Once you have passed segmentation, you want to further refine your campaigns to become more relevant. This is when we get 1-to-1 campaigns. You use uplift modeling techniques to score which customers have a high probability to accept a certain product offer.
The above graph gives an overview of the lift chart. The idea is that you stop targeting the entire segment, and instead select people that have a score above a certain threshold. The Y axis gives an overview of the lift. The X axis gives an overview of the percentage of the database you’re targeting. The most interesting line is the blue line which shows the tradeoff between the lift and the percentage of the people you’re targeting. If we would target the first 20% of the database this would result in a lift of 2.2. This means that the response on the email will be 2.2 times higher in comparison to targeting the whole database. Moreover, marketing fatigue goes down as you just stopped annoying 80 % of your customer’s with an irrelevant offer.
With the score you have identified for example people that have a high likelihood to abandon their market basket.
Using a campaign management tool like SAS Marketing automation, you can start refining the message you want to send to the selected person. For example; the men between 35 and 45 will receive an email with a woman on the beach and the women between 35 and 45 will receive an email with a visual of a guy in the mountains. Off course this is a simple example. It gets really interesting when you start to develop campaigns for every segment you have, combine this with the propensity scores, and use A/B testing to check which content is generating the most responses.
Interested to read my next updates on the following steps, towards event-driven marketing, optimized marketing and omni-channel marketing? Stay tuned!