Leveling up your Web Analytics Knowledge from Lv. 1 to 2

As you might’ve figured out, I have a fascination with web analytics. It’s amazing how it can help a startup go

from:

to:

I thought I’d use this blog post to share the love and help anyone interested in analytics get started (as well as organize my thoughts). I’ll discuss a few topics I think are pretty important to web startups and link to resources I’ve learned from. I’ll save the deep dives for later blog posts.

Get to product market fit first
A startup’s main goal is to get to product market fit. No other metric matters more in the early stage of a startup. It essentially means you need to have a damn good product that most of your customers love in a market big enough that you can conceivably make over $500M in. Marc Andreessen has an infamous quote that gets copy-pasta’d on startup blogs everywhere (including this one!) about how you know you’re in product/market fit:

And you can always feel product market fit when it’s happening. The customers are buying the product just as fast as you can make it — or usage is growing just as fast as you can add more servers. Money from customers is piling up in your company checking account. You’re hiring sales and customer support staff as fast as you can. Reporters are calling because they’ve heard about your hot new thing and they want to talk to you about it. You start getting entrepreneur of the year awards from Harvard Business School. Investment bankers are staking out your house. You could eat free for a year at Buck’s.

My initial reaction to this quote was “well, that’s cool and all… but how the hell do I measure this with numbers?” I think Sean Ellis’s 40% rule has been the best answer I could find.

Funnel Analysis
It’s important to understand whether the changes you are making to your website are actually making it “better.”  A great way to figure out whether you are actually improving your website is to do a funnel analysis (also known as customer lifecycle analysis). In a funnel analysis, you basically observe the behavior of every user that comes to your site and measure the conversion rate in each step of your product’s lifecycle.

The point is to see whether a change you’ve made to a site is actually improving your end goal overall (sign ups, revenue, etc.) instead of just a single conversion rate. Someone could probably improve the conversion rate of a page by promising nude pictures of [insert your celebrity crush here], but he is not likely to improve his overall signups. Dave McClure has funnel analysis well organized and explained with his AARRR metrics.

Viral Coefficients
One of the most intriguing analytics topics is virality. Virality measures how fast your app grows organically based on how many invites each user sends. The main metric that you want to measure is what’s called the “k-factor.” It’s a very simple formula that is calculated using two parts:

k = (average number of invites sent per user ) * (conversion rate of invites to customers)

David Skok has a great mathematical model behind virality. It’s quite a mindblower, so I’ll be taking a deep dive in my next blog post for an easier explanation of virality.

Figure out what metrics matter to you
Pick a few metrics that matter most to your business. This could be number of sign ups, avg. revenue/customer, or even something not profit-driven such as number of “happy” users (if you’re a non-profit or an enthusiastic blogger at least). Avoid vanity metrics. Then, take a deep dive into your data and find the metrics in your funnel that affects these metrics the most. Focus your efforts on tracking and improving a few metrics at a time. There are hundreds of metrics that you can track, so many that trying to track them all would be counter-productive and just distract you from bigger priorities.