Case Study

Quantified consumer sensitivity towards pricing with our AI/ML demand-sensing solution

Published on 24th May 2023 | By The X Future

For

Unilever

Customer Price Sensitivity Analysis

Demand Sensing Solution

Customer Price Sensitivity

Qualifying Customers

AI Demand Sensing

AL Solutions

Real Time Analysis

At A Glance

Need for detailed analysis of factors impacting in-store and e-commerce sales.
Need for deep understanding about the price sensitivity of consumers.
Bifurcated insights on other factors impacting consumer sensitivity.

Challenges

Unilever was looking for solutions that could help them in real time analysis of the factors that were impacting their sales volume; both in-store & e-commerce. To execute this they required real-time access to in-store sales data for an understanding of how consumers engage with their products. They wanted a clear bifurcation between price & other factors that impacted their sales volume & their consumers' sensitivity towards each of them.

Solutions

Sancode helped Unilever overcome their challenges with the help of our AI Demand Sensing tool. The following things were achieved:

Volumes of sales data along with 6 other factors were taken into cosideration from Unilever to accurately predict demand at the SKU + Channel + Warehouse level across every channel using an AI engine.
Data provided from their retailers' existing POS and order management systems; whilst also factoring primary level demand drivers like promotions, price changes, business goals, targets, and even seasonality; ensured optimal demand sensing at the most granular levels.
Unilever could dynamically adjust days on hand with optimal replenishment suggestions for every SKU X Channel X Mobile Distribution Center.
Unilever's sales, promotion, and supply chain teams could align and plan their production, inventory, and sales targets from a common base and successfully achieve their business targets.

Key Metrics

20%

Witnessed a reduction in year-end inventory of 20%

30%

Consistently provided an uplift in demand sensing accuracy of at least 30%

2.5%

Witnessed a 2.5% increase in revenue because of better pricing models