The Next Step in Predictive Analytics: Explainable Predictive Customer Insights

You may have figured out how to uncover specific data points that are relevant to driving your department KPIs and/or business.  You may have wielded those into forecasts, projections, or even data-backed predictions to help carry your business forward, allowing you to prioritize your financial and human resources to execute on that forward-looking and data backed strategy.  You may even be using a predictive capability that is nested in another tool to take your data investment and forecasts one step further.  Or you could be the cherry on top, and are currently using a data science backed predictive analytics tool (that sounds difficult, but we translate what that actually means below) helping to level-up your strategy and take more of the market share.  

If that’s the case: Excellent, that’s not easy.? 

If you are working your way there, kudos, and continue down that path.?

If you’re struggling to figure out how to accurately project things like customer churn, upsells, customer health, conversions, likelihood to purchase, and so many more business cases, then rest assured, you’re not alone. ?‍?

No matter the group you find yourself in, there is one piece of the puzzle that you’re likely missing and can be used by anyone who needs to track KPIs.  Explainability.  

WHY are customers, accounts, consumers, prospects, etc., doing what they do; what is driving that behavior?!  Can you confidently answer that?  And gut feeling does not count here.  If you can’t articulately answer that then you’re among the majority; however, the tide is changing.  Today, these answers can be obtained with no code tools, without the need of data scientists, analysts, or expensive outsourced developers – it’s now available to business users.

Let’s review how you can figure out what data is influencing specific customer actions.  With the right  predictive customer insight software you simply:

1. Upload your historical data: Use a pre-built connector or easily upload a .CSV that contains the different data points of each customer.
   
2. Select what customer insight you’d like to predict for this project: For this step, let’s say we’re wondering which customers’ health scores will be at risk this quarter.  You would select the customer health column in your file that contains your historical data.
   
3. Upload a .CSV or connect to the data that you’d like to make predictions on: Which customers would you like to receive customer health predictions for.
   
4. Run the project and evaluate your results: See which customers are most at risk for churning at their renewal and evaluate the data points that are most impacting their predicted churn.

The next obvious question is; if I know what data is affecting customer outcomes, what the heck do I do with it?  Based upon the prediction or project that you run, there are three things you can do when evaluating the data that is most impacting your predictions or current customer outcomes:

1. Double down: If the data point(s) is positively impacting your customer outcome, you might want to put more budget or resources there to continue or hopefully grow your desired outcome.

Example: The software predicted a certain subset of customers was likely to upgrade their subscription, and the data point that most influenced that prediction was the customer receiving a monthly product roadmap email.

With that information, you may want to increase your customer marketing budget so that you can take that email one step further and include video or offer a more robust communication that could lead to increased sales volume or  multi-product upsells.

2. Pump the breaks and evaluate: If the data point(s) is negatively impacting your customer outcome, then you need to uncover what’s exactly going on and put measures in place to update the current process, so it’s more helpful for your customers.

Example (from above): The customers who are predicted to have a lower customer health score, those who typically churn at renewal, were most impacted by whether or not they received an onboarding specialist.

In this case, you may want to update your process so that every new customer must be assigned an onboarding specialist so that they understand the full value and functionality of your product and are set up for success.

3. Do nothing…for now: Run a few different projects, make a few different predictions, and evaluate the data points influencing the state of your business.  Gather the different predictions and data points and create a prioritized strategic plan to generate more revenue, mitigate risk, know where to add resources, and how to make the customer experience even better for your customers.  Most companies like to run multiple predictions and truly understand what is most impacting the data and work to prioritize and make updates to company processes.

Bottom line is, we’re all human, and we can calculate only so much in a certain amount of time without the assistance of AI.  As a leader in business, it’s reassuring to take the gut and guess feeling out of business decisions and make them based on both the situation AND the supporting data.  Predictive customer insight software can not only help you forecast and predict what you should do next, but it tells you WHY and both parts are equally important to successfully drive business today.  Business continues to change, and customers’ needs and expectations consistently evolve, so companies need to rise to meet the demands in order to drive the success that they’ve set out to do.  And what better success is there than creating effective and efficient business processes and creating the best customer experience for your customers.  Happy employees and happy customers are what will drive your business to success.

Discover the impact of explainability; schedule your demo today.