Unlocking the full potential of your grocery retail data

Nick Calver, Marketing Director

Most CPG executives recognise the significance of advanced analytics but struggle with its practical implementation. Why do some succeed while others don’t?

Consumer packaged goods (CPGs) businesses that effectively leverage data, analytics, and insights on a large scale outperform their peers. Gone are the days of making commercial and supply chain decisions based on intuition and guesswork. In today’s trading environment, suppliers must strive for absolute operational effectiveness — reducing product waste, optimising promotional strategies, and ensuring consistent product availability — to maintain profitability; the margin for error has narrowed.

89% of CPG executives agree that advanced analytics are critical.

74% of CPG executives acknowledge they’re struggling to scale capabilities across their business.

Five steps towards becoming an insight-led CPG – accenture

Overwhelmed by too much data and not enough insight

Despite having access to a wealth of trading data — ranging from daily sales performance and availability to depot service and stock levels — many CPG companies find themselves overwhelmed. Drowning in a sea of data, executives struggle to extract meaningful insights from reports and spreadsheets.

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“We’re living in a world in which insight in the quickest possible time is now the priority.”

Scott Morton , Head of Customer Collaboration at Alpro

Getting insights is easier said than done

For CPGs taking on the data insight challenge, the issues of scale, inconsistency, and data diversity loom large. Multiple teams collecting multiple versions of data from multiple retail customers results in a huge data management headache, which limits analysis and prevents action.

It can be difficult for CPG companies to get the insights they need because:

Inconsistency means complexity. It’s hard for CPGs to analyse data when the data they get from different retailer portals differs in terminology, structure and meaning.
Time lags reduce accuracy. Ad hoc and infrequent data collection results in outdated information, which is less effective for analysing fast-moving products than a comprehensive daily picture of performance.
Not seeing the full picture makes analysis difficult. Despite having access to in-depth retailer data, CPGs struggle with efficiently collecting, collating, linking and analysing it, leading to missed insights.

Risking it all on guesses and gut

Without robust, reliable, consistent, and well-curated data, category analysts, account managers, demand planners, and product designers are left with one of two options:

Best Guess: When the data is out of date, incomplete, or doesn’t match, we have to make decisions based on partial information and guesswork
Gut Feel: With data proving too painful to procure and analyse, some CPGs base their decisions on what “feels” right and discount the data altogether.

Neither of these options is good for answering questions like:

Which of our products justify wider retail distribution thanks to stellar sales and which alternatives would we trade these for in a “one-in, one-out” shuffle?
Which of our promotions performs best and why?
What changes could we propose to our customers that would drive incremental sales for us both without damaging the category?
Where should we be launching new product variants and how can we monitor their performance in the first days and weeks so that we can collaborate with our customers to adapt to shopper demand?
How might we optimise our delivery case sizes to better meet customer ordering patterns?

Often questions like these are met with a shrug of the shoulders or, at best, a high-cost one-off analytical project against one retail customer data set… an unrepeatable project that may deliver little, even after weeks of effort.

Bridging the gap: from uncertainty to data-driven confidence

Recognising the pitfalls of relying solely on guesswork and intuition, the transition to a more data-driven approach becomes imperative. Demand intelligence platforms emerge as a crucial enabler for CPGs, transforming raw retail data into actionable intelligence that empowers performance across the organisation.

“CPG companies that effectively leverage analytics have seen up to a 10% increase in sales volume.”

Solving the digital and analytics scale-up challenge in consumer goods – McKinsey & Company

By implementing a central, consistent data management approach, demand intelligence platforms enable CPGs to move away from the precariousness of guesses and gut feelings to a more reliable, analytical approach. This strategy to simplify and unify is not only about managing data more effectively; it’s about transforming the entire business paradigm to foster agility and informed decision-making. It’s the foundation from which the organisation can:

Sense and respond: Timely and accurate sensing of market signals is crucial. Demand intelligence platforms offer the ability to swiftly interpret data, identify emerging daily patterns, and anticipate supply chain shifts, enabling businesses to adapt proactively to operational opportunities and risks.
Shape the future: With powerful analytics, commercial teams can leverage data to shape product demand. Strategic insights lead to informed decisions, guiding product development and initiatives that resonate with their shoppers needs.
Share collaboratively: The power of data is multiplied when shared across ecosystems. Businesses can unlock collective intelligence by fostering a culture of sharing insights and wins with retail partners and collaborators, driving mutual growth and fostering stronger alliances in the marketplace.

Together, these principles — Simplify, Sense, Shape, and Share — form the keystones of an effective data strategy. They ensure not just repeatability and consistency in analytics but also imbue the organisation with the velocity to act swiftly and the completeness to see the bigger picture. In an industry where sales volumes can surge by up to 10% through effective analytics utilisation, as noted by McKinsey & Company, the ability to navigate and capitalise on data with such a holistic approach is not just advantageous — it is essential.

A real-world example: Revenue Growth Management (RGM)

Challenge: A world leading confectionery company faced difficulties in measuring the returns on RGM investments, especially in promotions. There were concerns about negative ROI influenced by factors like cannibalisation and cost price adjustments.

Solution: Using SKUtrak the company was able to collect comprehensive data, consolidate it, and develop a model to understand how various factors influenced sales. This facilitated:

– Identification of baseline sales.

– Measurement of promotion uplifts.

– Analysis of cannibalisation impacts.

– Assessment of seasonaliity and other factors.

By feeding in profit margin data, the company could better understand how well each promotion worked, distinguishing effective strategies from less successful ones. This approach led to a clear investment roadmap, highlighting both areas of inefficiency and proven successful tactics.

Want to see SKUtrak in action? Contact us today for a demonstration and learn how to unlock the full potential of your retail data.