aka the “magic moment”

9 “quick” steps to discover your product’s aha-moment

without prior knowledge of data science

Abdelrahman Wahba
May 23, 2016 · 5 min read

By Abdelrahman Wahba

Among the activities I’m proud we did in my startup analyzing the reasons why people continue using iqraaly (our app for narrated Arabic audiobooks, audio articles and podcasts) for longer times, commonly known as the aha-moment or magic-moment of a tech-product.

I wrote about this extensively and in great detail in a previous post of mine, and upon publishing it, I thought I needed to compress it a bit for those who don’t have that much time or just need the gist.

So, 10 weeks of blood, sweat, shawerma’s and trawling through accumulated data of 600,000 users and 6,000,000+ monthly listened minutes taught us (Mahmoud Salaheldin, iqraaly’s Technical Manager back then and myself, the co-founder and General Manager) how it is done:

The gist

  • Observe your users over 6–12 months period of time
  • Isolate those who have stayed active for the whole period (hereinafter known as “forever-users”)
  • Compare their behavior on your product vs the rest — the churning users — and try to find patterns distinct to the forever users.
  • Said patterns become your hypothesis for the aha-moment, which you should test with new users, and drive them from on-boarding to said aha-moment with all the might and resources you can muster.

The more elaborate steps


Cohort analysis — Setup your user behavior data based on cohorts for analysis (daily cohorts at best). In semi-layman’s terms, group them by the date of starting to use your products and make said groups distinct in observing their behavior vs. time. This is a show-stopper, if you don’t have the framework to conduct cohort analysis, you’re screwed. And it is worth investing every penny to setup your data this way.


Define your “forever” users — they are usually defined in terms of a “very long” duration of activity on your product (6–12 months).


Identify interesting points of comparison — pinpoint certain points in the users’ lifetime to compare the churning users with the forever users. Defining those points can be based on how many users you have lost, some specific meanings said points have with respect to your product, some expectation you have from your users or basically random.


Brainstorm all possible user attributes for observation — that can be assigned to the users and may or may not be function of the user reaching the aha-moment. AND LIST THEM ALL! You will need this list later.


Select attributes (or combination of attributes) and plot their quantitative values (and cluster the qualitative values) at each point of comparison for forever users vs. churning users — and compare them


Observe the common patterns where the collective behavior of forever users converges or shows similarities, and use said similarities as a representation of the collective behavior of forever users.


Compare collective behavior of the forever users with the behavior of churning users and try to find points of convergence between both behaviors, and more importantly points of divergence.


Said points of divergence are highly likely to be the points where you have successfully converted regular users into forever users; hence, you should explore said points in your users’ lifetime with greater depth to try to identify, what exactly happened there. Subsequently, you shall adjust your product strategy to push such conversion onto the new users from the time of their on-boarding onto your product.


The previous 8 steps have created a hypothesis for your product describing a likely possibility of your product’s the aha-moment. Remember, correlation is not causation, until proven so. Therefore, you still have to verify your hypothesis by rolling out a strategy to test it and observe its results.

Once you have executed the above steps successfully and to your satisfaction, you are now ready to create and execute an aggressive marketing campaign to acquire users and convert them to forever-users. It is highly advisable to continuously observe your user behavior to make sure the hypothesis scales up with your product.

Useful Side Notes

  1. Usually you are trying to maximize lifetime and/or lifetime value, i.e. how long the user stays active on your product and how much money you make off of them. Keep this in the back of your head throughout the above process.
  2. A rule of thumb, you want your product to become among the users’ habits. Remember, frequency of use leads usually to intensity of use; frequent and intensive use usually leads to habit formation; finally, habit formation leads to increased user lifetime and lifetime value. This is what you are ultimately aiming for with your product.
  3. Another rule of thumb, the aha-moment is also a function of time, not just intensity. e.g. Facebook’s 7 friends in 10 days — the 10 days factor is highly important, not just the 7 friends. An explanation would be, you need to form the user’s habit while you still have his attention. Exceeding the allotted time window to have the user reach certain behavior milestones might prevent you from pushing your user towards the aha moment.
  4. All of the above is my personal experience, please provide your comments and insights so we can all benefit and discuss it together :)

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Egyptian Startup Manual

A compilation of thoughts and real-life experiences about starting a (tech) company in Egypt. It might apply to where you are from, or “geographical” differences might require you to tweak the knowledge to a local taste. Either way, it is guaranteed BS-free.

Abdelrahman Wahba

Written by

aka Abdo, Tech Entrepreneur, Director, Seasoned Product Manager, Business Development Pro, striving towards a BS-free, eclectic & expressive non-mainstream blog

Egyptian Startup Manual

A compilation of thoughts and real-life experiences about starting a (tech) company in Egypt. It might apply to where you are from, or “geographical” differences might require you to tweak the knowledge to a local taste. Either way, it is guaranteed BS-free.

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