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


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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, or even basic risk-pulse process, 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, and can be uncovered through basic analysis. Some are more individualized and require greater sophistication and sleuthing capabilities. 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 (front line employees) and external (social media, ratings, etc.) can provide useful information.

3. Customer transactional and relationship research—can keep track of perceived performance (such as changes over time) 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—querying, or debriefing, 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 algorithms, evaluating the interplay of multiple purchase and behavior variables to uncover defection behavior and proclivity 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.

And, how can the various data points from this RIS, or Risk Insight System, be analyzed, applied and leveraged to reduce churn? Answer: In virtually every way that can be conceived, including support staff hiring, training, and motivation, service process modification and/or improvement, customer support proaction and outreach, messaging and communication, product/service repositioning or design enhancement, array and availability of contact channels, etc. Perhaps the most important of these is reshaping the culture around customer-centricity and employee ambassadorship.

I’m going to be explaining much more about employee ambassadorship, and its customer and enterprise value, on October 8, during a webinar co-facilitated by me and Colin Shaw, CEO of Beyond Philosophy: http://www.beyondphilosophy.com/thought-leadership/webinars/employee-ambassadorship/ All those interested are cordially invited to attend.

Michael Lowenstein, PhD CMC
Michael Lowenstein, PhD CMC, specializes in customer and employee experience research/strategy consulting, and brand, customer, and employee commitment and advocacy behavior research, consulting, and training. He has authored seven stakeholder-centric strategy books and 400+ articles, white papers and blogs. In 2018, he was named to CustomerThink's Hall of Fame.


  1. Michael: that 20 – 40% figure surprised me because it seems so high. If the figure comes from a study, how did the researches define ‘loses?’ – an event that seems hard to pinpoint. Was the determination made based on a reduction of purchases, a postponement of a procurement, or “no, I’m never going to buy from you again!”? Curiously, a 2010 Accenture customer retention survey indicated the highest rate of churn was in retail at 18%, though this was in ‘mature’ markets and owing to customer service problems, rather than an aggregation of all causes for churn. (The emerging markets churn percentages were much higher.)

    In any case, your article does a great job of pointing out the value of assessing churn risk, and for understanding the often complex combination of events that are involved in customer attrition. Not enough companies look beyond departmental silos, assuming, for example, “service is just too backlogged. By the time they respond to problems, the customer has already terminated their subscription.”

    While churn can always be reduced, some is inevitable. The questions for risk management are a) what are the options? and b) what are the costs?

  2. …and have often been found in individual industry and multi-industry studies. In Customer WinBack, Jill Griffin and I identified annual voluntary customer defection rates by industry: Internet Service Providers, 22%, Clothing Catalogs, 25%, Residential Tree and Lawn Care, 32%, Newspaper Subscriptions, 60%+, etc. Also, by the way, churn rates have been identified in multiple CustomerThink blogs, including: http://www.customerthink.com/blog/customer_churn_and_retention

    As a further example, here are postpaid voluntary churn rates for the major wireless telecoms in Q2, 2012: Verizon’s monthly churn rate on its postpaid accounts was .84% (10% per year) compared to .97% churn rate for AT&T (12%), 1.69% for Sprint (20%) and 2.10% for T-Mobile (25%). The regional and niche telecoms have even higher churn rates.

  3. ….is proactive complaint gathering. Only a small percentage of B2B and B2C customers with complaints ever register them. This makes the inventory, or full profile, of customer complaints incomplete at best, rendering corrective or prescriptive action limited. So, monitoring otherwise uncollected complaints from online comments or ratings and soliciting unexpressed complaints through transactional or relationship customer research – something done by very, very few companies – can provide a tremendously actionable, and monetizing, data stream: http://www.customerthink.com/blog/customer_complaints_learn_the_real_value_of_getting_the_whole_picture


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