December 10, 2021
SaaS Learnings for Enterprise Analytics
Translate key SaaS success principles into enterprise analytics wins to advance your data-driven culture.
4 min read
Learnings from SaaS can teach us about enterprise analytics collaboration
Every weekend you can find me doing chores and exercising to the stories of successful B2B SaaS companies. My wife finds it a bit pathetic (sort of fair). But even she cannot deny that these companies are compelling—not so much their valuations, but the value creation confirmed by their mindblowing annual recurring revenue (ARR). Even Jessie had to ask “so how do they do it”?
In 10,000 words or more - I found myself on my soapbox - I say, “They fixate on user feedback, follow the data, reduce friction and simplify everything.” Why couldn’t I answer her that succinctly? When I finished answering her, I turned around to find her 5 blocks away on a dog walk.
But my succinct answer sounds good; it’s logical, obvious and practical. I stopped in my tracks: in the decade my team had built analytics and provided consulting related to performance/technology improvement, did we take this same approach? Do clients? We’ll develop each one of these areas in subsequent posts, but here’s how our team has translated the “best of” principles of SaaS to enable data-driven cultures in any organization:
Talk to your users:
Seek customer feedback. Always. The best SaaS companies’ lifeblood is feedback sessions because a first-hand perspective on where they are getting value and where they wish they found more value is irreplaceable.
How do you bring that in-house for enabling a data-led culture? Well, talk to your end-users. Ask open-ended questions about current analytics and the user experience. Increase the value by getting user feedback in the same medium they consume the analytics. How does your organization currently share and disseminate analytics? Is there a seamless mechanism for collaboration and feedback from all? Enabling feedback in a variety of methods with the analytics at the center is a huge opportunity to increase adoption through understanding your user.
The best SaaS products are always eliminating barriers, and the payoff is that users use the product because reducing friction gets them to value faster, saves time, and makes them like using the offering more.
How do you do this in your analytics environment? Much more coming in a subsequent post, but let’s start with access. Especially if your org uses multiple analytics software, is it easy for end-users to find/get to all of this content? Do they know what they have access to? Can they easily promote using content by sharing it with others?
Make it easier for people to access the plethora of what is available to them (and get user feedback on accessibility b/c it could be…surprising) and you have a better foundation to increase data-driven decision making.
Follow the data:
The irony of applying this to a framework for a data-led culture is palpable, but it’s a cornerstone of successful, growing SaaS companies. Why? Identifying a problem and having a hypothesis isn’t enough. Using—and rightsizing the amount of— data helps draw a conclusion and rapidly focus on the best option. Let’s translate it to your use case.
Is the organization measuring how many analytics dashboards it has to look at, or how much they’re actually being used? Do you know if users actually find all of these useful? What types of users are (not) using what is available? “Analytics on the analytics”, inclusive of all different software used for the builds to provide value for your organization, can be a value unlock. Consider offering this transparently to all users and accepting feedback here, too.
Much more to come in another dedicated post.
If you try to provide every filter, option and individual need “demanded”, you risk falling into a terrible build trap. This is how your org may have ended up with 250+ self-service dashboards in your ecosystem that have all become unused or have actually fragmented the decision makers.
Take a beat from Slack regarding making thigs simpler: “don’t make me [end user] think”. Instead of looking at user requests as a means to grow the BI shop and keep it busy on specs, reconsider the root cause, the “real” ask, and if you can reduce—not add-- features/options to meet needs. I assure you if it works for a platform with 12m+ daily active users it can work inside your org, too.
The concepts are simple but none of this is easy per se. Yes, leaders leading the charge helps. Education and data literacy helps. In addition, apply principles proven to outperform in other businesses like SaaS, along with technology and systems that enable these principles, and I suspect you’ll see some real ROI-increasing progress.
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