Using Data to Become a Customer-Centric Organization
Quality, not quantity, is key in using data to build customer relationships and personalize the products and services you deliver. By now, we all know that we have to know our customers, and we need to personalize the products and services we deliver to our customers. To keep up with our competitors, we need to become "customer-centric." Many executives will tell you that to become customer-centric, you have to buy more and more data about your customers and feed it into a data warehouse with lots of add-on software products.
That bit of conventional wisdom is not precisely true. The market for business intelligence and data warehousing tools and services is growing at an average rate of more than 50% and is expected to reach $113 billion by 2002.1 The race is on to develop better, smarter technologies to facilitate Customer Relationship Management and to buy more data about our customers and prospects. But there is little evidence to show that the mere presence of more knowledge and more technology will lead to better customer relationships. The key to success in today's environment is to know what information drives your customers' behavior and to apply that knowledge across the enterprise - in strategic planning, product development, marketing, customer service, and sales.
In short, a wealth of relevant customer data is a critical element in becoming a customer-centric organization. So, too, is a central repository to manage the data. Before purchasing either, companies need to have a plan. They need to know why they are gathering data, what data to gather to achieve the desired results, where to get the data they want and how they will apply the knowledge they gather. Quite often, companies find that they need less data - but more focused data - than they thought, and that much of the data they need is already housed within their enterprise.
Defining Data Requirements
In our experience, we've found it worthwhile for companies to take a structured approach to determining what data they need, particularly when third-party data is required. This approach includes three basic steps:
Start with What You Know
Begin by defining what you know about your customers. Profile your current customers by using information such as purchase history, length of relationship, purchase details, transactions with other divisions within the enterprise, demographics, credit/financial details of the transactions, and customers' most frequent means of interacting with you.
Often, companies have more data than they realize, because much of that data is "hidden." As enterprises implement various CRM systems in an attempt to understand and serve customers, they sometimes blur, rather than sharpen their view of the customer, according to a recent report by the Aberdeen Group of Boston.2 Sales force automation, telesales, customer support, and campaign management systems typically use different keys and look at customers from different perspectives. Consequently, the enterprise finds it difficult to connect these data sources and create a holistic view of the customer. The result is a weakened ability to deliver a single face to the customer and to show your customer that you know how to serve him or her individually. Understanding the data housed within all the information sources in your organization will sharpen your understanding of your customer and of your requirements for centralization of the data.
Define What You Want to Do with the Information You Gather
Understand your objectives behind gathering more information. Do you want to retain existing customers? Sell more products/services to existing customers? Attract new customers? Tailor the delivery of service to customers based on their value to your organization? Develop new products based on your customers' preferences? Enter or exit markets based on profitability and penetration within those markets? Your objectives may include all of these and others, but for purposes of defining your requirements, treat each objective as an individual project, and define the data elements required to support each project.
This approach will help you avoid overloading the end users with irrelevant information and will ultimately reduce your data acquisition costs by reducing the number of data elements you need to purchase. For example, Kraft General Foods developed a customer knowledge system that they use to build share in product categories. This tool enables Kraft sales reps to obtain, analyze, and apply customer knowledge. Since sales reps do not have the time or resources to sift through volumes of data and draw subjective conclusions, Kraft built the system to focus on the most important data elements that drive sales. As a result, Kraft sales reps use less than 20% of the data their competitors use, and realize a marginal sales growth of 3-4% versus a control group. 3
Define What You Don't Know
Define the gaps in what you know about your customers, what you want to do with the information and what you need to know about your customers, your prospects and your market(s) to achieve your objectives. Probably the most effective method of defining the gaps is to analyze your customers using your existing data. By segmenting your customers into unique groups with similar characteristics, you begin to draw a hypothesis of how they behave, what they purchase, and why they purchase. From this segmentation, further questions will arise - how does the demographic profile of one group differ from another? What motivates a segment to purchase from you at their specific life stage? What other hobbies or interests are common within segments? What level of service does each segment require to remain loyal to your organization? Through what channel do they wish to be contacted by you? From the questions that arise, identify those data elements you lack to answer the questions.
Once the gaps in data have been identified, the missing data elements may be gathered from a variety of internal sources - customer surveys, focus groups, other systems within the enterprise - or they may need to be purchased from a third-party provider. As always, measure the importance of the data against the ultimate objective of this project.
Filling in the Gaps
As a rule, the best source of data about your customers will be found inside your enterprise. Optimal match rates on purchased data are 70-90%, which means that as much as one-third of the records you purchase are unusable. The source of the data you purchase can be very reliable - Department of Motor Vehicles or drivers license records - or it can be inferred from less quantifiable information such as customer-provided warranty cards, surveys, magazine subscription lists, or census tract-level deductions. This information can be very useful directionally but your customers' actual experience with you is recorded inside your own four walls. Start with your internal information systems. Examples of information sources that can help you fill the gaps in your customer knowledge include:
o Complaints/inquiries to the call center
o Credit and financial information
o Business transactions with divisions other than your own
o Aggregate purchases/history of the relationship
o Purchase details
o Preferences based on actual purchase history/trends
The information found internally can be highly valuable in developing your customer relationships. For example, Nordstrom's department store, a renowned customer-centric organization, created the "Personal Touch" program to offer complimentary personal shopper service to customers. The program's objective is to form long-term relationships with Nordstrom's customers. The store's personal shoppers have access to two systems that capture customer information - a database of customers' likes, dislikes, lifestyle, and apparel needs that were gathered from telephone and face-to-face conversations, and a database containing customer purchase history. Armed with both previous purchase history and personal preferences, the personal shopper is able to offer more targeted selections and truly personalized service to the customer. 4
Following a review of internal data sources, some "human" information will probably still be missing. It is likely that your customer can provide this information. Specific behavioral and buyer preference information may be more effectively gathered through primary research such as surveys and focus groups. It is also possible to simply "ask" the customer for information, in exchange for some incentive. Many Web-based companies like Free-PC.com and E*TRADE are offering incentives for customer-provided information. E*TRADE offers 500 air miles for the customer's information, then follows up with a certificate for $50 toward the first investment. Free-PC.com joined with Compaq to offer free computers in exchange for personal data. They offered 10,000 computers to people who were willing to share personal information and receive targeted advertisements. About 300,000 people visited the site and thousands who tried could not get through to the website. It appears that for the right offer, people are willing to exchange some of their privacy. 5
Purchasing and Managing Third-Party Data
Once internal sources of data have been explored, it may be necessary to purchase third-party data to fill in the final critical gaps you have identified. There are many sources from which to purchase data and such information can be very useful in completing the profile of the customer. However it is important to understand the potential limitations of third-party data before buying.
Many reputable companies offer demographic overlays. These companies generally compile data from sources such as phone books and Departments of Motor Vehicles to arrive at household-level demographics. Demographic overlays are an important element of analysis, profiling, and targeting; however, an understanding of their relative accuracy is critical. Census studies have shown that more than 10% of the U.S. population moves every year, and an even higher percentage changes jobs every year. Add to that the previously mentioned typical match rates of 70 to 90% and it becomes clear that a significant portion of the information a company purchases will not match the records in its database.
In the United States, recently enacted privacy laws have made the acquisition of actual data more difficult. For example, under the Shelby Act, an amendment to the Drivers Privacy Protection Act, effective June 1, 2000, states must offer notice and opt-in rights to the consumer before making its drivers license and motor vehicle license lists available to direct marketers. In other words, the consumer must explicitly agree to allow this information to be sold for marketing purposes. This essentially renders two of the most credible third-party data sources far less reliable than ever before.
Similarly, Gramm Leach Bliley, the Financial Modernization Act, enacted in the United States on November 13, 2000, says that before a financial institution can share non-public personal information (NPPI) with anyone other than affiliates, it must provide the consumer with detailed notice and the ability to opt-out. NPPI has traditionally been the source data providers use to derive such demographic attributes as income, home value, net worth, credit card usage indicators, and credit worthiness, among others.
So it is critical to understand the limitations of the data you purchase and to carefully test the information you purchase. To do so, choose a few vendors for evaluation based on the available data variables, their overall match rates and their sources of data. Ask each vendor to enhance a sample of your data file and provide match rate reports. Check the returned data against actual data elements in your database. For example, match the "age" returned against customers for whom you have date of birth in your database, or check "home value" against the mortgage value on the customer's credit file if you house that information. Evaluate each vendor on the combined score of overall match rate, elemental match rate (the percentage of elements that are populated per customer versus the number you requested) and the accuracy of the data variables against the actuals in your database.
Putting It All Together to Build a Customer-Centric Enterprise
In the end, the point of gathering all of this data is to develop deeper relationships with your customers and ultimately sell more products and services. Today's customer is accustomed to a certain degree of "personalized" service as a baseline - the leaders of the premier customer centric organizations are focusing on using that data that contributes to higher sales and more personalized service to the customer. Marriott has invested heavily in a Customer Relationship Management system that incorporates both transaction and "human" data. Armed with this information, desk clerks see a display of any special needs, interests or preferences the guest expressed when making the reservation or in past visits to Marriott properties.6 Similarly, First Union has recently implemented an enterprise-wide customer-centric data warehouse. Information from within the warehouse is first used for customer analytics, with the objective of deepening the bank's understanding of who its customers are, and how these customers can be served, to increase the bank's profit per customer, and ultimately to increase customer loyalty by cross-selling the appropriate products when the customer needs them. Analytics incorporate both transaction data and demographic data and prompt the bank's relationship managers to react with a targeted solution to customer events such as major withdrawals/deposits, home purchases, etc. By understanding the customer's complete relationship with the bank, First Union is able to introduce new services to the customer - investments, home mortgages, insurance, credit cards, etc. - at the appropriate time. The bank expects this capability will contribute $100 million in revenues annually.7
To understand how companies can use customer information more effectively, the Accenture Institute for Strategic Change and the Goizueta Business School at Emory University recently conducted a study of the customer knowledge management practices of 26 leading firms. The study found that while the specific tactics varied by organization, the leading firms had at least four major strengths in common:
They Know Upon Which Customers to Focus.
For example, the leaders have each made a strategic decision to apply data to identifying segments as a basis for action, such as enhancing service delivery to the most profitable segment, encouraging low-cost transaction options to unprofitable segments, cross-selling additional products or services to segments that have the potential to become "best customers."
They Are Focused in their Objectives for Customer Knowledge Management.
The leaders in customer knowledge management know what they are going to do with the data - purchased and in-house. For instance, they may use it to increase market share in a specific market; encourage the use of alternative channels in low-profit segments; identify product improvements or additions; or cross-sell products and services.
They Aim for an Optimal Mix of Data-Driven and Human Knowledge.
The leaders creatively incorporate actual customer feedback into their analyses of transaction, purchase, and demographic data to complete the holistic view of the customer.
They Manage to Results.
Getting data into a repository is only the first step toward managing the customer relationship. Your efforts are only successful if they improve your bottom line or meet your strategic objective.
Conclusion
In building a customer-centric organization, information about the customer is critical - but it needs to be meaningful information that will help you meet your goals. To make sure they are pursuing and using the right data, companies can follow a structured, step-by-step approach: Begin with an understanding of your objectives; Study your own customer data; Identify the gaps in the information you have and the information you need; Ask your customers for information. Where needed, complement your data by evaluating and selecting the third-party providers that can effectively supplement your data. Then, and only then, build the systems and processes to support your customer centric environment.
References
1 1999 Business Intelligence/Data Warehousing Program Competitive Analysis Report, World Research Inc., San Jose, California.
2 DM Review, January, 2001. "Customer Data Integration: The Essential Component of Effective CRM"
3 "How Do They Know Their Customers So Well? Lessons from the Leaders in Customer Knowledge Management"
4 "How Do They Know Their Customers So Well? Lessons from the Leaders in Customer Knowledge Management"
5 "The Newest Currency. Are You Ready to Pay For It?" Bob McKim
6 "How Do They Know Their Customers So Well? Lessons from the Leaders in Customer Knowledge Management"
7 "How Do They Know Their Customers So Well? Lessons from the Leaders in Customer Knowledge Management"
