Home Retail How Data Analytics is Helping Retail Outlets with Critical Decision Making

    How Data Analytics is Helping Retail Outlets with Critical Decision Making

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    Stock-outs are the bane for any retail business since they are costly and result in long-term losses. Consumers shift to competitors when their needs are not met in today’s fast-paced world. Companies across the world are spending unprecedented amount of money in finding ways to prevent stock-outs and ensure that they can serve what consumers want, at the time when they want it! But it is more easily said than done!

    For example, a typical apparel retailer stocks thousands (some even stock millions) of different SKUs; additionally consumer demand, driven by fashion trends, can be very diffi cult to predict – especially for new launches and sales during holiday seasons. While stock-outs result in loss of valuable profits, too much stock has an inventory cost that can again reduce profits substantially.

    The science of is now showing a ray of hope to bring simplification to complex business problems, taking decision making from a rudimentary gutfeeling based to a much smarter fact-based one.

    Most retail organisations today operate in the master franchisee model where another company becomes the master franchisee and manages the operations of the different outlets of the brand. In such cases, it is important to understand the difference between what brand the company sells to its master franchisee stores and what the stores in-turn sell to the consumer. This metric is where the insights are hidden. Hence, it is very important that companies track, understand and action this metric to optimally manage stocks and avoid costly stockout or over-stock situations.

    Are there important insights about consumer choices in different geographical regions that can be uncovered through data analytics? There definitely are! For example, if a sports retailer could use data analytics to analyse his sales data during the IPL season in India, this will enable him to customise his promotional offers by specific geographies as he will now know in which cities the cricket fever is at a higher level and thus justify lesser promotional investment!

    costs in most cities have doubled in value in the last five year’s which is another huge problem faced by major retailers while looking at expanding their footprint in the Indian market face. Which is the best location for each store in which city and the size of each store is not a straightforward choice.

    A large enough product assortment cannot be held if the store is too small giving its competitors the upper hand in the market, whereas at the same time if a business is too large it can delay the breakeven period of the business. Given a limited budget, what would be a better location to open the next store – a or a mall address? Tough questions like these have vexed top company officials in their decision making for many years and most of the times their decisions have been based on the gut feeling for quite a few people. These gut feelings are not always successful as we can see a large number of retail businesses closing down in a city such as Bengaluru more often than not.

    Data analytics usage in this space has proved to solve this dilemma. It helps companies decide the optimal size of each new store they open, while ensuring that these decisions take into account key parameters such as real estate rental costs, catchment area, average incomes in the geographical area and presence of competition. Data analytics even helps retailers make differential decisions based on the location of their new store.

    Success or failure of a particular store can be identified with the use of a variety of advanced statistical algorithms. Analysis of this data provides independent parameters contributing to a store’s success and then measures the ROI of the investment made in real estate. Companies can judge different options for their future stores by using a simulator given to them by these data analytics companies. These results are constantly improved as more data from newer locations are got.

    Though all IT companies derive substantial income from data analytics and have been around for many years, there are now a few companies who are already showing the future path in this fast growing industry by providing insights into businesses at the speed of business. The future of analytics is focussed on bringing the science of analysis with the art of decision making as compared to most analytics companies who create standardised reports from data.

    “Simulation” and “optimisation” are advanced capabilities that not only require a strong mathematical base but also a deep understanding of the business context of the client being serviced. The next few years in the industry are going to reward theseplayers who bring such capabilities to large and small businesses.

    Statistics show that most IT projects fail – and the few that work result from the right use of analytics by analytics professionals, thus ensuring that they deliver better results on a consistent basis. Analytics is not just an application, it is the presentation of a strong data base that empowers all its users. The components of analytics must operate together and be rapid to deploy to cater to all kinds of business needs.

    A single analytics tool chain across differently structured data can be used to help complete environments for companies given they have the necessary analytic capabilities. The empowerment that analytics brings gives staff across the organisation the information they need to improve the organisation’s bottom line and helps the company make better Decisions.