The case for Product-Led Customer Success

Led by Openview Labs, product-led growth has become a recognised SaaS business model.  Much of the focus has been on the use of the product as the primary sales tool.  I want to focus on product-led customer success (PLCS) as an approach to build scalable, profitable growth through retention and expansion revenue.  

Product-Led Growth = 

Product-Led Marketing & Sales + Product-Led Customer Success

To secure the retention and expansion revenue that is key to SaaS growth, B2B suppliers must be able to convert the promises in the marketing and sales stages into measurable value, preferably a proven ROI.  Zuora CEO and Subscribed author Tien Tzuo said “We tend to meet each new challenge by creating a new stovepipe.”  That’s how most companies approach the delivery of customer success.  Rather than think about CS being built-in, we create it as an add-on.  This approach however does not deliver scalable margin, especially in businesses with low AOV.  

PLCS addresses this by building the success realisation process into the product.  A success realisation process follows five stages:

  • Discovery: Extending what has been learned in the sales process to provide context.
  • Benchmark & Goals: Determining the current and desired levels of the measures of value. 
  • Success plan: A customer specific project plan, including actions beyond the product, detailing what is needed to achieve their desired goals.
  • Advice & guidance: Context rich delivery of resources throughout the success plan to help the customer achieve their desired goals.
  • Success dashboard: Tracking & reporting of progress in the steps of the success plan and achievement of the desired goals.

To learn more about the process read our e-book “Product-led Customer Success

I believe PLCS will become the dominant model for B2B customer success for the following reasons.

It is what many customers want.  Customers have bought a product and expect that product to deliver the value that underpins their buying decision and what was probably promised in your marketing & sales.  In a blog about predictions for CS in 2019, CS software supplier Gainsight say “Customers are happier when their needs are anticipated and addressed through efficient digital means.”  Given a choice, busy customers don’t want to call a support or success agent, they just want a product that delivers what was sold to them.  

It delivers significant financial benefits for the supplier.  Shifting the balance between people and product-based costs provides massive opportunities for scalable profitable growth.  It also holds out the opportunity to shift from costs and low-value services revenue to recurring revenue: a mix preferred by investors for its scalability and predictability.  Openview’s Product-Led Growth Index suggests PLG companies attract higher valuation multiples.  We have created a PLCS financial model to help companies assess this impact on revenue, profitability and company valuation.  It is important to note that the product-led growth approach shifts the balance of costs way from sales, marketing and customer success to research & development.  Data from Openview show that some companies recognised as leaders in product-led growth spend up to 50% of revenues on R&D. 

Building the success process into the product creates opportunities for companies to understand better the relationship between value and the activities required to achieve it.  Mastering this relationship will deliver further benefits:

  • Companies will find it easier to direct product roadmaps to further improve value achievement.
  • Marketing & sales messaging will be strengthened with value-proven case studies and reference customers.  PLCS companies will be able to offer guarantees for their SaaS products.
  • Analysis of customers value achievement will refine ideal customer profiles, creating a virtuous cycle.
  • Confidence about proven value will enable companies to introduce performance-based pricing 
  • performance, where customers pay for the value delivered against that promised in the sales process.  

Products that are easy to use go hand-in-hand with PLCS.  Consumer software achieves this, even with complex products.  Where they lead, B2B will follow.  The techniques are known, it is the intent and desire that is often missing.   PLCS requires a level of ambition for software developers far in excess of what many now aspire to.  

Let make clear that product-led does not mean product-only.  I do not see the end of people as a means of delivering the advice and guidance customers need to achieve their goals with the product.  I do however predict a lower ratio as more of the success process is productised.  This will result in a shift in what CSMs do; guiding customers on the process and psychology of change rather than product use.

Single customer view

This has been an interesting week for those, like me, who believe in the importance of a single, rich view of the customer as the foundation for delivering great customer experiences.  Interventions that match the goals and specific, current context of the customer are not possible without this foundation.

First, Adobe, Microsoft and SAP announced their Open Data Initiative designed to make it easier for products from the three companies to share data.  Hot on the heels of that  was Salesforce announcement of Customer360, an initiative built on its acquisition of Mulesoft, to bring together in real-time data from multiple systems.

I applaud these initiatives but a single customer view, however important, is of little value to customers without mutuality.  In this context mutuality means using every piece of data for the benefit of the customer as well as the company.  All too often, customer data is used just for the company’s benefit.  As has been said many times, we, the customer, are often the product.  Our data is used for one purpose; to sell more to us with little thought to our goals and needs.  The backlash has started, witness the growth of data protection rules.  These are only necessary to control companies that exploit not value customers.  As tools to help us control access to and use of our data grow we will take more control of our data.

When I first discussed this idea in 2008, I talked about the ability to decide who can access personal and transactional data.  Back then, I called it CMR – Customer Managed Relationships.  We will gain the ability to share it only with companies who can show how they use it of our benefit and that has to go beyond simple personalisation.  I want a real benefit, often financial from what is increasingly one of my most valuable assets: I want to deal only with companies that share my values.  Meet my criteria and I will tell you everything, including what I have done and purchased with/from other companies.  Don’t and I will lock you out of everything.

In his opening address at Dreamforce18 Salesforce chairman and co-CEO Marc Benioff, said technology is neither inherently good nor bad; it’s what you do with it that matters.  For those of us who truly believe in customer focus and the benefits it brings, that starts with mutuality.  Its a pity this did not feature more in the announcements.


Did we really do those things?  I know hindsight is 20:20 vision but looking back at how we used to think about and practice customer success seems quaint and somewhat crude compared to how we now operate.  I am old enough to remember the days when setting up a CS capability was focused on adding a team of people to work with customers to help them achieve their goals and generate a return on what they spent on our product.   I’m embarrassed to say I did it myself and it worked – at a cost!  

When I look back, it seems wrong that we didn’t get to the root of the issue sooner – a product that should deliver what customers buy it for out-of-the-box.  Just like today, they only wanted, needed, to achieve their goals and deliver a positive ROI.  The old people-led approach worked, sometimes, but at a significant cost to both margins and customer satisfaction.  Customers thought they were buying a product that delivered what our sales folks had promised – a benefit, a desired outcome, a return on their investment.  It seems odd now that the only way we thought that could be done was by constantly phoning and emailing the customer, not really thinking they just wanted to get on with their job.   We all too often confused our need to contact the customer with what they really wanted.  Just think of all the work we have saved, for the customer and us, by building the success process into the product.  

Do you remember those playbooks everybody spent ages developing?  How arrogant we were to think that we could guide customers based on sketchy or non-existent research into their challenges and needs.  We even described customer journeys with phrases like ‘Land, Adopt, Expand, Renew’ – confusing what we wanted a customer to do with what they needed to do.  The basic idea of expert guidance was sound it’s just that we were lazy in how we implemented it.  Instead of putting the time and money into building a real 360, single view of the customer and researching in depth the work they do and the challenges they faced, we pushed customers down a path of our design.   At least now we drive next best actions in context, using a very rich customer data set and the role of customer research means we know far more about them than we did back in the day.  It might not be perfect but our advice is much closer to the customer’s current context.  And we do most of it in the product.   

Given the number of times it has happened, we again underestimated the impact technology would have, particularly AI. Building meaningful CS processes into the product gave us access to stacks of data on customers’ goals and how effective they have been in changing their processes and organisation.  Without this, AI and machine learning, which is so central to what we do struggled to identify the patterns behind real success practices.  No longer are we limited to looking just at product usage, although that is a still a very important piece of the jigsaw.  The ideal of software that learns how to achieve outcomes and automatically corrects itself, without human intervention is getting closer.

Fewer still saw how technology developments would lead to Google and AWS becoming the dominant players in the customer success software market.  We should have seen it coming; it had happened many times before.  For example, the first AI applications were built from scratch by developers as part of a specific application before becoming libraries that anyone could use and then services embedded in hosting infrastructure.  Customer success as an infrastructure service that you plug-in and configure is still in its infancy but I only see it growing.  

It is strange that at the time that Google were introducing Edge TPU, AI on a chip, we were still thinking that the success process could only be delivered by people.   Of course people still matter and play a vital role but the success coaches we now employ are a far cry from the product-centric CSM’s of old.  We have developed the role into a high level, high-skill change coaches whose focus is often around the psychology of change.  Whilst we have fewer of them, CS coaches are highly paid and highly valued by their employees and, more importantly, by customers.

What next?  I’m the wrong person to ask: I’m a historian not a futurist but try this if you want to know what I think about product-led customer success

Balancing people and product for profitable growth

This blog is a summary of an upcoming e-book “Product-led Customer Success”.

As CEO of Clicktools, one of the UK’s first SaaS companies, I was always seeking better ways to deliver profitable growth.  I have been an advocate of customer focus since the late 80’s: actions therefore were always based on the premise that what we did had to be good for customers AND the company.  We appointed our first customer success manager in 2004, following the lead taken by Salesforce and went on to build a very successful team of CSMs. Gross revenue retention was mid-90s and net revenue retention well over 100%.  Given that success, would I do the same if I was building a SaaS business today?

No: let me explain.

A B2B SaaS company sells a product that helps the customer achieve a goal; to deliver benefits. These benefits are the focus of the marketing and sales process and delivering those promised benefits, the customer’s desired outcome, is the whole premise behind the philosophy of customer success.

As a product company, it should therefore be an inherent part of the product.  Unfortunately, most B2B SaaS products don’t deliver.  Instead, CEOs build a team of people to fill gaps in the product’s capability.  Worse still, this knee-jerk response is seen as the best practice.  Think that through: so called best practice is to add recurring cost to your business and constant interruptions to your customer to address a failure to deliver on your value proposition – a product that helps customers achieve their goal.

If I were starting out again, my first response would be to look at how the delivering customer success can be built into the product.  Here’s why.

  • Helping to achieve the customer’s goal is a B2B SaaS company’s raison d’etre. It must therefore be the primary goal of the product itself.
  • We are approaching the point where contacts and notifications swamp our attention and reduce productivity.B2B customers are using an increasing number of apps, the suppliers of which each want time.  A buyer just wants a product that helps deliver their goals. A backlash to the interruption economy is coming.
  • Technology to deliver self-service customer success has come on leaps and bounds. The development of applied AI and machine learning, will further enhance the ability to deliver contextually rich guidance.  The companies that build success processes into the product will use these technologies to enhance their understanding of what drives success, furthering their advantage.
  • The basis of self-service customer success is a deep understanding of customer’s objectives and the changes they have to make to achieve them. The depth of understanding required to deliver self-service CS is itself a source of advantage.
  • Done well, it is a path to scale at higher margins. Recent researchby Notion Capital and Frontline  shows that many (but not all) self-service based SaaS companies are delivering leading financial performance.

What does it take to embed customer success into the product?  The starting point is a deep understanding of what the customer is trying to achieve (their desired outcome), the challenges they face in achieving their goal and how the best players overcome these challenges to achieve their goals. This of course includes the role your product plays in their work.  This understanding is the basis of customer success, however it is delivered.

Not helping the customer address the changes they have to make is essential to delivering customer success and thereby the retention, revenue and referrals business growth needs.

Deep customer understanding is the basis for building four capabilities into the product:

  • Discovery: Building a picture of how the customer currently operates
  • Goal setting: What the customer is trying to achieve, including guidance on     achievable and stretch goals.
  • Success plan:  The steps the customer needs to take and how the product is best configured to help the customer achieve their goal.
  • Working space:  A simple goal and activity tracking capability supported by delivery of contextually rich advice and guidance.
  • Success reporting: A dashboard showing progress towards the desired outcome, highlighting out of norm activities.

People tell me this is too complex.  I disagree.  I readily admit it will take work and some trial and error.  I think the problem is that much of what we describe as customer success is in fact supplier success; meeting our own retention and revenue goals, irrespective of the success the customer achieves.  It also seems that we can afford people to do this important task but we lack the vision productise it.

The sub-title of the blog is ‘Balancing people and technology to deliver customer success’.  I do not see product-led customer success as the end of the CSM. I do however see a change in the number required and what they do.  Much of the basic work of customer success; guiding product use, setting goals, process change advice will become part of the product.  CSMs will focus on the higher order challenges of change: helping customers build the case for change, shoring up their courage and providing examples of success.  It sounds a bit airy fairy but they become change counsellors more than process experts.

I am not saying no to people delivering CS; just don’t make it your first and only response. When your CEO comes to you at the next budget cycle and asks you how many more CSMs you need, say ‘None but I would like a small development team please!’

The robots are coming

The role of AI in Customer Success 2.0

It is one of the most talked about subjects in technology – artificial intelligence.  According to some, it is going to revolutionise the world.  Kevin Kelly (of Wired fame) describes AI as the second industrial revolution in an insightful TED talk.  This blog examines how AI will change the field of customer success; specifically the contribution it makes to the development of CS 2.0, where the product is at the heart of and the main delivery vehicle for customer success.

Let me begin with a grossly simplified view of the elements of AI.

Data capture and categorisation -> Machine learning -> Propensity modelling -> AI applications.

Data capture and categorisation

AI without rich data is an oxymoron.  Tools for smart capture and categorisation of data are the foundation of AI.  In the field of CS, a single customer view has always been; with the advent of AI is not optional.  Tools are emerging that capture data from documents and others that use AI to rate the quality of data.  And of course we are all aware of AI based intelligent assistants that recognise and respond to speech.  These will be used increasingly in a business context.

Machine learning

Machine learning is the automation of pattern identification in large data sets.  It answers questions like “What product usage data correlates with churn  are the characteristics and activities that correlate with retention?” or “What customer characteristics lead to churn?”

Propensity models

Propensity models use the patterns identified via machine learning to predict outcomes.

AI applications

These are applications of AI to do specific tasks.  These may be full automation of tasks or automation used to guide people in the completion of their work.

Here are a number of tasks where AI applications will contribute to customer success.  Many of these are already in use, although most are still a minority sport.

  • Conversational discovery.  Natural language interactions to collect information on the customer’s goals, challenges and modus operandum.
  • Customer ROI guidance.  Delivered in the application itself, AI will identify the actions a customer should take to achieve their objectives/desired outcome.
  • Personalised implementation plans.  On-boarding tailored to a specific customer’s situation and goals
  • Next best actions will replace playbooks.  Playbooks are typically a company’s interpretation of what a customer should do next.  Next best actions use more granular data patterns to understand context and suggest an action.
  • Customer health/engagement scoring.  AI driven health dashboards will improve the reliability of scoring and will self-adjust as continuous machine learning identifies changes in the underlying data.
  • Feature targeting.  Identify customers that can gain greatest benefit from new features and should therefore be targeted first.
  • Sentiment analysis. Discerning the behaviour and intent from the content and tone of customer conversations.
  • Upsell targeting.  Listing the customers most receptive to additional purchases and why.
  • Content curation.  Identifying the content which will be positively received by which customers.
  • Dynamic pricing.  Suggesting the best price for up-sells and renewals.

Here and now

I am not suggesting that these capabilities will be widespread this year, not even next but I think many underestimate the maturity, sophistication and speed of development of the technology.  Here’s a few things to take note of.

  • Research from IDC into the use of AI in CRM suggests that 55% of companies expect to have implementations (not pilots) established next year (2018).  This will generate additional revenue for the companies using AI of $1,100 billions by 2021.
  • AlphaGo, a Google AI programme beat the best two Go players in the world in 2016.   It was coached on how to play.  Its’ successor, AlphaGo Zero was just given the rules of the game and learned how to play itself.  It took AlphaGo Zero just three days to reach world beating standards using a fraction of the computing power.  Professional players say it uses moves never seen before.  This is what neural networks do and they do it far better and faster than people can.  Imagine giving an AI tool a set of business principles and letting it learn how best to deliver customer success.
  • AI tools are widely available and will become a utility within five years.  The IDC report above suggests spending on AI will grow to $46 billions by 2021.  That is more than the forecast market for CRM, itself one of the biggest technology markets. In November 17, Salesforce launched MyEinstein, a tool to allow administrators, not developers users to build their own AI applications.  Almost all customer success software has, or have plans for using AI in their applications.
  • Andreessen Horowitz, one of Silicon Valley’s leading technology investors believe that AI is a fundamental platform of the same order of importance as cloud and mobile.

AI and competitive advantage

Whilst the technology is vital, I don’t think it is the real source of competitive advantage.  Given its ubiquity, how can it be?  Sure, early movers will gain an advantage but technology that everyone has access to won’t sustain that.  There are two factors that will create the winning edge in the use of AI in customer success.

The first is data – the richness of the picture that companies can build about their customers.  Single view of the customer has always provided an edge.  AI provides the means to extract meaning out of much larger and more diverse data sets.  The more dots you have, the more patterns you can spot.

This is reliant on the second source of advantage; mutuality – a true customer first culture.  Mutuality is not just a belief that customer satisfaction or even their success drives what a company does but about taking actions that are good for the customer and the company.  It is about using the customer’s data to their benefit, not just yours.  It is about doing the right thing for the customer, not just selling them anything you can.  Customers will soon have the means to control access to their own data and will increasingly restrict access unless they can see something in it for them; unless they can see that the company is doing the right things for them.  Remember, it doesn’t matter how good your AI applications are, they are useless without data.

Changing CS

AI will radically change the customer success landscape.  Routine tasks will disappear: chatbots are already replacing the support team and that will extend into the simpler, routine tasks of customer success, particularly as more and more companies build this into their application.  So what of the CSM?  Well if they spend their time doing routine tasks like on-boarding, training and low-level process change; they will also go.  If they are change mentors, guiding people through the human aspects of change, then they will stay.  In proportion, we will need more of these.  We already do it is just that we can’t afford them given everything else a CSM has to do.

Make no mistake, AI will improve productivity and that will impact jobs.  In the early stages, AI will augment people but as confidence in systems grow, AI will take over some tasks.  Whilst I have concerns about individuals who won’t or can’t adapt, I am not fearful of the overall impact.  President John F Kennedy said “We believe that if men have the talent to invent new machines that put men out of work, they have the talent to put those men back to work.”  Throughout history, new technologies have changed work and jobs but the overall effect has always been growth.   People just do different things.  Question is, if you are in CS, what are you going to be doing?

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