Direct marketing allows businesses to target specific customer demographics like age, gender, and marital status. It has expanded beyond mail to include digital channels like text, email, and online ads. RapidMiner's direct marketing wizard helps businesses invest in the highest converting marketing actions and reduce costs through improved targeting. It provides a table of top customers to target, shows which customer properties most impact response rates, and evaluates the predictive model to determine if more customer data is needed.

1. Direct
Marketing

2. Jeffrey van der Eijk
Founding Partner,
Responsible for data
driven innovations
Economics,
University of
Amsterdam
More than 10 years of
QlikView, Business
Intelligence, business
innovations experience
Jeffrey@Xomnia.com

3. Wessel Luijben
Lead Data Scientist
Rapid Miner Expert
Artificial Intelligence,
University of
Amsterdam
More than 10 years of
RapidMiner and Artificial
Intelligence experience
Wessel@Xomnia.com

4. Direct Marketing is easy to
implement, by selecting
columns such as age, sex or
marital status in order to reach
the target audience.

6. 21
3 4

8. Direct Marketing
What was once a postal delivery channel now expanded to include text
message, emails, online ads and other digital advertising. Business
must account for spending across these platforms and provide analytics
to support ROI metrics. The RapidMiner Direct Marketing Wizard allows
businesses to invest only in marketing actions with the highest
conversion rates and helps reduce costs by improving targeting.

23. Age more than 2 standard
deviations

27. Customer Selection
At the very top, we have the a table of customers that we recommend for addressing in your
marketing campaign. These are the customers for which the generated model achieves the highest
confidence values.
Influence Factors
The following two images help you understand your customers and how the previous table was
derived. The bar chart shows which of the properties of your customers have the biggest impact on
whether or not they respond to your campaign. The decision tree next to it displays how you can
identify potential responders. Follow a path from the root to a leaf and observe the criteria
identifying this customer group. The leaf itself indicates whether there are more responders or
more non-responders in that group.
Sample Evaluation
The predictive model generated by this template utilizes the customer data you provided. It is often
costly to obtain such data since evaluation campaigns have to be run, etc. The elements in the
bottom row help you to decide whether or not you have collected enough data. The "Learning
Curve" on the left visualizes how the prediction quality increases as more and more data samples
are added while generating the model. If this line is still rising at the far right or rather unstable, this
indicates that the model may still benefit from adding more data.