How can you make better business decisions?
Imagine you work at a large bank. Your goal is to strategically choose which customer experience problems to solve first – where should you invest time and money to have the biggest impact?
One way to rank issues is by expected profit alone. But that discounts the customer experience.
Discounting the customer experience can be very risky. You reduce the lifetime value of a single customer. If they share their bad experiences with their friends or on social media, then the value of your brand suffers, and that can lead to much higher losses.
It’s time to consider customer journey analytics
Another way to view the prioritization of customer experience improvements is along these three dimensions:
- Customer pain
- Number of customers affected (size of the circle)
The size of a circle is determined by the number of people affected. The small circle in the upper left quadrant could be applicants for auto loans who are frustrated because they can’t complete the application using just the website. Loans are very profitable, so not making it easy for customers to apply is a big deal, but this may not impact very many people.
The larger cloud could be people who begin to open new accounts, but do not complete the process. The application may have been long or confusing, or they may have spent a long time in person or on the phone, so the pain level feels higher. But in the end, they decided the account wasn’t right for them. The average cost of acquiring new customers goes up since more abandoned interactions have taken place to get a win, and these non-customers now have negative experiences they might share with their friends. This is costly to the company in terms of lost revenue and reputation.
A simple view of cost per transaction might have resulted in efforts being focused on fixing the car loan application process. But, further examination using customer journey analytics helps you to come to a different conclusion. Given the number of people impacted, what customers experience, and the risk to brand and reputation, the data may suggest that focusing on fixing the account opening process is a better place to start.
Strategic thinking, and a quantitative analysis of customer data, often contribute actionable business metrics to the evaluation process, and make the resulting decisions stronger.
As one of our resident experts in data science, Bruce is passionate about customer journey analytics. He specializes in using statistics and machine learning to deliver actionable business insights that drive improvements in customer acquisition, churn, cross-sell, segmentation, loyalty, and revenue optimization.
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