Data Science Practicalities: Assessing Promotion Success and Promotion Strategy - Segment 2
In the rapidly evolving world of retail and manufacturing, the importance of a robust data foundation is becoming increasingly apparent. This is particularly true in the context of a "data-centric AI" approach, as championed by Andrew Ng [1]. To build a comprehensive, scalable data foundation for a promotion effectiveness measurement and planning solution, it is essential to focus on integrating and managing specific data domains.
Key Data Domains to Establish
- Sales and Transaction Data: Detailed transactional records, including SKU-level sales, store/channel/location identifiers, timestamps, and quantities, form the backbone of this foundation. Historical sales trends are crucial for baseline and uplift calculations, and data synchronization across multiple retail outlets and distribution channels is essential [2].
- Promotion and Trade Spend Data: Complete records of all trade promotions, discounts, rebates, and coupons applied, including timing and scope, are vital. Investment or spend data linked to each promotional campaign is equally important [2].
- Customer and Consumer Data: Shopper behavior and demographics, loyalty program data, and consumer segmentation provide insights into consumer preferences and purchasing patterns [2].
- Marketing and Advertising Data: Campaign-level data such as media spend, channel information, and impressions help align marketing efforts with sales outcomes [2].
- Inventory and Supply Chain Data: Product availability, stock levels, and replenishment cycles impact promotional success and should be included in the data foundation [2].
- External and Contextual Data: Market conditions, competitor promotion data, seasonality, and macroeconomic factors provide valuable context for promotion planning and analysis [2].
- Analytical and Measurement Data: Outputs from measurement models like Marketing Mix Modeling (MMM), Multi-Touch Attribution (MTA), and incrementality testing help mitigate each other’s limitations to confirm promotion impact causally [1][2].
Pragmatic Approach to Building the Foundation
- Data Integration and Quality: Aggregate data across domains ensuring consistency, accuracy, and real-time accessibility. Automated workflows and APIs can be used to consolidate retailer POS systems, manufacturer trade data, marketing platforms, and CRM channels [3][4].
- Governance and Compliance: Ensure data privacy compliance (e.g., GDPR) and maintain high data quality standards. Address data literacy so teams can interpret measurement results correctly [3][4].
- Technology and Tools: Choose platforms with built-in models supporting retail forecasting, trade promotion, and consumer insights specific to CPG workflows. Scalability and ease of integration with existing systems are critical to support future expansion and analytics sophistication [4].
- Pilot and Scale: Start with pilot programs targeting specific use cases like forecasting or incremental lift measurement. Validate with KPIs such as forecast accuracy and customer engagement lift before enterprise-wide adoption [4].
- Cross-Functional Alignment: Ensure the data foundation supports strategic, tactical, operational, and analytical marketing domains to create a holistic view of promotion effectiveness and enable continuous optimization [2].
By systematically organizing these data domains and embedding robust measurement methods, retailers and manufacturers can build a comprehensive, scalable data foundation that accurately measures promotion effectiveness and guides planning and investment decisions with confidence [1][2][4].
It is recommended to keep data aggregation level as low as possible to avoid aggregation bias. Financial data for products, which can change over time, provide critical cost information from the Finance department. At least 2-3 years of data are required to properly reflect seasonality and trends.
The 6-step process for building a promotion effectiveness measurement and planning solution is outlined. The Point-of-Sale (POS) table is the most critical table with actual unit sales data, including retail prices and discounts. Manufacturers can have visibility for both manufacturer margins and retailer margins, and it's important to track regular price reduction allowance, fixed trade cost, and variable trade cost.
A tool called "Source to Target Mapping" can help keep track of key information and bring transparency during the messy phase of data mapping. Personalized promotion, where only a subset of consumers is offered promotion discounts, makes promotion effectiveness modeling more complicated. Data foundation tends to be use-case specific and is usually considered as business domain knowledge.
Four primary keys are used to join data across tables: UPC key, store ID, day or week key, and event offer ID. In the case of manufacturers, syndicated data providers like IRI and Nielsen collect data from various channels and provide data to manufacturers after subscription fees. The executed promotion prices and tactics can be different from the original plans, thus it's highly recommended to use "executed" data.
Product master table contains product details with UPC key as a primary key, including product attributes, product hierarchies, and promotion groups or pricing groups. A central data lake with all the required data for promotion effectiveness modeling may not be built yet. It's important to use retail sales data instead of factory/distribution center shipment data due to forward buying from retailers.
Basket-wide or category-wide promotion events can make it hard to calculate discount amounts per item level, requiring the use of promotion discount allocation rules. Covid lockdowns in Year 2020 and 2021 may have distorted normal seasonality and trends, thus extra care will be needed to handle data from these periods.
- In the manufacturing industry, data-centric AI approaches require a substantial investment in finance, particularly when building a comprehensive and scalable data foundation for a promotion effectiveness measurement solution.
- Technology plays a crucial role in integrating and managing various data domains such as sales, promotion, customer, marketing, inventory, external, and analytical data, ensuring real-time accessibility and automation.
- To mitigate aggregation bias and accurately measure promotion effectiveness, it is essential to maintain a high-quality data foundation, encompassing at least 2-3 years of historical sales trends, customer behavior, and marketing data, along with financial data for products.
