Mainstreet - individuals with a medium likelihood to engage¶
Increase engagement by more personalized email content such as newsletter segments or personalized product recommendations.
The Likelihood to Engage model finds correlation between the past email behavior and the near-future email engagement. In other words, the model predicts the probability of the user opening an email in the near future, based on the past email interaction behavior.
Based on the user behavior, CDP defines the likelihood to engage segments as Newbie, Opt-Out, Forgotten, Phantom, Enthusiast, Mainstreet, and Sleepy. These segments help marketers create the most relevant strategies for each group.
Increase engagement by more personalized email content such as newsletter segments or personalized product recommendations.
Re-engage the high value customers through other channels such as Facebook, Instagram, direct mail, or online.
Understand the reasons for not contacting such users.
The Likelihood to Engage model predicts the engagement behavior of individuals based on past email interactions. This model scores all individuals in the customer pool.
For each contact, the model calculates:
CDP trains and tests the model on the historical data where the outcome is known. Post training, the model is deployed to predict the future email engagement.
The output of the model is the likelihood to engage for each customer. This probability is segmented in 10 equal-sized deciles, 1 being the most likely decile to yield engagement:
Decile | Description | Email segment |
---|---|---|
1 | High | Enthusiast |
2, 3, 4, 5, 6, 7, 8 | Medium | Mainstreet |
9, 10 | Low | Sleepy |
NA | Does not engage | Phantom |
NA | Does not qualify | Newbies, Forgotten, Opt-outs |
CDP displays the output from the Likelihood to Engage model in:
To learn more about how the Likelihood to Engage model can enhance your marketing workflow, contact us.