3 pillar formula to growth hack your app.

One formula that will tell you where to focus your growth hack exploits on.

Every app developer dreams of building blockbuster apps like Tinder or a clash of clans or Uber. Aren’t these three totally different apps from different segments? Yes, but they have one commonality, they’re all chart toppers. Now what if you can fit not just these three but every other successful app into a simple formula? Well you can.

Entry, Engagement, Ecosystem, 3 Pillars of App success

PS: Tl;dr version at the end.

3E Formula.

Every popular app gets a high score on when you apply the 3E formula. But what is 3E ? 3E formula helps you growth hack your app objectively.

App score (is proportional to) Entry x Engagement x Ecosystem

S ∝ En*Eg*Ec

Now, let us understand how 3E works. It is proportional the product of 3 functions.

Entry(En)

How many entry points does the app have, or what are the ways by which a user can come back to your app. This can further be expanded into multiple categories, like organic, forced, promoted, peer etc. In a gist, entry is essentially anything that you could think of to get the user land on one of your app screens.

Engagement(Eg):

People think engagement is the holy grail metric for app success. But did you know engagement can be broken down into just two type of actions? (Yes, for every app you can think of). A core action followed by a trigger to another core action.

Eg = Ca → Ta → Cb → … Tn → Exit

Every app has one (or more) core actions that can be performed in it. And the engagement gets better when completion of one core action(C) triggers(T) another. Sometimes the triggers work, sometimes it doesn’t. When it doesn’t user exits the app. We will talk more about this in another post.

Ecosystem(Ec):

Ecosystem can also be called Scale. This function will be boosted by user acquisitions. Also, this is one hardest variable to boost, since obviously it is dependant on user acquisition or size of user base.

I will explain improving each of the E’s in separate posts. But for now, let us see how these impact App score.

Let’s assume each of the E’s can have a score of 0 to 1 and hence obviously App score will also be between the same range.

I earlier wrote App score is proportional to En*Eg*Ev. For the sake of simplicity let’s write

S = k*En*Eg*Ev

And for this exercise let’s keep k = 100 (So that App score is between 0-100)

Now, if you’re still wondering how this works, let me show you some examples.

• Facebook

Facebook is one of the most popular apps. And it has a huge level of engagement (Eg). Why? This is because one of the core actions is reading updates, and when you’re scrolling the news feed, reading updates re-triggers the same action, since posts are all chained. And facebook has made the core action very engaging by showing you the right kind of posts. When do you exit facebook? When you don’t like any posts you are shown. And news feed does not trigger to another core action like notifications or messages.

Now, let us talk about Entry points for facebook app, for any other app like facebook, it is the notifications (peer entry) or organic thought to post something. But since, facebook has become ubiquitous in our lives, it has another super powerful entry point, which is muscle memory. Everytime you get bored, you muscle memory triggers facebook. People even call this addiction. So, it is obvious that facebook has high score for (En) also.

Ecosystem (Ec) for facebook is obviously super strong, since your grandma to pet dog is on facebook. It is almost like the upper cap for scale.

So, if I can assume facebook has En(0.6), Eg(0.8) and Ev(1)/

Facebook Score = 0.6*1*0.8*100 = 48

Let’s do another one. Google Maps.

Google Maps

• Entry (En): The entry score for Google Maps is more of less average, you open maps when you want to go somewhere (organic). Little chance of people forcing you to open maps. If you notice, to improve this value Google added local search, rating, photos, location history etc to maps, so that there are more reasons for people to open Maps (En ~ 0.4)
• Engagement (Eg): Google Maps has very strong Core Action, navigation, apart from other actions like search, reviews etc. But Navigation is a long and engaging core action, with a very little re-trigger value. But, the other local search, reviews, find address, phone number pictures etc actions are also picking up for them. So I would say Eg ~ 0.7
• Ecosystem (Ec): Again, if you look at Maps, it is preinstalled (until recently) on every smartphone in the market. So, it has a very high super high Ec score. (1)

Maps score = 0.5*0.6*0.9 *100 = 27

I guess you got the hang of it.

Ok. Now let’s create some more scores. In the format Score : (En)(Eg)(Ev)

Whatsapp 0.8*0.8*0.8 = (51.4)

Angry Birds 0.3*0.7*0.8 = (16.8)

Candy Crush 0.5*0.6*0.6 = (18)

Instagram 0.4*0.6*0.8 = (19.2)

Twitter 0.3*0.7*0.5 = (10.5)

Google Now 0.5*0.3*0.9 = (13.5)

Pokemon Go 0.7*0.9*0.3 = (18.9)

Uber 0.4*0.7*0.8 = (22.4)

You can go and try this for other popular apps as well.

How is this useful for me?

In a best case scenario an awesome app should have strong values (0.5+) for at least two of 3 E’s. And the 3rd E can be engineered. In the next best scenario there should be a very high E (0.7+) and then the other two E have to be engineered.

So you can use this as a filter and understand which E you’re inherently strong at and which E’s you have to engineer, and which E you have to spend on (like [Ec]Ecosystem for example).

Also, another important aspect of this equation is that 3 balanced E’s have more value than, high and low E’s.

• For example take twitter. If it can improve its scale (Es) by 0.1 the score will be 12.6, but if it can improve its App Entry points (En) by 0.1 it’s score jumps to 14.6.
• Similarly compare Google Now and Google Maps, similar levels of scale and Entry points, but when it since Google Now has a little less engagement, the score is just half of Google Maps’
• This also means you can get a decent 2 digit score by having average values for each of the E’s (0.4*0.5*0.5)

obligatory XKCD comic

tl;dr

• En = Number of times user opens the app.
• Eg = Number of minutes user spends on the app.
• Ec = Number of users who are using the app.
• Your success if the product of the above 3.

S = k*En*Eg*Ec

Try to fit your app into this equation and you’ll know what you should focus next on. App opens, engagement or scale.

PS: Would love to hear your comments and your thoughts on this thesis, also if you think it was useful to you, share it with you network. (It will help increase my Ec)