Database marketing success is largely a function of operational excellence combined with strategic deployment. Operational excellence focuses on “correct-fit” technology, while effective strategy results from leveraging customer data to achieve business objectives.
Integrating these two functions produces a critical asset that can drive an organization’s success in building mutually profitable relationships with its customers and prospects.
Strategy follows operations. Proper scoping, initial build, maintenance and matching of the correct user tools create the best environment for effective database marketing strategies. Strategic initiatives develop as a result of organizational objectives, available data and the power of analytic tools.
There is a powerful connection between a strong operations and technology foundation and strategic marketing success. The upfront investment in technology provides payback in applying strategic initiatives that are data-based.
Integrating strategy, operations and technology maximizes the opportunities for organizations to experience customer relationship management success. Successful integration provides the opportunity to conduct analytics that maximize strategic development. Combining customer data such as demographics and survey data with transactional data detailing customer behavior and lifestyle data presents a comprehensive vantage point for understanding customer behavior.
And a thorough understanding of customer behavior is required to apply highly effective marketing and communication strategies.
Discovery through asking the right questions. To best leverage these resources, a series of short-term, long-term and ongoing analytic initiatives are required to assist strategy makers. Immediately after the delivery of a new customer-marketing database, there is a “discovery” process that seeks to uncover new truths and debunk old ones. Data analysts must begin crunching numbers to answer questions that may have previously gone unanswered.
The general questions are:
· How many unique customers do I have?
· What common characteristics or behaviors exist that serve as a basis for segmenting similar types of customers?
· How many “best” customers do I have and what makes them “best?”
· Do I have a customer retention problem?
· What are the immediate opportunities that will produce initial wins resulting from the new marketing database?
· What is the most effective way to communicate (channel and messaging) with each customer?
On an ongoing basis, two other key questions must be answered: What are the key customer metrics that provide insight into shifting customer behaviors? Is there a customer scorecard in place? A customer scorecard, which summarizes key customer metrics and establishes goals, measures change. New customer acquisition, customer retention, customer reactivation, and customer migration strategies are typical areas of focus?
In additional to measuring the day-to-day, two other areas also should be examined:
· What are the longer term strategic opportunities? This includes: How do I identify those most likely to become best customers early on? How do I identify those most likely to attrite before they defect? How do I communicate with these potential best customer and possible attritors? Which customers will I have the greatest success with migrating up to best customer status? What are the hidden opportunities for engaging customers in new or existing products and services?
· As a whole, how is each campaign performing? This includes: How do I maximize the ROI on the given campaigns by identifying the optimum audience and offers? Which prospects are most likely to be converted to customer status? How do I evaluate the most effective campaign strategies (audience, offers)? Which type of customer programs are most effective (welcome, reactivation, etc.)?
The analytics activities required to answer these questions may involve statistical modeling, multivariate analysis, data reporting, spreadsheet analysis and/or other techniques. The most popular tools come from the world of campaign management applications, automated modeling, and enterprise reporting and market research.
Regardless of the analytic technique or tools used to accomplish the job, all have one thing in common – the results enable marketing strategies within the organization that can greatly improve marketing effectiveness.
An opt-out world. Further justification for “intelligent” marketing is that if you carelessly market to your customers, they may opt out. If they do, you lose them for the foreseeable future. If you want them back, the cost will be significantly greater than treating them correctly in the first place.
The virtuous cycle. So, how does one do it? Here is the basic process to build and refine an integrated database solution:
Data integration. You know certain information about your consumers: what products or services they have used and when, if they have contacted you with a complaint or a compliment, or even survey information about their preferences. But, is this information linked together? Does your sales force know that their next call had problems recently? Does your e-marketing know who has not interacted for over six months? This information must be linked together to create more information through the relationships.
Built-in responsiveness. If you have millions or billions of point of sale transactions, you cannot quickly explore that much data. You must build in responsiveness by creating summary variables or treatment drivers. This includes recency, frequency, and monetary measures as well as any other common sense measure for your particular business.
Analytics and measurement. How have past campaigns performed in terms of response, revenue, and margin? Who are your most and least profitable segments? This information is learned via standard measurement reporting and more advanced analytical techniques such as data mining.
Marketing strategy. Given the findings from analytics and measurement, decisions must be made about what to do next. Attack the deepest profit pool? Reduce cost by not marketing to the lower qualified segments? There are many alternatives. The key is to do something based upon the findings.
Integration and execution. It’s time to make this process a cycle by integrating the strategy. Maybe it is determined that more data is needed? Third party or demographic information may increase your understanding. Go to the “Data Integration” step. Maybe you are ready to implement the target campaign(s) or add new variables to existing campaigns? Go to the “Built-in Responsiveness” step. Maybe you are refining/tweaking a campaign that is already running? Next is the “Analytics and Measurement” step.
As time goes on, the “bad” campaigns will be eliminated and the good ones will become more effective and efficient. “Bad” means that although a campaign may very well be profitable, it might still be discontinued. It’s just that your marketing capital is better invested in an even better campaign.
Marketing campaigns, not applications. On a final note, as you flow through the virtuous cycle, you must remember that the database is always at the core. Do not make the mistake of thinking about your campaigns and marketing goals in terms of the campaign management or e-marketing application. If you do, your thoughts will be limited by its technical limitations.
For example, maybe your campaign management application does not easily split off more than one offer per segment. You will begin to think in terms of only one offer per segment. If applications can be switched, you can pursue a “best-in-breed” application strategy.
By keeping the database constant and integrating the answers to key analytic questions, you can maximize the profitability of your customer relationships.
