Master Data in eCommerce – Part 1: The Customer

For those of us in the customer experience space, we can’t open a blog post without reading that the customer is in charge.  They’re empowered by technology.  They expect more – demanding a contextually relevant interaction with the brand across all channels. A core requirement to delivering a superior customer experience is having a comprehensive single view of your customer. 

This is the first post of a three part blog series on the importance of master data in eCommerce.

Part 1:  The customer – We’ll look at some typical enterprise data points around the customer and identify where that data typically resides.  We’ll talk about how to collect and reconcile that data into a single view of the customer – the elusive golden record.

Part 2: The product – We’ll look at some typical enterprise data points around product (or productized services) and the multiple systems that typically house that data.  We’ll also look at PIM/DAM/MDM options.

Part 3: Bringing it all together – We’ll illustrate, though use case, how a documented inventory of managed data can be used to enhance the customer experience.  We’ll look at the notion of a digital content model that includes integrated customer and product master data.

Part 1:  The Customer

Let’s start with a vision of what a 360 degree view of the customer might look like –The MetLife Wall.  This is one of the bravest, most impressive modernization initiatives I’ve seen.  The 145 year old Fortune 50 global insurance firm set out to develop a single system to view and manage customer data that ultimately resides in 70 different systems.  

The Wall is used by Customer Service Reps, during in-person visits with agents and by claims & policy staff.  The system integrates with an enterprise implementation of SalesForce.  The ultimate vision is for the business to operate primarily out of two screens; SalesForce for sales & service, and The Wall as the single interface to the myriad of other line of business (LOB) systems that MetLife requires.  Also in the product roadmap is next best action guidance that’s based on the rich analytics that the system collects.  Imagine a scenario where The Wall identifies the customer as at a higher churn risk and provides guidance to the agent on how to best engage with the customer.  That next best action based on stage in customer journey is crucial as we face the challenge of how to ensure experience consistency at scale.

Now, MetLife invested something like $100M in the development of The Wall.  While we all can’t make that kind of investment, we can follow a structured best practice to get, over time, to a single view of our customer.

Step 1:  Collect the data

The first step in the journey is to inventory all of the current and envisioned sources of customer data.  As we’re mining this data we should keep in mind our customer experience strategy; the persona points, customer journey stages, goals & values, etc.  If you don’t have a defined customer experience strategy in place, stop and focus your attention there first.  

Systems/networks that may contain customer data could include CRM (customer service records), ERP (order data), POS (in-store purchases), eCommerce system (order data, saved carts, wish lists, abandoned carts, etc.), eMail (email offers sent, opened, clicked), website (visits, goals met, lead value, etc.), live chat (chat sessions), social networks (posts that include your brand name, offer likes, etc.), mobile apps (log-ins, key usage, app sensor activity like location, etc.).   Don’t forget to include any industry specific business applications, custom developed applications and data stores. 

Step 2:  Reconcile data into a single view of the customer

The next step involves consolidating each of the customer data points, from each of the systems/networks into a single customer profile.  For each customer data point we want to identify the system of record, any association to customer experience strategy (e.g., persona, customer journey stage, etc.) and the business roles (CSR, merchandiser, etc.) that need access to the data. That will help us build out use cases and work spaces for each role. 

Now, there’s a technical planning component of master data management strategy that we’re not able to get into within this post.  That includes things like real-time vs. batch based data transfer modes, profiling, cleansing, matching, linking, identifying and semantically reconciling data points.  The reconciliation exercise above will result in better business requirements during the technical planning phase.

Up Next:  The Product

The next post in this series will cover master data considerations for eCommerce product.  We’ll take a similar approach and show how customer & product data align.  That will tee us up for the final post that brings it all together. 

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