Everyone’s talking about AI and machine learning when it comes to customer experience, customer retention, and profitability. While many companies are venturing into advanced analytics, it can be challenging to know how to capture ROI more quickly.

Here are three great starter projects using IBM SPSS Modeler that can deliver quick wins for common problems, using your existing data.

Project 1: Reduce customer churn for machine learning ROI

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Start with a project focused on customer churn because the data is relatively easy to understand, and you may uncover insights you can use immediately. Given customer acquisition cost (CAC), it’s less expensive to keep customers than acquire new ones. So, you may also see greater returns on your time investment here. ROI is also created by adding up the small values regained across thousands, or millions, of customers, and using the right predictive analytics tools.

  • Develop and test numerous predictive models based on a range of algorithms

    The key is to build numerous predictive models and evaluate them side by side using a test set of data. This iteration helps you fine tune the attrition model that works best for you. You can accomplish this with many different machine learning platforms. It is up to you if you prefer to code in Python or R, or would rather use a drag-and-drop interface with a powerful engine under the hood. Since you’ll be building and testing many attrition models, consider tools that help you to explore and rapidly iterate.

  • Use your model to score all your customers and identify the greatest attrition risks

    Next, you’ll take your customer data and score it through the model. If you’ve used properly prepared historical data to build the model, and your model has good explanatory power, the output will look toward the future by predicting which customers are likely to leave. With these insights, your marketers can engage at-risk customers with targeted incentives to stay.

Johnson Controls is using AI to reduce churn and identify over $100M a year of protectable revenue


Johnson Controls had no usable data sets, no data science team or data engineers.

How could they rapidly build a global data team with new AI/ML capabilities to improve business outcomes on a major scale?

Is your company struggling with how to implement predictive analytics?

Project 2: Increase cross-sell or up-sell

This project is a great next step as it leverages data structures and propensity modeling similar to the churn project. You’ll start with similar inputs, but instead of targeting churn, your dependent variable (or target) is whether or not a customer represents a successful cross-sell from a particular marketing campaign.

  • Identify attributes of customers from successful cross-sell campaigns in the past.
  • Develop a model to pin-point current customers with similar attributes.
  • Predict who you need to focus on using a confidence score developed from your model.

Cross-sell and up-sell modeling can have an immediate impact on your bottom line. You’ll get insights on how to tailor marketing communications for different channels and customer segments. By targeting the right message to the right customer at the right time, you have a better chance of improving marketing effectiveness, and increasing the value of existing customers.

Project 3: Achieve other business objectives with predictive analytics

Your next project might vary depending on the needs of the business, but you can also capture predictive analytics ROI in the following ways:

  • Optimize account collections. Create a predictive model to prioritize which customers are more likely to pay, enabling your company to focus collection efforts on more receptive customers to maximize revenue.
  • Maintain or increase customer loyalty. Loyalty is increasingly hard to achieve, so it can be valuable to understand which customers are most loyal, and how differing experiences and customer journeys led them there. Harness these insights to identify specifically what’s working well so you can replicate, and even innovate, ways to exceed expectations.
  • Predict the behavior of new customers. How will new customers perform in the first 90 days or the first six months? You don’t know their past, but you do have data on how you acquired them. You can build models that predict those most likely to purchase again, respond to additional offers, or be at risk of leaving.
  • Fraud detection. Reduce risk and build ROI by detecting fraud faster, quantifying how it happens, and then refining business processes to more effectively prevent fraud.

Modeling fun for everyone

The key to machine learning ROI is choosing the right projects. Your company doesn’t need a PhD in math or data science to use predictive analytics tools. In fact, the opposite is true; the best modelers are those with a solid understanding of your business objectives, who can also understand what to do with the insights you’ll gain.

You can start building predictive models and achieve real ROI quickly. If you do have a data science expert on staff, IBM SPSS Modeler allows for advanced customization and also interfaces with R, Python, and other predictive analytics platforms.

Machine learning and predictive modeling are fast becoming an inevitability for all industries. Companies that better understand how to harness the value will be poised to capture more ROI, more quickly — and it accumulates at scale. Don’t be left behind: Start now, and start simple.

How Beyond the Arc can help — Machine Learning & AI

Our team includes experts in data science, passionate about helping companies make better decisions and take action based on data. We specialize in using machine learning to deliver predictive analytics ROI.

Take advantage of a complete predictive analytics platform — as an authorized reseller and certified IBM Business Partner, Beyond the Arc can provide IBM SPSS Modeler software, training, and hands-on model building and data strategy consulting.

Interested in learning more about how to deploy AI/ML? Let’s start a conversation.