Lean analytics is a consequence of something called Lean startup.
There's a guy named Eric Ries, who came up with a bunch of ideas on how to start companies
around the idea of customer development and continuous improvement.
Lean startup took off really well.
Analytics is the analysis of information and metrics to understand where you are and where
you should be going.
If you put the two together, Lean startup is all about this constant cycle of building
and measuring and learning very, very quickly.
The way you measure and learn is analytics.
"Lean Analytics" is a book that my co-author, Ben Yoskovitz and I are working on -- it's
nearly finished, it'll be published early next year -- that takes the lessons of Lean
startup and shows you how to use data to build a better business faster.
Sure.
I'm going to give you an example that has nothing to do with technology, which is great.
Good friend of mine in San Diego owns a restaurant called Solare.
Was down there a few weeks ago.His son yells to him across the room, he says, "24."
I'm an analytics guy, so I'm like, wow.
That must mean something.
"What does that mean, Randy?"
He says, "Oh, every night, we measure the cost of staffing divided by the gross revenues.
If that number's below 30, we're healthy.""Why is that?"
"Well, it's over 30, you had too many people on or they didn't sell enough stuff.
They didn't sell $200 bottles of wine.
If you're below 20, maybe there's another risk that you didn't have enough staff in
and people will be unhappy.
But I can do something about revenues by telling people to sell more.
I can do stuff about staffing by telling fewer people to come in that night."
That's a really good ratio to watch.
It turns out 24 is a good number.
Those are really simple metrics.
As he's trying to find what's the right number of staff or how hard should you sell a bottle
of wine, or things like that, these are good numbers, good metrics.
That's the kind of focus that many startups lack.
The reality is that digital companies tend to give off digital exhaust.
If you're an ecommerce site or a two setted marketplace or a mobile game, every tap, every
click, every like, every swipe generates data.
It's much easier to analyze that data.
The first places we're seeing this are in technology businesses.
There's another reason for that, as well.
If you look at what makes a startup, it's usually an organization designed to grow fast.
Often the only places you can grow fast are in technology because you have software that
can scale rather than people.
Or because the technology's causing a big disruption like the rise of mobile.
It tends to be focused on technology.
But the fundamental lessons are true for anyone.
I'll give a very good example of this.
In the book, we talk about the five stages of Lean analytics.
The first stage is empathy.
Get inside your customer's head, understand the market, understand the need, understand
whether your solution's going to work.
The second stage is stickiness, which is to build something people will want to use over
and over again.
The third stage is virality, which is, now make it so that when they use it, other people
want to use it as well.
Either they tell their friends or they invite their friends or there's something about how
to use the app, like Hotmail, where it inherently invites them in.
Then there's the revenue stage, where you're building sources of money and you're pouring
that money back into growth through advertising and so on.
Finally, there's scale, being able to do this through channels, in large ecosystems.Now,
that's a specific order, and we got yelled at a bit for doing it in that order.
But we believe that if you make a product that's not sticky, and then you try and grow
it by putting money in, you're wasting most of your dollars.
It makes sense to have a small, loyal bunch of users that do the thing you want and do
it repeatably and reliably and stick around, before you start trying to get them to tell
their friends.
Because otherwise, you're throwing bad money after good, right?
Once you know that people will tell their friends, then every dollar you spend on acquiring
a customer is going to acquire other customers, because it's viral.
You may as well do virality before you focus on pouring money back in.
There's a very methodical way of doing this.
There are a lot of examples of where you need to use this stuff and go out and talk to customers.
I'll give you a concrete one from consumer packaged goods.
Proctor & Gamble makes a lot of cleaning products.
For years, they had labs working on solvents and cleaning products to try and make better
things to clean floors.
They weren't getting very far.
They hired a design team called Continuum to come in and look at the problem and see
if they could do something.
The design team doesn't know anything about chemicals, so they said, well, we're just
going to go talk to customers.
They went out to talk to a bunch of customers and they just watched them clean up.
Sounds pretty boring, right?
What they noticed was that when people spilled things, they would usually go and get, like,
a brush and dustpan and sweep up the dust and then go and get a wet cloth and wipe up
what was left, like coffee grounds.
They'd see the coffee on the floor and sweep the coffee, they'd take a cloth, swipe up
the coffee.Wait a minute, the problem is not that we need new liquid.
The problem is we need a new brush.
So, they invented the Swiffer.
That developed half a billion dollars a year of new revenue in what was thought of as a
very static market, because they went out and did customer development.
They didn't say, hey, can we build a product people want?
They said, what's the need people have?
That's a very good example of big consumer packaged goods company taking a Lean approach,
doing a lot of sort of early stage empathy instead of just pouring money into a bad cleaning
liquid nobody wants.Early on, when you're a startup, don't think about yourself as building
a product.
Think about yourself as building a tool to figure out what product to build.
It's a big difference, because then you stop being in love with your product and start
realizing all you're doing is building an experiment.
Once you've done that, you start to learn because you start to build in measurements.
Every time you build a feature, you say, how am I going to measure whether this feature
made things better?
There are some basic things about analytics that you need to know.
This is where we spend a big part of the book.
I'll give you an example.
There's a difference between a segment and a cohort.
Those are two words that, if you were in analytics, you'd care a lot about.
You may not know what they are.
A segment simply means I'm going to take all the users on my site, I'm going to show half
of them a red page, half of them a blue page, see who likes it more.
Who does what I want?
Then, if it turns out everyone likes the red page, I'm going to make my site red.
Simple.
You can do this in email mailing lists.
You could have, if you have 20,000 email addresses, you try out different subject lines with 500
users each time.
When you find the one that does best, you send the rest of the people that subject line
and your conversion rates will go up by two or three times.
Pretty simple, right?
But most people don't do that stuff.
Now, that idea of constant experimentation runs anywhere in a company.
When you make a change, you want to see what the change did.
The problem is that your users are constantly coming to a website.
You may have users in January, users in February, and users in March.
And so, while segmentation is what's called a cross-sectional analysis, because you're
looking at all users and slicing them, longitudinal analyses, which means along the life-cycle
of a customer, like cohorts, allow you to compare different people's experiences.
You may find that the people that showed up in January, when you didn't have a very good
product, yeah, they don't do so much.
They don't buy so much.
The people who show up in February, after you made some changes, do really well.
The people who show up in March...by the time you get to April, the users who started in
April are doing really really well.
If you analyzed all the users on your site, it might look like they weren't doing so well
because you're muddying the waters.
You're blending in the January, February, March and April users.
If you just look at the April users, you'll find out, wow, those guys are doing great.
Right?
It's very important to analyze what's called cohorts, or groups of people over time, as
well as to do these cross-sectional analyses.
Imagine that I have users from January, February, March and April.
The January users are all grumpy and mad because my site sucked.
The February guys are OK.
The April guys are ecstatic.
And then I run some kind of a test.
So I do a cross-sectional study of those users, and I say hey, I'm going to show them A and
B, or red and blue sites.
And it turns out the guys that had the red page, do OK.
But when I look at the guys in April who saw the red page, they do awesome.
That's doing a cross-sectional analysis of a longitudinal study, or a cohort analysis.
Sounds pretty complicated.
But you can see how if you don't understand those basic blocking and tackling rules of
analytics, you're going to make some dumb decisions, because you're going to be dealing
with bad data.
The field of analytics has changed significantly.
In the past it was simply counting how many page hits you had.
Not very interesting, right?
Because that could be one person maniacally clicking your site, or 1000 people visiting
and leaving.
Increasingly, those tools have become sophisticated both in how they think about things.
Google Analytics recently changed itself so it's much more about visitors instead of visits.
But also, the JavaScript technology that's in browsers, or the capture technology that's
in mobile devices, has become much more sophisticated.
Now I can do things like measure how long you're on a page for.
Right?
Your engagement time.
It turns out, if you're on a media site, you spend about 60 to 75 seconds on a page you're
engaged with.
If you're on a software as a service site, that would be terrible, since you're not being
very productive, right?
You want to do a thing and move elsewhere.
If you're an ecommerce site, maybe you want them to move through the shopping cart quickly
and get to the purchase.
Measuring those kinds of subtleties.
How long does someone spend on a page, is important.
You can also start to aggregate things, like the cohorts and segments that we talked about
before.
So, show me engagement by the people who came in February, versus the people who came in
April.
It turns out it's better or worse.
Right?
You're seeing tools like Kissmetrics, Heaton Shaw's product.
Crazy Egg, which is another thing that Heaton put together which shows a heat map of where
people clicked.
You can start to see there's a company called Clicktail, that lets you replay users' visits
and see what they did.
It will even show you things like which form field someone didn't complete, so maybe there's
a certain part of the form where people are abandoning.
That's pretty important.
Increasingly, you can use these tools.
But you can also build your own hooks, right?
If you're using one of the frameworks, you can build these tools.
For starters, Ben, my co-author and I, have both started companies and had exits.
I was the co-founder of Co-Radient, and Ben is VP of product at GoInstant, which was recently
acquired by Sales Force.
We've had other startups that didn't go as well.
That's important, to have good and bad stories.
But I think one of the things we learned a lot from, is the startup accelerator we ran
in Montreal called Year One Lapse, where we brought five pairs of founders in out of a
field of about 130 people we interviewed.
We had them, for about a year, build products and launch them.
We learned a lot, and so some of the case studies in the book are from them.
But most of the case studies are from people that we know through our speaking or through
the events that we run, or through our networks.
In many cases, we've talked to VCs about their opinion across all the startups we've seen.
We've also talked to big companies like Totango, which measures software as a service -- like
Price Intelligently, which does automatic pricing tools.
A variety of other companies who have customers that will let them share their data anonymously.
So let's say you have 1000 customers that you do analytics on.
Maybe you're allowed to share some of that data.
We start to do things like, what is a normal conversion rate?
I can tell you, and these numbers are hugely varied.
But I can tell you right now.
If you run a software as a service company, and you use freemium as an acquisition tactic,
roughly 0.6 percent of your users who are free will convert to paying every month.
Roughly 2.3 percent of those customers every month will upgrade to a more expensive tier.
If you're doing things right, between 2 and 5 percent of those customers will leave every
month.
Those three numbers, you can build a business model.
I didn't know those numbers before I read the book.
By the way, those numbers are wrong, but they're a line in the sand and now you can measure
and find out what's right for your organization.
That's the way you've got to start thinking about this stuff is, what's normal, how do
I draw a line in the sand, what's my business model.
Right?
In the book, we have about 30 case studies.
We have about 15 patterns or best practices.
We have some tools that we use, there's something called, there's a variety of tools for looking
at the Lean structure like checklists and things like that.
Some stuff based on Ashmeyer's Lean canvas, which allows you to sort of measure where
your business is and sketch it on a notepad.
There are a bunch of sort of practical tools.
We walk people through this, like, which business model are you?
OK, then turn to chapter 12.
We try and lead people through the process of, what business are you in?
What stage is your business at?
What metric matters to you right now?
What's normal for that metric and how do you measure your improvement past that metric
so you can go to the next stage?