How a leading retailer increased their market forecast accuracy by 42% and optimized data-driven decision making

Their challenges

Allocate resources efficiently

As a manufacturer in the consumer goods market, they needed to determine key drivers that would guide their market strategies and production planning for both product and region categories. Not being able to forecast demand accurately and the lack of clarity over the impact of macroeconomic factors meant that they needed full visibility over resource allocation. This led to unnecessary costs and potential lost revenue. This was also necessary to help them uncover key demand drivers by product and region at a quicker pace to stay competitive.

Identify external drivers of demand

Currently using a bottom-up approach, their forecast method was limited to a simple univariate forecasting model. This introduced a crucial issue that impacted forecasting accuracy. The risks associated with only applying univariate forecasting models meant that they were missing out on the opportunity to apply leading indicators to their aggregated forecast as univariate forecasting models do not allow for that.

Key results

Ability to optimize sales performance with market-validated forecasts

The organization was now able to optimize their sales performance with forecasts that were validated by market and macroeconomic data. This meant that they could get visibility over the significance of each market driver and an overview of their impact on their business projections. They had the possibility to continuously track these as consumer and economic activity shifts.

Identified predictive market drivers at a product group and region level

After identifying their leading indicators and demand, they could now factor in the economic developments and business cycle impacting a specific product group. The retailer was now able to create forecasts easily for each product group and region. With the models built, it simply requires a quick update monthly, making the process repeatable, saving valuable time.

42%forecast accuracy improvement

By implementing best practices through all stages of the forecast process, the retailer achieved a double-digit MAPE forecast accuracy improvement.

How a leading retailer increased their market forecast accuracy by 42% and optimized data-driven decision making

Their challenges

Allocate resources efficiently

Identify external drivers of demand

Key results

Ability to optimize sales performance with market-validated forecasts

Identified predictive market drivers at a product group and region level

42%forecast accuracy improvement

Download case study

Resources

How can predicting market shifts drive dynamic adaptation in your business?

Still using Excel to forecast? Here are its limitations and how you can overcome them.

Are you on top of capturing changes in trends and patterns over time when forecasting?