Churn scores in Poplar
Knowing which of your customers is most likely to churn gives you the opportunity to act before they make that critical decision.
The most effective way to predict likelihood to churn is with machine learning. With machine learning, you can constantly keep your list of customers up-to-date with churn scores based on the historical data of similar shoppers, and plug your likely-to-churn customers right back into your stack, no PhD required. With churn scores in your stack, you're primed to jump in, offer a discount, a helping hand, or some other offer to keep them on board.
Faraday makes predicting churn scores for your customers intuitive & easy, and delivering them to any channel in your stack a breeze.
With churn score predictions in Poplar, you’ll give your team valuable insight to intervene before the customer makes that critical decision, exactly where and when they need it.
Follow the steps below to get your churn scores predictions into your Poplar account.
In this guide, we'll show you how to:
- Organize your customer data into cohorts
- Describe predictive models for churn scores with outcomes
- Deploy churn scores predictions to Poplar using Pipelines
Make sure you have a Faraday account (signup is free!) and that it's not in test mode.
You'll need the following cohorts available in your Faraday account:
- A cohort representing your customers — or create one first
- A cohort representing your churned customers — or create one first
Now you'll create the prediction objective(s) necessary to complete this use case with Faraday.
Outcomes use machine learning to predict whether or not people will exhibit a certain behavior.
Likelihood to churn
Let's make an outcome for likelihood to churn.
- In the navigation sidebar, choose Outcomes.
- Click the New outcome button.
- Fill out the form:
- Click the Save outcome button.
Faraday will do some magic in the background, so you can proceed with the rest of the instructions. When your outcome is done building, you'll get an email, and you can review your outcome.
Now you'll configure the pipeline that deploys your predictions to poplar.
Create your pipeline
- In the navigation sidebar, choose Pipelines.
- Click the New Pipeline button.
- Fill out the form:
- Click the Save pipeline button.
Your pipeline will start building in the background. You can proceed immediately with the next set of instructions.
Deploy your pipeline
- In the Deployment area, find the CSV module and click Add.
- Fill out the popup:
- Click the Finish button.
- Click the Test deployment button and confirm the results meet your expectations. Faraday will finish building your pipeline in the background. When it's done, you'll get an email—return to the pipeline and click the Enable pipeline button to activate it.
With your pipeline deployed, it's time to plug your churn scores into Poplar. Follow the steps below to create an audience in Poplar.
Creating a new audience in Poplar
- In your Faraday pipeline, click the Download CSV button under the deployment to download your churn scores as a CSV.
- Navigate to Poplar's Audiences dashboard, then click new audience in the upper right.
- Give your audience an appropriate name and optional description, then click create audience.
- In the view for your new audience, click upload CSV in the upper right.
- Select your Faraday deployment CSV in the file picker, and the page will load briefly.
- Map your CSV fields to Poplar fields. Most fields are likely to map automatically.
- As we already know that this CSV includes only the highest churn scores, meaning only customers most likely to churn, we can safely select ignore this column for the two final columns indicating predictive scores.
- When finished, click continue to start the upload process.
- Once the upload is finished, you'll be presented with a confirmation screen indicating the total records imported. Click finish importing to finalize the import. You'll receieve an email from Poplar when your audience is ready, after which you can use it to launch campaigns to target only the best fits.
🔒 It's a best practice to permanently delete any file that contains personally identifiable information (PII) after use. Any deployment from Faraday that is unhashed contains PII, and should be deleted after uploading it to your destination for security purposes.