This last week I attended a meetup and workshop in London organised by Customer Success Network, a European based not-for-profit community for customer success managers. It had the same title as this post.
An excellent session which started off with a few minutes of talking by Dan Steinman, GM Gainsight EMEA. I then facilitated one of the breakout workshop sessions on how good data should be used in QBR’s (Quarterly or Executive Business Reviews as they are commonly known). Here are some brief notes.
Dan started off talking about we all have some “good enough” data, which should be good enough for starters. In other words, don’t get hung up with not having a perfect set of usage data or reporting setup. You can easily get started with things that don’t require usage data but can tell you a lot about your customer and how to manage them. Things like:
- How long have they been a customer?
- How many renewals have they done?
- What is their ARR now vs originally?
- Are they paid up on their bills?
- # of Support cases open?
- Survey results?
In terms of the elusive product usage data though, you HAVE to get it at some point. Some ways mentioned: Segment.io, MixPanel, Google Analytics, Aptrinsic. I’ve used MixPanel which was okay but had great experience with Looker too and in my current work I use PowerBI where we actually focus on enabling the customer to have the same views and insights as the customer success manager.
On the last point above, this is holy grail territory in my view because then you and the customer can have truly meaningful conversations since there is a plain and evident, single source of truth you can discuss strategies around.
Back to product usage data. Your product/engineering team should want it as badly as you do. Start with the bare minimum – logins, pageviews, reports run, etc. Don’t accept no for an answer.
Muck in even if it means having to learn a new tool. I remember spending an enormous amount of time learning first MixPanel and then Looker in my last role. All the product team had done was create the connections with the usage data and the reporting tool but how to make sense of it was left up to your own devices. But oh how rewarding when it works and you start making sense of the data and having the right conversations with the customer.
And its not just your product/engineering team who should want it as badly as you do. Marketing and sales teams have spent decades and millions perfecting understanding of prospects. Once they understand that customers are the new growth engine, they’ll be on board to help you create and share access to the same level of understanding on customers.
Different use cases for data
The workshop breakouts were pretty much focused on different use cases for data. I facilitated the one on QBR’s. The activity was focused on mapping as-is and to-be QBR data definitions. First we defined traditional definitions. Next we challenged these. How else could we focus on predictive or future-focused growth measures? What were they?
The output was a view of mapped current and future-focused CS measures, and why you’d use them. Here is the groups output after I took the raw material, cleaned it up, tweaked it and added a little of my own spin.
The other breakout sessions all explored different aspects/use cases of data usage like:
- You work in a small start-up where customer success is just evolving. You want to able to demonstrate the role of the team to show the impact you are making internally.
- Your company has been expanding rapidly, the growth of MRR is now driven by expansions and upselling, which is owned by the Customer Success team. You’ve been asked by your CEO to demonstrate CS’s impact across the business to prepare for another round of funding.
- Customer Success teams are increasingly expected to become more financially driven. This exercise was intended to demonstrate their role in contributing to the growth of the company.
- Drawing out a success plan which would help the most immature customer success team understand:
- What value looks like and where CSM’s can get data from (even if they don’t “have any” today)
- How to track customer health through the life cycle with what they have
NOTE: There is an evolving document capturing the output of all the sessions.