Search Icon
ce 3

Five steps to creating an Omni-data view of your retail customer

By  Paul Cook  on  1 Apr 2015
  • Analytics Best Practice

In-store, online and mobile are the three channels customers use to engage high street brands.  It’s also where the simplicity ends. Retail is inherently complex, with many moving parts and actors.  

For retailers, it’s a herculean task to continuously manufacture, sell and deliver goods that can go out of fashion in a matter of days or weeks.  It’s a complexity that involves a concerted effort from many disparate teams within a retailer including e-commerce, finance, human resources, IT, marketing, merchandise, procurement and supply chain.

Customers don’t (or shouldn’t) see this intricacy in operations. However, when a shopper has a poor experience, it is down to the retailer failing to deliver on its promise.

Retailers that witness a drop in customer loyalty or lower than expecting revenue per customer often lack an Omni-view of the shopper. This is a shame because retailers have so detailed and relevant information about us. 

Sadly, for everyone, they just haven’t been able to collect and connect the many disparate data dots where customers interact with a brand – point of sale, home delivery, call center, social media, mobile, customer loyalty, store front…the list goes on and on.

This data challenge is holding back the industry from meeting the growing expectations of customers who are accustomed to and expected a personalized shopping experience.  What can retailers do about this expensive roadblock?  Create an Omni-view of your customers from the vast quantity of data sitting across your entire organization.

I have outlined five important steps your organization should consider in order to build a complete and accurate picture of your customer, using data from a number of invaluable internal and external sources:  

  • Create a customer profile It’s important to sketch a skeleton of the person you want to create with data. This will help you identify the data sources you need. The first step is to design a profile that will be used for visualization of information about the customer. 
  • Collect data sources Different operational teams use different data that, when shared, can help colleagues.  The priority is to get hold of the data across channels.  These could be from ERP, customer loyalty and points of sale systems.  The next step is to standardize the different types and formats of data so they can be combined into one view.
  • Connect data sources to customer profile Start to create a holistic view of the customer by tagging core data sources to the customer profile (data schema) such as  customer name, address and shopping history.  
  • Collaborate with colleagues  Before you continue to enhance the customer profile, you will want to allow colleagues to gain access to an analytics platform that includes a data store so they can add contextual information to the customer profile.  This is important because without breaking down internal operational siloes, you’ll never be able to create and act on a single view of your customer.  
  • Crowd populate your customer profile As soon as you have started to populate your customer profile, it’s time to ask your colleagues to add other relevant information such as shopping attributes, demographics and credit scores.   This is a continuous activity, which ensures that the information about the customer is up to date.  

Retailers are investing a lot of money trying to achieve an Omni-channel operation. This is a lofty and probably unrealistic endeavour considering how slow organizations change.  It’s also human nature to think and act through channels such as social media, mobile devices and email. 

So, instead of breaking down operational silos to reach customers, retailers should consider bringing the customer into their operation. It is much more cost effective to create an Omni-data view of the shopper than to change the behaviour, tools and processes of thousands of employees.

Retailers know how difficult it is to change consumers’ perceptions. It’s why they should factor in an alternative approach to customer analytics that is centred on integrating data, not legacy technologies.