Data Mining in the 21st Century
Kick old analytics habits to achieve better acquisition campaign performance
August 2006 By Peter Harvey
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With all the advancements in data mining, why do we keep hearing that direct marketers are experiencing declining results? Direct marketing service companies—specifically, the list industry—are at a turning point: Deliver the performance boosts that clients are demanding by leveraging both the data and data mining tools, beginning with the fundamentals, or someone else will.
A Different Mindset
While many direct marketers who deploy historical response list marketing tactics are experiencing diminishing performance, those firms leveraging advanced data and data mining tools are meeting with continuous improvement in results.
How can marketers get out of the former group and into the latter? First, they and their suppliers need to move from broad terms to the specifics of data analytics, while aligning practices and results. Over-use of the term data mining—like that of the term CRM—has caused it to mean all things to all people. Hence, it’s referred to as a multibillion dollar opportunity filled with experiences of non-quantified failures, or declining results. For this discussion, let’s put data mining in the context of exploration to boost performance, or more specifically, to increase marketing ROI.
In addition, when you hear direct marketers are experiencing declining results, this statement must be put into context. Within the direct marketing industry, list rental revenues have climbed into the billions of dollars, which in turn has driven hundreds of billions of dollars in product sales. This is where the finger is being pointed as the reason for such dismal results. Consider that the majority of list revenues follow the supply chain model from list owner to list manager, then to the list broker, on to the product seller and finally to the buyer. Minimal data mining occurs in this supply chain model.
As a result, direct marketers continue to challenge the list industry to find higher volumes of better-performing lists. The reaction to this challenge has been to add more lists—which are assumed to have certain attributes—into the merge process. This is where the finger accurately can be pointed as the cause for declining results. But the technology of merging many small response lists at 50 percent to 80 percent de-dupe rates, (containing prospects only reported to have the attributes of a potential buyer) is not data mining.
Data mining in acquisition marketing is the identification of attributes across an experiential set of solicitations, responders and converters that discriminate between those prospects who have exhibited the potential to respond or convert and those who have not. This type of data mining, done correctly, yields predictable results.
A Different Mindset
While many direct marketers who deploy historical response list marketing tactics are experiencing diminishing performance, those firms leveraging advanced data and data mining tools are meeting with continuous improvement in results.
How can marketers get out of the former group and into the latter? First, they and their suppliers need to move from broad terms to the specifics of data analytics, while aligning practices and results. Over-use of the term data mining—like that of the term CRM—has caused it to mean all things to all people. Hence, it’s referred to as a multibillion dollar opportunity filled with experiences of non-quantified failures, or declining results. For this discussion, let’s put data mining in the context of exploration to boost performance, or more specifically, to increase marketing ROI.
In addition, when you hear direct marketers are experiencing declining results, this statement must be put into context. Within the direct marketing industry, list rental revenues have climbed into the billions of dollars, which in turn has driven hundreds of billions of dollars in product sales. This is where the finger is being pointed as the reason for such dismal results. Consider that the majority of list revenues follow the supply chain model from list owner to list manager, then to the list broker, on to the product seller and finally to the buyer. Minimal data mining occurs in this supply chain model.
As a result, direct marketers continue to challenge the list industry to find higher volumes of better-performing lists. The reaction to this challenge has been to add more lists—which are assumed to have certain attributes—into the merge process. This is where the finger accurately can be pointed as the cause for declining results. But the technology of merging many small response lists at 50 percent to 80 percent de-dupe rates, (containing prospects only reported to have the attributes of a potential buyer) is not data mining.
Data mining in acquisition marketing is the identification of attributes across an experiential set of solicitations, responders and converters that discriminate between those prospects who have exhibited the potential to respond or convert and those who have not. This type of data mining, done correctly, yields predictable results.
Companies Mentioned:
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