Is your Customer Success (CS) team primarily measured against an NPS or CSAT score?
If so, you might have had some doubts creep in about the effectiveness of this team over the last year.
It’s OK to admit it. I get it.
You might be especially frustrated if your expectation from CS was to significantly reduce customer churn. Maybe you haven’t seen this materialize yet.
If so, this makes sense because NPS doesn’t correlate to churn.
So, now maybe you’re asking, “what is the point of CS?”
Or maybe you’re feeling a little duped. It’s a natural reaction.
If you can relate to this, you’ve likely fallen victim to the Customer Success Center of Excellence Fallacy.
What exactly is this fallacy? And how can you move past it?
Read on!
So, what is Customer Success? Or, rather, what should it be?
Quite simply, it is a function that ensures your customers are successful with your product. This requires things like:
These are good things. And they make sense. Your company built an awesome product, no doubt.
You want those who use it to love it as much as you do. These are all helpful things to that end.
But Google the question “What is Customer Success?”. Go ahead, I’ll wait.
No, wait, I’ll make it easy for you. Millions of results, right?
There’s no shortage of opinions on this topic. Let’s look at a couple of the top search results.
HubSpot says:
“Customer success is anticipating customer challenges or questions and proactively providing solutions and answers. Customer success helps you boost customer happiness and retention, thus increasing your revenue and customer loyalty.”
And The Customer Success Association says:
“Customer Success is a long-term, scientifically engineered, and professionally directed business strategy for maximizing customer and company sustainable proven profitability.”
Both of these quotes are great examples of putting too many expectations on CS. And vague expectations at that!
“Boost happiness and retention”?
“Increasing revenue and customer loyalty”?
“Maximizing customer and company sustainable proven profitability”?
Sheesh.
We’ve wandered directly into the realm of the Customer Success Center of Excellence Fallacy.
Look, I’m all for Customer Success becoming a Center of Excellence (CoE). Everyone should work to be better at their craft. And undoubtedly there are business benefits to this.
The fallacy isn’t the pursuit of becoming great at your craft.
The CS CoE fallacy is the belief that if you deliver an excellent customer experience, you’ll automatically reduce customer churn. And by extension boost revenue.
This fallacy is most noticeable when CS metrics become the proxy you use to gauge company health.
We often see CS teams measured by operational metrics such as Time to Value (TTV) or customer satisfaction scores (e.g. NPS or CSAT). So, “excellence” is often determined by putting a certain threshold on these metrics.
If your goal is to turn your CS team into a CoE, this is a fine approach.
If your goal is to boost company revenue, this approach will end in frustration.
Why? Let’s look at NPS and CSAT a little closer.
They’re lagging indicators. At best you’ll get a nice retrospective for what to do better next time.
But wouldn’t you rather predict a potential customer churn and do something about it before it’s too late?
Maybe next time someone in your organization suggests there is a strong correlation between customer happiness and churn reduction, you can ask a few critical questions:
My guess is the answers to these questions will lead you to the same conclusion I’ve come to.
CS can and should strive to be a CoE. But that alone won’t help to reduce customer churn or boost Net Revenue Retention (NRR).
Examples of the CoE Fallacy are everywhere. But I’m going to highlight one that most of us probably have firsthand experience with.
Ever shopped at Walmart?
Most of us have. It's the second biggest retailer in the world, just after Amazon.
Walmart doesn’t subscribe to the CoE Fallacy. They don’t invest in a high-touch customer experience.
Their profit model is to provide the lowest prices possible. And they’re even willing to price match to keep more customers.
This approach, of course, comes at the expense of customer experience.
It’s no secret. Walmart isn’t considered a home of excellent customer experience.
Regardless, they remain highly profitable year after year.
So is the Center of Excellence working for them?
Nope.
They've skipped it altogether, but they still have record profits!
Ok. How about Nordstrom? Ever shop there?
Nordstrom fully subscribes to the importance of customer experience. lt’s core to their business model.
People love going there because of the friendly service. And the fact they’ll take returns and exchanges sometimes even decades after the original purchase.
But does this turn into profit? Is the high-touch service they subscribe to driving revenue?
Not really.
In 2020, Nordstrom closed 1 out of every 6 of its full-line stores. And toward the end of 2022, Nordstrom’s shares fell 9%.
Can we directly attribute this to their belief in CoE?
Maybe. Maybe not.
But it’s clear their implementation of CoE didn’t ensure that customers continually shop with them. It didn’t provide ongoing profitability.
Certainly, profitability is what we’re all aiming for in business.
In an ideal world, we’d provide each of our customers with a fully personalized, tailored experience. So, we could fully maximize profitability for each customer.
But business is a game of systems. And this leads to perhaps is my biggest issue with the CoE Fallacy.
Centers of Excellence tend to create a one-size-fits all approach that doesn’t serve each customer appropriately.
They do this because they create a system in which to operate. These systems don’t tend to offer a lot of leeway.
So, we end up with every CSM setting up monthly business reviews that no one cares about…
These systems (or rules) tend to help people “get work done”. But they often lead to soul-less, check-the-box activities that no one cares about.
The reality is that the relationship between customer experience and revenue is complex and multifaceted.
This complexity typically requires a data-driven approach to unlock the best way forward.
Oversimplifying it leads companies to miss important signals and drivers that contribute to churn.
And tends to move companies even further away from being data-driven.
This leads to surprises when customers churn.
It also means that no one fully understands why they lost the customer.
To be clear, “using data” isn’t necessarily a cure-all. There are those that only “use data” on the surface.
For instance, they try to explain why a customer was lost using the metrics they tend to lean on (and understand).
This isn’t helping the long-term health of the business. But it’s all too common.
If you hear (or are making) any of the following statements, your org is at risk of falling prey to this error:
So how can you avoid this trap?
I firmly believe a better approach is to collect multiple metrics from all along the customer journey.
You also must make sure it’s the right data!
Because it’s all too easy to get it wrong…
At my last company, we received a request for a toothpaste company to measure their “customer passion” through a 100-question survey.
100 questions to assess a customers' passion for toothpaste! 🤦🏽♂️
It’s highly unlikely that they’d accurately gauge how a customer feels with this approach.
It may have seemed like a good idea from their team at the time. More is more! Right?
And the more information we have on our customers, the better we can treat them.
But really, they didn’t think through their customer profile. I mean, is toothpaste, a daily necessity, something that truly has passionate customers behind it?
A big maybe. And even with passion, will any customer spend the time needed to answer 100 questions about toothpaste?
These attempts to score customer attitude after the fact don’t put us on a path to retention.
A better way? Collect data at every point in the relationship. This is an important step to take.
And this approach means you can avoid the CoE fallacy!
Collecting customer data at every step in the journey provides the foundation for incredible things.
Even better, with this approach, you’ll see the big picture. You’ll better understand what is informing a customer's decision to cut ties with you.
More importantly, this approach enables you to be predictive rather than reactive. You can know a customer might cut ties with you—even before they themselves know!
Perhaps one of the best things you can do with this data is customize your approach to each customer.
The solution at its core is to understand each customer's individual needs and wants.
This is, of course, easier said than done. I think most of us want to believe that we have a deep understanding of our customer.
But I think there’s always room to improve. And better, faster ways to do it.
For instance, one customer might prioritize access to a specific feature. Another might place value on fast support ticket resolution.
The key to success is recognizing the uniqueness of each customer and delivering customized experiences.
By anticipating customer requirements, you can focus on what truly matters to them. Then you deliver that thing. This is what leads to customer retention.
There are two ways to achieve this. The first way is to allocate more resources. You could increase your CSM or AM coverage with dedicated teams for each customer.
This is a high-touch approach.
And it might make sense for some businesses. Or maybe a subset of high ACV customers.
But it won’t make sense as a one-size-fits-all approach.
Luckily, the solution to delivering tailored customer experiences doesn't always have to be a matter of throwing human resources aimlessly at a problem.
We know that’s not a worthwhile use of resources, no matter what the issue is.
There are other options. Even more effective options.
Specifically, there’s data science and machine learning.
The advanced abilities of machines can reveal insights that are beyond human capacity.
For instance, machines can analyze patterns and perform multi-variate analysis without much effort.
How’s your real-time multi-variate analysis capabilities? 🤪
But a machine? It’s a breeze.
Take for example a machine learning model that can determine which customers are likely to churn.
It can quickly analyze multiple, complex factors. And it has to. Because there are so many relevant factors…like firmographics, product usage patterns, relationship with the company (e.g. ACV, next renewal date), number of tickets logged.
And so much more!
By considering the interplay between these multiple factors, the model can determine the optimal time and action to intervene.
It can then provide an effective solution to retain the customer. And this ensures continued success.
This is exactly the approach we’re taking at Tingono.
But even more than this, we couple this machine learning power with automation. This enables you to not only quickly focus on what matters but also quickly act on that information.
Want to learn more?
We’d love to show you how we’re helping companies move away from their flawed CoE Fallacy, and towards data-driven retention and growth!