So Much Data, So Little Time

Many organizations – both large and small, ranging from multi-billion-dollar international companies to sole proprietors, simply have too much data on their customers to make sense of it in enough time to have an impact on their strategic or tactical decision-making. There are either too many customers (admittedly a good problem to have), or too many data points available on each customer (a more common problem), or both.

In both cases, such massive amounts of data often lead to only sporadic attempts to update and use the information in a beneficial way, or worse, to complete inaction in the face of a seemingly overwhelming volume of information. Think of it like cleaning up your home file cabinet: you know there’s important and valuable information in there somewhere, but you just don’t have the heart to get started organizing it all.

This is the fundamental problem of leveraging customer data to create actionable customer intelligence that can have a tremendous positive impact on an organization, from front-line customer-facing functions all the way to the back office. Why does this occur, and what can be done about it?

The Data Problem

Where is all this customer data coming from? Typically, every business has many different sources of customer data:

Point-of-Sale (POS) systems

Retailers often capture a customer’s name, address, and purchase information when a customer checks out and pays. Sometimes retailers often build in a short customer profile in the form of a few simple questions asked the first time that a customer makes a purchase.

Accounting, invoicing, and bookkeeping systems

Almost all businesses have some kind of accounting system to keep track of their income and expenses. Frequently, especially for non-retail businesses, these systems are also used to generate invoices for customers and to keep track of details like customer name and address, payment history, and what sorts of services have been provided when.

Websites

Active company websites that generate a lot of traffic will also generate a lot of data on visitors, whether through cookies, the “click stream” itself (a record of what activities have taken place on the site and on what pages), registration data, or shopping cart data.

Non-electronic files

Often businesses have tremendous amounts of paper, storing very valuable information on customer needs and preferences. These often take the form of one-off notes stored in sales peoples’ customer files, post-it notes stuck to computer monitors, miscellaneous project-based files sitting in file cabinets around the office, and other paper that never quite seems to make it into an organized storage system.

CRM systems

Customer relationship management (CRM) systems store and manage customer contacts and activities, focusing on providing functionality to customer service support teams. These systems contain enough information on customers so that a customer service rep will be able to answer intelligently any questions that a customer might ask about his or her relationship and be able to resolve customer service issues. They can also be used more broadly as sales tools, storing data on likely customers or helping customer relationship specialist’s cross-sell or up-sell when they are on the phone with customers resolving service issues. Often their functionality is closely related to that of SFA (sales force automation) tools; see below. 

SFA systems

SFA systems are software tools that allow sales people to keep track of their prospecting and lead-generating activities. Often, when a lead progress from being a prospect to a qualified lead to a contact to a “converted lead” or an actual purchasing customer, these tools store customer information as well as information on leads or prospects. ACT! Goldmine, and salesforce.com are popular SFA tools that enable the capture and use of tremendous amounts of data on prospects and, eventually, customers. 

Within all these different data sources, a wide variety of customer data is captured. These data can include:

• Operational data (data necessary to support a company’s legal and financial interactions with them, including legal name, address, methods of payment, purchase history, etc.

• Demographic data (descriptive data that goes beyond just the basic operational data to capture characteristics which allow more targeted product development and marketing, including age, sex, income, home ownership status, etc)
• Customer feedback data (data gleaned from customer service interactions, including communications preferences, customer satisfaction scores, complaints, product or service preferences, etc.

It is clear that many companies have too much data on their customers, stored in too many different places, for managers to make use of it all in a timely and efficient manner that can have a positive impact for both the customer and the organization. 

Worse yet, managers looking at different data on the same customers from different sources often draw different conclusions which may conflict with each other and stymie company-wide efforts to develop a coordinated, unified, and effective marketing effort. And even if organizations solve these thorny problems, there’s another one lurking right behind. In my next blogpost, I’ll talk about “analysis paralysis” and what to do about it.