Customer Success

The Customer Success Team Maturity Model

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NOTE: The thinking behind this model applies predominantly to enterprise SaaS operations. It also applies to the maturity of your customer success team, not the customer’s maturity in relation to their use of your product or their interactions with you.

I’ve already written about the phases of the model above in this post: Customer experience, the subscription economy and 10 ways success teams will make you win

This long overdue post dives into the functioning of the maturity model. It’s not so much about the elements of each phase which are covered by separate chapters in detail.

Becoming a successful customer success organisation takes time and you cannot become successful overnight.

So the first premise to understand is that you need to level up through different phases of maturity.

Then that input and outcomes are necessary at each phase.

Finally, that the impact this has over time are central to the functioning of the maturity model.

As mentioned in the note in the very first lines, this model applies to the customer success team, not the customer.

On the latter, a very fine model has already been proposed by Boaz Maor – more on that here: Why your customer health score may be quite useless: your framework to calculate CMI

Mine is for those that want to build a customer success team as well as showcase best practice for organisations who have done it well.

Having said that, of course impact on customers business has to be figured into the equation and you will see how I’ve done that.

So onto a little around the main building blocks of the maturity model.

The build, grow, innovate side of the phases should be pretty self evident and covered sufficiently in the first post I linked to so it’s really for input, outcomes, impact and time to be explained.

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First a DanelDoodle to visualise things on the inputs and outcomes side. The context for this as I describe it is the science of customer success. The very necessary data driven side of things, something that I believe needs emphasis.


Input should cover not just human activity but technological and automated activities.

Each chapter will cover this in more detail but it is alongside the next area, also to be covered in chapters, that is crucial to understand a connection

In other words, the causality between what you or the customer does and what outcome results from the action.

Examples of input are shown in the diagram.


Outcomes should as far as possible be quantifiable.

That doesn’t mean to say you should only use telemetry to understand an outcome based on an input.

You could use qualitative means, through a survey for instance, to gauge user sentiment.

This would amplify understanding you would typically get from only looking at usage data for instance.

Notwithstanding the way you measure an outcome, it is nothing unless you look at its impact.

Seen in isolation, an outcome would just be a number or score, but without contextual analysis of what impact it is having, it might be meaningless.

Examples of outcomes are captured in the diagram.


By this I mean impact on the customers business. Material effects from actions with resulting outcomes.

The emphasis which cannot be stressed enough, is impact on the customers business.

This is kind of the last mile.

In an enterprise SaaS business, while outcomes can be measured in terms of effects it has on users, impacts need to be measured in terms of how end customers are effected and how this impacts on your customer’s business.

The formula would look something like this: X (input) + Y (outcome) = Z (Impact)

A simple example: A mailing with an explanation of a new feature increases usage of that feature by staff which results in an uptick of end user satisfaction.


Time per se is not difficult to understand.

What I want to stress is the likelihood that at the beginning of the journey you will not be in a position to be tracking the kinds of impact I’ve referred to above as you will later on.

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That’s because you will be too busy building systems (often internal operations) and may not even have the capability to track inputs, outcomes and impacts.

The latter largely also depends on your customers involvement which will depend on the length of your relationship and their own maturity.

I’ve often said that anytime I start out in a new role or advising starting out anew, my starting point would be this dashboard above left.

As a Service, Customer Success

The customer success and experience iceberg

I’ve just started working on my new eBook / trend report and I got to thinking about the cover. A little back to front but it’s often a catalyst for thinking about any other major concepts I want to cover, or ways of explaining existing concepts (sense making in other words).

I have already largely defined the chapters, yet thinking about the cover got me fired up to come up with some new thinking. I also had fun thinking about the cover, it’s a great way to inspire you for the slog of writing a long piece like this.

I started out thinking about a cover through a crowdsourcing exercise which was also fun (part 1 and 2). At some stage past the first few iterations an iceberg came to mind. I think it had to do with the three major themes of the eBook / trend report: the subscription economy, customer experience and customer success. The iceberg easily covered all three with a submerged part, the visible part and the sea in which they are naturally found.

So here is the result in a quick doodle, a video and a few lightly detailed bullets below of the main elements of the iceberg. I’m just working out loud so this is far from final. I’ve already thought of improvements but I’ll include those in the final work and if you have any input I’d love to hear it. I’ll also be digging into any research around some of my hypotheses that these building blocks essentially are so if you have any please send my way.

customer success and experience iceberg


The ultimate point of a great customer experience and a customer success manager’s efforts is a customer that is highly satisfied. Most measures will lead up to this. In a subscription economy company but in general too, if a customer is sufficiently satisfied they will likely stay loyal which is also the ultimate point.


I main driver of satisfaction is when you get value from something. There will be a myriad ways of quantifying value but as long as a customer is deriving some material, financial or emotional reward for using a product or service, they should logically be satisfied.


When you unpack value you’ll likely find that that there are contributing factors that influence the eventual quantification of value. These can hopefully be defined as clearly articulated outcomes. This is not easy, especially when it comes to the softer type of outcomes like status or emotional wellbeing. Outcomes are ideally something a good customer success manager has identified and quantified (with solid metrics) upfront, and not realised unintentionally at the end of the journey. But there maybe be some element of backward engineering for some outcomes.

Method / Process / Tech

A good customer success manager will have a robust approach that can be applied as a set of key building blocks of activity which lead to the intended end points above. A repeatable and measurable process and methodology that drive intended outcomes. One thing I have added consequently and is not in the diagram, is technology. That is, a platform or combination of platforms that will facilitate the journey of delivering the intended end points.


success and impactYou have to be able to understand (through careful reporting and analysis) what is driving the intended end points. That has to be a well tracked and represented set of activities that lead to understanding. It is typically centred on user activity (of your product and/or service). The understanding will allow you to work with your approach to see what is working and what isn’t. The holy grail of customer success management is when your planned interventions (made up of the approach covered above) can be materially tracked and tied back to impacts on usage – see rough doodle for example. The way to connect these two is through insights on usage data.


Raw data primarily around how your product or service is being used is what I mean here. This often will stem from a key source which is the product or service being used. But sometimes there will be other sources of data that require integration into a single funnel which is then reported on and analysed for insight. For example you may want to take raw usage data and combine it with transactional data around the customer from a CRM system and together they enrich the meaning.


It’s a given I’m talking about a tangible product like a technology platform but it need not only be that. Service delivery around a product should also be considered. Even just a service in its own right. The key thing is that you have a product or service that is made up of interactions with customers and technology plays a role in either fundamentally driving the interactions or in the means of digitising them.