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Customer Intelligence stories from my uncle’s vegetable shop

Analytics and Customer Intelligence were alien words to me when I was just 12 years old. Most of them who use analytics in their day-to-day activities have not even heard of the term “Analytics”. Same is the case with my Uncle. He started as a small time vegetable seller and soon went on to run a very popular super market. It’s not only hard work that is the reason for his success, but also the crucial decisions that he took at various points of time, the strategies and novel methods that he implemented to take his one-time small scale shop to new heights.

Customer Intelligence lessons from vegetable vendor

With reasonable experience in the field of Analytics, when i look back now, I must give full marks to my Uncle when it comes to Customer Intelligence. And I assure you again, he knew none of those analytical terms. I still remember spending my entire summer holiday at his shop looking for chocolates, making friends and understanding the business. It now occurs to me that he implemented all the vital customer intelligence strategies very proficiently.

Here are some anecdotes from a small town vegetable shop in Southern India and customer intelligence lessons for today’s retail world.


To recollect some of the strategies, firstly, my uncle was very clear that it is important to understand the behavior of his customers very well. This, he believed was a very important factor in retaining one’s customer base. My uncle followed the simple technique of tracking how often a customer bought and the value of every purchase. A close watch of these aspects across his customers, helped him devise promotions and discounts and plan his inventory too  – inventory management in his case was critical, given that 60% of his business was in perishable goods.

For example, Pankajam mami stocked up on vegetables and condiments everyday while Marwari Seth did a bulk purchase every weekend. He found other customers who exhibited similar traits and more often than not meted out the same treatment for similar customers. For customers like Seth, he offered free home delivery on purchases above a certain amount and some exclusive discounts on select products too.

In today’s retail business too , the very similar approach is applicable –  RFM Analysis (Recency – Frequency –  Monetary) . The RFM approach requires the business to measure for every customer:

  1. How recently was a purchase made
  2. How frequently does the customer buy
  3. What is average value of each transaction / purchase

The next step is to assign weights to R-F-M attributes and calculate a composite score. The rationale for assigning weights however has to be backed by strong business case. Once the composite score is calculated, one could now decide on thresholds and create buckets or groups of customers so as to optimize the degree of heterogeneity between group and homogeneity among the group. Each of these strategic groups can now be profiled and an exclusive marketing /promotion campaign can be designed for them.

While the RFM approach is only one way to segment customers, there could be other mechanisms to identify groups of similar customers – geography, income profile, historical transaction patterns etc.


My Uncle has always been proud to say that he understands the customers’ preference very well. To capture the market and to retain customers are key and he had to come up with new and novel ideas to understand customer preferences. For this, he started making note of his regular customers’ purchases, engaged them in conversations which involved subtle probing only to ferret out information about their preferences and their inclinations. To his surprise, he found that there were a lot of interesting insights from their purchase patterns. For example, he gathered that Kamakshi paatti chose not to buy from his store unless he stocked country tomatoes!

Understanding and adapting to customer preferences is about giving them what they want, the way they want. If you are grappling with how to constantly monitor customer preferences, satisfaction and brand loyalty, our in-house solution ExTrack comes to your rescue.  We have helped a major Canadian retailer in executing a tracking survey, deconstructing survey data and dissemination of key insights with a quick turn-around time. The retailer was equipped with insights on changing seasonal preferences, readership on marketing flyers and campaigns, brand preference for various product categories. The interactive feature of the ExTrack tool gave the decision maker the ability to slice and dice data by multiple parameters and attributes to strategize by the demographic or age group segment.


Understanding customer preferences will also help you identify their choice/pattern of buying. This will help you a great deal in anticipating your customers’ next purchase. All my Uncle did was to predict and keep the stocks ready based on the notes that he had. For example, If Kamakshi paatti bought more than a liter of milk he knew she was expecting her grandchildren home and would be quick to offer sweets/ candies. knows you so well it wants to ship your next package before you order it. The Seattle based retailer in December gained a patent for what it calls “anticipatory shipping,” a method to start delivering packages even before customers click “buy.”

While careful observation and notes helped my uncle in his vegetable shop, today’s world of omni channel retailing calls for powerful algorithms to track and map customer purchase across channels – map purchases that are coupled in a single transaction or multiple sequential transactions across retailing channels.

BRIDGEi2i Customer Analytics

BRIDGEi2i’s powerful predictive and collaborative filtering algorithms help easily map and infer what do similar customers most often buy together – on and off season sales. This overlaid with our superior text mining also gives marketers insights on what customers are looking for, the trending discussion topics on shopaholic forums. We have helped a global retailer identify and recommend a set of books for customers based on their historical purchases and the book preferences of similar customers.

Instances from my uncle’s shop never fail to amaze me and each time these stories lend a new perspective to customer analytics and how businesses armed with data can turn the tables.

Keep an eye out for my next blog on my Uncle’s Marketing Effectiveness strategies.

This blog is written by Raghavendar Varadaraj, Analytics Consultant at BRIDGEi2i

About BRIDGEi2i: BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. Our analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems and make data driven decisions across pan-enterprise processes to create sustainable business impact. To know more visit

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The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position or viewpoint of BRIDGEi2i.