The biggest loss for a brand that operates offline is the store or facility closure. The cost of shutting shop when accumulated over time is massive. The only way to avoid this loss is to open stores and facilities where the success probability is high. Let’s take a look at how the cost of a shut shop accumulates and adds to losses, eating up from the successful stores.
Calculating the Losses Due to Store Closures
For a mid-range apparel brand in the Indian market that has around 500 active stores, let’s assume the following values:
- Z : The number of stores that has been active for more than 2 years and needs to be shut because of low performance = 20
- A : The capital invested in the opening of one store = INR 30,00,000
- B : Average monthly loss of a low performing store = INR 2,00,000
The total loss due to these store closures can be calculated using the following steps:
- Initial Investment Loss: This includes the capital invested in opening the stores that eventually shut down.
Initial Investment Loss = Z * A - Operational Loss: This includes the average monthly loss multiplied by the number of months the stores remain open before closing. Assuming stores are open for an average of 24 months before closure,
Operational Loss = Z * B * 24 - Total Loss: The sum of initial investment loss and operational loss.
Total Loss = Initial Investment Loss + Operational Loss
Calculation with Given Values
- Initial Investment Loss:
Initial Investment Loss= Z × A
Initial Investment Loss= 20 × 30,00,000
Initial Investment Loss= INR 6,00,00,000 - Operational Loss:
Operational Loss= Z × B × 24
Operational Loss= 20 × 2,00,000 × 24
Operational Loss= INR 9,60,00,000 - Total Loss:
Total Loss= 6,00,00,000 + 9,60,00,000
Total Loss= INR 15,60,00,000
As per the estimated numbers, over the course of two years, the apparel brand incurs a total loss of INR 15,60,00,000 due to these store closures. This impact varies for different brands under various categories and other real-world scenarios. The substantial financial impact underscores the importance of data-driven decision-making in retail.
Opportunity Cost:
The opportunity cost of preventing store closures is the potential revenue and growth that the brand misses out on when resources are tied up in unsuccessful ventures. If the 20 stores that shut down had been successfully located in high-potential areas identified through data-driven decision-making, they could have contributed significantly to the brand’s revenue. Instead of incurring a loss of INR 15,60,00,000, these stores could have generated substantial profits, boosting overall business performance.
Impact of data-driven decision making
The retail sector holds growth potential that surpasses any other industry today. The Indian retail market size was INR 91,891 billion in 2022 and the market will grow at a CAGR of more than 13% during 2022-2027. With a sector that beholds such massive opportunity, there’s still scope for optimizing strategic business units and processes to deter revenue loss and maximize ROI. One area that could substantially benefit from data-driven strategic decisioning is Retail Expansion to newer locations, which we will cover in detail.
The evolution of retail: from intuition to data-driven decisions
Traditionally, retail decisions were often guided by intuition and experience. More often than not such judgment calls have an underlying human bias that leads to uncertainty in results, and worst case, failure. But the digital age has ushered in a new era where data reigns supreme. The proliferation of smartphones, IoT devices, and sophisticated analytics tools has enabled retailers to collect vast amounts of data. This shift has transformed the retail landscape, allowing businesses to make informed decisions that minimize risk and maximize opportunity.
One example of this transformation is the shift from relying on historical sales data to predictive analytics. Now there are two elements to this statement: data and predictive analytics. Let’s dive deeper into how this becomes the combination for sustained growth.
Data; and Location Data
For over a decade, the only way to collect data for analytics and decision-making was online. E-commerce sales, transaction data, cart data, buyer journey, and behavior patterns, etc. However, this data does not accurately replicate the operations in the physical world. Brick-and-mortar retail cannot rely on online data alone for a strategic approach toward expansion, inventory management, supply chain, marketing, and other operations.
Hence, offline data that reflects the real-world characteristics of a location becomes key to deriving answers for the physical world. We, at GeoIQ have conquered this behemoth task of collecting such data, and moreover, transforming it into consumable data variables. One can derive more than 3500+ answers, such as population data, POIs data, infrastructure data, rental information, etc. for any address, catchment, or pin code.
Predictive analytics
The information, or data points for a location or catchment are just the first step, they do not provide any actions or outcomes. Hence analytics and predictive analytics come into the picture. For example, taking revenue prediction for a catchment as a north star for ensuring success, one can solve multiple problems in retail such as footfall prediction and cannibalization probability.
Other answers that retail brands can derive from analyzing and visualizing location data are:
- Demand mapping
- Identifying total addressable market
- Whitespace analysis
- Strategic site selection
- Trade area analysis
- Competition mapping
- Hyper-local targeted marketing campaigns
These solutions mostly sum up the importance of data-driven decision-making in retail. With street-level and address-level information and actionable insights, brands in offline space can witness a substantial impact on ROI and profitability.
Conclusion
When we talk about the importance of data-driven decision-making in retail, it is true for all brands and organizations that have a physical presence, such as banks, QSRs, quick commerce dark stores, and more. For strategic decisions in offline space, offline data holds critical importance. Leveraging this data to fuel machine learning models for predictive analytics helps unlock the future vision of the success or failure of a store or facility at a given location. The ability to analyze and act upon real-world data is not just a competitive advantage; it’s a necessity for sustained success in the evolving retail landscape.