AI systems allow retail and consumer product companies to move beyond existing strategies of challenging competitor prices or following google trends. It transforms the entire retail value chain to a predictive shopper-centric commerce ecosystem. With AI systems in its core, the companies can cater to various aspects of customer willingness to buy versus ability to buy and dynamic pricing intelligence to provide the best deal for the customer. Though dynamic market forces result in the wide range of price fluctuations driven by e-commerce businesses, AI system begins with consumer insights on how much a consumer willing to pay for a certain product or service that leads to a personalized experience while shopping online or offline.

Most manufacturers typically depend on syndicated data brokers like Nielsen and IRI to devise promotion strategies. While this data is still essential, depending solely on this data will not provide a big picture view of reality an ideal trade promotion optimization software should be able to provide accurate granular insights and support a forward-thinking strategy with a predictive and prescriptive analysis. Trade promotion optimization solutions are quickly becoming outdated. AI is creating a whole new category of trade promotion intelligence solutions that provides trade promo calendars at the individual retailer level based on custom goals and constraints.

The AI practitioners of pricing and promotion at ICURO innovated a holistic enterprise platform that works on the principle of adjusting its algorithm from learning from previous actions and working with auto optimization techniques to the new data patterns. As the parameters in these algorithms are always in a state of constant change, this AI platform can keep up with the transient scenarios, which adapts its algorithms to the ever-growing pool of information and fix prices dynamically. Our platform automates competitor price capture for multiple pricing scenarios using deep q-learning neural network that takes pricing and non-pricing factors such as digital footprint, product reviews in social media, loyalty scoring, customer and product lifetime value, and weather to name a few. It constantly evaluates trade promotion benefits predictive measures for ROI, uplift %, revenue, and volume down to the promotional product group or SKU level. The self-learning accuracy learns what promotions worked, and what did not, to continuously refine recommendations and optimize trade investments across channels, accounts, and tactics based on frequency, depth, placement, and offering.