Kenny Lee

VP, PMDO

March 7, 2014

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Leverage Data for Retail Analysis – In 4 steps

Using and understanding retail data to improve business.

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The current trend in data collection continues to be for businesses to move away from the paper-based data collection processes of the past, to data collection with mobile devices such as smartphones, tablets and notebooks which not only allow data collection to move freely among locations, and even among different classes of data, but more importantly allows data, in some cases to be instantly sent to databases for quick processing and analysis.

All retailers own data, and all retailers are looking for ways to use this data for business analysis, but not all retailers understand the steps necessary for leveraging their spatial data for retail analysis. So here they are:

1 – Profile customers

The first step to running a spatial analysis is trying to understand the existing customers. Without this insight, it’s not possible to measure the potential value of a given markets opportunities. Existing customer profiles can usually be extracted from transnational data bases, customer intercept surveys or loyalty program data. Prospective consumer base information can be derived from census or demographics data.

By profiling your customers, you’ll have a better understanding of who they are, where do they live, how they behave and what they want. You’ll begin to understand the differences between groups and associated product/services offerings. Another benefit of customer profiling is being able to identify geographical variances. Knowing where and how your customers are distributed is highly valuable information.

2 – Develop store catchment areas

Catchment area help define how far your customers are willing to travel to reach your store location. Having this area mapped out allows you to define the boundaries of your market, estimate market value based on customer profiles and visualize the market share across those catchment areas.

Catchment areas can usually be defined using the following data:
a) existing customer address/locations
b) drive times or drive distances
c) Gravity models

Developing store catchment areas is an essential step to assess your market value and share of catchment areas. By comparing store sales and customer profiles, you can begin to identify areas with strong and weak performance. You can also analyze the overlaps and cannibalization between catchments, monitor the impacts of competitor activities and predict new store investment. 

3 – Gather market insight, from macro and micro locations

At the macro level, market demand data should be collected by catchment area, and detailed micro location quality can be derived from a variety of sources including location databases, footfall surveys…etc.

Assessing micro-location quality can help you understand where the best possible locations are in a particular market, which includes knowing what other retailers and operations may be present.

4 – Develop fresh strategies

By profiling customers, developing store catchment areas and gathering market insight, you will have a formidable database of beneficial variables. The question is which of these factors will be most responsible for driving enhanced store performances? Typically, regression analysis is used to analyze the relationship between these variables (customer profiles, competitions…etc.) and store sales performance. Combining these factors will allow you to:

a) Turn around poor performing stores
b) Rank and prioritize future investment opportunities
c) Develop various KPIs to manage existing store networks
d) Develop sales forecast models to predict optimal sales performance of existing and proposed new stores. 

Also worth reading : 
Time Geography: Uncommon Analytic Approach
2 Ways Big Data Helps Location Analytics
How Is Big Data Changing Retail Analytics?