Price & Promotion Optimization
Use Cases

Promotion Optimization

for a Global Quick Service Restaurant

Business Challenge:

A global QSR chain was in need of improvement of its spending strategy for promotions.  It wanted to optimize its advertising spend related to gross rating points (GRP) acquisition and coupon-based discounts. 

For coupon-based, discount optimization, the timing and dollar amount of discount allowed per item had to be calculated for a multitude of scenarios so that decision-makers could optimize their strategies. Without curated datasets and machine learning, this would not be possible to the level of accuracy that was achieved.

Benefits:

98% daily prediction accuracy on promotion revenue.

 

Price Optimization for a

Global Personal Care Product Company

Summary:

This international, personal-care brand runs promotions in multiple channels for different product categories on an annual basis. It wanted to understand the impact of price differences with its competitors on its market share for a few of its most competitive products. 

A2Go established the basis for a multi-dimensional pricing model by understanding how market dynamics were affected by price changes. We called this a price-demand sensitivity analysis. 

In addition, we developed a microapp to deliver optimized predictions and prescribed actions for decision-makers. The microapp simulated market variations resulting from price changes, allowing for “what-if” scenario analysis, and then optimization analysis—in this case, on price related to market share. The app was delivered to the company via a web-based interface.

See below for screen shots of the pricing solution.

 

Example of one dashboard showing market share dynamics at different product prices.

 

marketsharedemograph

 

Price Optimization for a 

Global Online CPG Company

Business Challenges:

A major retailer offering consumer products online was challenged with ineffective pricing strategies and techniques.  The company needed to understand the demand drivers, monitor the marketplace effectively, and develop a pricing strategy to increase profits.

Working with the pricing teams, it was possible to use SMART AI to expand from a handful of pricing strategy alternatives to a multitude of alternatives that were defined systematically in order to consider the full scope of options.  In addition, the demand prediction of the company's online products improved dramatically, allowing foresight to avoid "stock-outs." 

 

Benefits:

15% higher revenues and improved customer satisfaction by avoiding stock-outs