October 17, 2023

Finding where to open the next retail store?

It takes just 2 minutes to analyze a new store location.

Finding where to open the next retail store?

At a glance

Challenge

To build confidence and expertise, advanced simulations and clear explanations of forecasting modules were essential. An ambitious rollout plan, with strong support for operational teams, ensured successful adaptation of the tool. The system became more responsive to business needs, with over 10 percentage points improvement in accuracy and a more stable forecast compared to the initial design.

Solution

The process began with estimating the total market size by integrating various data sources like external reports, demographic statistics, and raw datasets (e.g., using Airbnb listings to assess tourism impact in urban areas). This ensured high geographic detail while maintaining precision.

Following this, an interactive app was developed, enabling users to evaluate future store performance based on specific characteristics such as location, store size, and brand. The app features a machine learning model that predicts store sales by analyzing the competitive landscape and estimating the store’s market share within its area of influence.

Results

The solution improved sales forecast accuracy by 15% and reduced the time to analyze a new store location from 5-10 days to just 1-2 minutes. With faster and more accurate forecasts, the team could focus only on the most promising locations. Multiple validation scenarios demonstrated the solution's robustness, making it a key tool in the retail client's expansion strategy.

Challenge

To build confidence and expertise, advanced simulations and clear explanations of forecasting modules were essential. An ambitious rollout plan, with strong support for operational teams, ensured successful adaptation of the tool. The system became more responsive to business needs, with over 10 percentage points improvement in accuracy and a more stable forecast compared to the initial design.

Approach

Solution

The process began with estimating the total market size by integrating various data sources like external reports, demographic statistics, and raw datasets (e.g., using Airbnb listings to assess tourism impact in urban areas). This ensured high geographic detail while maintaining precision.

Following this, an interactive app was developed, enabling users to evaluate future store performance based on specific characteristics such as location, store size, and brand. The app features a machine learning model that predicts store sales by analyzing the competitive landscape and estimating the store’s market share within its area of influence.

Results

The solution improved sales forecast accuracy by 15% and reduced the time to analyze a new store location from 5-10 days to just 1-2 minutes. With faster and more accurate forecasts, the team could focus only on the most promising locations. Multiple validation scenarios demonstrated the solution's robustness, making it a key tool in the retail client's expansion strategy.

Our
AI-generated
summary

Our AI-generated summary

Our AI-generated summary

Changes in consumer behavior have led retailers to pursue higher levels of customer proximity, by increasing the number of stores while focusing on convenience formats. This shift in focus requires heightened agility when analyzing possible new store locations.

This project aimed at improving a retailer’s accuracy when predicting the sales potential of given store locations, while also increase the speed at which these forecasts are generated. The goal was to find the highest value locations for the retailer’s future stores.

We started off by estimating the total market size by combining different data sources, such as external reports, demographic stats and raw datasets (e.g., using Airbnb’s listings to plot the impact of tourism across key urban areas). This allowed us to have a remarkable level of geographic detail without foregoing precision.

Afterwards, we built an interactive app that makes it possible for the user to study the performance of future stores, given specific store characteristics such as exact location, store size and store brand.

This tool has a built-in machine learning model that forecasts store sales by looking at the competitive landscape and estimating the store’s market share within its area of influence.

The developed solution enabled the client to increase sales’ forecast accuracy by 15% and to decrease the time needed to analyze one new store location from 5-10 days to 1-2 minutes.

Since a solid forecast could be delivered much faster, the focus of the team shifted to promising locations only. Multiple validation scenarios were tested to prove the robustness of the solution, which allowed it to become a central piece in this retail client’s expansion process.

Our AI-generated summary

Our AI-generated summary

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