Everyone’s talking about Big Data when it comes to customer experience, retention, and profitability. If you want to begin using advanced analytics but don’t know where to start to realize value, here’s a quick guide to get you in the door. 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
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. Typically 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.
- Test case churn: The key is to build numerous models and evaluate them side by side using a test set of data, which helps you fine tune the model that works best for you.
- Implement: Next, you’ll take your customer data and run it through the model. While you’ve used historical data to build the model, the output looks 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.
In our next post, we’ll show you a quick walk-through of how Modeler, one of the premier predictive analytics tools, does its magic using a churn model.
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 is whether or not a customer was successfully cross-sold by a particular marketing campaign.
- Identify attributes of customers who were successfully cross-sold 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 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
Your next predictive analytics project might vary depending on the needs of the business, but usually includes one of the following:
- Optimize 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
Your company doesn’t need a PhD in math 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 in a matter of weeks. If you do have a data science expert on staff, Modeler allows for advanced customization and also interfaces with R, Python, and other predictive analytics tools analysts love.
As Big Data offers many valuable possibilities, predictive analytics is fast becoming an inevitability for all industries. The early adopters will be poised to surpass the competition because the ROI is quick and accumulates at scale. Companies that don’t want to be left behind should start now, and start simple.