Posted by Dan Wolchonok
The Sidekick growth team is a small, data driven and aggressive group within HubSpot that works on new, emerging products with massive audiences and a freemium business model (similar to Dropbox and Evernote). We are constantly pushing ourselves to learn new growth strategies, tactics, and techniques. I have personally become more data driven and model driven after joining the team, and wanted to walk through an example of one decision that became much easier with the use of our generic problem solving framework.
I am a big believer in the idea that complicated problems look simple when you are able to break them down. Don’t take my word for it - this is what was attributed to Einstein:
If he had one hour to save the world he would spend fifty-five minutes defining the problem and only five minutes finding the solution
The Sidekick growth team follows a very straightforward process that strives to take complicated choices, and analyze them to produce areas of opportunity:
Step 1: Choose a goal
Step 2: Build a model
Step 3: Analyze the inputs
Step 4: Identify opportunities
These steps are generic enough that they can be applied to many kinds of problems. Whether you’re on a sales, marketing, product, support, services, or any other type of team this framework is incredibly valuable.
Step 1: Choose a goal
Choosing the right metric / goal is very challenging and critical to our success. If our team optimizes for the wrong metric, it doesn’t matter how well we execute because our efforts won’t translate into success.
Our identified goal:
We ultimately chose to define our goal as increasing the number of people active on a weekly basis. Rather than pick any metric, a person has to take one of six key actions to demonstrate that they are getting value from our product. Brian Balfour talks about the cycle of meaningless growthhere, but the key takeaways for our product are that more people using Sidekick helps us to grow faster.
Some of the attributes we used when picking our goal:
It is a holistic representation of our product. We thought about all of the ways that someone uses Sidekick, and thought about the best way to represent them.
It’s authentic (hard to fake). If you optimize for a hollow metric that is easy to attain, but doesn’t translate into success later on, you might fool yourself into thinking you’re making progress. If we were to pick signups, we might crush that goal and get a lot of users to sign up, but they might not stick around.
It represents real value. If you solve for your own needs instead of the customer’s needs, you may be successful in achieving your goal but it won’t translate into true success down the road. We tried to pick a goal that represented users getting value out of the product, which results in people upgrading to the paid version of Sidekick.
Step 2: Build a model
With the goal established, we then set out to build a model to understand what will have the biggest impact on weekly active users (WAUs). If we simply tried to increase our top level goal on its own, we wouldn’t have an understanding of where to start. In order to understand where to focus our efforts, the model breaks down the goal into manageable pieces. The whole point of building the model is to understand what the inputs are, and what the biggest contributor to our goal is.
Our Excel model breaks down our goal (WAUs) into the individual components that drive it on a weekly basis. It’s a simple equation:
WAU = (New people) + (People from previous weeks who continue to use Sidekick)
We broke down each of these two buckets into their individual components.
WAU =
- New People:
- Channels:
- People we acquire through paid acquisition
- People we acquire through content marketing
- People we acquire through SEO
- People we acquire virallly from existing Sidekick users (invites, etc)
- Activation Rate
- Not everyone who signs up ends up using the product. Therefore, we measure the people who install our software and get it up and running correctly through an activation rate. Rather than look at the activation rate across all channels, it’s important to understand how each one is different, and if there are isolated pockets ripe for improvement. For example: users who read our content are more likely to set it up than someone who clicked through on an advertisement before signing up.
- People from previous weeks who continue to use Sidekick
- We look at the number of people who sign up each week, and then look to see how many of them are active each week since they signed up.
- We look at retention, which is critical in freemium businesses. In order to accomplish our goal of having millions of users, we have to retain the users we acquire.
This is a screenshot of our model:
The numbers in this screenshot have been changed so they are not reflective of our true numbers.
Step 3: Analyze the inputs
The model above is extremely valuable because it allows us to use our week-over-week growth to forecast the long term impact of any change. It’s incredibly hard to understand how multiple factors could interact over a long period of time. It might be possible for someone to reasonably predict the implications of any change, but without the model it is easy to be short sighted.
With our model built, it was easy for us to test the sensitivity of the inputs. For example, if we were to increase the number of users we acquire from our paid acquisition budget, how would that impact our WAUs in a year? Instead, if we focused on retention and user acquisition rates stayed the same, would we have more users a year later? What about if we improved the conversion rate for a different area of the funnel?
Rather than sporadically tackling new campaigns or projects, the goal is to understand what is the most impactful focus area for the business.
In looking at the Sidekick funnel, we found that two of our biggest drivers were retention and viral growth. We modeled how changes to each of them would impact our goal, and decided to focus on retention first before looking at increasing the number of new people through viral channels. At the end of the day, some of the factors that we always consider:
- What is the current state of the metric?
- How much do we think we can improve this metric? What’s the ceiling on any improvement?
- What are the resources required to have a meaningful impact? How long would it take?
Factoring in answers to those questions, and including the estimates in our model, we decided to focus on improving our retention in Q4 2014. There was a lot of analysis that went into picking retention; it was the result of repeating Steps 1 through 3 multiple times. By going through the process of evaluating different levers in the model, it becomes much easier to weigh different options against one another and impartially judge alternatives.
For the Sidekick team, it wasn’t as simple as saying that we wanted to improve retention. Just like WAU’s, retention in itself has many inputs that we had to evaluate.
Of the people that stop using Sidekick, we lose the majority of them in their first couple of weeks
In the hypothetical example below, we have sample numbers of how we retain users over time:
Cohort Size
|
Signup Week
|
Active 1 Week Later
|
Active 2 Weeks later
|
Active 3 Weeks Later
|
100
|
11/3
|
55
|
50
|
45
|
110
|
11/10
|
61
|
55
|
50
|
120
|
11/17
|
66
|
60
|
54
|
The numbers in this table have been changed from their real values for this post
Given the size of our user base, we determined that week 1 retention was our biggest issue and opportunity. If our existing user base was larger, our long term retention might have been a more important issue. The lesson is that your biggest areas of opportunity depend on your current context.
Once we isolated the fact that people stopped using it after their first week, we set out to understand why someone who installed Sidekick would stop using it after they signed up.
Step 4: Identify Opportunities
At this point of the process, we know what’s most important to our goal and the implications of an improvement. The next step is to start identifying how we can make an improvement. Depending on the lever, there’s a mix of elements that are helpful in breaking down the opportunity. We used quantitative analysis to identify a problem segment, qualitative analysis to flush out its symptoms, and used our understanding of our product to come up with ideas to address the issue.
To identify a problem area, we did a quantitative analysis of the people that only used Sidekick for a single week. We looked to segment these users to look for patterns, such as:
Where were these users coming from? Was there an issue for a single channel of users?
What technology were these users using? Was it an issue with Gmail, Microsoft Outlook, or Apple Mail?
What part of the application were they using the most?
How much do they use Sidekick? How many days did they use it? How much did they use it their first day?
In asking these questions, we found that Gmail users were more likely to stop using the product when compared with other email clients. This was a complete shock to us. We had figured that Gmail would retain fairly well, and that an issue would be likely to exist in one of our other email clients. We found that a large number of these people were only active the day that they signed up. To understand their usage on their first day, we created a histogram that showed how many tracked emails this population of users sent their first day.
The numbers in this chart have been changed from their real values for this post
For the Sidekick team, it wasn’t surprising that people who only tracked a single email their first day didn’t come back. The surprising element was that such a large % of these people were only tracking one email. We wondered why someone would go through the Sidekick onboarding process only to never use it again. Wouldn’t you at least test it out with a couple of friends or coworkers?
To understand why these people stopped using Sidekick, we sent out a simple email to a thousand users. I emailed them individually by BCCing them from my HubSpot account, asking for feedback on a specific question designed to bring insight to the pattern we discovered. We bucketed the replies to our email, and found that there were big opportunities to improve our week 1 retention.
The numbers in this chart have been modified from their real values for this post.
I was personally ecstatic when I saw these distributions. It wasn’t that a competitor was better, or that there was a mismatch between the features people were looking for and what our product offered. The issue was a psychological one:
We weren’t doing a very good job explaining what our product did, and how people could get value from using it.
Rather than having to build a lof of new features, we needed to experiment with explaining the value of the product. It’s much easier to test out different ways of describing the product than addressing weird edge cases or building entirely new features.
With our quantitative analysis done and having received qualitative feedback from the segment of users we were most interested in, we spent time brainstorming ideas to address the opportunity. We looked at how competitors accomplish the same task, how companies in other industries educate their new users, and researched why our most passionate users like Sidekick. I’ve included a list of sample experiments we’ve tested:
Only show the Sidekick web application once we have value to demonstrate
Show a video of someone using Sidekick and how they get value out of it
Ask users whether they intended to use Sidekick for personal or business use cases, understand whether we should try to change their mind or give them examples that align with their mind set
Show a narrative of how someone uses Sidekick over a period of time
Incorporate our onboarding into the Gmail interface rather than in our web app
Conclusion
Looking at the opportunity we have focused on for Q4 2014, it seems kind of simple and obvious. By setting an appropriate goal, understanding the inputs to that goal and finding the biggest contributor, it led us down a path to clearly define our next steps. While finding a solution isn’t guaranteed, the team is confident that if successful it’ll have a big impact on our trajectory for 2015.
This framework isn’t perfect and isn’t for everyone, for instance, if you are creating a new product or process and have a small sample size. However, for the Sidekick team, this process has been an enormous help in prioritizing where to focus energy and resources and get the team aligned behind a common goal.
This framework is incredibly valuable to the Sidekick team for multiple reasons:
It breaks down large, complicated problems into actionable and manageable tasks.
We have confidence that the opportunities we are working on will have a big impact.
We understand the relative importance of different initiatives and are able to make conscious decisions about areas to pursue and the resulting trade offs. It’s also easier to decide what we shouldn’t be working on, even if it may feel important.
Our team can see the direct impact on individual metrics, and understand how any improvements translate into the success of our team. Teams like being able to track their progress and see how their efforts translate into success.
It’s a repeatable and scalable process.
The insights aren’t isolated to technology solutions - they can be as simple as messaging and the steps instructions are displayed.
