The Power To Predict Can Give B2B Marketers An "Unfair Advantage"

Laura Ramos

When Sir Francis Bacon, coined the aphorism "Knowledge is power", he didn’t foresee a 21st century where technology and data science would more automatically and immediately turn knowledge into insight. Today, the phrase “Prediction is Power” may be more appropriate.

My colleague Mike Gualtieri works with applications developers and has been looking at the power that predictive analytics can infuse into a myriad of business applications these developers may encounter.

What I found interesting about his recent research is that he chose a marketing example (Figure 1, subscription required) to demonstrate this power.

At Forrester, we define predictive analytics as:

Techniques, tools, and technologies that use data to find models — models that can anticipate outcomes with a significant probability of accuracy.

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Chinese Banks Benefit From Customer Analytics

Gene Cao

China has experienced a fast expansion of credit card usage in the past 10 years, accumulating more than 390 million credit cards by the end of 2013, around 16 times more than 2003. But Chinese banks suffered from low activation rates of credit cards. In my recent report, I found China CITIC Bank (CNCB) faced a similar challenge; their 21 million credit cards had less than 20% activation before 2012.

In 2012, to increase the number of active credit card users, CNCB decided to revamp its customer analytics capabilities to better understand customer profiles and manage customer relationships. As a first step, the bank used SAS Enterprise Miner to deeply analyze both active and inactive cardholders and their usage scenarios and to measure the effectiveness of its credit card campaigns and programs through cardholder analysis for customer segmentation and marketing program effectiveness analysis including:

  • Cardholder analysis for customer segmentation.CNCB first collected and classified basic information about its cardholders from past marketing campaigns and transactional data. It defined four basic types of cardholders: inactive users, moderate users, convenience users, and heavy users. The bank spent two months to build data marts from the summarized data. It decided to focus on two groups of inactive cardholders: those who could be swayed by marketing campaigns and those who were heavy users of other banks’ cards but not CNCB’s through the analytics engine.
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