Data Science in Real Practice: Assessing Promotion Success and Strategic Planning - Sequel 2
Building a Data Foundation for Promotion Effectiveness Measurement and Planning in Retail and Manufacturing
A robust data foundation is essential for measuring and planning promotion effectiveness in the retail and manufacturing industries. This process requires a structured approach, focusing on data integration, accuracy, and advanced analytics methods, while fostering collaboration across various functions.
Data Collection and Integration
The first step is to gather comprehensive historical sales data, including SKU-level sales, promotions, pricing, and timing. Integrate marketing data, such as advertising campaigns, promotions channels, and budgets. Incorporate external factors that impact demand, such as economic indicators, competitor actions, seasonality, weather, and market trends. Lastly, collect customer behavior data and segmentation from POS systems, CRM, and e-commerce platforms to understand target audiences and their responsiveness.
Data Quality and Governance
Ensure data accuracy, consistency, and timeliness through automated cleansing and validation processes. Maintain centralized, unified datasets with clear metadata to support transparency and reproducibility. Address privacy and compliance, especially with consumer-level data, by relying on aggregate and anonymized data where possible, aligning with privacy-safe frameworks like Marketing Mix Modeling (MMM).
Analytical Frameworks and Measurement Techniques
Use Marketing Mix Modeling (MMM) to quantify the impact of promotions and advertising on sales, utilizing aggregated historical data to measure incremental effects without relying on individual user tracking. Combine MMM with multi-touch attribution (MTA) and incrementality testing to triangulate and validate promotion impact, reducing guesswork and improving decision confidence. Deploy Demand Forecasting and Planning models that incorporate sales history, promotion calendars, and external factors to predict future demand and optimize inventory and promotion timing/planning.
Cross-Functional Collaboration
Involve marketing, sales, finance, supply chain, and IT teams early to align on business objectives, data inputs, and planning cycles. Establish clear promotion objectives and link measurement outcomes directly to these goals for actionable insights.
Continuous Monitoring and Optimization
Implement dashboards and KPIs, such as incremental sales lift, promotion ROI, forecast accuracy, and inventory turnover, for ongoing performance tracking. Use AI and automation to enable near real-time MMM updates and agile decision making for frequent promotional adjustments.
Technology and Tools
Leverage open-source MMM tools (e.g., Meta’s Robyn, Google’s Meridian) to customize models and maintain transparency. Use data platforms that unify marketing, sales, and operational data and support advanced analytics workflows.
In summary, a robust data foundation is established by integrating quality historical and contextual data, applying advanced modeling techniques like MMM combined with incrementality testing, and embedding planning within cross-functional processes that support continuous learning and optimization of promotion effectiveness in retail and manufacturing contexts. This foundation enables smarter, data-driven promotion planning and measurement with improved accuracy and agility.
Sources: [1], [4], [5], [2]
Additional Information:
- In the financials table, gross/net revenue per unit of manufacturers are included together with retailer profit, as well as regular price reduction allowance, fixed trade cost, and variable trade cost.
- The ERD (entity relationship diagram) of the central data repository for retailers includes primary keys: UPC key, store ID, day or week key, and event offer ID.
- Building the central data foundation requires 2-to-6-month efforts of 1 or 2 full-time data engineers.
- Building data foundation is a foundational step and causes most mistakes and problems.
- Personalized promotion discounts make it complicated to decide on what effective promotion prices to use for promotion effectiveness modeling.
- A clear guideline and practical tips are rarely provided for this step.
- To ensure a comprehensive understanding of promotion effectiveness across various retail and manufacturing scenarios, incorporate financial data like gross/net revenue per unit, retailer profit, regular price reduction allowance, fixed trade cost, and variable trade cost into the data foundation.
- Collaborate closely with technology and IT teams to leverage open-source MMM tools and data platforms that support advanced analytics workflows, ultimately enabling smarter, data-driven promotion planning and measurement with improved accuracy and agility.
