5 Examples of How Retail Professionals are Using Analytics to Solve Their Problems - Great Learning
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5 Examples of How Retail Professionals are Using Analytics to Solve Their Problems

Whether you had to buy a new dress or go to a Kirana (departmental store), your shopping experience was not very different. Retail was the last domain one could have imagined changing drastically, but in came business analytics, and out went the archaic norms that once dominated retail. From labour forecasting, transportation costs, influencing in-store customer experience, to expansion plans, retail has used analytics to maximize their sales and profits, enhance customer experience, and check-out free stores. Retail companies have witnessed this paradigm shift because of the labour of data scientists and business analysts in the system so this serves as a wake-up call for retail professionals to upskill and take advantage of the growing popularity and use of big data analytics in retail. To get you started, here are 5 cherry-picked case studies where analytics resolved one or more retail problems:

  1. Run systematic remodelling experiments to optimize retail store sales – A top 10 speciality retailer wanted to remodel 27 stores, by taking a measured approach to remodelling based on incremental benefits. However, measuring the impact of remodelling was subject to misinterpretation due to factors such as seasonality and difficulty in identifying a control group. Learn how Fractal Analytics used Analytics to solve their problem by developing Trial Run proprietary algorithms.pgp business analytics (pgp-babi) great learning
  2. Reduced transportation costs for a convenience retailer – A leading convenience retailer was leveraging 3rd party logistics providers to run their transportation network. The client was interested in identifying opportunities to optimize the transportation routes from DC to stores in order to reduce transportation costs. Mu-Sigma developed a route optimization tool to develop optimal transportation routes from DC to stores thereby reducing 30% of transportation costs and the number of miles travelled. Read full details here.
  3. Improve customer serviceability, overall product offering along with customer retention & acquisition – Arvind Lifestyle Brands used retail analytics to design, source & manufacture products based on consumer preferences and thus be aligned to the market trends. Implement best practices in retail analytics and help business take data led decisions resulting in better customer serviceability, products and profitability. Read how.
  4. Improved labour forecasting and planning for a large retailer – The client had invested in purchasing and deploying an off­ the shelf demand planning suite. Despite the investments in the demand planning solution their SKU-store level forecasts had a high error and therefore could not form a reliable basis for labour forecasting. Mu Sigma helped reduce forecast inaccuracies to less than 5% for 95% of the stores resulting in improved labour plans. Know the full details.
  5. Use AI to enhance customer experience and drive digital sales – For a leading retailer, Fractal deployed an advanced AI-based framework to create features from every digital interaction down to minute click-events and identified high-impact causative issues that negatively impacted the customer sales journey. Read the solution and impact here.

Sources: Fractal Analysis, Mu-Sigma, NASSCOM.

pgp business analytics (pgp-babi) great learning

 

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