How many customers have you lost based on a bad experience? More than 80 percent of respondents have stopped working with a company after a bad experience, according to a recent international survey by Ovum.
The Aster Customer Satisfaction Index (CSI) and Customer Paths analytic solutions are great tools for getting insight into which experiences may be tied to these customer defections. But perhaps more important, these solutions can help you identify customers on those paths so you can redirect them toward positive outcomes.
Let’s consider how both of these solutions could help you prevent negative customer experiences through two simple examples.
When we implement the CSI solution, there is a discovery phase early on. In this phase, we explore your data to identify events, or series of events, that appear to be positively or negatively related to satisfaction. You can think of the latter as “bad experiences.” For example, maybe we identify a series where 1) a customer searches your web FAQ about a billing issue, then 2) calls customer support and presses “6” to speak to a representative about their bill, then 3) gets bounced from one support rep to a second support rep. Through our analysis, we may determine that this sequence is strongly correlated to dissatisfaction.
With the CSI Guided Analytics Interface, any business user can create a rule around this sequence of events. And through AppCenter, a user can generate a report so that every time a CSI analysis is run, a list of all customers who have passed through this sequence in the past 24 hours is pushed into your CRM for your senior customer support representatives to follow up with discounts and other offers.
Building a sequence rule in the CSI Guided Analytics Interface.
Let’s build on this analysis with the Customer Paths Analytic Solution. With the Path Analysis Guided Analytics Interface (a component of the solution), a business user can specify a path and identify customers on that path. And with AppCenter, you can leverage path analytics alongside Naïve Bayes text classification and Support Vector Machine (SVM) modeling to predict which customers are actually likely to churn. You can use these analyses to further target your efforts to redirect customers toward positive outcomes.
Identifying customers on a path in the Path Analysis Guided Analytics Interface.
Predicting customer-level churn with multiple analytic techniques in AppCenter and Tableau.
Hopefully, these simple examples help you understand the power of the CSI and Customer Paths solutions in addressing and preventing bad experiences, so you can prevent your most profitable customers from churning.
By the way, we recently launched an offering to implement the Path Analysis Guided Analytics Interface for $35,000. This is a great way to drive immediate value from your multi-channel path analytics and identify customers who are on paths to bad experiences. If you’re interested in learning more, send me an email at firstname.lastname@example.org.
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