Retail: More Effective Store Analysis with Location Data
For most Retail companies, brick and mortar stores form the pinnacle of their business. With an ever-growing ecommerce transformation, the catering areas for digital stores are vast and unrestricted, but for physical outlets, location is still the primary feature of importance to understand brand or product performance. Be it an electronics brand or an FMCG outlet, location information decides the expanse of catering area and customer market features.
Store Analysis?
The location determines how the store shall perform based on its aspects of;
- static Trade Area Potential
- dynamic Footfall
- Market Affluence
- road Connectivity and
- variations in Spending Capacity over time
The location determines whether it will be a destination store or a neighbourhood store.
The location determines whether it will be a market influencer, or whether it will be an impermanent player.
And lastly, location determines how internal factors of a store will play out in its performance. Thus it is impossible to do an Internal Store Analysis without considering Location Data.
Data To be considered?
Till now, Store Analysis has been worked out solely over Internal Data of the particular outlet, considering factors like ~
- Products/Brands
- Sales
- Customers
- Inventory
- Employee performance
But we at Data Sutram, have realized that that is not enough information to understand performance of an outlet completely. It is imperative that External Data ( i.e. Location data), along with information on other outlets i.e. overall brand/product performance must be married to internal data, to analyse and understand performance and each and every factor affecting sales of such an outlet.
Mistakes to resolve?
There are some commonly occuring problems when it comes to the traditional Store Analysis methods, which in turn contribute to a faulty performance understanding;
X. Wrong definition of Location type
X. Wrong Feature Importance selection
X. Wrong Target Market identification
Location Intelligence improving Store Analysis
Leveraging Location Intelligence in Retail stores, there are multiple aspects of data-driven insights and decision layers to be supplemented to reach an accurate analysis and extract essential information to make changes and expand successful store performance;
- Details of market to be catered to — Extensive and granular information pertaining to demography, facilities, market trade points, static and dynamic footfall, road traffic, land-use and property data etc are pertinent to understand catering area and target market in and out of store location.
- Location indexes by marriage of internal and external data — Useful business insights can be obtained on marriage of multiple data points to evaluate indexes like Affluence, Spending Capacity, Development, Connectivity, Activity, Footfall etc which are comprehensible to Retail brands, are mapped as per 100m granularity, so visualization and comparison of feature importance in store performance can be realized.
- Store performance measured against new indices — Finally, analysis of outlets considering internal and external data, comparing performances over all locations in an area, identifying and estimating feature importance and eventually, modulating perfect market mix for future locations as per definition of store type, is achieved as a modified Store Analysis study.
Data Sutram
The statement to the question, “Outlet Y is not performing”, can usually always be amended to “Outlet Y is not performing to Location X”. More often than not, a store’s failure is associated with Internal factors, when it is actually a Location issue. At Data Sutram, we offer you a complete understanding of your store’s performance as a feature of both internal factors and external features, and how they interact with each other. Our platform provides geospatial visualizations on granular Location data and Feature Indexing, while our Location Evaluation module provides you variation of performance down to each and every feature (external or internal), site selection intelligence, and predictive customer flow analytics.
And all this measured against one, single KPI of your choice to bring you more value.
