Discover How We Used Facebook Ads to Grow a New Mobile App from $0 to $15K MRR in Just 6 Months and Our Strategy to Hit $100K a Month.

Our client introduced a mobile app for Poshmark resellers in December and wanted as many to experience it and subscribe to the monthly plan.

An Error We Committed

We initiated a Facebook ad campaign with a "awareness" goal, not "installs." This sent them to a landing page that linked to the iPhone App Store and Android Play Store. Smart, right?

We got some installs, but we couldn't tell how many came from the ad versus organic/other channels because the objective we chose only reported landing page clicks, not app installs.

We didn't know which interest groups/audiences had the best cost per install (CPI) to optimize and scale our budget.

After spending $700 without adequate data (installs and trials report), we stopped the campaign and worked with our client's app developer to set up app events tracking.

This allowed us to create an installs campaign and track installs, trials, and purchases (in some cases).

Finding a Successful Audience

Once we knew what ad sets brought in what installs at what cost, we began optimizing and testing other interest groups and audiences, growing the profitable low CPI ones and eliminating the high CPI ones.

We did all our audience testing using an ABO campaign (Ad Set Budget Optimization), spending $10 to $30 on each ad set for three days and optimizing afterward. All ad sets under $30 were moved to a CBO campaign (Campaign Budget Optimization).

We let Facebook's AI decide how much to spend on each ad set, usually the one most likely to convert at the lowest cost.

If the CBO campaign maintains a nice CPI, we keep increasing the budget by $50 every few days or duplicating it sometimes in order to double the budget. This is how we've scaled to $400/day profitably.

Finding Successful Creatives

Per campaign, we tested 2-6 images/videos. Same ad copy and CTA. There was no clear winner because some images did better with some interest groups.

The image above with mail packages, for example, got us a cheap CPI of $9.71 from our Goodwill Stores interest group but, a high $48 CPI from our lookalike audience. Once we had statistically significant data, we turned off the high-cost ad.

New marketers who are just discovering A/B testing may assume it's black and white — winner and loser. However, Facebook ads' machine learning and reporting has gotten so sophisticated that it's hard to call a creative a flat-out loser, but rather a 'bad fit' for some audiences, and perfect for others.

You can see how each creative performs across age groups and optimize.

How Many Installs Did It Take Us to Earn $15K Per Month?

Six months after paying $25K, we got 1,940 app installs, 681 free trials, and 522 $30 monthly subscriptions. 522 * $30 gives us $15,660 in monthly recurring revenue (MRR).

Next, what? $100K per month

The conversation above is with the app's owner. We got on a 30-minute call where I shared how I plan to get the app to be making $100K a month like I’ve done for other businesses.

Reverse Engineering $100K

Formula:

For $100K/month, we need 3,334 people to pay $30/month. 522 people pay that. We need 2,812 more paid users.

522 paid users from 1,940 installs is a 27% conversion rate. To hit $100K/month, we need 10,415 more installs. Assuming...

With a $400 daily ad spend, we average 40 installs per day. This means that if everything stays the same, it would take us 260 days (around 9 months) to get to $100K a month (MRR).

Conclusion

You must market your goods to reach your income objective (without waiting forever). Paid ads is the way to go if you hate knocking on doors or irritating friends and family (who aren’t scalable anyways).

You must also test and optimize different angles, audiences, interest groups, and creatives.

“Poor distribution, not poor goods, is the main reason for failure” — Peter Thiel.

Here's an easy way to conceptualize "go-to-market" for your distribution plan.

One equation captures the concept:

Distribution = Ecosystem Participants + Incentives

Draw your customers' ecosystem. Set aside your goods and consider your consumer's environment. Who do they deal with daily? 

1. First, list each participant. You want an exhaustive list, but here are some broad categories.

• In-person media services

• Websites

• Events\Networks

• Financial education and banking

• Shops

• Staff

• Advertisers

• Twitter influencers

1. Draw influence arrows. Who's affected? I'm not just talking about Instagram selfie-posters. Who has access to your consumer and could promote your product if motivated?

The thicker the arrow, the stronger the relationship. Include more "influencers" if needed. Customer ecosystems are complex.

3. Incentivize ecosystem players. “Show me the incentive and I will show you the result.“, says Warren Buffet's business partner Charlie Munger.

Strong distribution strategies encourage others to promote your product to your target market by incentivizing the most prominent players. Incentives can be financial or non-financial.

Financial rewards

Usually, there's money. If you pay Facebook, they'll run your ad. Salespeople close deals for commission. Giving customers bonus credits will encourage referrals.

Most businesses underuse non-financial incentives.

Non-cash incentives

Motivate key influencers without spending money to expand quickly and cheaply. What can you give a client-connector for free?

Here are some ideas:

Are there any other features or services available?

Titles or status? Tinder paid college "ambassadors" for parties to promote its dating service.

Can I get early/free access? Facebook gave a select group of developers "exclusive" early access to their AR platform.

Are you a good host? Pharell performed at YPlan's New York launch party.

Distribution? Apple's iPod earphones are white so others can see them.

Have an interesting story? PR rewards journalists by giving them a compelling story to boost page views.

Prioritize distribution.

More time spent on distribution means more room in your product design and business plan. Once you've identified the key players in your customer's ecosystem, talk to them.

Money isn't your only resource. Creative non-monetary incentives may be more effective and scalable. Give people something useful and easy to deliver.

Non-technical talents can benefit data scientists in addition to statistics and programming.

As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.

Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.

Ready? Let’s dive in.

Best books for data scientists

1. The Black Swan

Author: Nassim Taleb

First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.

Three characteristics define a black swan event:

• It is erratic.

• It has a significant impact.

• Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.

People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.

Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.

Try multiple tactics and models because you may find the answer.

2. High Output Management

Author: Andrew Grove

Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.

That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.

Five lessons:

• Every action is a procedure.

• Meetings are a medium of work

• Manage short-term goals in accordance with long-term strategies.

• Mission-oriented teams accelerate while functional teams increase leverage.

• Utilize performance evaluations to enhance output.

So — if the above captures your imagination, it’s well worth getting stuck in.

3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers

Author: Ben Horowitz

Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.

Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.

It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.

Find suggestions on:

• create software

• Run a business.

• Promote a product

• Obtain resources

• Smart investment

• oversee daily operations

This book will help you cope with tough times.

4. Obviously Awesome: How to Nail Product Positioning

Author: April Dunford

Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.

How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.

You'll learn:

• Select the ideal market for your products.

• Connect an audience to the value of your goods right away.

• Take use of three positioning philosophies.

• Utilize market trends to aid purchasers

5. The Mom test

Author: Rob Fitzpatrick

The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.

Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.

6. Introduction to Machine Learning with Python: A Guide for Data Scientists

Authors: Andreas C. Müller, Sarah Guido

Now, technical documents.

This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.

Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.

If you know machine learning or artificial neural networks, skip this.

7. Python Data Science Handbook: Essential Tools for Working with Data

Author: Jake VanderPlas

Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.

Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.

The only thing missing is a way to apply your learnings.

8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Author: Wes McKinney

The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.

The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.

9. Data Science from Scratch

Author: Joel Grus

Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.

The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.

Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.

10. Machine Learning Yearning

Author: Andrew Ng

Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.

The book delivers knowledge and teaches how to apply it, so you'll know how to:

• Determine the optimal course of action for your ML project.

• Create software that is more effective than people.

• Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.

• Identifying machine learning system flaws

Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.

11. Deep Learning with PyTorch Step-by-Step

Author: Daniel Voigt Godoy

The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.

It comprises four parts:

1. Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)

2. Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)

3. Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)

4. Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)

We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.

Is every data scientist a humanist?

Even as a technological professional, you can't escape human interaction, especially with clients.

We hope these books will help you develop interpersonal skills.

ZERO GRACE/ZERO MALICE

Big companies and wealthy people make stupid mistakes too.

Your ancestors kept snakes and drank bad water. You (probably) don't because you've learnt from their failures via instinct+, the ultimate life-lessons streaming network in your head. Instincts foretell the future. If you approach a lion, it'll eat you. Our society's nuanced/complex decisions have surpassed instinct. Human growth depends on how we handle these issues. 80% of people believe they are above-average drivers, yet few believe they make many incorrect mistakes that make them risky. Stupidity hurts others like death. Basic Laws of Human Stupidity by Carlo Cipollas:

1. Everyone underestimates the prevalence of idiots in our society.

2. Any other trait a person may have has no bearing on how likely they are to be stupid.

3. A dumb individual is one who harms someone without benefiting themselves and may even lose money in the process.

4. Non-dumb people frequently underestimate how destructively powerful stupid people can be.

5. The most dangerous kind of person is a moron.

Professor Cippola defines stupid as bad for you and others. We underestimate the corporate world's and seemingly successful people's ability to make bad judgments that harm themselves and others. Success is an intoxication that makes you risk-aggressive and blurs your peripheral vision.

Stupid companies and decisions:

Big Dumber

Big-company bad ideas have more bulk and inertia. The world's most valuable company recently showed its board a VR headset. Jony Ive couldn't destroy Apple's terrible idea in 2015. Mr. Ive said that VR cut users off from the outer world, made them seem outdated, and lacked practical uses. Ives' design team doubted users would wear headsets for lengthy periods.

VR has cost tens of billions of dollars over a decade to prove nobody wants it. The next great SaaS startup will likely come from Florence, not Redmond or San Jose.

Apple Watch and Airpods have made the Cupertino company the world's largest jewelry maker. 10.5% of Apple's income, or $38 billion, comes from wearables in 2021. (seven times the revenue of Tiffany & Co.). Jewelry makes you more appealing and useful. Airpods and Apple Watch do both.

Headsets make you less beautiful and useful and promote isolation, loneliness, and unhappiness among American teenagers. My sons pretend they can't hear or see me when on their phones. VR headsets lack charisma.

Coinbase disclosed a plan to generate division and tension within its workplace weeks after Apple was pitched $2,000 smokes. The crypto-trading platform is piloting a program that rates staff after every interaction. If a coworker says anything you don't like, you should tell them how to improve. Everyone gets a 110-point scorecard. Coworkers should evaluate a person's rating while deciding whether to listen to them. It's ridiculous.

Organizations leverage our superpower of cooperation. This encourages non-cooperation, period. Bridgewater's founder Ray Dalio designed the approach to promote extreme transparency. Dalio has 223 billion reasons his managerial style works. There's reason to suppose only a small group of people, largely traders, will endure a granular scorecard. Bridgewater has 20% first-year turnover. Employees cry in bathrooms, and sex scandals are settled by ignoring individuals with poor believability levels. Coinbase might take solace that the stock is 80% below its initial offering price.

Poor Stupid

Fools' ledgers are valuable. More valuable are lists of foolish rich individuals.

Robinhood built a $8 billion corporation on financial ignorance. The firm's median account value is $240, and its stock has dropped 75% since last summer. Investors, customers, and society lose. Stupid. Luna published a comparable list on the blockchain, grew to $41 billion in market cap, then plummeted.

A podcast presenter is recruiting dentists and small-business owners to invest in Elon Musk's Twitter takeover. Investors pay a 7% fee and 10% of the upside for the chance to buy Twitter at a 35% premium to the current price. The proposal legitimizes CNBC's Trade Like Chuck advertising (Chuck made $4,600 into $460,000 in two years). This is stupid because it adds to the Twitter deal's desperation. Mr. Musk made an impression when he urged his lawyers to develop a legal rip-cord (There are bots on the platform!) to abandon the share purchase arrangement (for less than they are being marketed by the podcaster). Rolls-Royce may pay for this list of the dumb affluent because it includes potential Cullinan buyers.

Worst company? Flowcarbon, founded by WeWork founder Adam Neumann, operates at the convergence of carbon and crypto to democratize access to offsets and safeguard the earth's natural carbon sinks. Can I get an ayahuasca Big Gulp?

Neumann raised $70 million with their yogababble drink. More than half of the consideration came from selling GNT. Goddess Nature Token. I hope the company gets an S-1. Or I'll start a decentralized AI Meta Renewable NFTs company. My Community Based Ebitda coin will fund the company. Possible.

Stupidity inside oneself

This weekend, I was in NYC with my boys. My 14-year-old disappeared. He's realized I'm not cool and is mad I let the charade continue. When out with his dad, he likes to stroll home alone and depart before me. Friends told me hell would return, but I was surprised by how fast the eye roll came.

Not so with my 11-year-old. We went to The Edge, a Hudson Yards observation platform where you can see the city from 100 storeys up for $38. This is hell's seventh ring. Leaning into your boys' interests is key to engaging them (dad tip). Neither loves Crossfit, WW2 history, or antitrust law.

We take selfies on the Thrilling Glass Floor he spots. Dad, there's a bar! Coke? I nod, he rushes to the bar, stops, runs back for money, and sprints back. Sitting on stone seats, drinking Atlanta Champagne, he turns at me and asks, Isn't this amazing? I'll never reach paradise.

Later that night, the lads are asleep and I've had two Zacapas and Cokes. I SMS some friends about my day and how I feel about sons/fatherhood/etc. How I did. They responded and approached. The next morning, I'm sober, have distance from my son, and feel ashamed by my texts. Less likely to impulsively share my emotions with others. Stupid again.