Chapter 1. Introduction to Growth Marketing
At its core, this is a book about growth marketing and how advances in technology can help achieve your company’s goals faster—at a lower cost and with less risk—than ever before. In the last 10 years, “growth marketing” functions have sprung up inside Silicon Valley startups and yielded incredibly valuable companies like Facebook, Pinterest, Uber, and others that have institutionalized their growth marketing approach. As our experience with growth marketing has matured, we’re in a good place to document the best practices and modern approaches any company can understand and adopt to help them unlock their full growth potential.
For most companies, growth marketing is the way of the future for leveraging data and agility to scale revenue and increase customer lifetime value. As campaigns continue to shift more toward digital, it’s easier to track and monitor every move. Designing strategies around the entire customer journey and funnel, and constantly being ready to iterate and improve, will deliver clarity around attribution sources and fuel revenue growth. The days of guessing about how to invest your user acquisition budget are over. With a well-thought-out growth marketing strategy, you can now use real-time data to validate which efforts are working and which are not.
With a well-thought-out growth marketing strategy, you can now use real-time data to validate which efforts are working and which are not.
Entrepreneurs flock to Silicon Valley, affectionately called the “Valley of Dreams,” to build fast-growing startup companies fueled by venture capital. These startups aim to scale their growth to hundreds of millions of users and guide their company to a successful exit, whether that be an acquisition by a larger entity or through an IPO. For the venture capitalists funding these endeavors, the risks and the rewards are high; most venture capital funds have a fund life of approximately 10 years, in which they seek a liquidity event for their portfolio companies.1
With this additional pressure on venture-backed startups, companies know that they must accelerate growth at all costs. The reality, however, is that most venture-backed startups fail spectacularly. As discussed in “How VCs Deploy Operating Talent to Build Better Startups,” research estimates that between 30% to 40% of high-potential start-ups end up liquidating all assets within the first five years. If failure is defined as failing to see the projected return on investment—say, a specific revenue growth rate or date to break even on cash flow—then more than 95% of startups fail.2
Considering the risk of failure, startups have to come to market prepared to address the three biggest challenges to success: hiring the right people, acquiring and keeping customers, and optimizing for revenue growth.3 While every startup purports itself to be the next unicorn, it’s only the ones who are prepared to overcome these key challenges that end up becoming successful. The financial pressure and constant state of resource constraint is also one of the startup’s most significant advantages, because it fosters a creative, agile environment where teams are encouraged to experiment, learn, and outmaneuver incumbents and the competition. In this book, you’ll learn how to take advantage of the Lean AI + Customer Acquisition strategy for leveraging data and automation to scale your user growth.
One of the most crucial stages that has the potential to make or break any startup beyond determining your basic product/market fit is growth marketing. Growth marketing looks at the entire customer funnel as a singular object with many working parts, where experimenting with messaging at the top of the funnel and down-funnel hypothesizing and testing work together to increase customer acquisition rates by any means possible. Understanding the intricacies of this approach to marketing is critical to figuring out how you can use a new class of software—artificial intelligence and machine learning capable of making sense of immense amounts of data and market feedback—so we’ll spend plenty of time bringing clarity to the fundamental underpinnings of growth marketing.
In The Lean Startup, Eric Ries teaches entrepreneurs how to be hyper-efficient with resources in order to make the most viable business decisions. He argues that entrepreneurs should run small experiments all the time. Even though 9 out of 10 experiments may fail, the one that succeeds may make your business grow 100x faster. The growth marketer plays a crucial role in the Lean Startup process by constantly testing and tweaking to make the business grow as fast as possible. The key is to take action—to try, to fail, to learn, and, eventually, prevail.
Having a wide customer base is a key determining factor for revenue, and, consequently, the success of any startup. Acquiring this wide customer base is growth marketing’s primary function, making it an essential part of any marketing strategy. But the fact stands, the procedure requires immense time and intelligence to make a startup stand out from the crowd. In this book you will learn how to take advantage of leveraging artificial intelligence (AI) in growth marketing to help you crush your goals to acquire customers and drive revenue growth once you have achieved product/market fit.
Champions of growth marketing adopt a cross-functional approach with multiple stakeholders, including product, marketing, engineering, and data science. These teams enable an organization to “compete on the rate of learning” and create a path to hyper customer acquisition growth. In many ways, this book is an extension of the latter stages of a Lean Startup, after startups have reached product/market fit; the speed of learning needs to accelerate, and this book presents a framework for tackling that challenge. The emergence of growth as a function has changed people’s expectations around velocity. This means that learning needs to happen faster, because decisions need to be made faster to compete.
Taking the wisdom of The Lean Startup’s approach into the golden dawn of artificial intelligence, we can radically improve our chances of successful outcomes. A properly instrumented approach to modern artificial intelligence, machine learning, and automation combine to offer companies large and small the ability to conduct far more experiments simultaneously. Conducting experiments at scale improves the likelihood of finding successful experiments, some of which you’d never have taken the time to test in a pre-AI world. Incremental experiments that otherwise would have been sidelined for cost or complexity are now valid for observation in the world of autonomous marketing.
Taking the wisdom of The Lean Startup’s approach into the golden dawn of artificial intelligence, we can radically improve our chances of successful outcomes.
The Attention Economy
Lean Startup isn’t about being cheap [but it is about] being less wasteful and still doing things that are big.
For any business, the goal is to create sustainable and systematic customer acquisition strategies that keep revenue and profits flowing while keeping up with industry trends. As more brands continue to increase ad spend across a myriad of channels to acquire new customers, the average cost of expanding your customer base continues to climb year over year.
The average consumer’s attention is now literally worth billions of dollars, because that’s how much money companies are spending on their user acquisition efforts across mobile, desktop, television, radio, and/or voice assistants. Every digital interaction is an opportunity for brands to bombard users with advertisements in an effort to turn your attention toward their product or service.
While we may not think about it through this lens often, the fact is that human attention is a finite resource. For every 24 hours, Americans spend on average only 5.94 online, which means that companies only have those six hours every day to get the right messaging in front of the right audience and convert them into customers. With endless demand and a finite supply, human attention is arguably one of the most valuable resources in the world—companies are constantly and quite literally competing for your attention and your wallet. And the race for your time and money has only gotten tighter thanks to the addition of the apps and channels we use to live, work, learn, and play like Google, Facebook, Instagram, YouTube, Amazon, Netflix, Pandora, and Fortnite among many others. If six hours per day are spent online, then taking time spent on social media, streaming, and gaming sites reduces “marketable” hours to a small fraction of each day. But that’s where the challenge, and the opportunity, lies.
How you bring new consumers to your business is customer acquisition, which is also sometimes referred to as “user acquisition,” depending on the type of products or services you’re offering. Given the demand that exists to command human attention, one of the top challenges for any startup is acquiring and retaining new customers cost-effectively.
In the beginning, the vast majority of startups struggle to find users or customers. No wonder: if you’ve got a new product or service, very few people will be familiar with it or your brand. Regardless of the size of your business or startup, acquiring customers profitably is a critical aspect of running a business. It also acts as evidence of traction for your startup to customers, partners, investors, influencers, and prospects. All future startup growth depends on two things: acquire customers fast and acquire customers sustainably.
Most startups generally work with contractors or marketing agencies in the early days to help with growth marketing. The general approach is to broadly test many different paid and organic user acquisition channels to figure out what works and doesn’t. There is no generic growth playbook guaranteed to work across all different startups because every business is unique. I can confidently say there is no silver bullet to drive growth.
The real secret to scaling customer growth in a startup is to run as many A/B tests as possible. A/B testing, as it’s commonly referred to, involves testing a set of independent variables (offer, copy, pricing, etc.) to find statistically significant improvements toward reaching your business goals.
This approach would lead you to test, learn, and iterate as quickly as possible by finding small wins that end up compounding into massive growth in the long term. Obviously, the A/B testing and hypothesis development have to be scientifically based on some best practices, observable evidence, and statistical significance. But as an organization the big takeaway here is not to sit back and suffer from “analysis paralysis” where overthinking gets in the way of making decisions, working against your ultimate objective.
Your biggest leverage is to figure out how to increase the volume and velocity of experiments and tests you can run across the entire customer journey from different prospecting and retargeting channels as well as product features and experiences to help you better engage, retain, and monetize customers. The lifeblood of any startup is cash in the bank because you need to pay your expenses. The biggest expense for most startups—second only to payroll—is their user acquisition budget that the head of growth manages.
But there’s a problem with this conventional approach to scaling user growth in startups. Successful businesses following this paradigm can become highly dependent on people to help execute all the different A/B tests, which is both time and labor intensive. When I think about the next generation of growth teams they’re going to have to be much quicker in execution with the increasing pace of change in today’s world. Everything happens much faster now with even more pressure to produce results that are being tracked in real time. It’s hard for any startup to build a successful growth team quickly. The future will involve leveraging AI in growth marketing, as the only way any startup can survive and thrive in a highly competitive world is by getting A/B testing ideas quicker and implementing them faster than anyone else, because global competition means the time frame you have is shorter and shorter.
There is a smarter approach on the horizon, and becoming familiar with the pros and cons of this new way of thinking presents a major opportunity to businesses and leaders as well as employees. Today, we can leverage artificial intelligence and machine learning to enhance and manage your user acquisition channels, radically accelerate the velocity of A/B testing all the key variables (like audiences, geographic markets, creatives), and process all your user data faster to uncover better insights and make smarter decisions on where to invest your user acquisition budget to get the best return on investment (ROI).5
All of these major marketing platforms have application programming interface (API)6 connections, which make it much easier to capture and share data to automate the key levers for optimizing campaigns without being dependent on humans. This book will show you how to leverage these ideas, strategies, tools, and technologies to scale up your startup growth and stack the odds for success in your favor. To start, let’s move on to Chapter 2, where we will explore some of the building blocks and the possibilities you can unlock with Lean AI.
1 A liquidity event is an event that allows founders and early investors in a company to cash out some or all of their ownership shares. The liquidity event is considered an exit strategy for an illiquid investment—that is, for equity that has little or no market to trade on.
2 Drew Hansen, “How VCs Deploy Operating Talent To Build Better Startups,” Forbes. https://oreil.ly/ZNl9s.
3 “State of Startups 2018,” First Round. https://oreil.ly/UKk0A.
4 The average adult spends 5.9 hours per day with digital media, up from 3 hours a day in 2009, according to Mary Meeker’s 2018 Internet Trends Report.
5 ROI measures the gain or loss generated on an investment relative to the amount of money invested. ROI is usually expressed as a percentage and is typically used for personal financial decisions, to compare a company’s profitability or to compare the efficiency of different investments.
6 In basic terms, an API allows applications to communicate with one another. An API is not a database. It is an access point to an app that can access a database.