What is syndicated data?

Syndicated data is aggregated sales and market information collected by third-party research companies. It measures how products and categories perform across a broad set of retailers and channels, giving suppliers a market-level view that no single retailer’s portal can provide. In the European FMCG market, the main providers are Nielsen and Circana (formerly IRI). The data covers category sales, market share, pricing trends, promotional benchmarks, and distribution metrics, all organised into standardised hierarchies that allow for cross-retailer comparison.

Syndicated data is purchased on a subscription basis and typically arrives weekly or bi-weekly, with a lag of several weeks from the moment of sale to the moment the data is available for analysis.

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What syndicated data tells you

Syndicated data puts your performance in context. It shows how your brand is growing or declining relative to the total category, whether a trend you’re seeing in your own POS data is specific to your products or reflects a broader market movement, and how competitors are performing without you needing access to their internal numbers. For a trade marketeer preparing a category plan, syndicated data provides the strategic foundation: what the market looks like, where the opportunities are, and how your category is positioned relative to the rest of the market.

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Pros of syndicated data

• Market level visibility across all retailers and channels, not just your own accounts
• Competitor benchmarking without requiring access to competitor data
• Standardised category hierarchies that make cross-retailer analysis possible
• Long-term trend data that supports strategic category planning

Cons of syndicated data

• Data lag of several weeks, which limits its usefulness for operational steering
• No store level granularity, it shows category trends, not what is happening in a specific retailer or region
• Expensive, and the cost increases as you add more categories or geographies
• Category definitions may differ from how your retailer partners organise their assortment, requiring manual mapping
• Quality can differ, which costs (a lot of) time to resolve 

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What is POS data?

Point of Sale (POS) data is the transaction level information recorded every time a product is scanned at a retailer’s checkout or processed through a distribution centre. Retailers make this data available to suppliers through dedicated portals. In the Dutch market, the most commonly used platforms are SIS (Albert Heijn) and 7EVEN (Jumbo). Depending on the retailer and the agreement in place, this data can be available weekly or even daily (if PoS data is available at that granularity).

POS data is specific to your products at that retailer. It gives you visibility into sales volumes, promotional performance, inventory levels, and distribution, all at a level of detail that syndicated data cannot match.

What POS data tells you

Where syndicated data gives you the market context, POS data gives you the operational reality. It tells you whether a promotion is working at a specific retailer, which stores are underperforming and why, and how your inventory is moving relative to what was planned. For a trade marketeer preparing for a retailer meeting, POS data from platforms like SIS or 7EVEN is the source that makes the conversation specific and credible. 

Pros of POS data

• Near real-time visibility into sales performance at a specific retailer 
• Store-level and SKU-level detail that syndicated data cannot provide
• Direct insight into your own promotional uplift and inventory movements
• Faster feedback loop for operational decisions like replenishment and promo adjustments, allowing to steer more proactively.

Cons of POS data

• Only covers your own products, no competitor or category benchmark data
• Each retailer delivers data in a different format, with different naming conventions and product hierarchies
• Requires significant manual effort and know-how to combine across multiple retail channels
• Category structures in retailer portals often do not match syndicated data hierarchies, making cross-source comparison difficult

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Using syndicated and POS data in tandem

Neither source alone is enough. Syndicated data without POS data leaves you with market context but no operational grip. POS data without syndicated data gives you granular performance numbers but no way to judge whether they are good or bad relative to the market. The combination is where the real insight lives.

In practice, using both sources together means you can answer questions that neither can answer alone: Is this promotional uplift better or worse than the category average? Is an underperforming retailer a ‘you problem’ or a ‘market problem’? Where is your brand growing share, and where is the category growing faster than you?

For some FMCG brands, this matters especially in promo analysis. When 60% of the volume is sold in promo, understanding the interaction between market level promo trends from syndicated data and your own promo performance per retailer from POS data is critical for factbased steering. Without both sources in the same view, you are working with half the picture.

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Why combining them manually breaks down

The challenge is not conceptual, it is structural. Syndicated data and POS data are built on different product hierarchies, different category definitions and different time windows. A category that Nielsen calls ‘ambient beverages’ may be split across two different sections in SIS. An EAN code that appears in your syndicated export may not match the reference in a retailer’s portal. A product launch or packaging change can break the link between historical syndicated data and current POS data entirely.

The result is that combining these sources manually means spending hours, sometimes the better part of a week, on what should be a data preparation step rather than actual analysis. You end up with Excel prepped visuals that took longer to build than to present, and that are already partially outdated by the time they land in a retailer meeting. Category managers simply do not have the time or resources to do this for every account, every week.

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What becomes possible when the data layer is automated

When the harmonisation step is automated, both sources land in the same system, mapped to the same product structure, updated with every new data dump. The manual matching is replaced by an intelligent data layer that understands how Nielsen categories relate to SIS categories, how your internal product master connects to both, and how to handle EAN changes without breaking the historical trend.

Having a human in the loop remains essential. Automated harmonisation does not mean the data takes care of itself. The ability to review, adjust and improve data quality directly and see the result immediately in your dashboards, is what makes the difference between a system you trust and one you keep double checking. Data quality is the foundation. If the data fundament is not right, no analysis built on top of it will be reliable.

What this unlocks in practice, from our experience working with category teams across FMCG suppliers:

• Near real-time visibility into promo performance per retailer, without waiting for the weekly export cycle to complete
• Direct comparison between your brand performance and the market trend, from the same interface
• More category plans backed by actual data, not just for the biggest accounts, but across all retail partners
• Winning better retail deals by walking into a retailer meeting with insights the retailer does not already have

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From insight to fact-based forecasting

Combining syndicated data and POS data well does more than solve a reporting problem. It creates the foundation for moving from gutfeel to factbased decision making. When both sources are clean, harmonised, and available together, forecasting becomes possible: using historical patterns from both market-level and retailer-level data to predict what will happen rather than only explaining what did.

Two practical examples stand out. Price elasticity per SKU requires combining your own POS data with market pricing trends from syndicated sources without both, the calculation is incomplete. Promotional impact prediction works the same way: the uplift model is only reliable if it incorporates both in-store performance and category-level benchmark data. These are the analyses that move a trade marketeer from reactive reporting to genuine category leadership at the retailer table, delivering more value for both supplier and retailer by reducing waste and growing the category together.

Elho is one example of how this shift plays out. Before working with Captain, the trade marketing team spent up to 60% of their time harmonising data across 33 retail channels. After automating that layer, they scaled from 10 to 25+ data-driven category plans and grew significantly stronger at the retailer table. Read the full case here.

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Ready to spend less time on data crunching and more time on insight?

If combining syndicated data and POS data in your category work still means hours of manual matching every week, it is worth having a conversation about what is actually possible. We work with category teams across FMCG to make that step faster, more reliable, and more useful for the retailer meetings that matter.

Request a strategic conversation. We come prepared with practical tips for your specific data situation.

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