by Assaf Elovic

How my app grew by over 1M users in one month

All it took was this simple weekly approach and patience.

Building and promoting a new consumer product is one of the most challenging things you can do as an entrepreneur. While there are many approaches on how to design, test, build and promote apps, usually they don’t seem to bring real results.

Then you start wondering, maybe it’s the product? Maybe there’s not enough market fit? Or is it bad execution? Or maybe we should grow the marketing and branding budget! Maybe we’re not targeting the right audience? Maybe we should build more features!

When you start questioning everything, things usually get even worse. You start defocusing from the main goal and start wasting energy and money on all kinds of wide approaches.

The worst is when you think it’s all a matter of growing your marketing or branding budget.

Your goal should always be one: improving customer retention. For those who are not familiar with what customer retention is, click here.

A case study: why it’s important to keep your customers around

To make my point clear, I’ll let you in on a story I heard from a friend of mine, who’s the Co-founder and CTO of a very successful productivity B2C company.

In 2012, they released the first version of their app to the Android Google store, and a crazy thing happened. Within a few days after the launch, 500K users worldwide had downloaded the app. The reason for that crazy growth was mainly because there were no good apps in the productivity space back then. Over the next few months, they had grown to a few million users and raised over $5M from VCs.

Four years later, however, they still couldn’t reach a decent business model. He realized that despite the big numbers, there were very few users who were actually using the product long term.

So he decided to dig into the data and look for the reason. He found out that retention was very low. Even worse, he discovered that it hadn’t improved much in four years! That’s when it hit him to focus on retention instead of user growth.

Back then, VC’s poured millions of dollars into companies with large user growth because they didn’t know how to deal with or measure the crazy scale that mobile app stores and websites brought with them.

Today the case is different. The first thing you’ll need in order to raise money in B2C is to show retention growth. And there’s a very good reason for that.

Back to my friend’s story: with no retention, it didn’t matter how many users had downloaded their app. After a week, 95% of users stopped using the product. So even if they had a billion users, after a few weeks it would only be a number in their database.

Here’s a thought: if you have 100K users using your product every day, it’s 100X more valuable than having 100M users using your product once a month.

Most importantly, once you’ve reached a decent retention rate, you can be sure that your marketing budget will lead to the sustainable growth of your product and business.

The Lean Startup approach

Too many startups begin with an idea for a product that they think people want. They then spend months, sometimes years, perfecting that product without ever showing the product, even in a very rudimentary form, to the prospective customer. This is where the Lean startup comes in.

In short, the Lean Startup is a methodology that posits that every startup is a grand experiment that attempts to answer one main question — “Should this product be built?”

A core component of Lean Startup methodology is the build-measure-learn feedback loop.

The first step is figuring out the problem that needs to be solved, and then developing a minimum viable product (MVP) to begin the process of learning as quickly as possible.

Once the MVP is established, a startup can work on tuning the engine. This will involve measurement and learning, and must include actionable metrics that can demonstrate the cause and effect question.

Whenever my team and I are working on a product, here are the steps we’ve:

  1. Define the most important product assumption
  2. Design and build an MVP of how this assumption should be tested
  3. Target early adopters to test the MVP
  4. Apply the test results
  5. Repeat

This is how our growth looked in the first year (2017) of iterations:

User activity from March 2017 to January 2018

Slowly but surely right? Now let’s look at an example together and see what happened after enough iterations.

The process

Define the most important assumption

I’ll use my team as an example. We believed that there were no decent reminder apps that people actually like to use. The main reason, in our opinion, was that there is a lot of friction in setting a single reminder. Either you need to fill out a long form on a mobile app, or naturally ask an assistant like Siri — realizing that she doesn’t understand 50% of your requests.

So that’s when we defined our most important product assumption: if we could achieve understanding for almost every reminder request in natural language, users would use such a product long term.

Design and build an MVP

Since our assumption was focused on NLU (natural language understanding), we decided to focus solely on that. No branding, UX, or other features.

First, we hired data scientists to build a state of the art NLU algorithm for understanding complex reminder requests.

Secondly, since all we were validating was this assumption, we decided to build the MVP as a chatbot on Facebook Messenger, instead of going through the long and annoying process of building a mobile app.

Please note: If we were to build a mobile app, this would not add anything to testing our assumption, and would make our MVP longer and more complicated to design and build. Moreover, it might even defocus us from the main assumption. For example, what if users just don’t like to use new apps anymore? We might’ve concluded that our assumption was wrong, even though it was for a whole other reason.

It’s important to narrow your MVP as much as possible, so there are no distractions from your main assumption.

Target early adopters

We needed English speakers, since our NLU algorithms only supported it. Also, we believed that millennial moms would eagerly want a product like this, since they’re always on the move and very busy, while constantly needing to remember things. So we targeted some Facebook pages (with no budget) which were based on a community of moms, and successfully brought onboard a few hundred beta testers to try it out.

Apply the test results on the product

After our first iteration, we learned the following:

  1. There were many more ways to ask for reminders than we thought. But users really enjoyed the ease of setting reminders with a simple request.
  2. Users don’t always ask for reminders in a single request but break it down into a few steps.
  3. When working with chatbots, users would like the assistance of buttons to make it faster and easier to handle.

With these results, we prioritized and went on to our next assumption, which was to add buttons to the flow of setting a reminder (conclusion three). And guess what? That assumption also turned out to be true. For more proven assumptions, you can read my article on How to improve your chatbot.

Little by little, we improved our overall product and retention rate on a weekly basis.

We never tackled more than one assumption at a time.

Suddenly, we started discovering users who were with us for over six months! After a year of weekly iterations, we finally decided it was time to launch our product. We reached a Week 1 retention rate of 92% and Week 4 of 19%. It was way above the market standard, which was enough for us.

We published our chatbot on FB Messenger in mid Feb 2018, and within a month we grew by over 5,800%, as you can see below.

Daily new users from Feb 17 to March 17

This was mostly due to delivering a lean product that we knew people enjoyed and would recommend to others. Since then, we’ve grown to over 1M users worldwide and are growing by tens of thousands of users a day.

Total user growth from Jan 18' to May 18'

We’re continuing to work with this methodology, and it’s proven a success every day.

Also, we try to make as many data driven decisions as possible. Try collecting user data for improving user experience, and do A/B tests when there is no definitive answer. For example, if you’re not sure where a button should be placed or which title would attract more clicks, try placing it on one side for half the users, and on another side for the second half. Then, see which placement led to more clicks.

Final thoughts

Not only does this methodology help us focus solely on what’s the most important set of features users want, but it also helps us filter out features we believe are valuable, but that are actually not.

As they say in customer service: the customer is always right. The same goes for product development. Trust your customers, listen to them and engage with them, and you’ll understand what they want and don’t want. Never build things out of your own intuition, unless it’s to challenge your assumptions. At the end of the road, you’ll reach one of two conclusions:

  1. The product does not have enough market fit, time to move on.
  2. You have a product people want. Good job, you’re on the way to building a company!

Either way, you win.

Thank you very much for reading, if you found this useful, please give me some claps 👏👏👏 so more people can see it. For more articles, visit my tech blog at assafelovic.com. If you have any questions, feel free to drop me a line in the comments below!