Decoding Consumer Product's "Power Users"
Behind every successful product, lies a passionate group of users who engage daily. Lets understand all about them.
Welcome to yet another power-packed edition of my bi-monthly newsletter. I’m quite happy with how the growth curve on this one is going. Started with 100 odd subscribers last year and on to 1700+ now. It’s phenomenal to see so many of you read and respond to these newsletters every time.
If you’re receiving my newsletter for the first time today, you’ve either been added by a well-wisher or you’ve signed up yourself. But I assure you that it’s going to be a fun journey from here on. You are part of 1700+ avid readers of experiential content on product, design & technology. No ads I assure.
(And if you were forwarded this email, please take 7 seconds to click here and subscribe for yourself)
My interaction with product companies in the past one year has been soaring through the roof. Some of them are listed while the rest of them are Unicorns. And there’s one thing that’s stood out in my conversations with them: Retention
And always end up saying, “It’s wise to build your growth on data (read: traction) rather than intuition”
Today is deep dive into exactly that one concept which I feel sits on top of UX Research & Analysis.
There’s a tonne of content out there on high engagement users or so called “power users”. What I’ll try to do is rationalise them through a framework used by Facebook’s Growth Team (L30 Curve). While the concept of L30 is borrowed from Facebook, the perspective here is brand new.
So let's dive straight in:
WTF are Power Users:
Behind every successful product, lies a passionate group of users who engage daily.
Power Users drive growth for most consumer businesses. And they have the following patterns:
- Fall within the core demographic of your product
- Stay actively engaged with your brand (daily, weekly or monthly)
- Evangelise the brand across different channels
- Most Essentially: who are paying (and recurring) customers for your brand
For instance, in -
- DTC businesses: it’s repeat purchase customers who are also engaged with your brand on social platforms
- Consumer Tech Businesses (mobile apps): they are your MAU’s (month active users) who buy into your product’s paid propositions
We are going to dissect power users or PU’s from the lens of product companies (mobile apps). Lets dive deeper:
Typically, most businesses would calculate PU’s by dividing daily active users (DAU’s) with monthly active users (MAU’s). Since it’s a singular lens to evaluate active users, it has several disadvantages. And hence, a more comprehensive framework like L30 (alternatively referred to as the User Engagement Histogram or the Power User Curve) fits more appropriately for high-growth companies.
But if you’ve been calculating your user activities with a traditional formula of DAU/MAU, you are bound to face certain roadblocks.
Drawbacks of DAU/MAU:
- Singular (consolidated) metric for evaluating user engagement
- Lacks the depth of filters to dissect users over a range of factors, modules and activity periods
- Gives superficial understanding of activity levels and may not be inaccurate representation of the business metrics that your businesses is chasing
Let’s understand what the new way of calculating user engagement means.
User Engagement Histogram or Smile Curve:
It’s a histogram of users’ engagement by the total number of days they were active in a month, from 1 day out of the month to all 30 (or 28, or 31) days.
Things To Know:
- Plotting the entire month against % of users would give you an activity graph, like the one above
- Reading the curve: In the above graph at 30 the user percent is 7% it means that 7% of the users have logged in the app for 30 continuous days in that month.
- You could build various parameters within each day’s activity based on the metrics you wish to target, at different points of time in your product lifecycle:
- App opens
- Activity on certain key modules
- Conversion to paid customers
- User Interviews: Because the L30 curve gives you multiple insights about your daily activities, it also allows you to dissect your users based on their activity levels. This means that you can now talk to 5-10 users from each cohort (app instals, paid subscribers, lower activity levels, etc) to understand the ‘why’ behind their behaviour. This is so much more insightful than randomly picking an engaged user to build on to user research.
- Product Lifecycle: Based on your product’s lifecycle, the metrics you track would evolve. This curve would give you the possibility to build more measurable metrics across each module.
- Decision Making: Decision making on products cannot be intuitive. It needs to be driven by data + primary research. This is the perfect mix of data that leads you to dissect your user cohort across various activity levels and have in-depth conversations with them. Based on inputs from them, the decisions are sharper and have an actual impact.
Understanding the Curve:
Smile: Typically, for social apps, this curve would appear to be like a smile or should ideally start showcasing signs of an incline towards the end of the 30 day period.
An average of 20% to 30% of activity from your users is a good yardstick for a product that’s wired for daily usage. To set some benchmarks on mature products, FB, Twitter, Whatsapp fetch daily engagements north of 50% on this curve.
What matters is that, over time, the platform is able to retain and grow its power users: successive Power User Curves should ideally show users shifting over more to the right side of the smile. As network effects kick in, the density of users starts to thicken and will usually remain denser towards the end of the smile.
Not every product will have a smile, some of them will taper in their engagement towards the end of the 30 day period of usage. Especially if you are not a high-frequency product.
Based on the metrics & goals you set out for your product, you could evaluate your L30 curve and dissect it with varying levels of activities.
(If you’re enjoying reading so far, and haven’t yet subscribed, here’s your chance for regular updates on such high signal content)
While we’ve spoken about the L30 curve and plotting user activities, it’s time to now extract the most engaged users who follow the thesis of Power Users for your product and action upon them.
In the smile curve above, it represents a business whose 7% of total users are power users.
- Set Thesis: Firstly, define who a power user is for your product. Is it just an engaged user or a paying customer? Do they have to be paid customers or can they be sharing your product details with their friends and family? It’s for you to define. This would help you draw a pen profile of your ideal power user vs actual power user.
- Reach Out: Once you’ve established who your power user is and have identified them based on their activity levels, it’s time to reach out to them and let them know that ‘you know they exist’
- Gamify / Incentivise / Reward: Specialised engagement modules can be designed for power users, to help them stay engaged with the product. This could be incentivised in-app or offline but you should definitely gamify their engagement
- Evangelism: Try and push them to evangelise your product, by actually having first hand conversations with them. Make them feel like an influencer and give them certain referral credits or tokens.
- Retention: Build strong retention strategies along with them for some of your other users. Increasing the reasons why your power users become power users can help compound network effects.
Design Re-engagement loops: Once you've identified reasons why users drop out (of a module or of your product), you can design re-engagement loops
Advertising: Once you have assessed the activity levels of your users and should they fall above 5% to 10% on an average day, it’s a ripe product to bring in affiliates, advertising or collaborators.
Redesign: The core essence of redesign would be varying levels of activities. Eg: Where to place CTA’s based on high activities or drop offs. The analysis a UX team can derive out of this one metric is humongous. Monthly iterations help improve product efficiencies while ensuring that the sales effort to bring in new users isn't all down the drain.
The beauty of the Power User Curve over DAU/MAU is that it shows heterogeneity among your user base, reflecting the nuances of different user segments (and therefore what drives each of those segments).
We effectively implement this at Lights Out across all our UX processes and it’s shown amazing results in dissecting the user base.
I’d love to get feedback from you on how you find this mechanism in your product teams.
Well, that’s it for now. Will come back with another super interesting edition in 2 weeks from now. Until then,