$600 Bn Indian retail industry is projected to grow at CAGR of 12-13%, is still struggling with perennial problems to reach its true growth potential, thereby affecting retailer’s daily operation and efficiencies. Some of these problems can be significantly curtailed by using analytics.

While poor supply chain infrastructure and heterogeneous demographics are key problems disrupting retailer’s sales planning and operations, the appropriate utilization of data helps in streamlining store, backend operations and enhancing overall customer experience to push sales. A McKinsey’s study shows that utilization of vast amount of data generated by retailer can help retailers in addressing some of these issues while generating 3-5% of additional sales growth and 1-4% of improvement in net margin. Despite such immense utility of data, it is seen that nearly 50% of the analytics projects fail to deliver the expected outcomes. (Gartner)

Analytics Projects: Implementation Highlights

Bottom-up approach rather Top-down approach:

The first pitfall in analytics projects is typically in the approach adopted by retailors .Their focus is to improve high level outcomes without complete understanding of low level factors responsible for better high level outcomes. A grocery retailor wanted to improve the customer footfalls in the retail store. It had already worked and exhausted the options of catchment area, retail display, discounts, lane management and employee training to enhance customer experience. However, it had overlooked the options of targeted advertising using the inputs from loyalty program to improve the footfalls. Identification, analysis and execution of targeted mailing/messaging to specific audience in catchment area started yielding better results for retailer.

Involve user & align data storage strategy to analytics strategy:

Second most important factor in analytics project is development of tool and to ensure that raw data can be directly feed into the tool to see the outcomes. It is also of foremost importance that business user understands the representation and assumptions. In most cases, the design of tools is outsourced to an external vendor who applies his assumptions to design the model. Secondly, the data structure and business architecture is made by one vendor and the analytics layer above it is made by some other vendor. This leads to distortion in terms of data collection as it is generated by different users across various functions of the retailor.

Action-focused instead of trend-focused analytics:

The most critical part of analytics is deriving actionable insights. The current business intelligence (BI) tools give insights about only trends of performance indicator . It does not prescribe the actions one should take or avoid to ensure the PI remains under control limits or comply with business performance.

As the competition in retail industry is increasing, it is critical for retailer to enhance the competitive edge to stay ahead of competition. Adopting data based improvement programs can definitely help retailor in improving its operations and fare better than the competitors.

REFERENCES:
http://www.mckinsey.com/industries/retail/how-we-help-clients
http://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/applying-advanced-analytics-in-consumer-companies
http://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers
http://www.mckinseyonmarketingandsales.com/how-leading-retailers-turn-insights-into-profits
http://www.forbes.com/sites/mckinsey/2013/07/22/big-data-analytics-and-the-future-of-marketing-sales/#5f244904344d
http://www.slideshare.net/hitaishi9/retail-assignment
https://www.tacoma.uw.edu/sites/default/files/sections/MilgardSchoolofBusiness/Why so many analytics projects still fail by Haluk Demirkan for Milgard MBA NL.pdf

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