Transforming Trade Spend into Measurable ROI
CPG
Food
Manufacturing
Enterprise

Transforming Trade Spend into Measurable ROI

Technology used

AWS Redshift
Matillion ETL
Pyramid Analytics
SAP
Circana EPOS Data
Assosia Pricing Data

Solution

End‑to‑end Trade Spend Analytics capability connecting EPOS, pricing, billing, and promotions into a governed platform. Delivered a defensible base and incremental dataset and full Promotional P&L visibility.

Results

10% trade spend efficiency target established and tracked; single source of truth created. Shift from data reconciliation and debate to insight‑led commercial decision making.

The Challenge
The Solution
The Results

The Challenge

Trade promotion is one of the largest and least understood cost areas in consumer goods. For this client, a significant proportion of revenue was being reinvested into trade spend, yet there was no reliable way to determine whether promotions delivered a return.

The organisation faced a fragmented data landscape and slow, manual processes that limited their ability to act on insights at the pace required by commercial teams. As a result, decisions were often driven by instinct and disconnected spreadsheets rather than consistent financial evidence.

01
Manual post‑promotion analysis: Data was pulled from multiple sources and reconciled manually, taking weeks to produce a view—by which time the opportunity to influence future promotions had passed.
02
No clear view of ROI or profitability: Trade spend was tracked as a cost line without linking incremental sales back to the investment, leaving no true return‑on‑investment view.
03
Decision‑making driven by instinct, not insight: Budget decisions were shaped by precedent and persuasive spreadsheets rather than consistent, trusted data.
A single, governed view of trade investment transformed commercial conversations—from debating the accuracy of the data to focusing on what actions to take and where to reallocate spend for maximum return.
Tim Andrews
Tim Andrews
Head of Decision Intelligence
United Kingdom

The Solution

Snap Analytics designed and delivered a scalable Trade Spend Analytics platform, bringing together fragmented data sources into a single, governed foundation and enabling financially robust decision‑making across commercial teams.

Data Integration and Harmonisation
Connected EPOS, pricing, SAP, and promotions data into a unified platform. Established master data alignment, validated pricing against promotional activity, and ensured consistent time alignment across sources.
Base and Incremental Dataset
Developed a robust analytical dataset separating promotional and non‑promotional sales. Combined statistical modelling with rule‑based logic to address real‑world complexities such as cannibalisation and data inconsistencies.
Promotional P&L
Built a comprehensive view of promotional performance, combining incremental sales, trade spend, and cost data to calculate margin impact from both manufacturer and retailer perspectives.
Analytics and Visualisation
Delivered intuitive dashboards and self‑service analytics in Pyramid Analytics. Enabled users to analyse campaign performance, compare promotions, and explore ROI without reliance on the data team.
Scalable Cloud Data Architecture
Deployed the solution on AWS Redshift with Matillion pipelines, ensuring transparent data lineage and enabling the client’s internal teams to maintain and evolve the platform independently.

The Results

Trade Spend Efficiency Target Established
Introduced a clear 10% efficiency benchmark, providing a measurable goal for ongoing optimisation.
Single Source of Truth
Replaced fragmented, manual processes with a governed dataset integrating all key data sources.
Full Promotional P&L Visibility
Enabled financially grounded analysis of every promotion, improving the quality of retailer negotiations.
Faster, Actionable Insights
Eliminated weeks of manual reconciliation, allowing teams to act within the planning cycle. 
Smarter Budget Allocation
Identified underperforming campaigns and enabled evidence‑based reallocation of trade spend.
Shift to Insight‑Led Decision Making
Commercial discussions moved from validating data to focusing on strategic decisions.
Foundation for AI‑Driven Analysis
Established a clean, governed data model ready for AI‑enabled, real‑time analysis in future phases.

The client is an enterprise‑scale UK consumer goods organisation operating across food and beverage categories with a portfolio of well‑established brands. In a highly competitive and margin‑pressured market, effective trade investment is critical to maintaining retailer relationships and driving growth. Strengthening data‑driven decision‑making allows the business to optimise promotional performance, improve profitability, and respond more dynamically to changing market conditions.

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