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Marketing Analytics

RFM and data mining have been used for years by Forune 1000 companies to gain significant advantage over their
competition.  Data mining uses a number of different algorithms (e.g., neural networks, logistic regression, decision trees,
K- Means, etc) to develop response models and customer segmentations that support targeted marketing efforts and
messaging.

Response Modeling

Response models predict which customers will behave in a particular way in the near future.  For instance:

  • Who is most likely to respond to my direct mail campaign?
  • Who is most likely to attrite/churn?

The notion is, "why spend marketing budget on customers that won't respond?"..."why spend money trying to retain
customers that are happy and don't plan to cancel their relationship with us?"  By only spending budget on those you
should be targeting, you have more funds left over to spend on additional campaigns or contribute to higher profits.

The graph below shows the result of using a predictive model designed to estimate the probabiliity of responding to a
campaign for each of our customers.  Customers are then ranked from highest to lowest along the horizontal axis based
on their probability to respond.  The vertical axis measures the number of responses expected.  In this case, we see we
can get 94% of the expected responses by contacting only the top 40% of our customers.

Gains Curve

If a direct mail piece costs a total of $3.00 and our customer list is 75,000, we are saving $135,000
(.6 x 75,000 x $3.00) by only targeting the 40% most likely to respond.

Customer Segmentations

The notion behind customer segmentation is to divide your customer list into separate groups based on their profiles
(e.g., purchase history, income level, industry, etc).  Everyone in a given segment is similar to each other, while each
overall segment is different from other segments.  By gaining a better overall understanding of customers, then
grouping them into categories, companies are able to better optimize marketing programs and allocate marketing dollars
more effectively.  Let’s look at the below example.

The below chart shows how customer segmentation, based on “clustering” can help us more effectively commincate to
our customers.  This example is based on data about customers and the drugs they purchase along with demographic
data we have obtained.

Customer Segmentation Consulting

This shows that drugX (green) is really only purchased by customers in cluster 2, 3, 4 and 10.  DrugB is only purchased by customers in clusters 1, 5, 8 and 9.  And drug Y is purchased by everyone.  Can you imagine how much more targeted this company's marketing efforts can be  based on this information?  For example, you could create a marketing message for clusters 2, 3 and 10 that spoke to the benefits of drugs X and Y.  This message would be considered as being very relevant to these customer while a message about drugC would not.  Such targeted messaging leads to increased response rates, sales and customer satisfaction.

A large statistical software company called SAS has a number of case studies on response modeling and customer segmentation that include the ROI from these types of studies.  Click here to go to their site to see real world examples across all industries.

The challenge in using data mining is that you'll need to spend tens of thousands of dollars on the software and hire a statistician.  Small businesses cannot afford this type of investment, even with the expected positive return.  Your real alternative is to hire a consultant that has the experience and software to perform these types of analyses for you. Please call or email us today to learn more about Marketing Analyltics and Stafford SBSG’s unique capabilities to kick-start your marketing capabilities today!

RFM is another analytical technique used by marketers. It is easier and cheaper to utilize than data mining and has yielded positive ROI for companies for over 40 years. Click here to learn more.
 


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