Hacking Growth: How Today's Fastest-Growing Companies Drive Breakout Success

by Sean Ellis

Twice a week we’d look at the results of each new experiment, see what was working and what wasn’t, and use that data to decide what changes to test next.

Product teams do the market research, develop the product specifications, and assess the market size. Then, only once the product is properly defined, do they turn it over to the production side of the house—engineering or manufacturing—who then return the finished product ready for market. At the same time, marketers begin working on marketing plans once they’ve received the research and specs from the product team—often contracting with outside agencies, who are even further removed from key personnel,

to plan the advertising and promotion. Only once the product ships does the company work to maximize sales, and sales reports from the field are fed back to the product and marketing teams to guide the next product release. This highly inefficient cycle can take quarters or even years to complete, creating a debilitating lag in both responding to changing consumer demands or technological developments, and in rolling out the new capabilities, product improvements, and marketing channels through which to reach customers.

“If you’re pushing code once every two weeks and your competitor is pushing code every week, just after two months that competitor will have done 10 times as many tests as you. That competitor will have learned 10 times, an order of magnitude more about their product [than you].”

Creating cross-functional growth teams is a way to break down these barriers. Cross-functional teams not only smooth and accelerate collaboration between the product, engineering, data, and marketing groups, they motivate team members to appreciate and learn more about the perspectives of the others and the work they do.

the language they use to describe the product to their friends can unearth benefits, features, and language to use in your own product promotion.

to be dispassionate about your product. The feedback will be worthless if you’ve spent the whole time selling. You’ve got to be listening and observing, not pitching.

Twitter team asked users who had gone dormant and subsequently returned: (1) Can you tell us why you signed up in the first place?; (2) What didn’t work for you? Why’d you bail?; (3) What caused you to come back and try it again?; and (4) What worked this time?

failed, close up shop. They had considered employing several growth hacks to drive more adoption. For example, they thought about requiring people to whom users sent photos to also sign up for the app in order to download the photos. But they decided against that because they were afraid it would annoy people. But remember that growth hacking involves more than picking from a menu of hacks; it is, rather, a process of continuous experimentation to ensure that those hacks are achieving the desired results. If they were truly practicing growth hacking, they would have run a test to determine whether or not their assumption was true.

Let’s say experiment A is testing a small change, such as the color of the sign-up button. As results start coming in, it becomes clear that the increase in the number of new visitors signing up is very small—garnering just 5 percent more sign-ups than the original button color. Besides the obvious assumption that changing the color of the sign-up button may not be the key factor holding back new users from signing up, it’s also an indication that you’ll have to let the experiment run quite a long time in order to have enough data to make a solid conclusion. As you can see from the chart above, to reach statistically significant results for this test, you’d need a whopping 72,300 visitors per variant—or, in other words, you’d have to wait 72 days to get conclusive results. As Johns put it in an interview with First Round Review, “That’s a lifetime when you’re a start-up!” In a case like this what a start-up really ought to do is abandon the experiment quickly and move on to a next, potentially higher-impact, one.

I do not bet on small changes or something obviously will fail.

The growth hacking process is designed to help discover the most cost-effective ways to acquire new customers—and then optimize those efforts to drive growth.

first text they see must send the right message fast;

“How is this thing you’re showing me going to improve my life?”

From those shoppers who visit the website, they want to learn whether they downloaded the app and if not, what held them back; for the existing app users they ask what would make them likely to refer the app to their friends. From these surveys, they learn that a significant number of the website visitors didn’t know about the app, and those who did were happy ordering from their laptop and didn’t see the need to use it. They also learn

that a good portion of the app’s users say that they would recommend it to others and would be even more likely to if they were given a discount off their next order or a coupon.

Adding additional channels will be even more important as your growth takes off. One reason is that you will almost inevitably reach a natural ceiling with any given channel, after which you just won’t be able to squeeze

growth teams should periodically shift their focus to the next stage of the customer funnel, moving on from acquisition to activation and then on to retention,

“We don’t mind tricking people into seeing content they’ll love,” says founder Eli Pariser. “If they don’t love it, they’re not going to share it. Virality is a balance of how good the packaging is and how good the content is.”16 The takeaway is that while finding the right words to appeal to people is vital, offering true value is a necessary ingredient for achieving viral growth.

Chamath Palihapitiya at Facebook reminds us, he’d told the growth team not to even think about instrumenting virality at first and to focus instead on building a great product.

you’ve got to make the experience of sharing the product with others must-have—or at least as user friendly and delightful as possible.

VIRALITY = PAYLOAD × CONVERSION RATE × FREQUENCY

you need to choose how invites will be delivered. In the best viral loops, the delivery is a natural result

The best way to do this is to create a double-sided incentive,

Too many companies add referral programs into their products as afterthoughts (another reason why the product managers, designers, and engineers should be involved in growth team efforts, so they can take these considerations into account when building the initial product)

it’s crucial that you never assume why users are behaving as they are; rather, you’ve always got to study hard data about their behavior and then query them on the basis of observations you’ve made in order to focus your experimentation efforts most efficiently on changes that will have the greatest potential impact.

you should be tracking all essential steps of the customer journey to that moment of activation.

those steps are: map all of the steps that get users to the aha moment; create a funnel report that profiles the conversion rates for each of the steps and segments users by the channel through which they arrive; and conduct surveys and interviews both of users who progressed through each step where you’re seeing high drop-offs, and those who left at that point to understand the causes of drop-off. You can then use this information to create new, highly targeted, and high-impact ideas to experiment with to improve your results.

DESIRE – FRICTION = CONVERSION RATE

communicate relevance, show the value of the product, and provide a clear call to action.

The more information people put into the product, the more their commitment increases, through a concept called stored value.

The company went from having one home page to more than 26 variations based on key accounts, time of day visiting the site, business vertical, and more.

Here we recommend a two-pronged approach that involves (1) optimizing the current set of product features, notifications, and subsequent rewards from repeated use; and (2) introducing a steady stream of new features over a long period of time. Getting this balance right is extremely important.

Growth teams can play a pivotal role in evaluating the appeal of planned new features by experimenting with offering customers prototypes or beta versions. New features should be road tested with a very small percentage of users, as these experiments create rafts of data that help companies refine new features before making them widely available. While the product teams in most organizations are responsible for designing new features, the growth team will undoubtedly come up with new feature ideas for the product team to try from the constant surveying and data analysis they conduct. Similarly, data that the team synthesizes can uncover new product optimization opportunities that may not have been discovered in market research or strategic planning.

As users become more experienced at using your product, features they haven’t used yet—and new ones being introduced—should be brought to their attention, gradually and in a way that allows them to tackle learning a new feature only after having achieved mastery of the previous one.

All ongoing onboarding messaging can be tested by growth teams, such as by sending email to test groups with different versions of explanations and imagery explaining the features being promoted. If email messages prove effective in driving more use of the features, the team might then experiment with including highlights about the features in the product itself, such as by adding a promotional video about a feature to the landing pages of several other features.

people are leaving because the grocery chain doesn’t carry an important brand that they want to buy, the growth team may not be able to prevent their churn. In that event, the best the team can do is alert the purchasing department of the high demand for that brand (which they certainly should, as the absence of the brand may be stymieing not only the retention rates for the app, but the growth of the chain overall).

Ad-based growth teams should be optimizing pricing in a similarly fluid way. The largest advertising platforms, such as Google and Facebook, use an auction model to set the value of their ad inventory, which essentially means that when advertisers want to place an ad they set a bid price for what they’re willing to pay, then the site gives the ad space to the highest bidder (this is a dramatic oversimplification; the auction processes are dauntingly complex). When that bidder’s budget is exhausted, often by hitting a daily cap or other restriction, the next highest bidder is given the inventory, and so on, until all available inventory is used. These companies also use elements such as ad quality and customer response to factor into which

bidding system nd bitcoin price.