Reengage Audiences Likely To Churn by Showcasing Products Based on Past PurchasesUse Case
Food & Beverage
Furniture and Home Goods
Outdoor Equipment & Sports
Travel & Hospitality
Target segments at risk of churning and reengage them by using a custom time period feature to showcase products similar to ones they've liked or purchased in the past.
- "We miss having you around! We thought you might like these new collab sneakers."
Personalize with Bloomreach
Using the power of Bloomreach's predictive modeling. You have the ability to proactively identify customers who are likely to churn or you know, stop using your product and then win them over before they do so. So this is offered as one of Bloomer's prediction presets and to quickly walk you through how this is set up, it asks for a couple of questions. So number one, what is the future, the forward looking time frame in which this churn will be calculated for? So we could say, are they going to churn in the next one day, one month, one year? We'll go with one year for now and then the activity event. So what defines churn? Is it if somebody does not return to your website or is it if somebody does not return to place another purchase? So we'll go with purchase for now. Once I've selected this, it's going to show you this validation section on the right side to show you how many target customers there are versus eligible. So it's not gonna be a black box uh prediction you'll have an understanding of if the outputs will be sta statistically significant or not So this particular prediction determines whether a customer who bought something during a defined previous period is likely to purchase something in the defined future period as well. If a customer is unlikely to purchase in the defined future time frame, they're deemed as having a high probability of churning. And this is on a scale from 0 to 11 being that they are very likely to churn now for the customers who have a greater than 70% probability of churn. I wanna send them an email showing products similar to the ones they have previously purchased. So I can set up this recommendation model using one of our out of the box templates. So in the recommendations builder, I open this up and I can see that there is this preset for more like this. So I can come in here, select the product catalog and um select what attributes I want the products to be similar based on. So I could say I want this to be based on the category as well as the brand. I can select additional attributes. If I, if I'd like for number four, I can blacklist certain items. So I could say don't show items that were purchased by this customer in the last one year. Once that year is up, the products will be re reintroduced into the recommendation model. And then step number five, I want to take the power of the customer data platform and pair that with the catalog data to say. All right, if the catalog has a certain brand, I want that to match the customer's brand preference. So I could pull this in here. The catalogs brand equals the customer's brand preference. All right. Now, all this is set up. I want to plug this into our scenario builder here to create that customer journey. So I'll set this up to be triggered right now. I'll pull in this condition node to set up the audience from here. I'll search for that prediction that we just built out. So turn prediction here, I want this to be greater than 0.7. So as a reminder, it's a scale from 0 to 1. So if it's greater than 10.7, we know that it's greater than 70%. So label this as greater than 70% churn likelihood. And the last step here is to pull in that email to personalize with those product recommendations we just built out. So I'll open this up, build it off of a template that we have out of the box. I'll drag in the dynamic content element here to set up my recommendations block. I'll search for the dark mode recommendations. I'll select the recommendation module for more like this. So the the recommendations we just built out and I can go ahead and apply that there. So now the product recommendations are going to be generated differently for each individual customers based on the attributes we have set up here and based on the customer's brand preference. So this is another example of how you can use bloom reach to make your marketing efforts more efficient while also optimizing your email sends with dynamic audience building.