Demand forecasting is a crucial task of goods analytics for retail chains, CPG manufacturers, and quick service restaurants chains. Improving demand forecast accuracy reduces the risk of warehouse overstocking, allows safety stock optimization, ensures the availability of goods on shelves, reduces write-offs, and ultimately increases company gross profit. The AI-powered solution significantly improves demand forecasting accuracy by using machine learning technologies. It speeds up and considerably simplifies the forecast preparation and reconciliation processes.
Trade promo planning is a crucial process for retailers, CPG manufacturers, and quick service restaurants chains. A promo calendar, as a result of the promo planning process, affects the RTO and margins. The SBDA AI-powered trade promo planning and optimization solution is designed to make this process more efficient. Not only does the system allow companies to devise and analyze various promo scenarios to choose the optimal one for their goals, it also increases the return on marketing investments, cuts time spent on calculations, and reduces the likelihood of human error.
The chain's share in various market segments and product categories, RTO, and marginality depends on the degree of the pricing policy’s efficiency. Based on the black price tags, consumers determine the relative price level of product categories and make purchasing decisions. The AI-based SBDA platform allows for the creation of price baskets. Identifying products with high and low price visibility (key-value indicators and back basket, respectively), analyzing the competitive environment, setting price rules, and configuring their activation scenarios: acceptable price bands and conditions for following competitors. Taking the price elasticity of demand into account for different daily time intervals, the system recommends prices that allow the retailer to maximize profits.
This solution is a system that increases production planning and shift scheduling efficiency in the SKU - work center - time period granularity. The program contains an optimization model for constructing production schedules, including expert rules that determine the manufacturing technology of each SKU and possible production restrictions. If required, this solution allows for rapid changes to production plans and significantly increases the speed of calculating various production scenarios.
The solution is an AI-based recommendation system for optimizing search performance and personalizing search results, catalog, and recommendation blocks on the website and in the mobile application. SBDA models allow for search quality improvement by processing typos within the search string and working flexibly with synonyms. On the other hand, they allow for personalized search results by ranking products. Lists of products relevant to the query are listed to maximize conversions into purchases, ARPU growth, the average spend, the number and variety of SKUs, categories in baskets, and the maximization of pLTV over a given period. This solution allows for audience loyalty growth through the most relevant search results and user convenience.
The SBDA solution automates creating and sending messages across digital channels by using mathematical models for audience segmentation, customer profiling, and prediction of customer experiences. This solution increases user engagement through more relevant communications and, as a result, increases sales along with the number of transactions in a mobile application.
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