Is It Time To Call Time On NPS?
NPS works great in the B2C world, it’s near real time, it’s broadly drawn and it’s super simple to collect. In B2B not so much, it all too often arrives too late, comes from too few respondents and requires too much effort. Is it time to call time on NPS or can it be saved?
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Net Promoter Score (NPS) was born at the dawn of the Software As A Service (SaaS) age. Developed by two consultants at Bain and first released into the wild in a 2003 Harvard Business Review article is it for many the gold standard measurement for customer loyalty. Used in both business to consumer (B2C) and business to business (B2B) organisations it has been in action ever since.
The history and detail of NPS can be found here, a worthwhile read for anyone using this tool to measure loyalty. Before I revisit the utility of NPS in a SaaS B2B setting though let’s look at why it’s such a great tool in a B2C setting.
NPS In A B2C Setting
For anyone unfamiliar with NPS there are two things you need to know. Firstly the calculation of NPS is based on one and only one question marked on a scale from 0-10.
What is the likelihood that you would recommend Company X to a friend or colleague?
Secondly high NPS has been shown by Fred Reichheld at Bain & Company to correlate strongly with customer repurchases, referrals and other actions that contribute to the growth of a company. Having a lot of people scoring you at 9 or 10 (promoters) on the question above is a good thing.
You generally hear people talking about two types of key performance indicator (KPI), lagging or leading. A lagging indicator tells you about something after it’s happened whereas a leading indicator either helps predict what’s going to happen or tells you what is happening in more or less real time. The key difference being the speed with which you can react to issues or to opportunities. Although NPS is strange mixture of the two: it asks you based on your past experience with a company about what you might do in the future; it has come to be seen in B2B circles primarily as a lagging measure of customer satisfaction and loyalty. However, in a B2C setting it’s different. As NPS can be measured broadly across the customer base in a more or less continuous fashion it allows a company to collect and react to results in next to real time.
For example, shortly after a flight you get a text from an airline that asks “Based on your flight with us today how likely are you to recommend ABC airline to a friend or colleague” and you can respond by text straight away on 0-10 scale. If something went badly wrong on the flight then it’s likely the airline will receive a lot of low scores and can more or less immediately investigate what went wrong, take steps to avoid a repeat and apologise to the affected customers. Note that while the survey didn’t stop the specific incident from taking place it did allow for almost immediate action to address the issue: itself a good way to build and maintain loyalty. As it’s easy and generally acceptable in a B2C environment to survey customers after a transaction like a flight then averaged across all flights and all customers who respond to the NPS survey an airline can get an excellent and robust idea of its overall loyalty level on a day to day basis.
NPS In A B2B Setting
In a B2B setting things are different. A small SaaS company may only have a couple of hundred customers and no obvious meaningful ‘transaction’ (like a flight) to survey on a frequent basis that clearly sums up the overall health of the relationship. While you could for example survey customers after each support ticket it would give you a fairly narrow view of the overall NPS between you and the key decision makers at your customer. If you do wish to ask the key decision makers regularly then you have survey fatigue to worry about. As a consequence in many companies what I would term relationship NPS tends to be measured as part of a broader customer satisfaction survey on a periodic basis, usually quarterly or semi-annually. Which makes it very much a lagging indicator. And while that’s better than nothing at all it’s not that great.
I mentioned that NPS was born at the dawn of SaaS or while we were very firmly in the age of on-premise software. In those days data was much harder to come by living as it did in little islands in customer data centres across the globe rather than nicely consolidated into multi-tenant SaaS platforms that make looking at everyone’s metadata straightforward for SaaS vendors. So 15 years ago a periodic customer satisfaction survey wasn’t just one of the best ways to assess the overall health and likely loyalty of a customer base, it was more or less the only systematic way to do it.
Not any more.
SaaS companies have lots and lots of data. Without doing anything clever It’s now much easier to measure and demonstrate the value you are delivering to customers than it was in the on-premise age. As that value trends up and down customer satisfaction and NPS should follow so that in real time companies with the right instrumentation should be able to predict movements in NPS. Furthermore they will be in a much better position to react to and investigate bad, or good, results from their surveys that don’t correlate with value delivery.
More excitingly in the learning industry a buddy of mine Lori Niles-Hofmann has begun the conversation about digital body language. Human body language often tells a different, more truthful story than the words someone uses. Digital body language, in this case user behaviour on your platform, may also be able to give us a much better read on the satisfaction of the users of a system than the results of an NPS survey of a few key stakeholders (Machine learning and AI may have a part to play in enabling this type of measurement to take place.) Once companies understand what good or ‘satisfied’ digital body language looks like on their platform they can begin to focus on helping customers achieve that as broadly as possible across their user base. By designing systems that allow for real time measurement (both by the vendor and the customer) of digital body language and by developing strategies to incent, or educate people on, those behaviours companies can potentially make huge strides in loyalty and satisfaction. They will then be able to close any gaps that arise between the reality and the perception of value. Of course this won’t solve relationship problems stemming from unqualified CSMs or ineffective support agents but it will put the conversations to address those things into a much clearer context.
On the flip side companies like Entelo are building NPS collection into their platforms. Done well this is smart. It makes measuring NPS subjectively on a one-to-one basis both real time and something customers see as a benefit rather than as an imposition. Importantly it allows Entelo to act fast to correct issues. This brings the same immediacy that customers are used to in their B2C lives into their B2B relationships. This, in combination with a digital body language style approach to the real time measurement of user satisfaction would allow B2B companies to put themselves into the same position as their B2C relatives and begin to truly measure and act to improve NPS and customer satisfaction in real-time.
So it’s not time to call time on NPS. It is time though to have a radical rethink about how we measure B2B NPS in real time, as broadly as possible and at the user level using tools like digital body language and on platform NPS measurement. Companies that can do this and bring the benefits of NPS with B2C immediacy and accuracy to the B2B environment while thrive, those that don’t risk being left behind.