The key to successful target specific #growthhacking

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Reading Oliver Blanchard’s useful book ‘Social Media ROI’ which I bought when I was working at Sony on marketing leading up to the James Bond movie ‘Skyfall’ I tweeted a quote from page 18 earlier today – which was rewarded with 18 Favourites:

But then that begs the question, what is the key to successful target specific #growthhacking from the expert’s point of view? And the answer is finding a metric you can measure early in a user’s lifecycle, according to Alistair Croll and Benjamin Yoskovitz. Simply put the key to the growth hacking process is the early metric, which is also known as a leading indicator.

The extract below from their book ‘Lean Analytics’, explains in detail what that means for growth hacking.


Growth hacking
Most startups won’t survive on gradual growth alone. It’s just too slow. If you want to grow, you need an unfair advantage. You need to tweak the future. You need a hack.

Growth Hacking is an increasingly popular term for data-driven guerilla marketing. It relies on a deep understanding of how parts of the business are related, and how tweaks to one aspect of a customer’s experience impact others. It involves:

Finding a metric you can measure early in a user’s lifecycle (i.e. number of friends a user invites) through experimentation or, if you have the data, an analysis of what good users have in common.

Understanding how it’s correlated to a critical business goal (i.e. long-term engagement.)

Building predictions of that goal (i.e. how many engaged users you’ll have in 90 days) based on where the early metric is today.

Modifying the user experience today in order to improve the business goal tomorrow (i.e. suggesting people they might know), assuming today’s metric is causing a change in tomorrow’s goal.

The key to the growth hacking process is the early metric, which is also known as a leading indicator—something you know today that predicts tomorrow. While this seems relatively straightforward, finding a good leading indicator, and experimenting to determine how it affects the future of the company, is hard work. It’s also how many of today’s break-out entrepreneurs drove their growth.

Attacking the leading indicator
Academia.edu founder Richard Price shared stories from a recent Growth Hacking conference at which several veterans of successful startups shared their leading indicators.

Former Facebook growth team leader Chamath Palihapitiya said a user would become “engaged” later if they reached seven friends within ten days of creating an account. And Josh Elman, who worked at Twitter, said the company had a similar metric: When a new user follows a minimum number of people—and some of those follow back—the user is likely to become engaged. In fact, Twitter has two kinds of users: “active” ones who’ve visited at least once in the last month; and “core” ones who’ve visited 7 times in the last month.

Onetime Zynga GM Nabeel Hyatt, who ran a 40-million-player game, said they looked at first-day retention: if someone came back the day after they signed up for a game, they were likely to become an engaged user (and even one that paid for in-game purchases). Hyatt also underscored the importance of identifying one metric that matters, then optimizing it before moving on to the next one.

Dropbox’s ChenLi Wang said the chances someone becomes an engaged user increase significantly when they put at least one file in one folder on one of their devices.

LinkedIn’s Elliot Schmukler said the company tracks how many connections a user establishes in a certain number of days in order to estimate longer-term engagement.

User growth isn’t everything, however. You may be trying to hack other critical goals like revenue. Elman told us that early on Twitter focused their energy on increasing feed views because they knew their revenue would be tied to advertising—and that advertising could only happen when a user looked at their Twitter feed. Number of feed views was a leading indicator of revenue potential even before the company hit the Revenue Stage.

What makes a good leading indicator?
Good leading indicators have a few common characteristics:

Leading engagement indicators tend to relate to social engagement (links to friends), content creation (posts, shares, likes), or return frequency (days since last visit, time on site, pages per visit).

The leading indicator should be clearly tied to a part of the business model (such as users, daily traffic, viral spread, or revenue.) After all, it’s the business model that you’re trying to improve. You’re not just trying to increase number of friends per user—you’re trying to increase the number of loyal users.

The indicator should come early in the user’s lifecycle or conversion funnel. This is a simple numbers game: if you look at something that happens on a user’s first day, you’ll have data points for every user. But if you wait for users to visit several times, you’ll have fewer data points (since many of those users will have churned out already), which means the indicator will be less accurate.

It should also be an early extrapolation so you get a prediction sooner. Recall that Kevin Hillstrom says the best way to understand whether an e-commerce company is a “loyalty” or an “acquisition”-focused organization is to look at how many second purchases happen in the first 90 days. Rather than wait a year to understand what mode you’re in, you look at the first three months and extrapolate.

You find leading indicators by segmentation and cohort analysis. Looking at a group of users that stuck around, and another group that didn’t, you might see something they all have in common.

Correlation predicts tomorrow
If you’ve found a leading indicator that’s correlated with something, you can predict the future. That’s good. In the case of Solare, the Italian restaurant, the number of reservations at 5PM is a leading indicator of the total number of customers that dine on any given night—letting the team make last-minute staffing adjustments or order additional food.

UGC site reddit has been fairly public about its traffic and user engagement – after all, it derives revenue from advertising, and wants to convince advertisers it’s a good bet. About half of all visits to the site are logged-in users, but these users generate a disproportionate amount of site traffic. reddit’s engagement is good. “Almost everyone who makes an account comes back a month later,” says Jeremy Edberg. “It’s a couple of months before people stop coming back.”

Is there a leading indicator in reddit’s site traffic? The table compares logged-in users (those with accounts) to anonymous visitors by the number of pages they view in a visit.

Table: reddit’s page views for logged-in versus non-logged in users

reddit_data

Click for full-sized image of table of data.

This data suggests that loyal, enrolled users—those that return each day to the site and have an account—view a higher number of pages per visit. Is that high number of page views by a first-time visitor a leading indicator of enrollment?

Causality hacks the future
Correlation is nice. But if you’ve found a leading indicator that causes a change later on, that’s wonderful, because you know how to change the future. If a high number of page views on a first visit to reddit causes enrollment, what could reddit do to increase the number of page views, and therefore increase enrollment? This is how growth hackers think.

Consider what Circle of Friends’ founder Mike Greenfield did when he compared engaged to not-engaged users—and found out that many of the engaged users were moms. Whether or not someone was a mother was, for Mike, a market-focused leading indicator of that person’s future engagement. He could decide how many servers to buy in six months’ time based on how many moms signed up today. But what really mattered was this: he could target moms in his marketing, and change the engagement of his users dramatically.

Mike’s hack was market-related, but growth hacks come in all shapes and sizes. Maybe it’s a change in pricing, or a time-limited offer, or a form of personalization. The point is to experiment in a disciplined manner.

Product-focused growth hacks—what Chamath Palihapitiya calls “aha moments”— need to happen early in the user’s lifecycle in order to have an impact on the greatest number of possible users. That’s why social sites suggest friends for you almost immediately.

You can use promotions and experiments to try and identify a leading indicator, too. Music retailer Beatport ran a Cyber Monday promotion to maximize total purchases. A week before the holiday, they sent all their customers a 10% discount code. Those customers who purchased something with the code were then sent a second, personalized code for 20% off. If they used that code, they were sent a final, one-time-only, time-limited code for Cyber Monday that gave them 50% off their purchase. This approach increased purchase frequency, and encouraged customers to max out their shopping cart each time.

While we don’t have data on the effectiveness of the campaign itself, it’s clear that the company now has a wealth of information on who will respond best to a promotion and how discounts relate to purchase volume—and they’ve made their loyal customers feel loved as well.

Growth hacking combines many of the disciplines we’ve looked at in the book: finding a business model; identifying the most important metric for the stage you’re at; and constantly learning and optimizing that metric to create a better future for your organization.

Or for a concrete illustration simply see page 35 of the startup metrics slideshare below:

Lean startup metrics

It’s about startups “picking a single metric that they can literally bet the company on,” says Suhail Doshi, Co-Founder of Mixpanel. Click the video to jump to the 14 second quote, or check out Mixpanel for free to see how it can help figure out that all important specific metric for your business!

CBA vs ROI (cost benefit analysis vs return on investment)

 

** Updated to include a new comparison example between CBA and ROI in the second table below **

A great table and explanation of the difference uses and value of the two forms of measurement for social marketeers, from Angie Schottmuller in Search Engine Watch:

Cost-Based Analysis (CBA)
Return on Investment (ROI)

Formula
Benefits – Costs
( Benefits – Costs… Continue reading | 3 Comments