Today I’m pleased to share a guest post by Adam Rogers at Kayako.
There is an abundance of metrics out there that can measure the quality of your customer support. But which one really shows how loyal your customers are?
When measuring customer support, metrics help you see whether the service you’re providing meets the expectations of your customers. The overall goal is to maximize loyalty and reduce churn.
On the opposite side of loyalty is churn because, simply put, a loyal customers is less likely to churn. But how do you find a customer that might want to churn? Which support metrics can you analyze and rely on to find the true meaning of why a customer wants to leave?
Normally you’d start by looking at the interaction they had with your support team, pulling up the customer’s data on their customer effort score (CES) and customer satisfaction scores (CSAT), and later Net Promoter Score (NPS).
Why choose CSAT and CES to predict churn?
CSAT and CES are closely related because they measure two vital things at the beginning of the support journey.
- CES is a survey question appearing as a Likert scale (scale of 1 to 5), “How easy was it for you to get your problem solved?” It is a strong predictor of future customer loyalty – those with high effort scores are less likely to become return customers.
- CSAT directly measures the customer’s satisfaction with your product or service. It also helps identify pain points in order to see which aspects of your support could be improved.
These metrics measure everything involved in the whole support process: the ease of getting in touch with you, the actual conversation you have, and any follow up correspondence you might have sent.
The CSAT and CES metrics are easy and quick ways to keep track of your customers’ satisfaction. Keeping your support easy and efficient is one of the easiest ways to keep your customers happy and to view their risk of churn.
But what are the limitations of CES and CSAT?
It really comes down to some of the underlying numbers.
For example, you might have a customer who rates their interactions with support really highly; let’s say they give you over 90% on all five cases they create with you – that gives you a really high CSAT. But the customer then decides to leave, and you are left baffled because CSAT indicates that they were happy.
When you drill down into the issues, it turns out the customer actually reported the same problem affecting them five times in a row. They were happy support fixed it every time but were unhappy with the product, in general, for having that problem.
The same applies for CES, too.
A customer can have a really low effort experience contacting you, but if they have to consistently get in contact, that’s another kind of problem the stat is not going to highlight. When looking just at ease of contact and problem resolution – you’ll find these are the customers sitting with a high level of product frustration, or not seeing enough value to stay. These customers are at a high risk of churning.
It can take up to twelve good experiences to overcome the unhappiness caused by one bad experience. Customers don’t easily forget a problem!
NPS is a good way to see how customers feel about your product
Net Promoter Score (NPS) helps you identify which customers are at risk of churning. NPS was specifically developed to measure customer loyalty. It’s a measurement that calculates the likelihood of your customer recommending your product or service to someone in their network.
NPS surveys can capture an overall trend in customer loyalty, but the results won’t tell you WHY that trend is happening.
How do I measure customer loyalty with metrics?
The key is to combine and layer NPS data with other customer metrics to get a more complete picture of your users. Layering NPS and customer satisfaction scores on top of each other paints an interesting picture of whether your customer support is driving loyalty or if it’s a possible reason for churn.
The trick is not to view them in their own world, but view them together. If you read them without comparing them to each other, you can get a distorted view.
How to combine your NPS and CSAT scores
1. Pull data from your NPS surveys and recent satisfaction scores. Place them in a spreadsheet with the corresponding survey responses (order them using your customer or account names).
2. Use a scatter graph to plot customer data for both NPS and satisfaction.
3. You’ll be left with four types of customers based on these types of clusters.
|Example: CSAT and NPS combined|
These are promoters of your brand or product, but they have suffered bad service experiences.
These are loyal customers who have also had really great experiences with your support team.
These are customers who are not satisfied and are not loyal.
These “satisfied but disloyal” customers are an important segment of your customer base that you might not otherwise notice without running NPS surveys.
Naturally, you’ll want to jump on the Churners category and try to convince them to stay – don’t. This will be time consuming, and in most cases, it’s too late to rescue these customers. Especially when
satisfaction doesn’t drive loyalty: 60-80% of customers who ultimately churn said they were satisfied or very satisfied in their last CSAT survey.
If you want a shot at turning around any customers at risk of churn, focus on your Wildcards. These customers are sitting with a high level of product frustration, or not seeing enough value to stay. To keep them engaged, invest in education, product fixes, and proactive customer success calls to drive higher value and to help them achieve their objectives.
Nurture your VIP Support customers to become promoters
It’s important to focus on the VIP Support category because there is a very strong chance you can turn them into Advocates who help you drive new business.
When analyzing this category, you’ll see a high number of responses with low satisfaction scores but middling to high NPS responses. These are the customers that need love and are at risk of churn! You can prioritize these customers early on by giving CES its own quadrant. You can do this by calculating the average of CES over the lifetime of support tickets vs. likelihood to repurchase (based on CSAT and NPS results). Their average CES score should show how close or far they are from being an Advocate. The easier you make it for these customers, the more likely they are to repurchase.
To make these customers promoters of your services, invest in support to improve the experience they have with your support team. Try some tactics to make them feel appreciated as a customer:
- Set some time aside and really hear out the problems they have,
- Offer them some personalized support, and make sure you take care of their issue,
- Or send them some merchandise with a personalized thank you note.
Don’t silo your customer data
There’s not one true metric to measure loyalty. But by charting your NPS with other data, you’ll have a much more complete view of the state of your customer base. It will be clear which customers are unsatisfied with your product or service and at risk of churn. Take the time to nurture these customers, and you can move them on the NPS chart to Advocates.
Adam Rogers is a content marketer at Kayako. His writing helps customers get better at customer service. You can find Adam writing about marketing and books on his own blog, as well.
A few small quibbles here:
1. The author mentions that CES is driven by a Likert scale, but it's not. CES is commonly expressed using a numerical rating scale. A Likert scale asks respondents to rate their level of agreement to a statement.
2. The author quotes a stat that it takes 12 bad experiences to overcome a good experience. The source here is Help Scout, but where they got this stat is a little murky. I wrote a blog post about this with a quote from the author that Help Scout attributed (she references a different stat, but it was purportedly contained in the same study):
Also, Customer Effort is now on a 7-point scale, not the original 5-point scale.
Yes. Good call.
1. I would definitely use a Likert scale for rating CES. As Jeremy Watkin pointed out the CEB blog post, I'd use it because of similar findings in the Effortless Experience book, e.g. "The company made it easy for me to handle my issue" which can also be seen in this powerpoint: http://www.nuance.co.uk/landing-pages/enterprise/ces-europe/files/2014%20Nuance%20CES%20London%20-%207%20Analyst%20Keynote-%20The%20Effortless%20Experience%20-%20Rick%20DeLisi.pdf
Using the 5 or 7 point scale, probably comes down to personal preference and how much data you're collecting.
2. Nicely pointed out there Jeff and thanks for investigating those.
From a pure methods perspective, 5-pt semantic and 7-pt end-anchored scales have non-matching standard distributions. As someone who has had a lot of experience welding broken programs back together, I'm a pragmatist when it comes to methodological purity versus inertia of history, so scale consistency is the winner with existing programs. While you can do scale translations, they're not nearly as easy as advertised – if done correctly – especially with multi-country data sets. Scale transitions are recommended to have redundant metric/anchor waves during swap-outs, which should only be done if the current metric system is deficient to the point of value of deletion.