Our proprietary experience analysis methodology—known as @Risk Analysis—is based on decades of social science academic research and practical in-market business application. It is based on a fundamental principle of human behavior:
Individuals are far more likely to take action in response to negative events than positive ones.
Although the principle is straightforward, its practical implications for customer experience research are profound:
- Dissatisfaction is statistically much more predictive of what a customer will do than satisfaction
- A detailed understanding of specific problem experiences is significantly more actionable than a generalized scalar view of “satisfiers”
Verde clients use @Risk analysis to understand why their customers behave in a certain way and what actions to take to alter those behaviors. The approach identifies specific experiences driving unwanted behaviors, isolates those with the greatest negative impact on customer value and prioritizes all experiences on basis of financial impact. Below is a brief overview of how we conduct an @Risk program and the typical outputs from the work.
START WITH EXPERIENCES
At the center of any @Risk project are the experiences you create for your customers. These are really the only things you can control: customer attitudes and behaviors are the outcomes of these experiences.
@Risk analysis is formally based on Attitude-Behavior Consistency Theory. Using this model, we will evaluate your customers’ experiences on the basis of personal centricity, consistency and emotional impact.
AVOID “HOW MUCH DO YOU LOVE US”
Traditional customer satisfaction research grossly underreports dissatisfied customers due to response scale bias. The data is general and hard to act on. And since satisfaction has been established to weakly correlate—if at all—to long term loyalty behaviors, starting and stopping with satisfaction is a suspect measurement tactic to begin with.
@Risk analysis goes straight to the heart of the customer experiences that matter most to your customers and your top line in a way that satisfaction analysis cannot.
GET COMPREHENSIVE AND GET SPECIFIC
@Risk analysis is based on direct proactive inquiry of problem experiences versus passive “open text response” methods often used to collect dissatisfaction data. It expressly links qualitative and quantitative research and actively drives to implementation of findings. In a typical program we may identify and evaluate up to 500 specific experiences on the way to findings, spanning core product experiences, sales and support enablers and relationship accelerators.
SEPARATE THE CRITICAL FEW FROM THE TRIVIAL MANY
@Risk analysis will prioritize which dissatisfiers are imposing the greatest collateral damage on your revenue, share and brand. A typical analysis will isolate between 5 and 8 critical sub-optimal experiences that account for the majority of a client’s market risk.
eliminate the problems YOU CAN, RESOLVE THE REST
@Risk analysis will make clear for you not just which experiences are driving your customers away, but how well your problem resolution processes are performing at pulling them back. We conduct detailed analyses on the loyalty behaviors of customers with/without problems, those who contact you and do not, and those who do/do not have their problems fully resolved—and tell you precisely what to do for each customer type to improve retention, spend and brand consideration.
LOOK FOR RISKS TO MARKET REPUTATION
@Risk analysis takes a particularly close look at how customer experiences impacts your word-of-mouth reputation. Managing these critical experiences accelerates customer acquisition and strengthens overall brand equity.
@Risk analysis concludes with rigorous Action Planning. Based on Hoshin management principles and governed by TQM exercises, Action Planning is an integrated component of the program plan that bridges clients from data to market impact in 9-18 months.