations.

Enhance in-store promotions and advertising

We also support your teams in evaluating the impact of promotional campaigns. By harmonizing also retail media data, we allow you to analyze how your digital initiatives, like influencer campaigns, retail media networks, or social media affect sales on Amazon or in the stores. This way you can get a clear picture of what is working and what’s not.

By analyzing category elasticity, you can pinpoint where price adjustments or promotions will yield the highest return on investment, optimizing both budget and strategy.

Gain a competitive edge through data-driven decisions

By unifying all shelf-related data, we equip brands to spot competitor innovations early, and respond proactively. We also help you prioritize high-impact actions, such as improving product page quality through fresh reviews, addressing gaps in product descriptions, or optimizing product assortment. Ensuring all departments – from trade and brand teams to e-commerce and analytics – operate from a shared “source of truth” enhances collaboration and decision-making, driving consistent growth and market leadership.

The future of CPG: data-driven shelf mastery

As retail continues to evolve—driven by AI, e-commerce, and evolving consumer habits—the ability to leverage data will determine which CPG brands thrive and which fall behind.

By unifying fragmented data and giving CPG companies direct access to retailer insights, we eliminate blind spots and empower your teams to act with confidence. The result? Higher shelf occupancy, smarter pricing, and a measurable edge over competitors.

Ready to win at the shelf?

For CPG companies, the shelf is no longer just a retail fixture—it’s a growing battlefield where data is the sharpest weapon. Redslim equips brands with harmonized data, so you can transform your sales strategies from reactive to proactive.

Discover how Redslim can revolutionize your CPG strategy and help you win at the shelf today. Schedule a meeting with our experts and claim your data-driven dominance.

Why Data Foundations Matter for AI | Redslim

Mengshan Chen — Thu, 12 Feb 2026 23:00:00 GMT

AI is rapidly becoming a competitive differentiator across industries. Across fast moving industries such as Consumer Packaged Goods (CPG), from assortment, forecasting and product inventory optimization to personalized promotions and pricing, AI promises faster answers.

Yet many executive teams find that despite significant investment, AI initiatives struggle to deliver consistent value, scale or trust. While organizations rush to launch AI initiatives, Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.

The issue is not the AI itself. More often, it is the data foundation that AI is relying on. Nearly 63% of organizations are unsure if they have right data management practices for AI. In the industries where margins are tight, volumes are high, and decisions must be made at speed, AI is only as effective as the data it relies on. Without clean, harmonized, and consolidated data, AI becomes a risk rather than an advantage.

For C-suite leaders, this raises a fundamental concern: how to trust AI-driven decisions, how to prepare their organization for an AI era?

Three well-known AI behaviours

There are three well-known AI behaviours that help to explain why data foundations matter so much.

1.Sycophancy

AI systems are designed to recognize patterns and respond based on the information they receive. AI models learn patterns from data, not designed to challenge the validity of that data. When underlying data reflects incorrect assumptions or inconsistent business definitions, AI tends to reinforce those perspectives. If a product attribute is mis-segmented, or if metrics are inconsistently labelled, AI will not detect or correct these issues. Instead, it will reinforce them across insights.

2. Anchoring

AI systems are quick to establish foundational assumptions based on their initial data inputs. When this data is incomplete or inaccurate, these early insights become reference points, or ‘anchors’, that shape the future predictions and recommendations. If there is significant variation in quality or consistency across different systems, AI is likely to pick up flawed historical information and embed those inaccuracies throughout its ongoing analyses.

3. Garbage In, Garbage Out (GIGO)

The principle of garbage in, garbage out may be decades old, but it has never been more relevant. When data contains errors, duplicates, or conflicting records, AI scales those issues at speed.

These behaviours highlight a simple truth: AI amplifies whatever foundation it is built on. When data is clean and consistent, AI generates insights and recommendations that can be trusted. When the underlying data is flawed, AI can inadvertently accelerate risk rather than delivering the promised benefits.

Today, most organizations have data flowing in from every corner, retailers, customers, digital platforms, partners, and far-reaching markets. It is only when this data is cleansed, harmonised, and integrated that AI can truly amplify value. The difference between merely collecting information and harnessing it for competitive advantage lies in the reliability of the underlying data foundation.

Quality data prevents false anchoring

Around 43 % of organizations cite data quality as the main reason their AI projects stall, AI models cannot deliver reliable outputs without clean, well-structured inputs. Even small errors in data can lead to significant consequences such as having too much stock, sending incorrect pricing signals, or running ineffective promotion campaigns.

One principle remains central in our industry: we can’t compromise on quality.

Quality data is the foundation of trust, giving us confidence in the results we get. When data is clean, AI tools are better equipped to do a much better job by providing more accurate performance summaries and forecasts. There are fewer mistakes from AI-generated answers, and analysts can trust that what they learn is based on reliable facts.

Harmonized data enhances alignment within organizations

One of the most common barriers to AI success is misalignment across functions. AI does not naturally bring teams into alignment. This means that if different parts of a business use different definitions, AI-generated answers will simply reflect those differences.

For AI to be truly useful and trusted across the organisation, it is important that everyone is working with the same definition and harmonised data. Carlsberg showed that creating a single, unified data foundation, where product attributes, metrics and time periods are standardized, helps the whole business fuel growth. When various strategic business areas such as commercial strategy, revenue growth management, innovation, and finance can rely on one trusted source of truth, the alignment helps teams work better together and make informed strategic decisions.

AI-ready data foundations are a strategic imperative

Across industries, AI is quickly moving from experimentation to execution. As organizations look to scale AI across planning, operations and commercial campaigns, one factor consistently determines success: the reliability of the data foundation.

An AI-ready data foundation has become a strategic investment rather than a technical one. Without this foundation, AI risks reinforcing inconsistencies and driving decisions based on fragmented or biased data.

For C-suite leaders, strong data foundations support more accurate forecasting and planning, tighter alignment between sales, operations, and finance, and more confident decision-making. They also make it possible to scale AI across brands, channels, and regions without increasing complexity.

Most importantly, trusted data accelerates value. It allows organizations to adopt new AI capabilities and move beyond pilots to impact at scale.

The question that matters

AI technology will continue to evolve. But competitive advantage will not come from technology alone. It will come from organizations that invested early in getting their data right.

For leaders, the question is no longer “Should we invest in AI?” It is “Which components of our data foundation must evolve to support AI-ready data with confidence?

In an industry defined by speed and scale, AI success starts with data you can trust.

Discover how our data harmonization services can help you build a solid data foundation to prepare your organization to be future ready. Or contact us to learn how we simplify complexity for your business.

Data Harmonization Trends to Watch in 2026 | Redslim

Reka Toth — Tue, 20 Jan 2026 23:00:00 GMT

In today’s hyper-competitive markets, data is no longer just a byproduct of business—it’s the lifeblood of strategic decision-making. But raw data from global markets, different systems, and in inconsistent formats, is almost worthless if it’s not transformed into actionable intelligence.

As we look ahead, future trends in data harmonization are reshaping how leading consumer packaged goods (CPG) and consumer health care (CHC) companies manage their data. It’s no longer enough to align basic attributes like product category or brand name. The new trend is all about sophistication, scalability, and intelligence.

In this article, we present the three major changes that characterize the future of data harmonization and explain what they mean for organizations that want to stay ahead.

Scaling harmonization beyond basics: From dozens to hundreds of attributes

Traditional data harmonization focused on a limited set of core attributes like brand, size, flavor, and package type. Although these characteristics are very important, they are not enough anymore in an era where consumers demand transparency, and businesses demand precision.

But to meet the demands of consumers and businesses today, brands must look beyond the traditional attributes and include hundreds of enriched characteristics such as sustainability metrics (e.g., carbon footprint, recyclability), traceability of ingredients, allergen information, ethical sourcing details, and packaging composition. With these granular attributes companies can:

Deliver personalized marketing campaigns
Optimize supply chain sustainability
Comply with evolving regulatory requirements
Drive innovation through data-backed R&D

But more attributes don’t necessarily mean better insights. The real value comes from strategic control over data enrichment. Leading organizations are seeking more flexibility within a global data structure, and are creating their own attribute taxonomies that perfectly fit their brand and market-specific considerations.

According to Soren Altmann, partner at Redslim, notes, “What we are seeing now is that dimensions are blurring together and they are creating more complex harmonization challenges.”

Indeed, nutrition claims are related to regulatory standards that are, in turn, related to consumer sentiment data. Integrating all of these is essential to maintain synergy. The future belongs to those who will be able to not only harmonize product data but also the entire ecosystem of attributes that influence consumer behavior and business outcomes.

Dynamic period alignment: Harmonizing time for smarter analytics

Time is not one-size-fits-all in data analytics—and we recognize this as one of the future trends in data harmonization.

In the past, the measurement of retail and sales reporting depended on uniform timeframes – usually four-week cycles or monthly periods. But today, Marketing Mix Modeling (MMM) and Revenue Growth Management (RGM) require a much more flexible approach. Media campaigns, seasonal promotions, and influencer launches hardly ever fall on typical calendar weeks. They follow consumer behavior, market readiness, and competitive dynamics.

This shift necessitates period alignment tailored to specific use cases—a critical evolution in data harmonization.

Imagine trying to measure the ROI of a three-day social media campaign using monthly sales data. The insight is lost. So, what can leading companies do instead?

They can adopt flexible temporal harmonization frameworks that align data based on:

Campaign duration
Promotional context
Geographic rollout timing
Product lifecycle stage

Such precision ensures that they accurately capture the marketing impact, they build reliable price elasticity models, and they optimize their promotional strategies in real time.

But flexibility introduces even more complexity. The key challenge? Maintaining data actionability across diverse analytical models. Harmonized data must remain consistent enough to be trusted, but at the same time, it also has to be flexible enough to adapt to unique business questions.

The solution lies in intelligent data layering where the core product data remains stable, while time-based dimensions are standardized with multiple period views. The result? Analytics that are not only accurate, but immediately actionable.

AI-driven enrichment meets human governance

Artificial intelligence (AI) is no longer a futuristic concept. It’s already a driver of data excellence. In the realm of data harmonization, AI is emerging as a game-changer, especially with vast and complex data sets.

AI-powered enrichment significantly speeds up the way product attributes are identified, classified, and harmonized, especially for emerging characteristics like clean label claims, digital shelf compliance, or e-commerce metadata. Machine learning models can scan thousands of product descriptions, extract key features, and at the same time, suggest standardized values on a large scale, thus cutting down the manual work by up to 70%.

However, AI is not the ultimate solution.

As Eric Bensimon, co-CEO and founder of Redslim, puts it: “AI becomes more central to how businesses use data. We are strong believers that AI can accelerate the availability of the data by making suggestions on how things can be harmonized or enriched. However, when technology delivers scale and speed, human expertise makes sure that enriched data, with transparency needed to trust insights, are decision ready across teams.”

The future doesn’t belong to fully automated systems, but to AI-assisted human governance. Human judgment for making strategic decisions concerning attribute definitions, data hierarchies, and brand-specific logic is indispensable.

Platforms like Redslim’s Enrichment Hub exemplify this hybrid future. It provides collaborative settings where data teams, brand managers, and regional leads can review AI-generated suggestions, make real-time modifications, and ensure global consistency while preserving local specificity.

The combination of automation and control realizes the potential of harmonized data being fast and scalable, as well as aligned with business intent. It’s a framework designed for agility, compliance, and long-term adaptability—the pillars of a future-proof data strategy.

Why these trends matter now

These three trends—scaled attribute harmonization, use-case-specific period alignment, and AI-powered collaborative governance—are redefining what’s possible in data management.

For CPG and CHC leaders, the implications are clear:

Better decision-making: Enriched, harmonized data fuels advanced analytics, enabling predictive modeling and real-time optimization.
Faster time-to-insight: Automation and intelligent frameworks reduce harmonization time from weeks to hours.
Stronger compliance & transparency: Detailed attribute tracking supports ESG reporting, regulatory adherence, and consumer trust.
Global scalability with local relevance: Centralized governance ensures consistency, while flexible frameworks allow regional customization.

Organizations that invest in next-generation data harmonization today will dominate the markets of tomorrow. Those that delay risk falling behind in an era where data agility equals competitive advantage.

Final thoughts

The future trends in data harmonization represent a fundamental shift in how data is structured, governed, and utilized. It’s a move from rigid, rule-based systems to intelligent, adaptive frameworks capable of handling the complexity of modern commerce.

To thrive in this environment, companies must:

Expand their harmonization scope beyond basic attributes
Implement temporal flexibility for advanced analytics
Leverage AI as an enabler—not a replacement—for human expertise
Invest in platforms that support collaboration, control, and scalability

Data harmonization is no longer a back-office function. It’s a strategic imperative—one that fuels innovation, drives growth, and builds resilience in uncertain markets.

The future is not just data-rich. It’s harmonized, intelligent, and trasparent.

Is your organization ready? Discover how our data harmonization services can help you unlock real-time insights, align disparate systems, and future-proof your data strategy. Or contact us for a demo to learn how we turn complexity into clarity for your business.

Retail Media Data Harmonization Explained | Redslim

Mark Bortacki — Mon, 12 Jan 2026 23:00:00 GMT

Retail media has become one of the most influential channels in global advertising, with brands now investing over $150 billion annually while still struggling to answer a fundamental question: is it actually working? What began as a tactical experiment has rapidly evolved into a core growth lever, and industry analysts forecast that retail media will surpass television advertising spend within the next few years. For CPG and Consumer Healthcare manufacturers, retail media is no longer a nice-to-have optional budget line; it has become a strategic lever, influencing portfolio visibility, price negotiations, and even SKU survival on retailer platforms.

But while investment has scaled rapidly, the industry has encountered a familiar obstacle: advertising is growing faster than its measurement. Retail media has the potential to link advertising to actual purchase behavior more precisely than any channel before it — but only if the underlying data can be trusted, compared, and integrated.

The issue is not a lack of data. It is a lack of harmonization.

The Fragmentation Problem: Abundant Data, Limited Insight

Retail media generates more granular data than any other advertising channel. It spans:

sponsored search
display and video units
loyalty-driven offers
offsite targeting through retailer data
programmatic powered by first-party audiences
in-store media, including digital screens and retail networks
digital shelf impressions, conversion, and sentiment

Yet every retailer reports performance differently. One network counts “sales attributed to impressions”; another uses a click-based attribution model; some others apply a loyalty viewback window; further ones only count sales within online channels. Two identical products, on two similar campaigns, may appear to perform radically differently in retailer reports — even if the commercial outcome is the same or better in the channel that appears “weaker.”

According to IAB Europe, the lack of standardized measurement frameworks across retail media networks makes it nearly impossible for brands to evaluate performance objectively or optimize investment across retailers. As budgets grow, this fragmentation creates risk: brands are increasingly asked to justify spend with incomplete or incomparable data.

Without harmonization, retail media remains measurable only within individual networks, rather than as a commercial lever in the broader marketplace.

Where Commercial Consequences Show Up

Retail media’s measurement problem is not merely analytical; it is commercial. Poor or inconsistent data impacts:

1.Pricing and promotions

Retail media influences price perception and promo performance — but if sales uplift is measured differently per retailer, brands can over- or under-invest, or fail to scale a strategy that works elsewhere.

2.Assortment and portfolio decisions

Digital visibility affects product survivability. If measurement cannot show whether spend protects or accelerates distribution, teams cannot evaluate which SKUs to support, launch, or delist.

3.Joint business plans

Retail media is now part of negotiations. But if commercial teams must rely on retailer-owned measurement alone, brands face asymmetric information during contract planning.

4.Media optimization

Teams cannot shift budget across networks or channels without a unified view. Even when performance looks strong in one network, it may simply be using more generous attribution rules than another.

The commercial cost of fragmentation is not inefficiency. It is misguided decisions at the heart of brand competitiveness.

Closed-Loop Measurement: The Promise of Retail Media

The unique value of retail media is its access to first-party shopper behavior. Unlike traditional advertising platforms where performance is inferred from proxies (clicks, impressions, modeledmodelled conversions), retail media can connect directly to:

electronic point-of-sale (EPOS) data
loyalty transactions
in-store and e-commerce baskets
repeat purchase and shopper lifetime value
digital shelf traffic, share of search, and conversion

Boston Consulting Group describes closed-loop attribution — linking ads directly to sales — as the “holy grail” for advertisers. This is the promise of retail media: not merely optimizing campaigns, but informing category strategy, revenue growth management, and shopper targeting.

But this promise depends on a critical prerequisite: data must be harmonized across retailers, channels, formats, markets, and commercial metrics.

Without harmonized data, even the most sophisticated closed-loop attribution tells only part of the story — and cannot be compared across retailers.

Why Data Harmonization Is the Enabler

Harmonization ensures that different datasets can be integrated, compared, and evaluated using a consistent framework. In retail media, this means:

shared definitions (e.g., what counts as attributed sales)
standardized taxonomy (product, category, brand, retailer rules)
consistent attribution windows
unified KPI frameworks across markets and networks
integration of media, EPOS, loyalty, and digital shelf signals
data governance and stewardship to sustain quality over time

With harmonization, retail media can be evaluated using the same commercial language that drives pricing, promotion, assortment, and retailer negotiations. It becomes measurable not as an ad channel, but as a commercial growth tool.

Without Harmonization	With Harmonization
Siloed retailer reports	Cross-retailer comparison
ROAS-focused decisions	Incrementality-based decisions
Data shapes media plans	Data shapes commercial strategy
Visibility buying	Value buying
Negotiating in the dark	Negotiating with evidence

How Harmonization Transforms Retail Media Value

It unlocks true incrementality

Instead of reporting efficiency (ROAS), teams can measure whether campaigns drove net new growth rather than shifting demand within the portfolio.

It protects distribution

When sales uplift is measured consistently, spend can be directed strategically toward under-pressure SKUs or at-risk listings.

It improves revenue growth management

Media signals inform pricing elasticity, promo depth decisions, and category cannibalization risks.

It aligns eCommerce, sales, marketing, and category teams

Shared data eliminates debates over whose data is correct. Decision-making accelerates, grounded in one version of truth.

It creates win–win retailer partnerships

When data is shared on neutral terms, retailers and brands can jointly pursue category growth rather than negotiate from asymmetric data positions.

A Roadmap for Retail Media Maturity

Organizations that want to scale retail media as a commercial growth lever should prioritize:

1.Standardized KPI and attribution frameworks

Consistent windows, attribution definitions, and incrementality rules across retailers and markets.

2.Unified product and category taxonomy

Global mapping of SKUs, variants, pack sizes, channels, and retailer-specific IDs.

3.Integration of core commercial data

EPOS, loyalty, digital shelf, panel, and retailer-direct signals unified into one foundation.

4.Data governance and stewardship

Defined ownership, quality checks, source validation, and audit layers to maintain trust.

5.Commercial enablement

Training category, marketing, and commercial teams to use harmonized insights to shape negotiation, pricing, and investment decisions.

When these foundations are in place, technology such as AI-driven optimization, bid automation, and predictive modeling actually becomes reliable — because the underlying data is coherent.

Conclusion: Retail Media’s Next Era Is Commercial, Not Technical

The future of retail media will not be defined by the next ad format, algorithm, or dashboard. It will be defined by whether brands and retailers can evaluate its impact in commercial terms.

When EPOS, loyalty, digital shelf data, and media signals speak the same language, brands can:

justify investment with confidence
optimize based on incrementality, not impressions
negotiate using evidence grounded in market performance
grow category value collaboratively with retailers

Data harmonization is not a technical upgrade. It is the commercial backbone that determines whether retail media becomes a scalable growth engine or remains a fragmented cost of doing business.