High Performance Marketing:
Getting Started with Your Database Marketing Environment
As a database marketing consultant I sometimes feel that one of the most valuable things that I tell clients is also one of the least understood things that I tell clients. I emphasize it is essential to create a plan that reflects the vision of where the organization is to go. Once that is done, all the rest is just implementation.
Without underestimating the potential complexities of the plan for a comprehensive database marketing infrastructure, it is less daunting by asking essential questions before getting started:
- When does a company know it is time to begin when the plan does not tell them is where to start?
- How does an organization identify an appropriately sized portion of "this plan" that is most likely to create an initial success that will lead to positive organizational momentum?
While there are no pat answers or absolutes, there are three common starting points. Each in its own way is focused on helping the organization create knowledge and empower users to make better business decisions. The three common starting points and the most common reason(s) for starting with these areas are as follows:
- Developing analytics: the ability to focus on a specific business driver and create insight about that business driver in the shortest time possible that needs only to be understood by a concentrated number of individuals to affect broader organizational change.
- Creating a reporting environment: the ability to create a common understanding of business issues, progress or insights across a relatively broad range of individuals in a relatively short period of time
- Establishing a data warehouse or data mart: the need to create a pool of data from which insight can be generated or/and to eliminate the debilitating effects of the organization's various constituencies relying on differing core information sets to drive business conclusions.
Deciding which of these three starting points is right for you will likely be driven by budget, timeline and existing skills. Figure 1 outlines typical considerations associated with these choices.
Regardless of the path chosen, there are looming pitfalls that should be avoided. These include:
- Avoid trying to create a single, be-all segmentation schema for your customer base. Most experienced direct marketers learn that while the concept of a single segmentation schema is very seductive, the reality is that a series of customer segmentations around key business issues/products/channels/drivers tends to prove more valuable.
- Don't put too much faith in demographics. When dealing with the issue of customer acquisition, many direct marketers feel there is little else to rely on and, therefore, try to build a look-alike profile using demographic information. Demographics rarely provide sufficient differentiation to create effective marketing distinctions. As an alternative, try to focus on behavioral data or inferential material as the basis for your analytics - even if that means you are using less data.
- Focus on the broad business drivers for initial insight and then "peel back the onion." It is very common to see scarce analytics resources be diverted to response modeling on a very specific offer when broader issues like price sensitivity, profitability and lifetime value are left unaddressed.
For Reporting Environment:
- Don't over-engineer your reports. Get the basic information out to as broad an audience as possible and create a series of enhancements for more detailed reports or drill-down capability to be enabled.
- Try not to convert all existing reports because they exist. Not all existing reports are used or useful in their current form. Make your goal to understand what data is important and try to deliver information more efficiently than before, not the same as before.
- Remember to support both reporting and inquiry capabilities. A lot of systems focus only on delivering static views of information. This often leaves the organization's thought leaders hamstrung. Ultimately, without their support, your project may fail - even if you have the right long term vision and plan. Try to create a mix of static and dynamic environments from the initial phase and expand breadth over time instead of delivering breadth and expanding functionality over time
For Data Warehouse or Data Mart:
- Limit the data that you deliver in the initial phase. Attempting to design and develop an environment that possesses all the data that any business user will want is the surest recipe for an over-budget, over-timeline project. Try to focus the data delivered on the most commonly needed data arranged in the most common dimensions.
- Do not design your data marts with your statistician's needs in mind. I do not believe that I have ever met a predictive modeling resource who was satisfied with the detailed level of the information provided or the amount of transformations precalculated for him. This highly skilled audience should not be your focus. Empower these highly skilled individuals with access (including access to core systems or extracts) and focus the data warehouse or data mart efforts on the needs of more common business users who rely on IT for data access.
- Design with an eye toward your end-user toolsets. This often means abandoning some highly popularized data arrangement principles such as third normal form. Hopefully you will rest comfortably knowing that IT performance and design elegance do not count as much in the real world as end user access performance and satisfaction.
Remember, building up your database marketing infrastructure is a marathon, not a sprint. Therefore, start small and set expectations about incremental value creation and multiple-phase rollout and extensions of initial functionality with your organization's key constituencies. And the next time your consultant delivers words of advice about a plan, get the follow-on advice regarding where most others start their plan implementation.
For more information on related topics visit the following related portals...
Steve Schultz is a leading customer relationship management (CRM) practitioner who combines an understanding of information technology with extensive business process design experience and information-based decision-making methodologies. As executive VP of Client Services for Quaero (www.quaero.com), he helps clients identify, justify, implement and leverage leading edge analytical CRM environments to create or/and improve their database marketing capabilities. Schultz has worked with companies in the financial services, telecommunications, retail, publishing and hospitality industries. Contact him at firstname.lastname@example.org.
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