Dan Wolchonok

Product Guy at Reforge

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Accounting for Growth

July 8, 2018 / / 0 Comments

You’d be crazy not to measure your active users, at the appropriate frequency for your app. That may be daily, weekly, monthly, or yearly. (DAU / WAU / MAU).

As you monitor these key metrics, you’ll want to understand how and why it’s changing. I am a big fan of how Jonathan Hsu breaks it down in accounting for growth, breaking out the components that add or subtract from your top-level numbers. Understanding the dynamics that contribute to your growth (or lack thereof) are critical for members or your team to understand what drives success. Not just the PM that owns the feature, but many of the team members.

I’ve had conversations with PMs at many companies who may know their top-level numbers, but don’t have a good handle on why they’re going up or down. As Jonathan outlines in his post, two very different businesses can have the same MAU numbers, but one is much better than another.

There are a couple of scenarios I think are important to understand:

  • If your retention rates are poor and new signups are helping you grow:
    • When will your growth rate flat line?
    • Could signups go down? What would happen if your growth flat lines or if your user base shrinks?
  • If your active user numbers go up:
    • Is that because of a one-time increase in new signups? Do you expect that to continue?
    • Is it because of resurrecting users (making dormant users come back)? Is this a sign of a pattern you could invest in making stronger? Why do people stop using it in the first place?
  • If your active user numbers go down:
    • What component decreased? Was it isolated to resurrected users / signups / retaining existing users? If it was isolated, was there a product issue that contributed to it?
  • Are you soliciting feedback from each of these buckets of users? There is gold to be mined by segmenting and surveying the users with the most valuable feedback.

In addition to knowing your high-level active user numbers, you should know why you’re growing. It’s helpful to understand what the future looks like if things stay the same or worsen. Even if you know, does the rest of your team know? When everyone understands how your product and business will be successful, they’ll have the context to more effectively make the critical decisions in building tech products.

If you’re looking to get started with this type of analysis, Jonathan Hsu’s 8-ball analysis or the lifecycle feature in Amplitude is extremely helpful.

Flying blind: not setting or measuring product metric goals

January 4, 2018 / / 0 Comments

I love building new products. Ever since I was building junky web apps as a geeky high schooler, I always get excited the first time something actually works. It has always felt like magic. Now that I’m older, I increasingly feel the pressure of showing my impact. After the initial euphoria passes, I now immediately measure the metrics that represent success. Something that has been bothering me lately is that regardless of your methodology (waterfall, agile, scrum, burndown, trello anarchy, etc), I never hear others talk enough about product success metrics.

When I joined HubSpot I learned from many others about behavioral analytics. Sadly, I find myself constantly fighting responses when I speak with friends in the industry such as:

  • “We forgot to add tracking”
  • “We want to ship it and see how it does”
  • “We don’t have any specific goals for this release other than to improve the design”
  • “What should we measure?”
  • “We can’t afford to use behavioral analytics, it’s too expensive”

This is how I want to react every time I hear one of those answers:

A guy in a panda suit breaks a computer on someone's desk

Just kidding. I am always asking questions to understand the rationale so I can try to help add perspective.

These are the tough questions I want to ask in response:

  • What’s more expensive? A behavioral analytics system or shipping the wrong features / wasting the time of your product and engineering team?
  • If you hear feedback from a couple of customers, is that representative of all users?
  • How do you know that the users are actually doing what they say they’re doing?
  • Do you think you’ll get a team’s best work if the only goal is to release their work?
  • What do you think will garner more resources in the future? “We improved the experience, just look at it!” vs. “I increased conversion rates of signup to value by 10%, with an expected lift in revenue of Y”.

I don’t think you need to spend weeks off in a corner crunching numbers to come up with the answers to these questions. My suggestion is to spend 30 minutes thinking about a goal, why you’re working on something, and then a simple mechanism to measure success.

I push teams to answer these questions:

  1. What represents success for this release/feature?
  2. What is the current baseline?
  3. What is the hypothetical ceiling of improvement?
  4. Given the baseline and ceiling, how much do you think you can improve the metric?
  5. What will be the mechanism to track success/failure?
  6. When should you evaluate progress?

You don’t have to be super fancy and build Excel models, but at least spend 15-30 minutes thinking through the basics for a new feature. Regardless if you’re building something brand new or iterating on an old feature, I always think it’s worth considering the above questions.

As the saying goes, “if you can’t measure it, you can’t improve it”.

Segment Your User Base: Depth of Engagement

December 30, 2017 / / 3 Comments

If you haven’t read Jonathan Hsu’s 8 part Medium series on Social Capital’s diligence process, add it to your reading list. I didn’t immediately grok all of the concepts in the post, but it has had an incredible impact on how I look at product metrics.

It appears it’s a big part of their recent announcement of how they are able to fund early-stage companies focused exclusively on their metrics.

One of the concepts that struck me was the depth of engagement. It shows you how engaged different portions of your user base are. You don’t need a ton of fancy data science techniques to get a glimpse into what your user base is doing. All you need a fairly straightforward SQL query to get you started.

It starts with a fairly simple concept: how many users are active for 1 day in the past month? How many are active for 2 days in the month? It’s really simple to generate a histogram (this is fake data) that looks like this:

Count of Users by Days Active

In this fake example there are 100k monthly active users (MAUs) in this hypothetical product. I think this is very telling and interesting from a strategy and operational perspective, but there’s a different view that I now prefer. I prefer to look at this chart on a percentage basis (the % of MAUs), and look at it cumulatively. This is what it looks like:

CDF of Monthly Active Users by Days Used

How to read this chart: 33% of the MAUs are active for a single day of the month. It may be the first day of the month or the last, but the people that fall into this bucket were only active for a single day in the month. 53% of the MAUs were active for 2 or fewer days – you add up the 33k and the 20k from the histogram to get the 53%. In Jonathan’s example there’s a little bit of a spike of users that are active every day of the month – in a bunch of the examples I’ve seen in the B2B space there’s a nice healthy bump around 20 days, which makes sense when you consider that B2B apps are most likely used every business day, rather than every day.

This is a powerful way to slice up your install base very quickly. I push for taking the MAU install base and slicing it up into types of users. Here’s a hypothetical set of groupings:

  • Low engaged users (66%): 3 days of activity or less
  • Medium engaged users (14%): 4-10 days of activity
  • Highly engaged users (10%): 11+ days of activity

There are a bunch of plays that I could see happening for each of these buckets:

  • Sales: I could see sales following up with customers that fall into the highly engaged bucket. If they’re free, I could see them seeing value in paid tiers of your product. If they’re already paid customers, they are probably the most likely bucket to see value in additional paid options.
  • Services: I could see customer success reaching out to the low engaged bucket to understand why they aren’t using the tool more frequently. In a B2B company where customer success is focused on retention, this is an area of high potential churn.
  • Product: I could see the product team looking to build features that address the missing functionality users need to use it more. They could also work on retention hooks that pull users back into the product / get them to see more value in the tool.
  • Marketing: I could see the marketing group targeting users based on the bucket they fall into and how they might see value from additional features.

If you’re interested in doing this yourself, check out this Jupyter notebook for sample code.

How to quickly format retention data in less than 5 minutes to maximize learning

July 18, 2016 / / 2 Comments

Formatting a retention data set is critical whether it’s for yourself or in a situation like a job interview. Once you’ve asked all of the questions necessary to understand the data set, you should format it to maximize your ability to analyze it. Here’s my step by step process:

Export the raw data from your analytics system

It should look like the below. The cohorts are in the first column, the size is next, and then the number from the original cohort that retained in each subsequent period.

retention-1

Create another table that computes the percentage values of the original cohort size

retention-1

Add a conditional formatting element to show the size of the cohorts over time (conditional formatting -> data bars)

retention-4

Apply conditional formatting to the table that contains the percentages. It should now look like this:

retention-3

Adding summary rows

Then I create two new rows at the bottom of the percentage table. The first is the average of each column, and the second is the percentage decrease for each additional week.

retenion-5

retention-6

Wrap it up

Now you have a nicely formatted retention table that shows you:

  1. The size in cohorts over time
  2. At a glance how your cohorts retain over time
  3. Where there are good pockets and bad pockets of retention
  4. The average of cohort retention over time
  5. Which weeks have the biggest drops in retention
  6. Whether your retention levels off over the long term

Where there are many more graphs you could create off of this data set, I think this sets the right foundation for how to quickly look at the data and answer some important questions.
Are there other ways you visualize this information? Do you recommend doing this another way or formatting it differently? Let me know in the comments below.
Disclaimer: all of the data in these screenshots was made up for this example.

The Retention Questions You Should Be Asking in a Job Interview

July 15, 2016 / / 1 Comment

In my experience, job applicants rarely ask the right questions when faced with a retention exercise. You can create a retention chart for anything, but I think these are the questions you simply have to ask:

  1. What do the cohorts measure?
  2. What is the criteria for retention (the % of the cohort that returns)?
  3. How can I segment the retention data?

There are many follow up questions you should be asking based on the answer.

Question 1: What do the cohorts measure?

  • Is it any People? Companies? Revenue? People using it at a company? Is it a subset of any of those concepts?
  • If it’s people — what kind of people? Is it people who did some action? Example: is it people who signed up for a product? Is it people who saw some value from your product? Bought a product? What is the trend of the cohorts over time? What could affect that? How do you expect the numbers to change over time?

Question 2: What is the criteria for retention?

  • How is retention measured? If it’s people, what action do they need to take to be counted as having been retained? Why was that chosen? Why is it important?
  • If it’s revenue, how is it measured?
  • If it’s an attribute of a person or a company (maybe number of people active), how is that measured? Why was that chosen? How is it important to the business?
  • Does it level off? If you have a budget to acquire people in the cohorts, how long must they stick around in order to sustain a profitable business?
  • How does the data change over time? Are there patterns that you would expect to see, or cliffs where things drop off?
  • Do you expect to see patterns horizontally (step function changes from one cohort to another), vertically (time barriers where cohorts worsen or improve), or diagonally (holidays, press spikes, re-engagement campaigns)?

Question 3: How can I segment the retention data?

  • Are there pockets in the data that are better or worse than the overall average?
  • Is there a feature that results in better retention?
  • Is there a platform (ex: Android, iOS) that is better than another one?
  • Is there a cohort acquisition source that performs better?
  • Is there a geography that performs better in retention?
  • Is there a cohort attribute that results in higher retention? Age / sex / profession of people? Size of company? Industry of company?

If you’re doing a retention exercise in an interview, you don’t even really need to look at the data, you could just ask questions the whole time.

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