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Tag: Defection patterns

‘At-Risk’ Customers: Do You Have a System for Identifying, Stabilizing Them?

About a decade ago, my consulting colleague Jill Griffin and I identified seven distinct customer life stages for our 2001 book, “Customer WinBack.” These life stages, or components of the life cycle, could be applied to customers of any type, and any size of enterprise. We considered the most serious, and potentially impactful, of these to be customers to be those “at risk.” These customers have a proven high probability for defection. A decade later, that perspective hasn’t changed. Because the average company loses between 20 percent and 40 percent of its customers a year, isolating drivers of risk and stabilization (i.e. repairing and rebuilding the relationship) are priorities for any enterprise.

About a decade ago, my consulting colleague Jill Griffin and I identified seven distinct customer life stages for our 2001 book, “Customer WinBack.” These life stages, or components of the life cycle, could be applied to customers of any type, and any size of enterprise. We considered the most serious, and potentially impactful, of these to be customers to be those “at risk.” These customers have a proven high probability for defection. A decade later, that perspective hasn’t changed. Because the average company loses between 20 percent and 40 percent of its customers a year, isolating drivers of risk and stabilization (i.e. repairing and rebuilding the relationship) are priorities for any enterprise.

We identified multiple causes of value, or trust, breakdown leading to the stage, or state of risk. It could be a negative transactional outcome with the supplier of the product or service, or a series of them (at least as perceived by the customer), or any of several factors that can impact favorability levels, with the result that the “secure” customer will begin to exhibit specific behaviors and actions that indicate a weakening of the relationship. The sooner intervention begins with an at-risk customer, the easier and less expensive it is to fix the problem; so, it is clear that the best plan is to have a system for identifying these customers. That said, many companies have no such system in place.

Here is what we recommended for a first-alert information system that receives input from a variety of “nerve centers” throughout the organization, and also outside. Some of these are reactive, providing insights after the fact. Some are more proactive, helping anticipate trouble spots and problems looming on the horizon that may adversely affect customer loyalty. Some are Big Data-related. Some are more individualized. Some are both. These include:

1. Purchase and use data—declines in purchase or use activity represent proof that customers are spending elsewhere, and are a defection risk.

2. Listening posts—relatively few customers, whether B-to-B or B-to-C, actually complain. But internal (frontline employees) and external (social media, ratings, etc.) can provide useful information.

3. Customer transactional and relationship research—can keep track of perceived performance with customers overall, as well as with specific customer groups and individual customers.

4. Accounts receivable—“short payment” and slow payment are tripwires to customer discontent and risk.

5. Customer network—advisory boards and online communities can uncover areas of concern.

6. Churn modeling—advances in data mining software have enabled more companies to apply sophisticated churn models, evaluating the interplay of multiple purchase and behavior variables to uncover defection patterns that might otherwise go undetected.

7. Account event milestones—firms like banks, credit card and insurance companies have discovered that certain milestones—such as renewal dates—can trigger risk and relationship reconsideration.

Perhaps the most leveraging element of both risk and stabilization in customer experience is interaction with employees. Beyond employee engagement and its somewhat tangential influence on customer behavior, employees have the capability to be “ambassadors of value,” as well as conveyors of insight. If they are proactive, involved and committed on three levels—to the company, to the value proposition of the company’s products and services, and to the customer—then risk can be mitigated and the solid relationship, leading to customer loyalty behavior, can be regained.

I’m going to be explaining more about employee ambassadorship on October 8, during a webinar co-facilitated by me and Colin Shaw, CEO of Beyond Philosophy.

Author Michael LowensteinPosted on September 23, 2013Categories CRM, Data, StrategyTags Account event milestones, Average company, Behavior, Company, Consulting colleague, Customer, Customer behavior, Customer loyalty, Customer loyalty behavior, Defection, Defection patterns, Defection risk, Distinct customer life, Employee, Enterprise, First-alert information system, Information, Insurance companies, Isolating drivers, Life, Life cycle, Life stages, Nerve centers, Probability, Relationship, Relationship reconsideration, Risk, Sophisticated churn modelsLeave a comment on ‘At-Risk’ Customers: Do You Have a System for Identifying, Stabilizing Them?
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