Behold the power of predictions on Paid Social. 👍
Predicting who will become a high value customer, then using that as your lookalike to acquire others who’ll behave similarly — that is today’s flywheel for successful acquisition.
Breaking performance metrics by going where the conversion probability is hot and avoiding wasted spend — that’s an efficient retention strategy.
Seeking incrementality in your activity and using control-group theory to nudge customers along their purchase journey — we’ll do that too, with the optimal investment.
Make marketing positively influence outcomes, not game them.
This article introduces the notion of “Recipes” for marketers with a customer-obsession.
Take action cross-channel on your customer predictions, driving business value at scale with efficiency.
Suffer from “one-and-done”? We’ll take care of that.
Our prediction of when Churn Risk is high allows you to mitigate it when the time is optimal. In this article we cover stimulating second purchases from newly acquired customers and the problem that mounts when you fail to do so.
Next up, it’s a biggie — Customer Lifetime Value.
We plunge deeper into the Predictive Marketing Platform by looking at our CLV model and how this prediction can be used to nurture consumers when the timing is most appropriate for them.
Knowing how consumers are likely to behave in the future allows you to make calculated investments at the right time to drive short and long term results, as we illustrate in this chapter.
The second article in a series that unveils Programmai’s Predictive Marketing Platform.
We cover our Purchase Propensity model and how it can be used to predict a consumer’s likelihood to convert and the advertising required to drive maximum efficiency and incrementality.
The debut article in a series that unveils Programmai’s Predictive Marketing Platform, from start to finish.
We begin with why we’ve built this product, then discuss our unique use of first-party data in order to understand the complex interactions of customer features and values, so that we’re able to deeply learn about what’s worked historically to predict what’s likely to happen in the future.