AI-driven solutions to revolutionize your CPG Analytics
Not just time series but also leverage generative capabilities to refine product offerings & promotional strategies
Enhanced feature attribution & mapping for multi-touch consumer journeys. Understand carryover and saturation effects on the incremental sales. Model the true impact of your marketing efforts.
Oversee supply shortages by analysing regular sales and inventory levels with full transparency. Get accurate demand Forecasting with Long Short-Term Memory (LSTM) networks
Build fool-proof sales and incentive compensation plans by using AI from Genetic algorithms for compensation plan optimization to creating multiple scenarios for optimization
Enhance trade promotion effectiveness, and preserve brand equity, sales growth, and channel partnerships with gradient boosting and causal inferences - not just bayesian models
Find the right product placement, use convolutional neural networks for planogram, cluster stores for effective promotions, and ensure seamless customer experience at stores.
In an era where it is difficult to predict when and how customers buy, build strong relationships, and identify where to be at the right time with attribution modeling and non-linear forecasting with MMM
Sales Key Performance Indicators for in CPG data analytics industry encompass account efficiency distribution, efficiency factory outlet, store efficiency, sales growth metrics, trade promotion & customer retention rates Marketing Key Performance Indicators are brand equity, brand performance, distribution performance, media performance & household Performance
In the Consumer Packaged Goods (CPG) sector, the scope for analytics is endless. Some of the key AI-powered solutions are: For supply chain, predictive demand forecasting with neural networks, optimized inventory using reinforcement learning, and supply network visualization via graph neural networks. In sales and marketing, personalized product recommendations with collaborative filtering, dynamic pricing models using Bayesian optimization, and campaign effectiveness analysis powered by causal inference. Automated planogram generation with computer vision, assortment optimization through integer programming, and shopper behavior prediction via agent-based modeling support category management
Businesses can employ optimization algorithms to find the equilibrium between competitive pricing and profitability. These algorithms consider cost structures, competitor pricing data, and market demand elasticity, ensuring businesses make data-driven decisions to achieve a competitive yet profitable pricing strategy. Models such as Cost-Plus Pricing or Value-Based Pricing are often adopted to strike the right balance.
