Data Intelligence, Business Analytics
Inductive Decision Tree (IDT) or Logistic Regression analysis may be used to identify individuals or households who should NOT be the target of direct marketing campaigns, thus increasing response rates, lowering cost-per-click e-mail costs and increasing the return on investment (ROI) per responder. Conducting this analysis requires first collecting data on customers who do not wish to be directly marketed to. In Canada, a person may request that he or she be exempt from direct marketing campaigns in one or more of the following ways:
- 'opting out' of an e-mail marketing campaign
- returning a hand delivered letter marked 'Do Not Mail' or 'Return to Sender' via Canada Post
- contacting a customer call centre representative and asking to be exempt from future marketing campaigns
- registering his or her name on the Government of Canada's 'National Do Not Call' list
A data analyst may use one or more of the above data sources to create a boolean 'Contact?' field for each customer record--uniquely identified by an e-mail address, phone number or mailing address. FSA (first three digits of a Canadian postal code) may be used as a unique identifier when e-mail, phone number or full mailing address is not present. In this case, the number of 'Contact? = 'Yes' instances may be calculated for each FSA, and a calculated index variable 'Contact?_FSA_Index' can be the target variable profiled.
At this point, an important decision must be made: will the analysis try to answer the general question, 'Who does not want to be directly marketed to by us via any channel?' or will three different analyses be conducted for e-mail, regular mail and telemarketing respectively?
For our purposes, let's focus on identifying individuals who have opted out of e-mail marketing campaigns and compare them to a sample who have not. A stratified sampling technique will ensure an adequate number of 'Yes' and 'No' values for the 'Contact?' variable if e-mail opt-out customers are a significantly smaller segment.
Behavioural variables such as websites visited, ads clicked on, and time spent on the web may be used to profile the difference between 'Yes' and 'No' Contact? groups. Depending on the e-mail provider, detailed information for each accountholder may also be available: such as gender, age and postal code.
At the very least, FSA should be a standard field of information tagged onto every e-mail customer's record before he or she is marketed to: it should be kept populated by e-mail marketers. Demographic, attitudinal and psychographic data from Generation5, Environics Analytics and MapInfo may be appended onto customer records which contain a valid FSA.
An Inductive Decision Tree CHAID Algorithm can then help profile the significant difference (if any) between customers who have opted out of e-mail marketing campaigns and those who have not. Segments containing e-mail accountholders where the probability of an e-mail opt out is, for example, .8 or greater should be removed from future e-mail marketing lists.
If the model holds true, future e-mail marketing campaigns will generate a higher response rate.