The Loyalty Power of A Well-Designed, Well-Maintained Customer Information System

by Michael Lowenstein on March 23, 2016

Michael Lowenstein, Ph.D., CMC, is Thought Leadership Principal for Beyond Philosophy

Francis Bacon said that knowledge is power. Nowhere is that saying more true than in compiling and applying customer information. An effective, strategic use of customer information can help make a company highly loyalty-driven and defection resistant.

Customer data collection, data warehousing, and data mining have become almost commonplace words in the sales, marketing, and customer service lexicon today, thanks in large part to the dramatically lower costs associated with obtaining and storing data and the power, and applicability, of predictive analytics and data graphics. The greater availability of current customer data information, in turn, spurred the rapid development of sophisticated and flexible software to apply this information to customer loyalty and customer win-back programs.

Trying to manage customer retention or recovery without such data, and the system to route and apply it, is a lot like trying to find something on the floor of your garage, at night, without a flashlight. A well planned customer information system (CIS) can take much of the peril out of customer loyalty program planning and evaluation. A company-wide CIS enables your firm to integrate sometimes scattered and inconsistent customer databases, and transparently tie together geographically and organizationally dispersed customer operations.

Widely dispersed databases are a common problem, for example, in most insurance companies. And so are the resulting problems. For example, every potential customer who asks an agent for a quote on a policy triggers a complex underwriting process that involves collecting and matching data on the applicant’s driving record, auto registration, demographics and even family risk (the presence of a teenage driver).

Precious time can be lost in getting risk-related data from multiple external sources, converting it for use in an expert underwriting system, and delivering the final determination to the agent. But that’s not all — if the individual’s self-reported risk factors don’t match what the external data shows about that individual, the policy coming back may well bear a different price than what the agent quoted.

That’s why Allstate Insurance’s successful push, beginning over 20 years ago, to create a comprehensive CIS for its army of agents, has had a profound impact on the company’s ability to better serve its customers. The payoff has been substantial. Underwriting time has been cut in half, enabling Allstate agents to provide quotes within 24 to 48 hours. Policies can now be issued within three to five business days. And the behind-the-scenes data streamlining has eliminated $3 to $4 million dollars per year in unnecessary data costs. Data quality is so much improved that Allstate now guarantees that the rate an agent quotes is the rate by which the policy will be written. It’s a far cry from Allstate’s redundant data days in the early 90s when each of its regional offices was purchasing state motor vehicle and auto registration data from six to eight sources. Through its CIS system, Allstate now gets one cost from each data provider and sets up the necessary conversion, deduplication (eliminating customer record duplication) and data processing procedures to ensure that incoming data is fed to Allstate’s expert underwriting system and from there to the agent’s desktop system.

But the CIS quickly proved its worth beyond underwriting. Allstate has been generating leads for its agents for years, but with the data warehouse capabilities in place, the company has been able to prequalify leads using ideal customer profiles and personas it developed from transaction history and enhancement data. By matching incoming leads against that profile, Allstate can determine the customers most likely to be profitable or to buy a life, auto, or homeowner product.

A company-wide CIS can eliminate the problem of incomplete and dated product and service information. Instead of relying on paper records or isolated computer profiles in customer transactions, the information can be put on-line and updated on a continuous, real time basis.

In the world of database technology, there are almost as many ways to capture customer information as there are ways to use the information that has been gathered. The end goal, always, is to have an information system sensitive enough to understand the needs of every customer. Here are four widely used ways to capture customer data.

  • Building the database one sale at a time. When a consumer purchases batteries from his local Radio Shack store, and the clerk logs the customer’s name and address into the computer, they’re building a database to help determine if you receive a follow-up catalog and which products are emphasized. When customers swipe their frequent shopper cards at the supermarket, they’re helping the chain identify products and promotions of possible future interest. This detailed customer information is very valuable because of the insights into future cross-sell/upsell opportunities it provides.
  • Customer surveys. Surveys are an effective, though sometimes misused, way of gathering customer information. Some of them — the self-completion type often seen in hotel rooms, on rental car counters, in retail stores, on planes, or in supermarkets — provide potentially misleading data due to their low representation of customers. Surveys mailed to customers or e-mailed are also subject to potential bias due to sometimes low response and the lack of detailed information given by respondents. When using surveys for data collection be sure you use data collection methods that are representative of customers or customer segments you want to learn about.
  • Personal, in-depth interviewing. One of the best, and most accurate, methods of collecting customer opinions and perceptions, as well as demographics and lifestyle information, are via telephone, Intranet or personal interviewing. Trained interviewers can generate detailed information by probing customers’ specific reasons for answering in certain ways. It is also the most expensive method, but companies can get more relevancy and objectivity from this type of data than almost any other. Again, given the worth of the information collected, it may well justify the level of expense. For example, a pharmaceutical company which released a new drug may contact a cross-section of doctors who have recently prescribed the product to gain valuable insights about prescription habits.
  • Front line dialogue. When employees collect customer data as a consequence of their routine contact, the cost is extremely low. In addition, this dialogue helps nurture relationships with the customers, showing them that the company takes a genuine interest in their welfare. Since we know that one of the barriers to customer registration of complaints is belief that the company isn’t interested, this direct collaboration of information helps to demonstrate value in their contact.

Discount brokerage firm, Charles Schwab’s goal is to get customers to consolidate their assets — with Charles Schwab. The difficulty comes in learning how much those assets are since new investors routinely test the waters by investing only a portion of the amount they have available to invest. To accomplish that goal, Schwab adopted some basic principles for conducting dialogue with clients.

  1. Ask detailed questions — A new customer is asked several straightforward questions when they become customers. Included in those questions are “how much do you have to invest?” “do you use a computer?” “are you on the internet?” The answers to the questions are used to build a customer profile.
  2. Track behavior — Each time a salesperson talks to a customer, information gleaned from the talk is added to the customer profile.
  3. Incorporate surveys and demographic information — Schwab certainly doesn’t reject statistical information on customers. But rather than making the data the centerpiece of its marketing strategy, Schwab uses it to supplement the more personal, dialogue-based data it develops on each customer.
  4. Translate correspondence into usable information — Schwab records all correspondence, including email, using software that reads and categorizes each message. For example, the software will understand that a customer wants to change an account or has a complaint about a trade. According to Schwab, the text program can successfully categorize 60 percent of correspondence into customer profile data. Humans must read the remaining 40 percent.
  5. Make analytical deductions — Customers may not be forthcoming about their assets, so the company has to deduce the potential value of the customer based on the information it has gathered.
  6. Connect the data points — Schwab is able to measure the true extent of a relationship by mining data. For instance, it recognizes multiple accounts owned by one customer — say, the person with a $200,000 IRA who manages $3 million in retirement funds at her privately held company.

Some database experts are predicting that knowledge discovery will start to eclipse data mining techniques in the next five years or so. A knowledge discovery database (KDD) works like this. Instead of mining layers upon layers of customer transactional and lifestyle data for knowledge nuggets, a set of flexible knowledge-required algorithms is established and the available data is searched to find exceptions to this rule. Of course, the success of KDD within an organization is dependent on the firm’s ability to provide clear direction for the goal statements which in turn can be formed into specific goal-searching algorithms. This cannot happen without the right level of campaign knowledge and customer experience data that has been successfully captured, categorized and monitored. Like all CIS successes, the power of KDD is only as good as the knowledge transfer and the commitment of staff to using and improving the system.

Bottom-line, a major key to making your business customer-centric loyalty-based and defection-proof is information that leads to insight — information about your customers, information about their needs and preferences, information about your own business and its strengths and weaknesses. If you can get the information superhighway running past your door, you’ll find that keeping and creating loyal customers is easier than ever before.

Michael LowensteinThe Loyalty Power of A Well-Designed, Well-Maintained Customer Information System