Why a CSM's First Impression Means So Much

“My good opinion once lost is lost forever.” 

― Jane Austen, Pride and Prejudice

Eye contact. A smile. Friendly conversation. We all know first impressions mean a lot when we meet someone new or interview for a job. The same is true when it comes to customer interactions. How things go at the outset makes a big difference in the final outcome. Research offers intriguing insights why getting off on the right foot in the SaaS business is so important for reducing churn and building customer loyalty down the road. 

Most managers, including many prominent Customer Success consultants and authors, assume that all customer interactions have equal importance. Science, however, suggests that some carry far more weight than others. While the ultimate goal may be continuous improvement at every point along the customer journey, managers should start by concentrating on the critical few areas that yield the greatest impact. And the most essential exchanges occur in the very beginning. 

What starts right, stays right

Research from the wireless industry shows that first encounters matter.¹ Investigators studying wireless subscribers hypothesized that customers “anchor” their satisfaction and value perceptions based on their service history, incrementally modifying their beliefs by incorporating new information after each interaction. They found that customers who had many months of positive experiences early in the relationship weighed them more heavily than they did later experiences. But if new customers had early disappointment, they became particularly vulnerable to churn. 

Creating positive outcomes from the beginning yielded significant financial impact. The study found that one in four longer subscriber durations could be attributed to a series of satisfactory experiences. Doing a better job right from the start made a big difference. Simply having agents spend twenty additional minutes helping wireless customers activate and successfully use their phones cost the company $888K more each year, but the revenue increase due to churn prevention was estimated to be a whopping $4.48M. This represented gain of 2% in company profits, or an ROI of about 4:1.

Roots in biology

Neuroscience explains how anchoring works on a cognitive level. Our minds use reward prediction error (RPE) to gain new knowledge and skills because it is the fastest and most efficient learning method.² The brain subconsciously encodes differences between how rewarding something is compared with how rewarding it was expected to be. The brain then recodes expectations after each experience, and with successive cycles, outcomes eventually match expectations. RPE therefore serves as the anchor by which the brain evaluates its next learning experience.  

Learning is a mentally costly and permanent process. The brain consumes a great deal of energy building new circuitry by releasing neurotransmitters, firing millions of neurons, and modifying synaptic weightings. Given the high resource burden it places on the body, the brain is selective about what it learns, and it creates efficiencies by constructing new neural connections upon old ones. As a result, neural architecture has intrinsic latency. Once the mind learns, the underlying neural patterns are difficult to change, which explains why perceptions linger. 

When circumstances are unique, however, our expectations are undefined, and our protective evolutionary biology kicks in. In these cases, RPE is very high, and the brain subconsciously reacts to the increased uncertainty. We perceive novel situations as risky, and our cave man brain releases a neurotransmitter called norepinephrine, a stress hormone that increases attention and concentration and facilitates learning. The amygdala, the part of the brain responsible for encoding and evaluating our emotional responses, is on high alert. Norepinephrine also catalyzes our autonomic “fight or flight” system, readying us for possible action. Just like our primitive ancestors, new situations put us on edge, grab our attention, and sharpen our senses. We’re ready to learn quickly because our survival may depend on it.

Learning is rarely a matter of life and death in the modern world, but our brains are conditioned to respond to new situations in much the same way. Faced with uncertainty, the brain sets the first and most impactful cognitive anchor upon which all subsequent learning is based. Our neurobiology therefore predisposes us to automatically place more importance on first impressions. Subsequent learning then reinforces our initial experiences, and in time our cumulative perceptions evolve into long-term biases. First impressions are meaningful because it’s how our brain works on a fundamental level.   

Getting off on the right foot 

Customer Success Managers face a challenge to make their customers’ journeys optimally productive and enjoyable from the outset. Customers’ tendency to quickly judge the value of the product and the quality of the relationship means onboarding must go smoothly. CSMs should do their homework, researching the customer and their business and reviewing account history during the sales process. During the call, the CSM should take the time to understand and respond to the customer’s cognitive state, both effectively (meeting utility needs) and affectively (meeting emotional needs). When CSMs are mindful of conversations that gratify both conscious and subconscious needs, they not only solve problems but promote the conditions that lead to stronger relationships. If action items must be addressed after the call, prompt follow-up and follow through are critical because the customer is primed to learn if the CSM can be trusted and relied upon.  

As the customer learns to use their new software, they continue to refine their understanding about the nature of the relationship, too. The CSM should check in frequently in the early stages, helping the customer overcome obstacles in a friendly way. In the first few months, customers will not only come to appreciate the value of the product, they will do the same with the CSM and the company they represent, and the positive effects will stick. If the findings in the wireless industry are any guide, the financial outcomes are dramatic.

Contrary to popular belief, science shows not all interactions are created equal—first impressions really matter. Research from another industry and advances in neuroscience confirm the effects and demonstrate the financial impact. For CSMs, doing things right from the beginning sets the stage for stronger relationships and significantly lowers customer churn.

Excel-lens is a publication of Service Excellence Partners. We increase customer loyalty and business performance in the cloud computing industry. Contact us today.

Sources:

1. Bolton, Ruth N. “A Dynamic Model of the Duration of the Customer’s Relationship with a Continuous Service Provider: The Role of Satisfaction.” Marketing Science, 17 (1), 1998, 45-65.
2. Frank, M., Munakata, Y., Hazy, T., and O'Reilly, R. (2012). Computational Cognitive Neuroscience, Kindle Edition.

Are CSMs Taking a Critical Process for Granted?

It’s time to view relationship-building from a new perspective 

Studies have shown that people switch vendors for three reasons:¹ 
1. Expectations for quality and value go unmet
2. Customers lack personal attachment to the supplier 
3. It’s easy to switch  

When all three factors are present, churn results. Factor 3 above depends on product complexity, cost to change, and competitive pressures. Customer Success teams address the first factor, building value through software usage, answering questions, and demonstrating the benefits promised during the sales cycle. But the second factor, building personal attachment, rarely gets the attention it deserves, yet it’s a major driver affecting churn.  

Now wait a minute! Everyone agrees that building customer relationships is important. It’s intuitively obvious that better customer relationships lead to greater retention and loyalty. After all, people prefer to do business with people they know, like, and trust. But many companies take relationship-building for granted. They believe it happens on its own—simply hire friendly front-line employees, give customers what they need, and better relationships will somehow result. But if relationships are so important, why leave them to chance? Is there a better way? 

Relationship is a process

Psychologists say relationships have a beginning, middle, and sometimes an end.² Think about how you became friends with someone. You met, discovered you had a common background and shared interests, and you found each other likeable. Something “clicked.” You connected because the person showed empathy and realness, and you immediately felt comfortable with them. After subsequent contacts, your relationship deepened through mutual connection, caring, confiding, and trust. You found you were helping each other achieve goals and deal with daily frustrations. You maintained your relationship through periodic social get-togethers or by enjoying common interests. If you’re fortunate, this person is still in your life. But as you know, friendships can lull when the other person gets busy, goes through life changes, or moves away. Friendships can also abruptly dissolve due to a “falling out.” 

Imagine if all your business relationships could be close friendships. Most will never be, but all relationships follow the same pattern of initiation, maintenance and dissolution. Relationships, like all processes, occur in a sequence of steps, and outcomes obey the laws of cause and effect. The strength and quality of the relationship that develops depends on a number of factors, some of which can be controlled and others cannot. For example, people can't change their basic nature and few will ever become best friends. However, it is possible to become more likeable. Since some relationship factors are controllable, influencing them improves relationships. 

Relationship factors

Subconscious social signals lie at the heart of our relationships. David Rock, founder of the NeuroLeadership Institute, distilled neuroscientific research and social evolutionary theory into a simple model to describe the reflexive behaviors all humans share.³ His SCARF model outlines the core psychological drivers hardwired into our subconscious:

• Status—how we perceive our importance relative to others
• Certainty—our ability to predict the future
• Autonomy—our sense of mastery and control over events
• Relatedness—our connection and sense of safety with others
• Fairness—our perception of equitable exchanges 

These factors influence relationships positively or negatively. For example, consumers who call technical support complain when technicians make them feel stupid. The customer subliminally perceives the interaction as a status threat. In extreme cases, the conversation provokes anger and causes customers to terminate their contracts. “You’re not treating me like a valued customer!” they exclaim. Most of the time provocations are unintentional and subtle, but even minor comments at the wrong time can leave lasting impressions. 

Conversely, SCARF techniques can promote positive encounters that lead to stronger attachments. For example, a Customer Success Manager begins an onboarding call saying, 

“I saw on your LinkedIn profile that you’re from Chicago.” 

“That’s right. I grew up on the South Side, 120th and Pulaski,” the customer says.

“No kidding!” the CSM exclaims. “I’m from Orland Park!”

The CSM is building relatedness right from the start, sending a subliminal signal that she’s a friend, not a foe. After the small talk, she says she will help him get his account set up and it will take only fifteen minutes. This creates a sense of certainty in the customer’s mind. By proactively sending these and other signals, the CSM produces warmer, friendlier interactions.

In a similar manner, CSM leaders should look closely at how interactions occur throughout the customer lifecycle. They can do this by mapping the experience from the customer’s perspective and by identifying the customer’s practical (effective) and emotional (affective) needs at each step. Then, executives can close performance gaps by implementing process improvements and measuring the impact on customer churn. Often simple changes in the right places can make significant improvements through training, website revisions, and e-mail edits. 

SaaS companies tend to take the process of building relationships for granted. Like all processes, however, those better designed and managed produce better results. CSMs should be more mindful, both in what they do and how it impacts the customer’s brain. Proactively and systematically creating the conditions for friendly attachment leads to stronger relationships and reduces churn.  

Excel-lens is a publication of Service Excellence Partners. We increase customer loyalty and business performance in the cloud computing industry. Contact us today.

Sources:

1. Gremlera, D. and Brown, S. (1996) Service Loyalty: Its Nature, Importance, and Implications, University of Idaho and Arizona State University, USA
2. Blieszner, R. and Roberto, K. A. (2003). Friendship across the lifespan: Reciprocity in individual and relational development.  Also in F. R. Lang and K. L. Fingerman (Eds.), Growing together: Personal relationships across the lifespan (pp. 159-182). Cambridge, U.K.: Cambridge University Press.
3. Rock, D. (2012) “SCARF: a brain-based model for collaborating with and influencing others.” Neuroleadership Journal

Cohort Analysis Done Right

Use a p-chart to properly monitor shifts in customer churn

Let's say you run a Customer Success team and your manager asks you to perform cohort analysis in order to better understand customer churn behaviors. Your customers renew on a monthly basis, and you’re interested in measuring their initial fallout rate. Using data collected over the past year, you count the number of users who canceled their subscriptions after the first 30 days and divide that number by the total number of new users during each month.¹  Your Excel spreadsheet table is shown below:

You then plot the data:

The results concern you. Although the data points vary somewhat, it appears your 30-day churn rate has been increasing all year! Using the trend line function in Excel confirms it—the first month’s churn rate has grown from about 2.2% to 3.2%!!

You share this information with your boss and she’s not happy. Clearly your Customer Success team is not doing the job. She expects immediate improvement. Or else. 

Simple, obvious... and wrong

But hold on a minute—you’ve made a serious mistake. You haven’t managed your team poorly. You’ve analyzed your data improperly!

Your error has to do with statistical sampling. Randomness occurs in all data, and experimenters must be careful to separate the “signal” from the “noise.” Statisticians warn of two types of experimental error: 

• Type 1: False Positive—a result is determined when in fact there’s only randomness 
• Type 2: False Negative—randomness is determined when in fact there’s a valid result

Without using the proper statistical methods, people can easily jump to the wrong conclusions. Overly simplified, standard tools in Excel make the situation worse. As a result, managers tend to over-react, seeing a problem that isn’t there and making changes that can do more harm than good.² Managers can also under-react, missing the cues to make changes when it’s time to do so. Most businesspeople lack a strong analytical background, so statistical errors are quite common, often leading to disastrous consequences.   

Using the p-chart

Fortunately, manufacturers have been using powerful statistical methods for years, and these practices can easily be applied in software companies. Lean Six Sigma, a quality improvement method, incorporates a broad range of tools that ensure teams properly analyze data and reach valid conclusions. You can use these techniques to your advantage. 

The control chart is a handy Lean Six Sigma tool helping managers avoid Type 1 and Type 2 errors. Control charts are used to determine if a process is in a state of statistical control, i.e., if the observed variation is due to randomness and not any particular cause. There are many different types of control charts depending on the type of data involved, but all follow the same basic structure. The p-chart (or proportion chart) uses the binomial distribution. It is the most appropriate control chart to use in this example because there are only two possibilities (the customer cancels or renews) and results are expressed as percentages.³  

The control charting procedure is as follows:

1. Determine rational subgroups. A rational subgroup is a homogeneous set of data that provides a representative sample, such as a batch of parts manufactured during a shift. Many SaaS companies define their subgroups as monthly “cohorts,” the set of new customers who sign up for software during a given month. Usually there’s nothing different about a customer who subscribes in January versus one who subscribes in February, and for the purposes of evaluating churn, it’s more accurate to use sequential, fixed sample sizes instead of varying sample sizes.⁴  In order to calculate the mean with reasonable accuracy, statisticians recommend using around 25 subgroups. In this case, we’ll choose fixed sample sizes with 168 customers in each of 24 subgroups (4032 customers/24 subgroups = 168 sample customers/subgroup).⁵  Below is your new sample set:

2. Plot the dots. As you can see, the shape looks a little different, but it still appears there’s some trending in the data. 

3. Calculate and apply the center line and control limits. 

• Compute p-bar (or average proportion) for the data set. In this example, 114 out of 4,032 customers churned after the first 30 days, a p-bar of 2.8% (114/4032). Draw this line on the chart. 
• Calculate the upper process control limit, UCL. This line shows the mean plus three standard deviations. The line is important because the probability of finding a data point more than three standard deviations away from the mean by chance is less than 1%. The UCL calculation for a p-chart is:

UCL = p-bar + 3 * Square Root {p-bar * (1 – p-bar) / n}

where n is the fixed subgroup sample size

In this example, UCL = 0.028 + 3*SQRT {0.028*(1-0.028)/168} = 0.0662 = 6.6%. Add this line to the chart.⁶  

4. Interpret the results. As you can see, most data points fall in the vicinity of p-bar and none exceed the upper process control limit, UCL. This is your first indication that observed variation is likely due to randomness and not any special causes or shifts in your process. For good measure, add lines at +/-1 and +/-2 standard deviations (probabilities of data points in these ranges 68% and 95% respectively) and add them to the chart. Mark zones “C” between the centerline and 1 standard deviation, “B” between 1 and 2 standard deviations, and “A” between 2 and 3 standard deviations from the mean as shown below.

Then apply three additional statistical tests to rule out any possibility that something is amiss:⁷ 

• Nine points in a row in Zone C or beyond on one side of the center line?—NO 
• Six points in a row steadily increasing or steadily decreasing?—NO 
• Fourteen points in a row alternating up and down?—NO 

Congratulations, you can breathe a sigh of relief! You’ve correctly established that despite appearances, there’s no trending in the data, contradicting what you originally thought. You explain to your boss that nothing has changed and results are due exclusively to randomness. The data shows that your onboarding process is in a state of statistical control and will routinely produce 30-day churn averaging about 2.8%. What’s more, you can accurately predict that 2/3 of the time future churn will be measured between 1.5% and 4%, 1/3 of the time less than 1.5% or over 5%, and only on rare occasions will it ever exceed 6.6%.  

Next steps 

You can continue to add new data points and monitor process stability using the limits you calculated above and by applying the same interpretation procedure. If any of the four statistical test results are positive, take immediate action and determine the underlying cause. You can use p-control charts to investigate customer defections at other periods of time, such as subscriptions after 60 days, 90 days, or upon annual renewal. Several Lean Six Sigma statistical packages are commercially available online (many of which are SaaS), making control charting much easier. 

Once your Customer Success process is stable, it’s possible to systematically improve it. Although keeping things under control is always your first priority, making things better is your end goal. In my next blog, I’ll describe how Lean Six Sigma methods can be used to help you improve customer retention.

Excel-lens is a publication of Service Excellence Partners. We increase customer loyalty and business performance in the cloud computing industry. Contact us today.

Notes:

1. Technically, calculating ratios when denominators vary causes an “average of the average” problem in which comparing percentages between periods introduces significant measurement error. This discussion was deleted for brevity. 
2. Human over-reaction to Type 1 errors is a fascinating subject, one with neurobiological and evolutionary explanations. It’s beyond the scope of this blog, but humans are preconditioned to see patterns and jump to the wrong conclusions, hence the need for robust application of the Scientific Method. 
3. Note that p-control charts can be used with varying sample sizes under certain circumstances (computing average n or using multiple control limits), and that other types of attribute control charts can be used for smaller sample sizes. For simplicity, this discussion was omitted.
4. Homogeneity extends to type of customer, including the market segment and associated value proposition, as described in my previous guest blog. If churn behaviors vary significantly by customer type, you should stratify your data and chart each segment separately.   
5. A rule of thumb is to choose subgroup sample sizes for attribute data such that np>5; in this case, churn is about 3%; n>5/0.03 or n>167
6. Note that in this case, the lower process control limit is negative and can be neglected; this is a “single sided” investigation where the minimum churn is 0.0% and we are interested in detecting a shift upward.
7. Montgomery, D. C. (2005), Introduction to Statistical Quality Control (5 ed.), Hoboken, New Jersey: John Wiley & Sons, ISBN 978-0-471-65631-9, OCLC 56729567