A leading CPG company had entered a new product category a few years back. The category didn’t have much of marketing spend by most of the brands. The marketing team was looking to spend their marketing dollar most wisely so as to improve their ROI.

• The project was successfully executed in about 7 weeks with the majority of the time being spent in readying client data for model development.
• The AbsolutData team developed Hierarchical Bayes based regression models for 1500 product-market combinations to measure effectiveness of various marketing activities such as feature, display, TPR,TV advertising, radio, coupons, in store promotions.
• Various model forms including linear, log linear, log-log and mixed models were estimated and compared during the modeling process.
• The Market Mix Models helped us to identify changes in sales to changes in marketing activities on a weekly basis. The framework enabled us to identify the incremental sales due to the various marketing mix components for different product categories.
• The Marketing Mix Models measured the Return on Sales across different marketing activities which was further extended to measure the Margin ROI for the marketing activities.

The project deliverables to the client included:

• Model Parameters, Model Description and Model Accuracy measurements and recommendations on Model implementation.
• Model Performance Matrix that tracks model prediction versus actual behaviour.
• Easy to use interface to carry out “what if” scenario analyses.