Churn Prevention

Know which customers are likely to churn and why. Use all available information about your customers—not just the obvious signals—to determine who’s likely to churn so you can take preventative action and avoid attrition.

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Early Action to Reduce Long-Term Costs

Customer churn is a killer for any business. It keeps acquisition costs high, complicates long-term planning, and often means that the expense of signing a customer was higher than their investment in your product.

Even if you turn a profit before a customer leaves, you lose additional cross-sell, upsell, and referral revenue. And for every customer who complains, provides critical feedback, or warns that they’re planning to leave before doing it, there are several who close their accounts or stop buying without notice.

Keeping Customers Costs Less Than Finding New Ones

Warning signs can be incredibly difficult to detect when manually sorting through a sea of customer records. Idell Trade can change that—and help you take action.

Find red flags: Analyze customer data like product usage, purchase history, and other relevant risk factors that aren’t as obvious as complaints.
Take preventative action: Reach out to at-risk customers to hear about their experience, gather feedback, and turn problems into solutions.
Avoid attrition and improve ROI: Maximize the lifetime value of each customer by keeping churn at bay and ensuring that your customer acquisition budget is well spent.

"The goal of the use case was to analyze POS data and detect fraud. We wanted an easy way to load the data from the Server and write calculated identifiers back. The analysis of the data was easy and fast thanks to the very successfully create interface in Idell Trade. Automodell made it quick and easy to extend analyze to other areas of applications. The exellent results speak for themselves, so we have been very satisfied with our cooperation with Idell Trade for a long time. The support is remarkable and leaves nothing to be desired.

Overall, I had a positive experience with Idell Trade. It is a unified platform where I can judge my data overall and we can easily decide where we need improvements and what is working well. Due to its machine learning, I am confident about my decision that keeps my brand standing out in a competitive world. I found features of Idell Trade to be extremely useful from data preparation to data analysis as an experienced user of data mining projects utilizing open programming languages, developing predictive models, and placing them in a visually appealing presentation.

Idell Trade Studio is a awesome visual workflow designer. The way they present visually is so unique. It helps in speeding and automating the creation of visual models. With the help of it we are able to create point + click connection to database enterprises. It helps in creating models in only 5 clicks by automated machine learning due to which we were able to do our work fast.

Idell Trade Studio helps us evaluate and communicate our concepts in a simple and understandable way, and also expedites our data-driven transformations. As an outcome of all this, our information collection, model confirmation, data augmentation, and visualization methods have all changed considerably. This has completely changed the approach we handle our database schemas, how we achieve success and make long-term plans, and how we make decisions. We've deployed Studio throughout our whole business since it's applicable for each and every use case, sparing us a tremendous quantity of effort and resources.

How to Start Preventing Churn

Use our churn modeling template to get started quickly in the Idell Trade platform. This template lets you optimize and evaluate a decision tree model.

Step 1

Load a customer dataset with all available information about customers, not just the obvious warning signs. Examples include: age, technology used, length of time a customer, average bill, number of support calls, and whether they’ve left in the past.

Step 2

Edit, transform, learn (ETL) and prepare data. Mark the target label column (i.e. the churn indicator) and convert the numerical churn column to binary.

Step 3

Model validation is key! This cross-validation splits the dataset for training and then for independent testing. This splitting is done several times to get a better performance estimate.

See Churn Prevention in Action

Know which customers are likely to churn and why, and turn prediction into prevention. Request a free AI Assessment to determine the feasibility and business impact of the your high-priority use cases.

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