Growth hacking. There’s no doubt you’ve heard this term thrown around recently. But what exactly is it and do you actually need to be doing it?
The long and short of it is yes, you definitely need to be growth hacking. We do it here at Sisense and soon I’ll guide you through exactly how we found our North Star metric and what steps you need to take to find yours.
But before we do that, let’s talk about what exactly a “North Star” is in the first place.
What is a North Star Metric?
When you first think of the North Star you probably imagine a guiding light in the sky that can help you if you ever find yourself lost in the middle of nowhere. While a North Star metric won’t be found among the stars, the concept isn’t too far off.
Your North Star metric is your guiding light when it comes to growth hacking. It’s the metric that shines above all other metrics. It’s a powerful driver that should capture the core value your product delivers to customers.
How We Found Our North Star
Something all businesses think about on a constant basis is churn. Why do customers churn? How can we reduce churn? Where does a customer’s decision to churn really come from?
No small task, as Head of Product Intelligence, I was given the job to find a way to prevent customer churn through recognizing suspect patterns in data and using it to proactively fix any related issues internally and for our customers. But with so much data available, it can be daunting to figure out exactly where to start. Here’s what I did:
We always stress to our customers that when implementing BI they need to involve the stakeholders in their organization that will actually be using the dashboards from the very beginning. Finding a North Star metric is no different. I started by sitting and interviewing stakeholders and Customer Success Managers in order to define, understand, and distinguish between success and failure stories.
Take away: Talk to people! You can choose to dive straight into the data and try and find your way but if you can’t define how your project will affect the people that will be using it, you’re likely missing a huge piece of the puzzle.
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Once I was able to define what a success and a failure looked like for our internal stakeholders and Customer Success Managers, I began exploring the data.
First, I built a data exploration dashboard that presented, side by side, the usage patterns of happy customers (those that renewed) and sad customers (those that churned). Combining usage data from our events tracking system, demographic data from our CSM, and license details from our back office database, I focused just on the first 12 months of usage.
During this first phase of research I looked for differences between the two populations by observing their usage patterns. I compared any and every measure I could think of, including:
- MAU % Growth: The growth of number of Monthly Active Users during month to month period.
- License % Utilization: MAU/licenses purchased represents the license utilization ratio. When license utilization is greater than one – it’s time to grow the account. When license utilization is less than 0.3 we have an adoption problem and potential churn risk.
- Stickiness: Daily Active Users/Monthly Active Users (ratio of DAU out of MAU), determines how often the product is being used. DAU/MAU that is closer to one (greater than 0.7) reflects daily use.
I started first by plotting these different measures, one by one, on a trend line to get a sense if the data was even interesting to look at. The x-axis of my trend line represented the age of the account, with my goal being to find the change in usage patterns over the two populations over time. I then plotted each measure, again one by one, on a box plot to present the metric by time. Box plots help to compare between distributions and in this case, I was looking at the distribution between each metric over age/time.
From here it was all a matter of trial and error. I tested select use cases but couldn’t find a significant difference between the two populations.
Take away: You won’t find your North Star metric right away, and that’s okay. But, if you want to get there fast you need to play with the data in any and every way you can think of. Don’t be afraid to try new things!
When I couldn’t find any significant difference between the two populations I realized I needed to know more. I talked with more people and thought deeper about the story behind the happy and sad customers. The key point that continued to come back to mind is that a customer needs to get real business value in order to renew.
In order to really see if a customer is getting business value, we needed to focus in on dashboard consumption actions because they indicate how end users are interacting with the system. It became clear that those accounts that churned were focusing mostly on data preparation actions and not end-user engagement – likely leaving them with little reason to justify having BI.
In order to take into account size of a customer’s organization, I created a dashboard that broke down consumption ratio, which calculates what percent of the overall actions are dashboard actions such as querying, creation, sharing, and interacting.
This calculation follows the reasoning behind the 90-9-1 Rule for Participation Inequality in Social Media, and would answer the question of what percent of the customer’s total actions were dashboard consumption related.
The moment I put this measure into a box plot it became crystal clear that there is a difference between the happy and sad customers, regardless of customer type or license attributes. I was on to something! But I couldn’t rest easy just yet.
I exported my results and loaded them into R in order to ensure my findings were statistically significant. In order to do this, I applied a statistical significance test and found that the only significant measure was the percent of dashboard consumption ratio.
Later on, I built a decision tree and this exact same measure appeared to be the strongest churn predictor again. I realized that, although customer demographics have different benchmarks, whenever the percent of dashboard consumption does not “beat” the percent of data preparation, a red flag needs to be raised. Success!
Take away: Dig deeper. It may seem repetitive to continue to run tests upon tests but this is your overarching, most important metric. Attacking it from all sides with statistical tools and techniques is a must.
Now that you’ve found your North Star metric, it’s time to roll it out to those who you involved all the way back in step one. Present the metric to them and get them used to using it in their day to day report.
Whenever you’re dealing with a customer based benchmark, which we are here, make sure to create an alert based system so that users don’t have to constantly be checking in manually. Of course, we use Sisense Pulse, set the appropriate threshold, and let Sisense do the rest.
Congratulations! You found your North Star metric. Your work, however, is never complete. We know all too well that businesses change faster than a speeding bullet. It’s important you’re constantly monitoring your North Star metric and running tests to make sure it stays accurate. If anything changes, commit to following the winning data and make changes as you go.
Take away: Once you find your North Star metric take a minute to celebrate. Remember though, it’s not something you can set and forget. You need to iterate, test, and commit to following the data to improve your product.
All data represented in this post is for informational purposes only and is not accurate.