I was reading some blogs covering the Growth Hacking event from Open Network Lab and took away with some thoughts around what Andy Johns has shared. It is very interesting and impressive how the growth hackers in Silicon Valley has approached to find opportunities to optimize by testing while focusing on influencing very few KPIs (key performance indicators).
For those who aren’t familiar with the term Growth Hacking, it is:
One who’s passion and focus is pushing a metric through use of a testable and scalable methodology. “Growth hacker” is a new word for most but a long held practice among the best internet marketers and product managers in Silicon Valley. (1)
A growth hacker really is just a marketer, but one with a different set of challenges to tackle and tools to work with. There are a few key differences between startups and big companies. For more detail check (3)
In many big companies, experts doing Analytics and Optimization are not called ‘growth hacker’, but certainly, could be called as such. In most cases they are measuring, setting hypothesis to test, testing, optimize, rinse, and repeat to improve their digital marketing or business performance. While big companies may not really try to compound the growth in traffic like startups trying to grow beyond 20% plus, typical digital marketing team in big companies are a small part of the organization which runs like a little startup. If not focused on building traffic growth, they are usually focused on optimizing the crap out the site to improve conversions, conversion rate, customer satisfaction, etc.
While big companies may not really try to compound the growth in traffic like startups trying to grow beyond 20% plus, typical digital marketing team in big companies are a small part of the organization which runs like a little startup. At least from an operational standpoint when the team is doing tons of testing.
If not focused on building traffic growth, they are usually focused on optimizing the crap out the site to improve conversions, conversion rate, customer satisfaction, etc.
Either way, there are many interesting things we can learn from those growth hacker cases, but let me highlight the two cases in various blogs I read.
Facebook – many small optimization efforts leading up to big growth
- In early days of Facebook; they obtained more funds from steady growth
- Added a huge amount of functionality, but no big achievement to win significant number of users
- For all the functionality on the site, growth team measured if any lead to increase to user retention for the new users who just joined Facebook
- The research resulted in a conclusion that the new functionality did not contribute to improved retention of the new users or did not contribute to maximizing retention.
- Built assumption that helping user connecting with their friends is what they need to focus growing retention
- Set KPI as “get a user 10 friends in 7 days and they’re converted”
- Solely focus and optimize against this KPI by building strategies and tactics to perform against the KPI
What’s interesting is that they’ve done this for 2 years without adding new functionality and just kept on optimizing, while MySpace was focusing on monetizing (even placing ads on user registration form). The result was, as you may know, Facebook is now a very successful social networking site.
The user growth team uses the 3 user personas (called modules) to optimize the page for each of these users in order to maximize retention. For example, Facebook will show a new user a whole bunch of tools to help them find their friends and connect. Its all about converting that new user into an engaged user as fast as possible. (convert in this case means new user connecting with 10 friends within 7 days)
Twitter – Improving user experience and user growth accelerated
- Growth rate was slowing down
- Worked really hard measuring and optimizing the service, but didn’t contributed to significant growth in users
- Thoroughly researched users’ pain points from user’s point of view.
- Built an assumption that the user registration form was the major point of friction causing churns
- Over time, the team was able to: ax the feed of tweets, shrink to only a few profile pictures, minimize the copy, cut the search bar, and enlarge the signup window so it took 1/3 of the page. — End result: 2-3x uptick in signups within 24 hours.
- Moved picking a twitter handle to a secondary page and suggested available names to the customer. Users don’t care about how cool or appropriate their handle is. Now Twitter gets them through the door into the product much faster. — End result: a 2x uptick in signups within 24 hours.
As you can see these successful companies had already started optimizing from their early days of their growth stage, and growth hacker was able to contribute in accelerating their already growing user base. These optimization efforts are no different from what the analytics and optimization teams are doing in big corporations. Silicon Valley likes to come up with these buzz words to define specific work, methodology, role, etc. However, an important thing is to really to take away with their key learnings and apply that towards our day to day job.
These optimization efforts are no different from what the analytics and optimization teams are doing in big corporations. Silicon Valley likes to come up with these buzz words to define specific work, methodology, role, etc. However, an important thing is to really to take away with their key learnings and apply that towards our day to day job.
Here are my key takeaways after reading some of the blogs on growth hacking where analytics/testing folks in big companies should walk away with:
1. Number of KPIs don’t matter
Are you tracking many KPIs? think twice or even more about what KPI means, KEY performance indicator. Many of these growth hackers in the early stage of start-ups are short on resource and budget. So in order to move quick and be effective, they really have to focus on the critical few KPI and keep on optimizing the crap out of it. In some cases, the growth team will refer to that key measure as a “North Star Metric”.
2. Collaborate and perform
I remember in early days of eMetrics Summit and in other ‘web’ analytics conferences, many people were curious how one company gained resources or budgets to buy tools, or learn how to get executive’s attention, etc. It is interesting to see how these start ups were able to collaborate across team/departments by creating a small elite team and tackle common problems. Definitely not about getting more from the top.
For example, in Quora, they started with engineers, designer, product manager, and data scientist/analytics, total to less than 10 people for the growth team. They’d create their process, methodology, and optimize against that common goal, and if needed a tool they would build it. Probably not asking their executives for more money.
Interesting that you hear comments like, tools like Google Analytics provide too much data, so they’d build tools and iterate to capture great data against that one KPI.
3. Re-think 80/20 rule in analytics, and execution
In many cases for Facebook, they’d spend 5 days analyzing data and spend 2 days executing. So if you’re spending 80% developing or executing and spending 20% on analysis, then you might want to consider flipping that to spend 80% on analyzing and 20% on executing. This is actually a big deal, as you think about this, what probably comes to mind (especially for BIG companies) is how slow your IT or web engineers are and how you/marketers expect analytics team to turn around with reports quickly.
It is very common to see a reality where, say start to the end of a project is 100%, analytics person analyze and come up with reports in 20% mark, and then the rest of execution takes 80% of the project time. Hmm, no wonder resources are always short somewhere… Perhaps things will look very different if you spent 80% on analyzing to understand your customer and spend 20% on execution like what Facebook did.
If you’re serious about making an impact on customer journey or user experience, maybe you should start evangelizing the culture where you obsess about understanding your customer and expect speed on the execution side. I don’t think it is about adding more resource/budget to the engineers, it is probably more about the mindset (hence hiring great talent) and getting the process right and optimizing the process. Facebook or Quora’s case really justifies that as they hire best in class talents, and even build tools to measure (saves cost and execute faster).
With these 3 key takeaways in mind, I think many folks practicing analytics and optimization in big companies could re-think about their role and the potential impact they can have on their business.
In big companies, your team or department may only be a sub set of the company’s main function or goal, so when you treat your department as a small company like a startup, having that ‘Growth Hacker’ mindset could have a huge impact on how you view and work solving problems. You can be more focused on the critical few, from customer’s point of view, at a smaller budget, collaborate more, and perform by adding value to your shareholders.
When you perform well, people will start to learn from your efforts, strategies, tactics, methodology, mindset, etc. That will translate to building growth hacking culture in your company.
Enjoy growth hacking!!
(1) What is a growth hacker? – http://www.aginnt.com/growth-hacker#.UiLBz2RgaSx
(2) Andy Johns’ “The Case for User Growth Teams” – http://quibb.com/links/andy-johns-the-case-for-user-growth-teams
(3) Explained: The actual difference between growth hacking and marketing – http://thenextweb.com/insider/2013/05/05/the-actual-difference-between-growth-hacking-and-marketing-explained/
(4) 元Facebookのグロースハッカーが明かす！「ユーザー獲得」成功と失敗の変遷 – http://www.find-job.net/startup/event_growth0726