Granular Demand Prediction
Global Electronics Distributor: This company was challenged to understand its global demand for each product in each country. Demand in individual countries varied, and pricing was elastic and could collapse as demand volumes grew. SMART AI was applied to accurately predict demand in each market, allowing for better control of profit-optimization efforts.
Logistics costs were another significant problem that needed to be optimized for company decision-makers. Analysis of internal datasets enabled the decision-makers to define country-specific sales strategies that optimized logistics costs and drove global sales optimization.
Result: 10% increase in revenue
Demand Prediction per Customer Segment
US-based B2B Rental Business: A B2B rental business was struggling with unpredictable customer rental cycles, return-date variability, and "available-to-rent" quantity per site.
Data analytics was used to classify customers into segments based on pricing preferences and rental patterns. It was then possible for the decision-makers to forecast sales and services and establish sales and services' priorities aligned with the goal of optimizing revenue.
Result: 5% higher revenue from the same inventory
Regional Sales Demand Predictions
Quick-Service Restaurant: A quick-service restaurant chain in Brazil with over 1,000 locations needed to improve efficiency within its operations, marketing, and sales. It asked for assistance in reducing waste from overestimated sales forecasts and in minimizing revenue shortages stemming from material shortages.
A2Go developed an analytic-demand-prediction microapp to predict 30- and 60-day sales numbers for the company's major products. The microapp was used to understand primary demand drivers related to advertising spend and regional pricing data in order to identify patterns essential to providing demand granularity at the regional level.
Results: 90% average prediction accuracy for 30-day demand prediction and 93% accuracy for 60-day demand prediction