“Growth Hacking” is not just a buzzword in Silicon Valley. It is a core competence that leading tech companies have developed to systematically grow their customer base. I have worked in several global growth roles at Google over the last seven years. During that time, I realized there is a fundamental difference in how successful tech companies in Silicon Valley approach growth challenges compared to the rest of the world. I think that indie hackers, like you and me, can apply these lessons to our own homegrown startups. Here are five powerful Growth Hacking lessons that you can learn from leading growth experts of Silicon Valley:
1) Make Growth Hacking part of your culture
Most of Silicon Valley’s marketing and product organizations understand the importance of “Growth Hacking” (they call it just “Growth”, which is pretty revealing about how core it is to them). It is an essential part of operations; woven into the DNA of the most successful tech companies. In Europe, most internet companies do not know what Growth Hacking really is, and even less use it effectively. Our recent Growth Akademie research showed that 74% of digital professionals have not heard of the term “Growth Hacking” or have not had experience with it.
The most popular view of Growth Hacking promotes it as a silver bullet or a collection of secret quick fixes. However, I believe this view is too short-sighted. To get the fullest picture, we have to ask what Growth Hacking actually means. The most applicable definition is from growth expert Andrew Chen:
“Growth Hacking is a multi-disciplinary skill set combining elements of marketing, product management, data analytics, and coding to answer the question ‘How do I get and retain customers?’. It basically enables your product to market itself by optimizing the entire customer lifecycle or by plugging it into big platforms. Additionally, most growth tactics are free or do not require big budgets.”
Here are some examples of typical Growth Hacking approaches:
- Optimization of customer interactions throughout the funnel. For example, accounting for customer lifetime value and retention. On the contrary, traditional optimization usually focuses on acquisition cost and volume.
- Alignment of product and marketing channels to create powerful and innovative acquisition and retention opportunities like Growth Loops (see section 4 for more).
- Product integration into big platforms like Facebook or Youtube that allow your product to grow on the back of these platforms.
- Constant optimization of the customer experience through experimentation.
2) Always be experimenting
The most successful businesses in Silicon Valley learned that relentless experimentation is key for growth. A fellow Google growth expert summed it up as follows:
"Traffic that does not contribute to experiments is a lost learning opportunity. Always be experimenting."
This illustrates how Growth Hacking is all about learning, and how experimentation is intertwined with that. So, what do solid growth experiments look like? A crucial part of growth experimentation is data-driven hypotheses. Based on data points you basically think about product or marketing changes that could improve performance. You try to formulate this in a concise statement: “If ___, then ___, because of ___.”. Then “translate” this change into an adjusted variant of your current experience. For example, creating different landing pages or app flow variants.
Finally, you “A/B test” these variants against your control group— usually matching your current experience—by randomly assigning real users to different experiments. The goal is to find significant differences that allow you to reject or confirm your hypothesis, and thus create a more efficient/more retentive/more engaging funnel.
Common pitfalls in the "Growth Experimentation Process" can be avoided if you know about them. Here are two vital tips that you should know about in order to avoid pitfalls:
- Hypotheses should be formulated before the test and should be based on data.
You might ask yourself: “Why do I need experiments if I have data from market research or customer analysis?” It is tempting to misuse these sources for causal implications, and either fish for data points that support what you suspect, or, if you cannot find corroborating data, simply modify your suspicion. Be more systematic about it and build a foundation in the form of a hypothesis before looking for evidence. That being said, these sources are crucial for experimentation, but more so for generating speculations than drawing causal conclusions. Another good idea is to use them for pre-validation of your hypotheses which will increase your success rate tremendously.
- Do not change many variables at once.
The most common pitfall during Growth Hacking experiments is changing too many variables at once. Ideally, you alter only one variable per test. For example only change your pricing, and keep everything else the same. This way, the disparity in performance can be explained by different prices. If you change, for example, the value prop and price at the same time, you most likely will have a problem determining what caused the performance change. Furthermore, testing multiple variables in one experiment requires more traffic and usually more time to get significant results. To avoid both these issues, some startups apply “phased testing plans” that systematically test and optimize one variable after the other.
3) Build Behavioral Economics capabilities
Another rising trend in Silicon Valley is the infusion of Behavioral Economics insights into Growth to create products that are valuable to customers and their needs. This seems like a logical extension once you learn how Growth Hacking is rooted in experimentation and data analytics. Made popular by influential researchers like Nobel Prize winner Daniel Kahneman or Wall Street Journal columnist Dan Ariely, Behavioral Economics is at the intersection of psychology and economics. Compared to standard economics, this school of thought explains how choices like buying decisions are actually made without assuming that people are rational (with “rational actors” being a baseline assumption of traditional economics).
Employing insights from the field of Behavioral Science allow you to improve your user experience and tackle Growth Hacking challenges from a completely different angle. This is especially useful for creative parts of Growth like hypothesis generation or landing page optimization. Taking into account powerful concepts such as “Social Proof” or the “Endowment Progress Effect” have accelerated the growth of numerous tech companies while providing additional value to their customers.
A great example of employing “Social Proof” as a growth tactic is LinkedIn’s user experience. Their sign-up page for new users features pictures of people similar to them and allows them to “find your colleagues”. According to the Social Proof method, people are heavily influenced by their peers; seeing that other like-minded professionals have already joined helps new customers decide whether to join or not. Additionally, the skills section of their profile page is a feature built solely on the premise of a user’s social proof. It leverages the idea that your credibility as an expert is enhanced through endorsements by your peers.
LinkedIn also utilizes the “Endowment Progress Effect” to motivate individuals to complete their LinkedIn profile. The closer the goal is (or perceived to be), the more likely users are to follow through completing it. Showing users their progress encourages profile completion more powerfully than just reminding them via email or notification.
Navigating these Behavioral Economics nuances requires a specific skill set that involves not only subject matter expertise but also strong ethical judgment. To amplify business growth in the long run, Behavioral Economics has to be considered in a systematic and ethical way. Consequently, startups and innovative established players have started to form specialized Behavioral Economics teams or they tap into expert services like Behavioral Consultancies such as BEWorks, co-founded by Dan Ariely.
4) Think Growth Loops, not dead ends
Tech businesses in Silicon Valley learned that thinking big goes hand-in-hand with aggressive target setting. Product and marketing teams have to drastically rethink their approaches to growth in order to achieve these targets. Thus, growth experts started to engineer predictable growth models based on growth loops rather than traditional linear activities that evaporate shortly after implementation. Growth loops are when the cost to gain a new user is vastly outweighed by the output of that user (usually by gaining more users). This process repeats, and more users beget more users, until you’ve reached critical mass on your market. Let’s look closer to why Growth Loops are such an essential part of Growth Hacking:
- Growth Loops are usually baked into a product. They’re designed to leverage interactions of new customers to generate even more new customers. (A great Acquisition Loop example: Instagram’s new user sign-up aims at inviting your friends to join as well.)
- For existing customers, Growth Loops add notification triggers to useful and repetitive product interactions. They ultimately help to keep your product top-of-mind by bringing back customers and reinforcing the value of the product (a great Retention Loop example: Hubspot sends notifications via a Chrome extension once status of contacted prospect changes). Growth Loop approaches like these generate robust and predictable compound effects that apply to all new users as well.
Contrary to Growth Loops, Linear Channels can be defined as activities that imply a ‘dead end’ without multiplying the loop effect (for example, display ads usually bring only a single visit per click and do not generate additional visits). This isn’t necessarily the wrong method; you still need strong linear channels to feed and kickstart your loops in the first place. But focusing most of your efforts on Linear Channels rather than lasering-in on Growth Loops leaves you with huge untapped potential for growth.
5) Speed over perfection
It profoundly struck me how quick Silicon Valley businesses are in shipping their products. More than once, I personally witnessed products that launched even though they were not 100% finished. At first, it seemed counterintuitive; it confused a European like me, who was accustomed to caring more about quality and excellence before shipping. However, the ship-fast strategy makes more sense once you’ve learned about the three dominant characteristics of most technology and internet products:
- One big advantage of technology products is that you can easily collect real customer data, which allows you to rapidly optimize the product experience and even product-market-fit directly after launch. This also means you cannot optimize your product if you haven’t shipped it yet. You can do crazy market research and refine all product details as much as you want prior to a launch, but nothing beats real market response in form of customer data.
- Compared to traditional products, digital products usually have inherent network effects. As in, the individual value of a product increases the more customers are using it. A great example is payment solutions or digital wallets: everybody in the business world needs to send their money to others. There is an ever-present an intense need for software like this. Once these network effects kick in, they exponentially attract customers and therefore often create monopoly-like situations, which also means high barriers to entry once a certain company reaches a greater size (ie. hits critical mass, and starts strangling other competition from even joining). This doesn’t necessarily mean you can’t enter the market anymore, but you should focus on a slightly different segment than the dominant player. For example, Snapchat successfully focused on self-destroying messages and stories compared to Facebook’s feed and status updates combined with regular messages.
This gives you an idea why the best products do not inexorably win in today’s competitive tech world. This does not mean Silicon Valley's leaders neglect technical solutions or product design. It’s actually the opposite: solving real customer problems and a great product design turned into the standard requirements, and the real differentiator for success lies more in clever distribution and marketing. To quote Peter Thiel, co-founder of PayPal and early Facebook investor:
“Superior sales and distribution by itself can create a monopoly, even with no product differentiation. The converse is not true. No matter how strong your product; even if it easily fits into already established habits and anybody who tries it immediately likes it, you must still support it with a strong distribution plan.”
Hopefully these growth hacking tips can help you succeed like they’ve helped me. By utilizing these strategies, you can level up your startup, and learn from the success of Silicon Valley while on a budget. If you’d like to talk strategy, share you own successes with growth hacking, or just want to plug your own experiments into Growth Looping, leave a comment below and get the conversation started! If you want to dive even deeper, feel free to apply for next Growth Akademie cohort.
- Nikolas Vogt, Global Growth Marketing Lead, Google Assistant | Founder, Growth Akademie