Understanding Customers Beyond the Enterprise | Maavrus

Written by Mahadevann Iyerr

May 6, 2022

Understanding Customers beyond Enterprise

We are moving slowly into an era where big data is the starting point, not the end. –  Pearl Zhu.

As organizations increasingly adopt digital initiatives, they tend to capture a lot of digital footprint data about their customers and their behaviors – data around their purchase patterns, product returns, feedback, browsing behavior, etc. They then analyze this data to understand more about their customers and leverage that to enhance their product or service value proposition. However, this data has an intrinsic limitation – it speaks only about that share of spending that the customer is making with that organization, and obviously cannot provide any insight into the customer spending or engagement beyond that organization. 

Why is customer data beyond the organization important?  

Let us take the example of a fashion retailer. The average annual shopping by a customer with a retailer could be around 3-4 times per year.  However, based on the affluence of each customer, they could be spending with other fashion retailers or e-tailers as well. So it is possible that “CUSTOMER  A” who spends Rs 5,000 annually with you, maybe spending his/her entire fashion shopping with you, and it is equally possible that “CUSTOMER B” who spends around Rs 10,000, is only spending 20% of their wallet with you, and is shopping 80% elsewhere.  

How will Augmenting customer data, from beyond the boundaries of the organization help the companies? 

  • A Better understanding of its customers, not only based on him/her spending within the company but also based on their wallet share. A customer with a smaller share of the wallet is more likely to migrate than someone with a higher share. 
  • Companies can segment customers based on their overall spending behavior, and get an outside-in perspective of which segments, treat the company as “First-among-equals”, and the ones that do not. 
  • Understand the spending patterns of customers beyond the company’s category of offering, and get a larger understanding of their lifestyle choices.

How do we go about augmenting this data? 

Companies with customer loyalty programs can build strategic alliances, with other companies from non-competing industries, to provide value to customers who shop across the partner network. So a fashion retailer could partner with other category retailers, telecom providers, ride-hailing apps, and financial services companies to name a few.  Co-brand credit cards are another way to provide additional benefits to loyal customers, beyond what the retailer or credit card company can provide individually.  

 In a world, where data is the new oil, such partner networks can provide rich information beyond the possible 3-4 times that a customer shops with them; how much and where is their grocery spend, what is the telecom package they are using, what type of rooms they use when staying at a partner network hotel, do they book luxury or economy rides on ride-hailing apps, etc.

It is also possible that with partners from certain industries like banking etc the customer demographics data is likely to be of far higher quality. This access to a larger and richer data set can help retailers better understand each customer’s profile, family sizes, affluence levels, and proclivities. It can also help them to understand if there are changes in the customer’s spending patterns, new family additions, changes in affluence level, etc.  

 As a company starts to build this big data, it is possible for it to:   

  • Run models to see if there are correlations between customer spending within the company and partner companies. This will help companies identify headroom with existing customers, and also identify the best look-a-like segment to reach out for acquisitions. 
  • This segmentation at a larger share of customer wallet will also help the company focus its qualitative insights on the right target segment in the market, and better understand the WHY behind the WHAT. 


Off-course any customer-level data augmentation needs to be done with clear opt-in by the customers, and they are being made aware of the purpose of data collection.  


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