When it comes to data, direct marketers place a heavy burden on IT. We are always looking for more when it comes to understanding customers. We want more information about their needs. We want more information about their spending habits. We want more information about their preferred marketing devices. We want more information from more of our transaction systems. However, sometimes more is just more. In many ways, it is not surprising that the most overweight nation in the world also has the world's most voracious, and often nondiscriminating, data appetite.While it is not New Year's Day, it is time for overweight data hogs to consider a New Year's resolution to get a handle on their weight problem. Here are some disciplined eating habit analogies we can apply to our data appetites to help us become more discerning carnivores.
Let's begin by addressing the first step in making any change - admitting that we have a problem. No more excuses this time. We consciously wanted to gather all the data that we could to develop customer insights. We consciously exploited lower costs to help justify gathering more data. We are forever searching for more and are never satisfied with what we have. If that's not an addiction, I am not sure what is. These weight loss and eating principles to should streamline our overly curvaceous data figures.
Try Smaller Portions: While data storage costs have decreased dramatically over the years, the total cost of data acquisition can still be relatively large. This is often driven by the costs to extract, transform and load information from a data source into an accessible data repository. Before incurring such an expense, consider obtaining a one-time extract from that data source and performing your data mining tasks using that extract. If any of the new data contained in that one-time extract proves valuable, then proceed with the transformation and load processes to obtain a recurring extract that is populated into an accessible data repository.
Focus on Complex Carbohydrates: Not all data is created equal just as not all sources of energy for the human body are equal. The complex carbohydrate of data is the behavioral piece of information. It is this information that is often essential to generating the most leverageable customer insights. This information most commonly is sourced from transaction systems and customer interaction points. Most often, only transaction information is available in source systems. Key data about the interaction is often lost due to a dependence on company resources to input into arcane systems what occurred during the customer interaction that was nontransactional in nature. Gathering this information on a sample basis (see "Try Smaller Portions" above) may provide some customer insight and justification for deploying customer touchpoint systems that improve the quality of this data moving forward.
Plan Meals: How many times have you stood in front of that refrigerator or pantry and concluded that there is nothing to eat. When this occurs, often you try a multitude of solutions hoping that one satisfies the desire. This is true of organizations that do not plan their customer contact strategies. Organizations should challenge themselves to leverage data mining to develop customer segmentation and to understand segmentation migration patterns. Armed with this understanding, the organization can focus its contacts on items it has a data-driven basis to believe will be effective in accomplishing the overall goal. This, in turn, will serve to focus the data needs that are used to derive the contact strategy.
Cook without the Excess Fat: Be judicious about what you add to your key data repositories and what data you add to your purchased data requests from third-party vendors. Getting all 500 variables for all of your customers and prospects is not always such a deal when compared to purchasing and retaining the most predictive 20 to 30 variables. If information has not proved to be predictive in the first 200 models that were developed, what makes you think that it will suddenly become predictive with the 201st model? Additionally, challenge yourself about what the useful lifetime of historical information truly is. Don't keep it around just because you have it. Adding history and unused variables tends to complicate your data structures and makes them more difficult to use over time.
Remember to Work Out: In the same way that an effective diet is about more than the type and quantity of food that is consumed so, too, is changing traditional views about data consumption. One key to helping make the transition is to view direct marketing as more than just a direct response process. Look at ways to empower the customer touchpoint instead of just focusing on driving traffic to the customer touchpoint. This allows marketing to view its data needs and obligations in a streamlined fashion. When exercised, this new perspective helps marketing eliminate information that the customer touchpoint finds non-utilitarian or too complex to be meaningful during a real interaction.
The customer data diet is not a fad. It is a long-term approach to a healthier marketing organization. By actively applying some of these suggestions, you and your IT department will manage your ever-expanding data appetite more effectively.
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