We believe that ongoing financial success for retailers will depend on growing the number of happy and profitable customers. Happy households become more engaged. They will spend more money, shop more frequently, and broaden their spending across the retailer’s available assortment. We enable retailers to set individual household objectives (increase spend by x, increase frequency by y, increase breadth by z) and then provide purposeful and integrated merchandising recommendations. Our self-learning AI based algorithms continually adjust and improve these recommendations based on shopper response, assuring continuous improvement and rapid action with minimal analytic resources.
We believe that ongoing financial success for retailers will also depend on merchandising synchronization. Existing customer analytics solutions focus on historical effectiveness measurement. They aggregate individual customer data by Items or Stores before the analysis thus losing the crucial Customer dimension. They provide tactic specific analytics (for instance to improve promotions), with no link to any customer strategy and no ability to institutionalize or to automate learning. For example, existing Pricing and Promotion Management solutions are divorced from customer analytics, lack integrated forecasting and do not provide integrated Price, Ad Promotion, and Personalized Offer recommendations. As another example, existing Assortment and in-store Placement systems don’t work in tandem with Pricing and Promotion systems.
Loren Johnson