How To Score Problem Interviews During the Lean Startup Process

If you’re a fan of Lean Startup, you know about Problem Interviews. They’re the part of a startup where you first go out and speak to people you think might be customers, and try to determine if they have the problem you want to solve.

This post isn’t about the interviews themselves—there’s a ton of good thinking on how to conduct them already out there, and for many entrepreneurs they’re a rite of passage where you realize that your worldview is radically different from the market reality, and (hopefully) adjust accordingly.

But I do want to float a somewhat controversial idea, and get some feedback.

Yes. This is where YOU participate.

I want to talk about scoring interviews. It’s something we’re working on for the book; and it’s surprisingly thorny and controversial. Consider this a sneak peek, but also an opportunity to help us run an experiment. Let’s start with the idea first.

How to score interviews Interviews are designed to collect qualitative data. They’re meant to indicate strongly (or not) that the problem(s) you’re looking to solve are worth pursuing. They’re hard to do well, and take lots of practice and discipline to master. If you do it right, you’re left with a ton of insight into your customers’ needs and thoughts.

Unfortunately, those reams and reams of notes are messy. Interpreting and sharing qualitative data is hard, and often subjective.

So we want to try and score them. Scoring interviews is designed to help you quantify your results, without getting overly scientific.

The challenge here is that you can’t beat a forest of qualitative data into a carefully manicured lawn of  quantitative data. We’re not even going to try that. And we’re also not proposing that you go overboard with this method: if you’re not good at collecting and interpreting qualitative data, it’s going to be difficult to get very far at all (through the Lean process or through any startup.) But our hope is that this method helps coalesce things a bit more, giving you some clarity when analyzing the results of your efforts.

During the Problem Interviews, there are a few critical pieces of information that you should be collecting. I’ll go through those below and show you how to score them.

1. Did the interviewee successfully rank the problems you presented?

Yes 10 points
Sort of 5 points
No 0 points

During a Problem Interview you should be presenting multiple problems to the interviewee—let’s say 3 for the purposes of this post—and asking them to rank those problems in order of severity.

  • If they did so with a strong interest in the problems (irrespective of the ranking) that’s a good sign. Score 10 points.
  • If they couldn’t decide which problem was really painful, but they were still really interested in the problems, that’s OK but you’d rather see more definitive clarity. Score 5 points.
  • If they struggled with this, or they spent more time talking about other problems they have, that’s a bad sign. Score 0 points.

It’s important to note that during the interview process, you’re very likely to discover different problems that interest interviewees. That’s the whole point of doing these interviews, after all. That will mean a poor score (for the problem you thought you were going to solve), but not a poor interview. You may end up discovering a problem worth solving that you’d never thought about, so stay open-minded throughout the process.

2. Is the interviewee actively trying to solve the problems, or have they done so in the past?

Yes 10 points
Sort of 5 points
No 0 points

The more effort the interviewee has put into trying to solve the problems you’re discussing, the better.

  • If they’re trying to solve the problem with Excel and fax machines, you may have just hit on the Holy Grail. Score 10 points.
  • If they spend a bit of time fixing the problem, but just consider it the price of doing their job, they’re not trying to fix it. Score 5 points.
  • If they don’t really spend time tackling the problem, and are okay with the status quo, it’s not a big problem. Score 0 points.

3. Was the interviewee engaged and focused throughout the interview?

Yes 8 points
Sort of 4 points
No 0 points

Ideally your interviewees were completely engaged in the process; listening, talking (being animated is a good thing), leaning forward, and so on. After enough interviews you’ll know the difference between someone that’s focused and engaged, and someone that is not.

  • If they were hanging on your every word, finishing your sentences, and ignoring their smartphone, score 8 points.
  • If they were interested, but showed distraction or didn’t contribute comments unless you actively solicited them, score 4 points.
  • If they tuned out, looked at their phone, cut the meeting short, or generally seemed entirely detached—like they were doing you a favor by meeting with you—score 0 points.

4. Did the interviewee refer others to you for interviews?

Yes, without being asked 4 points
Yes, when you asked them to 2 points
No 0 points

At the end of every interview, you should be asking all of your subjects for others you should talk with. They have contacts within their market, and can give you more data points and potential customers. There’s a good chance the people they recommend are similar in demographics and share the same problems.

Perhaps more importantly at this stage, you want to see if they’re willing to help out further by referring people in their network.  This is a clear indicator that they don’t feel sheepish about introducing you, and that they think you’ll make them look smarter. If they found you annoying, they likely won’t suggest others you might speak with.

  • If they actively suggested people you should talk to without being asked, score 4 points.
  • If they suggested others at the end, in response to your question, score 2 points.
  • If they couldn’t recommend people you should speak with, score 0 points (and ask yourself some hard questions about whether you can reach the market at scale.)

5. Did the interviewee offer to pay you immediately for the solution?

Yes, without being asked 4 points
Yes, when asked 2 points
No 0 points

Although having someone ask to pay or throw money at you is more likely during the Solution Interviews (when you’re actually walking through the solution with people), this is still a good “gut check” moment. And certainly it’s a bonus if people are reaching for their wallets.

  • If they offered to pay you for the product without being asked, and named a price, score 4 points.
  • If they offered to pay you for the product, score 2 points.
  • If they didn’t offer to buy and use it, score 0 points.

Calculating the scores score of 25 or higher is a good score. Anything under is not. Try scoring all the interviews, and see how many have a good score. This is a decent indication of whether you’re onto something or not with the problems you want to solve. Then ask yourself what makes the good score interviews different from the bad score ones. Maybe you’ve identified a market segment; maybe you have better results when you dress well; maybe you shouldn’t do interviews in a coffee shop. Everything is an experiment you can learn from.

You can also sum up the rankings for the problems that you presented. If you presented three problems, which one had the most first place rankings? That’s where you’ll want to dig in further and start proposing solutions (during Solution Interviews.)

The best-case scenario is very high interview scores within a subsection of interviewees where those interviewees all had the same (or very similar) rankings of the problems. That should give you more confidence that you’ve found the right problem and the right market.

The One Metric That Matters

We’ve talked about the One Metric That Matters before and it’s important to think about it even at this early stage in the Lean Startup process. The OMTM at this point is pain—specifically, the pain your interviewees feel related to the problems you’ve presented. It’s largely qualitative, but scoring interviews may put things into perspective in a more analytical way, allowing you to step back and not get lost in or fooled by all the interviews.

So are you ready to help us?

Here’s the thing: we’d would love to speak with people that are currently in the middle of doing Problem Interviews, and have them try out our scoring methodology. We need feedback here to iterate and improve the concept for the book.

So if you’d like to help please contact us or reply in the comment thread below.

Lean Analytics and the One Metric That Matters: A Presentation for Acceleprise

Alistair and I are starting to take our proverbial show on the road. We’re “getting out of the building” to share some of our material, test it, and collect feedback. Build ➔ Measure ➔ Learn applies to books, too, and to presentations.

Yesterday I did a presentation for Acceleprise, a Washington, DC-based accelerator that’s focused on B2B startups. The presentation is embedded below.

There are a couple useful references/resources in the presentation:

You will start to see the evolution of the material we’re working on for the book: how Lean Analytics fits into Lean Startup, the One Metric That Matters, how to find the right metrics, how to balance gut and analytics, and so on.

Parts of the presentation are very visual, so some of the meaning may be lost, but hopefully it’s helpful and interesting enough that you send us feedback, ask questions, and (if you haven’t already!) sign up for future updates on the book.

The One Metric That Matters

One of the things Ben and I have been discussing a lot is the concept of the One Metric That Matters (OMTM) and how to focus on it.

Founders are magpies, chasing the shiniest new thing they see. Many of us use a pivot as an enabler for chronic ADD, rather than as a way to iterate through ideas in a methodical fashion.

That means it’s better to run the risk of over-focusing (and miss some secondary metric) than it is to throw metrics at the wall and hope one sticks (the latter is what Avinash Kaushik calls Data Puking.)

That doesn’t mean there’s only one metric you care about from the day you wake up with an idea to the day you sell your company. It does, however, mean that at any given time, there’s one metric you should care about above all else. Communicating this focus to your employees, investors, and even the media will really help you concentrate your efforts.

There are three criteria you can use to help choose your OMTM: the business you’re in; the stage of your startup’s growth; and your audience. There are also some rules for what makes a good metric in general.

First: what business are you in?

We’ve found there are a few, big business model Key Performance Indicators (KPIs) that companies track, and they’re dictated largely by the main goal of the company. For online businesses, most of them are transactional, collaborative, SaaS-based, media, game, or app-centric. I’ll explain.


Someone buys something in return for something.

Transactional sites are about shopping cart conversion, cart size, and abandonment. This is the typical transaction funnel that anyone who’s used web analytics is familiar with. To be useful today, however, it should be a long funnel that includes sources, email metrics, and social media impact. Companies like Kissmetrics and Mixpanel are championing this plenty these days.


Someone votes, comments, or creates content for you.

Collaboration is about the amount of good content versus bad, and the percent of users that are lurkers versus creators. This is an engagement funnel, and we think it should look something like Charlene Li’s engagement pyramid.

Collaboration varies wildly by site. Consider two companies at opposite ends of the spectrum. Reddit probably has a very high percentage of users who log in: it’s required to upvote posts, and the login process doesn’t demand an email confirmation look, so anonymous accounts are permitted. On the other hand, an adult site likely has a low rate of sign-ins; the content is extremely personal, and nobody wants to share their email details with a site they may not trust.

On Reddit, there are several tiers of engagement: lurking, voting, commenting, submitting links, and creating subreddits. Each of these represents a degree of collaboration by a user, and each segment represents a different lifetime customer value. The key for the site is to move as many people into the more lucrative tiers as possible.


Someone uses your system, and their productivity means they don’t churn or cancel their subscription.

SaaS is about time-to-complete-a-task, SLA, and recency of use; and maybe uptime and SLA refunds. Companies like Totango (which predicts churn and upsell for SaaS), as well as uptime transparency sites like Salesforce’s, are examples of this. There are good studies that show a strong correlation between site performance and conversion rates, so startups ignore this stuff at their peril.


Someone clicks on a banner, pay-per-click ad, or affiliate link.

Media is about time on page, pages per visit, and clickthrough rates. That might sound pretty standard, but the variety of revenue models can complicate things. For example, Pinterest’s affiliate URL rewriting model, which requires that the site take into account the likelihood someone will actually buy a thing as well as the percentage of clickthroughs (see also this WSJ piece on the subject.)


Players pay for additional content, time savings, extra lives, in-game currencies, and so on.

Game startups care about Average Revenue Per User Per Month and Lifetime Average Revenue Per User (ARPUs). Companies like Flurry do a lot of work in this space, and many application developers roll their own code to suit the way their games are used.

Game developers walk a fine line between compelling content, and in-game purchases that bring in money. They need to solicit payments without spoiling gameplay, keeping users coming back while still extracting a pound of flesh each month.


Users buy and install your software on their device.

App is about number of users, percentage that have loaded the most recent version, uninstalls, sideloading-versus-appstore, ratings and reviews. Ben and I saw a lot of this with High Score House and Localmind while they were in Year One Labs. While similar to SaaS, there are enough differences that it deserves its own category.

App marketing is also fraught with grey-market promotional tools. A large number of downloads makes an application more prominent in the App Store. Because of this, some companies run campaigns to artificially inflate download numbers using mercenaries. This gets the application some visibility, which in turn gives them legitimate users.

It’s not that simple

No company belongs in just one bucket. A game developer cares about the “app” KPI when getting users, and the “game” or “SaaS” KPI when keeping them; Amazon cares about “transactional” KPIs when converting buyers, but also “collaboration” KPIs when collecting reviews.

There are also some “blocking and tackling” metrics that are basic for all companies (and many of which are captured in lists like Dave McClure’s Pirate Metrics.)

  • Viral coefficient (how well your users become your marketers.)
  • Traffic sources and campaign effectiveness (the SEO stuff, measuring how well you get attention.)
  • Signup rates (how often you get permission to contact people; and the related bounce rate, opt-out rate, and list churn.)
  • Engagement (how long since users last used the product) and churn (how fast does someone go away). Peter Yared did a great job explaining this in a recent post on “Little Data”
  • Infrastructure KPIs (cost of running the site; uptime; etc.) This is important because it has a big impact on conversion rates.

Second: what stage are you at?

A second way to split up the OMTM is to consider the stage that your startup is at.

Attention, please

Right away you need attention generation to get people to sign up for your mailing list, MVP, or whatever. This is usually a “long funnel” that tracks which proponents, campaigns, and media drive traffic to you; and which of those are best for your goals (mailing list enrollment, for example.)

We did quite a lot of this when we launched the book a few weeks ago using, Google Analytics, and Google’s URL shortener. We wrote about it here: Behind the scenes of a book launch

Spoiler alert: for us, at least, Twitter beats pretty much everything else.

What do you need?

Then there’s need discovery. This is much more qualitative, but things like survey completions, which fields aren’t being answered, top answers, and so on; as well as which messages generate more interest/discussion are quantitative metrics to track. For many startups, this will be things like “how many qualitative surveys did I do this week?”

On a slightly different tone, there’s also the number of matching hits for a particular topic or term—for example, LinkedIn results for lawyers within 15km of Montreal—which can tell you how big your reachable audience is for interviews.

Am I satisfying that need?

There’s MVP validation—have we identified a product or service that satisfies a need. Here, metrics like amplification (how much does someone tell their friends about it?) and Net Promoter Score (would you tell your friends) and Sean Ellis’ One Question That Matters (from—”How would you feel if you could no longer use this product or service?“) are useful.

Increasingly, companies like Indiegogo and Kickstarter are ways to launch, get funding, and test an idea all at the same time, and we’ll be looking at what works there in the book. Meanwhile, Ben found this excellent piece on Kickstarter stats. We’re also talking with the guys behind Pen Type A about their experiences (and I have a shiny new pen from them sitting on the table; it’s wonderful.)

Am I building the right things?

Then there’s Feature optimization. As we figure out what to build, we need to look at things like how much a new feature is being used, and whether the addition of the feature to a particular cohort or segment changes something like signup rates, time on site, etc.

This is an experimentation metric—obviously, the business KPI is still the most important one—but the OMTM is the result of the test you’re running.

Is my business model right?

There’s business model optimization. When we change an aspect of the service (charging by month rather than by transaction, for example) what does that do to our essential KPIs? This is about whether you can grow, or hire, or whether you’re getting the organic growth you expected.

Later, many of these KPIs become accounting inputs—stuff like sales, margins, and so on. Lean tends not to touch on these things, but they’re important for bigger, more established organizations who have found their product/market fit, and for intrapreneurs trying to convince more risk-averse stakeholders within their organization.

Third: who is your audience?

A third way to think about your OMTM is to consider the person you’re measuring it for. You want to tailor your message to your audience. Some things you share internally won’t help you in a board meeting; some metrics the media will talk about are just vanity content that won’t help you grow the business or find product/market fit.

For a startup, audiences may include:

  • Internal business groups, trying to decide on a pivot or a business model
  • Developers, prioritizing features and making experimental validation part of the “Lean QA” process
  • Marketers optimizing campaigns to generate traffic and leads
  • Investors, when we’re trying to raise money
  • Media, for things like infographics and blog posts (like what Massive Damage did.)

What makes a good metric?

Let’s say you’ve thought about your business model, the stage you’re at, and your audience. You’re still not done: you need to make sure it’s a good metric. Here are some rules of thumb for what makes a number that will produce the changes you’re looking for.

  • A rate or a ratio rather than an absolute or cumulative value. New users per day is better than total users.
  • Comparative to other time periods, sites, or segments. Increased conversion from last week is better than “2% conversion.”
  • No more complicated than a golf handicap. Otherwise people won’t remember and discuss it.
  • For “accounting” metrics you use to report the business to the board, investors, and the media, something which, when entered into your spreadsheet, makes your predictions more accurate.
  • For “experimental” metrics you use to optimize the product, pricing, or market, choose something which, based on the answer, will significantly change your behaviour. Better yet, agree on what that change will be before you collect the data.

The squeeze toy

There’s another important aspect to the OMTM. And I can’t really explain it better than with a squeeze toy.

Nope, this isn’t me. But sometimes I feel like this.

If you optimize your business to maximize one metric, something important happens. Just like one of the bulging stress-relief toys shown above, squeezing it in one place makes it bulge out in others. And that’s a good thing.

A smart CEO I worked with once asked me, “Alistair, what’s the most important metric in the business right now?”

I tried to answer him with something glib and erudite. He just smiled knowingly.

“The one that’s most broken.”

He was right, of course. That’s what focusing on the OMTM does. It squeezes that metric, so you get the most out of it. But it also reveals the next place you need to focus your efforts, which often happens at an inflection point for your business:

  • Perhaps you’ve optimized the number of enrolments in your gym—but now you need to focus on cost per customer so you turn a profit.
  • Maybe you’ve increased traffic to your site—but now you need to maximize conversion.
  • Perhaps you have the foot traffic in your coffee shop you’ve always wanted—but now you need to get people to buy several coffees rather than just stealing your wifi for hours.*

Whatever your current OMTM, expect it to change. And expect that change to reveal the next piece of data you need to build a better business faster.

(* with apologies to the excellent Café Baobab in Montreal, where I’m doing exactly that.)

Finding people to talk to

When High Score House joined Year One Labs, we didn’t let them code for a month.

The story of how they found their target market is fascinating. It underscores one of the basic tenets of Lean: get out of your building and actually talk to people.

The modern world isn’t inclined to physical interaction. We have dozens of ways to engage people at a distance, and when you’re trying to find a need, they’re mostly bad. Unless you’re face-to-face with prospects, you won’t see the flinches, the subtle body language, and the little gasps and shrugs that mean the difference between a real problem and a waste of everyone’s time.

That doesn’t mean technology is bad, however. On the contrary, we have a set of tools for finding prospects that would have seemed like superpowers to our predecessors. Before you get the hell out of the office, you need to find people to talk with. If you can find these people efficiently, that bodes well: it means that, if they’re receptive to your idea, you can find more like them and build your customer base.

Here are some dumb, obvious, why-didn’t-I-think-of-that ways to find people to talk with, send surveys to, and grow your mailing lists and outreach efforts.

Twitter’s advanced search

For startups, Twitter is a goldmine. Its asymmetric nature—I can follow you, but you don’t have to follow me back—and relatively unwalled garden means people expect interactions. And we’re vain. If you mention someone, they’ll come find out what you said and who you are. Provided you don’t abuse this privilege, it’s a great way to find people.

Let’s say you’re building a product for lawyers and want to talk to people nearby. Put keywords and location information into Twitter’s advanced search:

 Right away, you’ll get a list of organizations and people who might qualify:

Now, if you’re careful, you can reach out to them. Don’t spam them; get to know them a bit, see where they live and what they say, and when they mention something relevant—or when you feel comfortable doing so—speak up. Just mention them by name, invite them to take a survey, and so on.


Another huge boon to startups everywhere is LinkedIn. You can access a tremendous amount of demographic data through your searches:

You don’t need to connect to these people, because you can just find their names and numbers, look up their firms’ phone numbers, and start dialing. But if you do have a friend in common, you’ll find that a warm intro works wonders.


Facebook is a bit more risky, since it’s a reciprocal relationship (they have to friend you back.) But you’ll get a sense of the size of a market from your search results alone, and you might find useful groups to join and invite to take a test or meet for a focus group discussion.


Google makes it really easy to target campaigns. If you want to promote a survey or signup somehow, you can do so with remarkable precision. In the first step of setting up an Adwords campaign, you get to specify the location, language, and other information that targets the ad:

Once you’ve done that, you can create your message. This is an excellent way to try out different taglines and approaches: even the ones that don’t get clicks show you something, because you know what not to say. Try different appeals to basic emotions: fear, greed, love, wealth, and so on. Learn which gets people clicking, and which keeps them around long enough to fill out a survey or send an email.

Some of this stuff seems blindingly obvious. But a little preparation before you get out of the office—physically or virtually—can make all the difference, giving you better data sooner, and validating or repudiating business assumptions in days instead of weeks.

Share your Lean Analytics story with us!

We want to make sure our book is chock full of case studies and stories from people that are using analytics and Lean Startup on a daily basis. We want to provide real numbers –where possible– so you can benchmark yourself and your startup or project against others. The more people share with us, the more we can as a group help each other and improve practices around Lean Startup and Lean Analytics.

With that being said, if you have a story, case study, anecdote or number you want to share, please do so!

Here’s our brief survey:

It should take ~10 minutes to complete. In return, we’ll buy you a beer (or another drink of your choice.) That’s assuming of course that we get to your city or meet you in-person somewhere (we’re not sending beers through the mail or sending out “free beer” coupons … sorry!)

A few things to remember:

  • We’re happy to keep things anonymous, so just tell us you want it to be that way in the survey.
  • The more specific you are, the better. If you can share real numbers with us – KPIs, benchmarks, targets, etc. – that’s fantastic. Again, if you don’t want us to share your name or company name publicly, we can hide that.
  • We may publish your stuff in our book. And if you don’t want it to be anonymous, we’ll thank you several times over publicly. Fame and fortune … here we come!
  • You’re genuinely going to help a lot of people. That’s a big deal.
Share Your Lean Analytics Story >>

Behind the scenes of a book launch

A couple of weeks ago, we launched the Lean Analytics website. Since we’re writing about lean analytics, we figured we should probably track a few things along the way. In the interest of transparency, here’s what we did and how it went.

Creating the site

Our first step was to create the website. We did this with the help of Nudge Design, a designer who knows WordPress really well and was able to turn things around quickly. We had to work fast, because Ben’s company had completed its recent acquisition by Salesforce and Startupfest was around the corner.

Setting up analytics and goals

We put Google Analytics into the site, and set up a couple of goals. These were simple enough. We wanted to know how many people would:

  • Click on the book cover to find out what an MVC was.
  • Sign up for our mailing list. (Come to think of it, you should probably do that now.)
  • Complete our brief survey to tell us about themselves.

Here’s what we configured in Google Analytics:

Note that we couldn’t easily track mailing list signups, since they happened elsewhere. We also configured the survey completion goal half-way through the launch; more on that later.

Generating tracking codes

We planned on telling some of our friends and proponents about the book. These are people with big followings: Tim O’Reilly, from our publisher; Eric Ries, the Lean Startup founder and series editor; Avinash Kaushik, arguably the smartest (and most irreverent) person in analytics; and Julien Smith, a Montrealer and bestselling author.

We also wanted to use our blogs (Solve for Interesting and Instigatorblog) as sources for traffic, as well as two events we’d be speaking at (Startupfest and Lean Startup Machine.)

Tracking all of these mentions across the “long funnel” from the first mention to the eventual visit is accomplished by embedding tags in the URL. Google has a page that helps you build these tracking URLs, allowing you to segment visitors by source, campaign, and so on.

The tags help distinguish visitors by their source, the campaign used, the medium, and so on. We didn’t want to burden our proponents with too many codes, but I did generate some codes for myself which identified different media (LinkedIn, Twitter, Facebook) to demonstrate how it works.

Generating short URLs

A long URL from Google’s tool won’t fit easily into social media platforms. Plus, it looks messy and it’s hard to remember. So we needed to shorten these, which we did by generating short URLs with

For many of the shared URLs, we just used the default random string of letters that gave us, but for some of the events we generated custom ones that were easy to remember. For example, in my presentation on coefficients of friction at Startupfest, I used in my closing slide.

Giving the codes to famous people

Armed with these codes, we sent them to our proponents, and they mentioned the site to their followers. The race was on: who would generate the most traffic? Who would send us the most completed surveys?

We could have overwhelmed these proponents with too much tracking data, but that’s too big an “ask.” Giving them a single URL to mention is good; in fact, the less work, the better. I’ve run campaigns in the past where we actually gave each proponent a calendar invite, so they’d remember to do the thing we asked at the time we asked. Famous people are busy; you have to make it easy for them wherever you can if you want their support.

Watching the results on provides some detailed information about the way short links are shared, so we were able to see how each proponent’s URL spread across a particular medium. Here’s Eric’s mention spreading across Twitter on the day he announced it.

Note that we can also tell some things about how his audience interacts with content—some use Facebook, some Hootsuite, and the vast majority came through Twitter’s shortened URL ( which in turn encapsulated our URL.

Watching realtime on Google Analytics

Google Analytics has a beta feature in which realtime traffic scrolls across your screen. Here’s what we saw on the receiving end of Eric Ries’ mentions. Note that any shortfall (people who clicked his link, but for whom we didn’t see a visit) is an indicator of problems with the site (slow loading, for example, may cause someone to cancel their visit before the page loads.) This is known as the sessions to clicks ratio.)

I’m not sold on the benefits of realtime data unless you can react to things in realtime, but it is a good tool for seeing if you’ve got something hideously wrong (such as the wrong landing page in a link) so you can go fix it. Plus, it’s fun to watch.

Seeing the Facebook page traction

We also set up a Facebook page. Once it hit 30 people, we had access to some stats on traffic and growth.

Facebook doesn’t give you a ton of data, but it’s enough to see whether traction is growing and to know how big your reachable audience is.

Join us on Facebook »

Traffic to our own blogs

Ben and I both wrote posts about the book on our own sites. I created different URL tags for Facebook, LinkedIn, and Twitter, turned these into three different URLs, and used a different one on each social platform. The results show me how useful each of the three sources is for traffic to the site.

Note that this is traffic to the Solve For Interesting post about the book, which in turn links to the book site. As such, it’s part of a long funnel that begins with a tweet on Twitter; leads to this page; and then (hopefully) sends traffic to the Lean Analytics book site.

Seeing the Mailchimp traction

We use Mailchimp to track our mailing list size, as well as to manage subscriptions, opt-out, and other aspects of staying in touch with our readers. Mailchimp provides charts and data on signup growth.

Once we start sending out messages and posts, Mailchimp can measure things like unsubscriptions, bounce rate, and so on. When we first sign up subscribers, and clean up junk email addresses, we don’t know how many of those 572 subscribers are actually useful.

And by the way, if you haven’t already, you should probably sign up for the mailing list and take the survey. The form is over on the right, at the top of this page. Go on; we’ll wait.

Once we sent the first blog post to the mailing list, we had a better idea of our list quality. Here’s mailchimp’s dashboard for a mail sent to all the people who asked to receive blog updates.

Our open rate was 55.5%; the industry average for our industry (“consulting”) is a paltry 15.8%. And Mailchimp thinks we can actually reach 308 people as a result of all this work. We’re not currently using Mailchimp for other platforms like Twitter or Facebook, though.

At this point, we have to confess to something: we didn’t track survey completion properly. We use Wufoo for our survey, and only the paid version is able to direct someone to a separate page on completion of the survey. We’re cheap bastards, and hadn’t paid for this, so half-way through the launch we realized the error of our ways, turned up the forwarding feature, and sent respondents to a thank-you page.

This matters because the only way we could easily track survey completion was when someone came back to that page. We should have generated Google Analytics “events” when someone put their cursor in the signup box, and embedded an event in Wufoo when someone filled out the survey. We could even have used a tool like Clicktale to understand which fields in the form weren’t being completed. Our bad.

What it means is that some people who mentioned our book later in the launch process (Avinash, Julien) look like their followers are more likely to complete a survey than those who mentioned it early on (Tim, Eric.) It’s not their fault; it’s ours. This should underscore the challenge of Getting Your Shit Together First.

Setting up a Google Analytics dashboard

Finally, we set up a custom report in Google Analytics to compare traffic, survey rates, and the effectiveness each proponent had on our goals. We have data on pretty much the whole campaign; and we know who we have to buy drinks for, and whose blog generates the most traffic.

Some things we can tell from all this:

  • Julien had a tremendous number of followers visit the site (and Chris Brogan, his co-author, helped spread the word) but relatively few of them completed our survey compared to Avinash’s followers.
  • Eric Ries’ followers were much less likely to wonder what an MVC was and click on the picture of the book than Tim’s were.
  • “Uninvited” visitors from Twitter and Facebook clicked on the MVC picture, but Twitter users were far more likely to finish the survey.
  • Ben’s blog drove some traffic, but mine didn’t even make the top ten. Sigh.
  • Google Plus drove traffic our way, mostly due to Tim O’Reilly’s use of it; LinkedIn mentions didn’t drive anything.
  • It turns out the Facebook page did in fact generate quite a bit of traffic to the site.

And so on. Here’s a second custom report, which compares the unique visitors and goal completions to the referring URL.

Sadly, of the 99 people who followed links to Solve For Interesting (see “traffic to our blogs” above) only ten clicked through to the book site; and of those, only one completed the survey. Ben, on the other hand, had eight people do so. In other words, Ben’s followers are eight times as likely as mine to complete an online survey. Ouch.

Finding the unknown unknowns

Avinash likes to point out that Donald Rumsfeld was an analyst.

There are known knowns; there are things we know that we know. There are known unknowns; that is to say there are things that, we now know we don’t know. But there are also unknown unknowns—there are things we do not know, we don’t know.

Known knowns are simply facts we regurgitate (time on site.) Known unknowns are simply analysis we perform based on things we already believe are important (“which sites send me the most traffic?”) But it’s the unknown unknowns that really matter, because that’s where the magic comes from. Here’s a great session from him at Strata earlier this year.

Admittedly, we have a very small site and a very small amount of traffic. Right now, it’s enough to go through by hand. But as it grows, having the system tell us what’s interesting is critical. Here’s an example of Google Analytics’ “intelligence events”. Basically, Google forms an opinion about what it expects to happen, and then tells  us when that opinion was wrong—when something was unexpected.

In this case, Google expected 1.18 to 4.74% of visitors to complete the survey, but on the 14th, 9.4% of visitors did so. We can then drill into this to see what provoked the change. (Note that we’re lying a bit here to make a point—the increase in exits from this page was almost certainly due to us changing the survey landing page halfway through the launch process. But the point is still valid: often, the most interesting thing that happens is a deviation from your expectation.)

One more piece of eye candy: We can further segment visitors by other dimensions and find out more detail. For example, when we split our four proponents’ traffic into new versus returning visitors, we find out that Julien’s returning visitors are much more likely to complete the survey.

(Again, these results are skewed because Tim and Eric didn’t have the benefit of the survey landing page to track the people they sent. We have 572 people in the mailing list, but only 186 visits to the survey landing page—clearly, Tim and Eric were responsible for a whole lot of survey enrolment. But you get the point.)

So what?

Right now, the thing we care most about is the followers and survey respondents we have. We want people’s attention (and nearly 3,000 of you have  visited the site, so that’s good.) But we also want the permission to reach out to you in order to ask you questions, send you updates, and ultimately, have you buy the book or pay to see us speak. For us, that’s the One Metric That Matters.

We know, from this data, that we should work on Facebook, Julien, and Ben’s blog for traffic. And because we know which people sent us posts, as we write things we’ll learn whose followers like what kinds of content—which means we’ll be able to ask different proponents to promote different content based on what their followers seem to like.

It’s early days for the blog, but we’ve got a pretty solid foundation in place with which to understand our audience in the coming months.

Putting it all together

We put all of this together into an infographic. It’s awesome. But we want something in return. We want you to sign up.

So put your email in the signup form below, and we’ll send you a beautiful, informative, no-holds-barred look at the campaign from start to finish.

You’ll also get future updates from us, and a bunch of chances to participate in the creation of the book by sharing your stories, taking surveys, attending regional events, and more.

Note: If you’re already a subscriber to our list, we’ll be sending the infographic in a couple days.