In the post-COVID19 world, many aspects of business have changed in unique ways – and so has the consumer response to goods and services. Companies are increasingly witnessing consumer attention disparity, changing rules of purchase criteria and different consumer responses to promotional tactics. These widespread changes have necessitated a refinement in the analysis of shopper data (both structured and unstructured), choice and purchase behavioral patterns, online activity (both for the consumer and manufacturer) and consumer activism. Fortunately, marketing, sales and behavioral science experts across industries have recognized this opportunity to improve promotional and marketing intelligence and respond to change.
In the fiscal year 2021-22, leading consumer-packaged goods (CPG) companies spent an average of 20-23% of revenue on trade promotions. But the inability to link trade promotion spending and retail execution impact marketing ROI – and the customer experience. Many marketers struggle to manage the promotion budget and fail to increase marketing ROI due to ineffective resource allocation to marketing tactics. To resolve this challenge, adopt data-driven intelligence. It can promote the right product, at the right place and time.
Approaching AI-driven Sales and Promotion Planning
The size and scale of the CPG industry are enormous – multiple brands, billions of global consumers, and the supply-chain engine that supports the industry. This entire process generates vast amounts of transactional data. In the past, marketers and business development analysts have analyzed this data with statistical methods that are cumbersome and time-consuming, yielding mixed results. But Wipro is pioneering the art of possible with AI-based trade promotions and marketing driven by data science.
It starts with a dataset consisting of 104 weeks of reasonably contiguous data points to generate and train the AI algorithm. If less data is available, it is possible to start with a smaller data set, and the algorithm can be trained and improved as the repository of data points grows and becomes more integral. Training the algorithm is a phased approach with multiple other factors contributing to its prediction and forecasting accuracy. It is important to note that Data Integrity is crucial and primary for model success.
Data inputs range from basic (sales, scan, promotion history, promotional sales, shipments), mid-complex (marketing tactics, black swan events, unexplained anomalies) to complex (customer reviews and feedback, customer behavior, transaction documents, etc.).