Case Study: Flyer Optimization Retailing Service Provider achieves 10% incremental sales and 13% incremental margin per ad with Kinaxis

Flyer Optimization Retailing Service Provider - Customer Case Study

Rubikloud, a Kinaxis company, helped a large retailer solve a common promotional planning problem: forecasting the cross-product effects of flyer promotions on total cart value. The retailer found many flyer blocks didn’t increase sales or trips and even reduced margin, while print and distribution costs amplified the losses.

Rubikloud’s AI promotion-forecasting engine used retail-focused machine learning to weigh factors like placement, promo mechanics, seasonality and residual basket effects, revealing which offers truly drove incremental value. By reordering and featuring higher-performing ads (e.g., front-cover placement) the retailer saw a 10% incremental sales lift per ad, a 13% incremental margin lift per ad, a 53% increase in forecast accuracy, and was able to reduce total flyer pages while improving overall promotion performance.