Do you know which customers are likely to churn? Which prospects are likely to convert?
Historical path analysis is a critical factor in such predictions. The problem is path analysis is hard. And even when companies have such capabilities, they often reside in the hands of a few specialists – or vendor consultants.
The business analysts, marketers and customer support professionals who could ultimately act on these predictive insights to improve customers’ and prospects’ journeys are effectively left out in the cold. Even the specialists are ultimately confined to the limits of their tools.
Ask anyone who has used a traditional business intelligence tool to understand customer paths. It requires significant time and patience to shoehorn this type of analysis into a tool that was not designed for it. To begin with, just manipulating the data to build an event table for a BI tool is a significantly high hurdle. And even at the end of such a project, organizations end up with a static, inflexible report on historical data that does little to help businesses prevent future churn or accelerate future conversions. (This is hardly a criticism of BI tools, as their benefits and value are well documented. I’m only pointing out that path analysis historically is not one of their strong suits.)
Other advanced approaches leverage statistical tools like R and programming languages like Python. They may incorporate sophisticated analysis techniques like Naïve Bayes text classification and Support Vector Machine (SVM) modeling. But, at the end of the day, these are not tools or techniques for businesspeople.
And at the end of the day, what matters is providing your business teams the opportunity to influence the customer experience in a manner that is positive for your business.
The solution is to bring path analysis – including predictive path analysis – to the business. For such a solution to succeed, it must be:
The new Predictive Paths capability in the Teradata Path Analysis Guided Analytics Interface makes this interface a solution to consider.
Using the interface, marketers and analysts use a simple form to specify an event of interest – a churn event or conversion event, for example – and whether they want to see paths to or from that event. The interface returns results in the forms of several visualizations, including tree, sigma, Sankey and sunburst diagrams, as well as a traditional bar chart.
Within the tree diagram, users can select partial paths to their event of interest and create a list of users who have completed that partial path but not yet completed the final event. For example, if you are looking at an online banking data set and see that a path of “fee complaint, to fee reversal, to funds transfer” precedes a large number of churn events, in three clicks you can generate a list of customers who have completed the path “fee complaint, to fee reversal, to funds transfer” but not yet churned. Thus, you have just used Predictive Paths to identify potential churners without writing a line of code.
This video demo shows how marketers and business analysts can predict next steps for customers with the Path Analysis Guided Analytics Interface.
Watch this short video to see how Predictive Paths works within the Path Analysis interface. If you’re interested in bringing these capabilities to your business teams, please contact your Teradata account executive today.
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