Data Science Practicalities: Assessing Promotion Efficiency and Planning - Installment 2
In today's dynamic retail landscape, understanding the impact of promotions on sales is crucial for both retailers and manufacturers. Here's a step-by-step guide on how to build a data foundation for promotion effectiveness measurement and planning.
Defining Data Requirements
The first step is to define clear data requirements aligned with promotion goals. This includes data on sales history segmented by product, channel, and customer type, promotion details (type, timing, discounts), market and competitor pricing, customer behaviour and demand indicators, supply chain and inventory levels, and external factors like market trends and economic indicators.
Establishing a Centralized Data Repository
Establish a centralized and standardized data repository where all relevant data streams converge. Standardize formatting and data ownership to ensure consistency and ease of analysis.
Leveraging Multiple Data Collection Methods
To avoid blind spots, leverage multiple data collection methods such as automated web scraping for competitor prices, field data collection from sales teams for market intelligence, customer surveys for price sensitivity and perception, and third-party market data and transaction history analysis.
Implementing Robust Data Validation Protocols
Implement robust data validation protocols to ensure data accuracy, including automated outlier detection, cross-referencing sources, and routine audits. High data quality is crucial since inaccurate data can severely compromise promotion measurement.
Incorporating Advanced Marketing Measurement Methodologies
Use Marketing Mix Modeling (MMM) for quantifying the incremental impact of promotions relative to other business drivers. Combine MMM with Multi-Touch Attribution (MTA) for detailed customer journey insights. Deploy incrementality testing (e.g., randomized control trials) to validate whether promotions caused actual lift in sales beyond baseline trends. These approaches together form a triangulated measurement system delivering clear, causal understanding of promotion effectiveness.
Integrating Demand Forecasting and Planning
Integrate demand forecasting and planning that uses historical sales patterns, promotions calendar, and external data to predict demand fluctuations. Align forecasts with stakeholders across sales, marketing, finance, and supply chain to synchronize promotion planning and inventory.
Monitoring Promotion Impact in Real-Time
Monitor promotion impact in real-time with a promotion effectiveness evaluation module that tracks outcomes during campaigns to provide timely feedback and allow agile adjustments to promotional strategies.
Common Use Cases
For Retailers
- Optimizing discount strategies to balance margin and sales volume
- Understanding which promotional tactics drive incremental store or online sales
- Aligning promotions with inventory to minimize stockouts or overstocks
- Competitive pricing intelligence to respond dynamically to market moves
For Manufacturers
- Measuring trade promotion effectiveness with distributors and retailers
- Planning production schedules and supply chain based on forecasted promotion-driven demand
- Evaluating the ROI of co-marketing activities with retail partners
- Incorporating consumer and market data to tailor promotional offers
This structured approach ensures the promotion effectiveness framework is comprehensive, accurate, and actionable for both retailers and manufacturers.
Remember, building a data foundation is a critical and often challenging step. It requires at least 2-3 years of data to properly reflect seasonality and trends, and it's considered foundational and causes most mistakes and problems. The Point-of-Sale (POS) table is the most critical table with actual unit sales data, including retail prices and discounts.
Data foundation tends to be use-case specific and is usually considered as business domain knowledge that will be picked up from on-the-job training. For retailers, a central promotion effectiveness data repository needs to be built to have a 360-degree view on promotion effectiveness. For manufacturers, syndicated data providers like IRI and Nielsen collect data from various retail channels and provide data to manufacturers after subscription fees.
In conclusion, by following this structured approach, you can build a robust promotion effectiveness measurement and planning solution that provides valuable insights for both retailers and manufacturers.
- As we delve deeper into science and health-and-wellness, therapies-and-treatments could benefit from this data-driven approach in retail, specifically by implementing advanced marketing measurement methodologies similar to Marketing Mix Modeling and Multi-Touch Attribution, which are used for quantifying the impact of promotions, to better understand the efficacy of various therapeutic interventions.
- In the realm of technology and data-and-cloud-computing, this framework's emphasis on a centralized and standardized data repository can be paralleled with the need for robust, secure, and accessible cloud-based platforms for storing and processing health data, ensuring that data consistency and ease of analysis are maintained for improved diagnosis and treatment planning.
