Stuart Balcombe
February 24, 2022
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4 minutes
This week weâre dissecting one of the top challenges being talked about on the interwebs when it comes to customer success and onboarding right now.
How do you scale customer success?
Thereâs lots of debate about whether teams should be using a low-touch, tech-touch, digital-touch, high-touch, mid-touch, or any other type of touch success motion.
And honestly (whispers quietly). Thereâs no right answer.
The only âtouchâ that really matters for your customers, is the one that helps them make progress towards their goals.
Not enough onboarding and success playbooks are being built around the path successful customers are actually taking to get there. We often think that mapping the golden path through our own product is enough, but itâs not.
What we really need is to reverse engineer the path our most successful customers took to achieve their goals and match our engagement model to give them what they need at the right time.
Hereâs a 3 step framework for doing it in practice:
Often the experience that we shape for our customers is driven by whatâs most convenient for the company.
For example: We need to collect this data, we want to schedule this call, we want to send this email.
But these actions are not necessarily relevant or motivating to customers - so the best way to build a more effective path to success is to look at the successful paths that have already been taken.
Hereâs what Dan Ennis told me about the signals he looks for in the data.
âWe know customers that have churned, we know customers that have grown ,so we can plot back against that, looking at milestones, looking at health triggers, looking at different usage trends, adoption trends, all those different kinds of things â we try to make everything that we are really data-driven, thatâs one of Mondayâs core values. We try to take a look at mapping: What do successful customers look like? So we can say, this is what people on that path do and customers that churned, customers that werenât successful. What did that look like?â
Itâs important to note that while Monday is operating at a pretty massive scale, this approach can also be applied with just a handful of customers. By manually digging through the email communications, call notes and product interactions of your âbestâ customers youâll start to see patterns that you can use as the jumping off point for more scalable experiments.
Donât forget that segmenting your âbestâ customers by attributes like desired outcome, industry, and seat count will help you identify more specific value paths to optimize.
Once you know what a successful path to value looks like, and the common objections and sticking points along the way, you can start to formulate a hypothesis about how to help more customers find and stay on the happy path.
Back to Dan Ennis âThen we really use a lot of that data to be our north star when weâre testing and kind of following almost a scientific model of why we think that this is what being successful looks like. Letâs compare it against three accounts that we know are successful. So there is some validation in that. weâre able to validate our thoughts. To customers that we know have been successful.â
The critical questions to answer before you run an experiment?
The best part? Intentional experimentation doesnât have to mean taking weeks to build new processes or adopt a new system.
You can start small and iterate over time. Here are 2 example experiments you could test in the next week:
We started this playbook by looking at the data. Now weâve run our experiment we can go back to the data and ask.
Did our experiment improve the outcomes for the people in the test vs those who came before?
Are we better at delivering outcomes today, than we were yesterday?
Now we know what the path to success looks like, and weâve tested our interventions to keep people on it, weâre ready to operationalize that tactic and bake it into our standard process (an Arrows template is a great way to share this plan with customers).
But as Jerry Henry, Director of Professional Services and Onboarding at Sendoso described. Itâs critical to build continuous feedback loops into everything you do.
âwe keep track of days in a certain stage in onboarding, so we have kickoff configuration, integration, enablement â So if we are noticing one stage is taking longer than the other. Are we losing momentum during that stage? And then at the end of onboarding, we send a survey out to kind of capture some CSAT from our customers â We review it every Thursday with our onboarding managers â Weâve noticed that this is a common trend where weâre missing this or maybe we didnât have enough content that we leave as takeaways, things like that. And then we use that to change how weâre doing things.â
A playbook that is documented is a playbook that can be improved and optimized.
So how does all this connect to scaling customer success?
âknowing that we want to reserve our human interactions for times that are going to be most strategic and impactful to have kind of a just-in-time human interaction is a lot of the way we look at it. So part of that is proactive lifecycle based. And so we factor that around where the customer is in the life cycle and how we allocated resources, frankly, is just digging into the data.â
As Dan Ennis from Monday.com explained. We can now more strategically deploy our limited resources instead of making blanket statements about engagement models based on the number of customers we need to serve.
If you want to hear first hand from someone currently doing the work to ensure successful customer outcomes at scale, the full episode of Happy Customers with Dan Ennis, Scale Team Manager on the Customer Success team at Monday.com is well worth 30-minutes of your time. We talked about:
â Prioritizing customer touch points
â Making reactive customer outreach feel proactive
â Designing experiments to improve customer outcomes
â Using data to strategically include a human in the loop
â Collaborating with other teams to map the customer journey
Listen to the full episode (30-minutes of data-backed tactics) and let me know what you think.
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