Lean Analytics – Notes from a fantastic Udemy Course

You’re building a new product or service and you’re sold on the whole Lean Startup idea already. At this point, you’re looking for some specific pointers on how you actually get “started”.

Look no further. Start by watching the online (and free!) Lean Analytics course over at Udemy by Alistair Croll and Benjamin Yoskovitz.

The Udemy course is a great introduction to a book of the same name written by Alistair and Ben.

What about the book?

Yes, get the book as well. But I’d recommend you try the Udemy course if you want to take a quick deep dive right away.

The Udemy Course
The Book

For instance, the book talks about 6 different types of business models (SaaS, Two Sided Marketplaces, UGC etc) – and takes a deep dive into each of them. While Ben and Alistair only cover the SaaS business model in this course, you’ll still come away with a good “feel” for how to look at any business from a Lean Analytics perspective.

MoveIt

There are exercises at the end each lecture in the course. If you’re working on a startup right now, make sure to do those exercises. You’ll come out of the course much smarter – and probably with a very different perspective of your startup than when you started.

My startup is called MoveIt. Its an employee engagement tool – that helps to create communities at the workplace around the concept of physical exercise and charity.

I did the lecture exercises for MoveIt. This post has my answers for each of those exercises. I hope you will find that useful as a guide to complete the exercises for your own startup.

Attribution for all of the Slide Images

I took a lot of screenshots of the slides while going through the course, because I knew I would want to use them as quick reference afterward. The slides are most useful, when you hear Ben and Alistair explain them during the course.

I’ve used those screenshots throughout this post – because I can’t do a better job than the authors to explain many of the concepts that they teach. But I hope you find the quick reference to the slides (via the screenshots) as useful as I do.

Blanket Attribution: Every one of the slide images below are snapshots from the Udemy Course. Here’s the link to the course, once again. Yes, I know – there’s probably nothing like Blanket Attribution and also that the original content is not Creative Commons licensed to be able to make an attribution in the first place. If you’re from Udemy and you want me to take this post down or want me to link to the images differently, I’ll do so gladly.

Overview of the course

The course has 6 lectures:

  1. Introduction
  2. Good Metrics and Bad Metrics
  3. Lean Analytics Framework
  4. The One Metric that Matters (or OMTM, for short 🙂
  5. What Growth Rate is Healthy? (or how to “Draw a line in the Sand”)
  6. Lean Analytics Life Cycle

As mentioned above, each of the above lectures has exercises at the end.

Let’s get started.

Lecture 1: Introduction

What is Lean Analytics?

Let’s start first by asking what is Analytics:

Its the measurement of movement toward your business goals

Here’s what Lean Analytics is:

In a startup, the purpose of analytics is to **iterate to product/market fit** – before the money runs out. Or your patience runs out.

You’re thinking about Lean Analytics the right way, if you’re asking the question

“What do people wanna do on the site?”  #ThisIsWhatYouNeedAsAStartup

rather than

“Are people doing what we want them to do?” #Don’tDoThisIfYou’reAStartup

Don’t let the Reality Distortion field make you delusional

“Startups see the world as it should be and then build the pieces that are missing”

– Paul Graham

The reality distortion field is a necessary attribute to be an entrepreneur. But if you let it get to the point of delusion, you’re in trouble.

How do you avoid becoming delusional? Check out the Lean Startup cycle below:

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You’ll need the discipline to focus on the Measure and Learn phases of the cycle – as much the Build phase.

Don’t sell what you can make, make what you can sell.

Exercise for Lecture 1: Introduction

Question: What are the metrics that matter “most” for your business right now?

Here are the metrics I came up with for MoveIt:

  1. % of “active” users
  2. % of the charity goal achieved

Lecture 2: Good Metrics vs Bad Metrics

Good Metrics

Good Metrics are:

Understandable: Its ideal if the person you’re sharing the metrics with, gets a quick picture of what your business does and how well you’re doing. Keep it simple.

Comparative: Absolute numbers (eg: “Active Users”) are okay. But if you have numbers that you can compare say, month on month – you’re onto something more useful.

Ratio/Rate: The % of “Active Users” is more useful than saying “10 Active users last month” and “20 Active Users This month”.

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Behavior Changing: If a metric does not change how you behave, that’s a bad metric.

If you’re measuring something and you don’t know what to do when that number moves (or doesn’t move), that’s a bad number.

First think about what decisions you’re gonna be making with the data you collect – that’ll ensure that you’re on the right track.

Types of Metrics

Qualitative vs Quantitative: 

Quantitative:

  • “What” is going on?
  • More useful when there is significant traffic

Qualitative:

  • Warm and Fuzzy Feedback. Very useful in the early days of the product.
  • More Insightful than Quantitative
  • “Why” is something going on.

“Discover Qualitatively and Prove things Quantitatively”

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Exploratory or Reporting:

Exploratory:

  • These tend to jump out at you at unexpected times, for example when putting a Pivot Table together
  • A speculative “mindset” and “Divergent” thinking are required for making these types of discoveries
  • This is stuff we don’t know we don’t know

Reporting:

  • This is stuff we know we don’t know

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Alistair shared the following very interesting slide while talking about Exploratory metrics:

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Vanity vs Actionable:  

  • Vanity: Does not change behavior [Bad Metric]
  • Actionable: Much better.

Some examples of Vanity Metrics:

  • “Time on site” is probably useful if you’re in the media space – but not if you’re building a productivity app.
  • Twitter Followers, Likes: Can you get your followers to do what you want them to do when you want them do it? If not, who cares?

Here’s some examples of common vanity metrics:

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Leading vs Lagging:

Lagging metrics are the one that are useful for doing retrospectives after a certain event has happened. Leading metrics on the other hand are great predictors of the future.

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Measuring lagging metrics is okay for starters. For example, first measuring Churn [that is, the number of people leaving your funnel] and then discovering that you have more people leaving than being added to the funnel, is a start.

But you wanna be able to predict that kind of Churn behavior in your audience – and then be able to react to that problem quickly enough – to keep the customer in your funnel.

For example, a good leading indicator in this case would be something like Customer Complaints – if that’s on the rise, then it’s probably time to see whether there’s a certain areas of your product or service that has a fundamental problem and needs immediate fixing. Maybe you find that the new version of the product is more buggy or that the Customer service team is overwhelmed and the response time is too slow.

Correlated or Causal

Look at the picture below. It indicates that both ice-cream consumption and drownings are related in some way. When one goes up, so does the other.

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But of course, we’re not idiots to believe that. So, when we dig deeper, we realize that during the summer season, people tend to eat more ice-cream and also go for more swims.

The two metrics (ice-cream consumption and drownings) are correlated (that is related in some way) but impacted by something else.

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Correlated helps us predict the future, causality helps us hack the future.

Figure out the causality from the correlations. This is what growth hacking is about.

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Cohorts, Segmentation, A/B Testing and Multi-Variate Testing

These are some important concepts in the world of metrics. The following slide explains all of these concepts quite well.

Each of the squishy wavy “bars” below represent users who registered and started using your product in subsequent months. So, starting at the very bottom, in light blue are the users from January. And so on, for the months of Feb, March, April and May – with the May users shaded in purple.

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According to Alistair, Cohorts are perhaps the least understood concept (including among investors). But we’ll come back to Cohorts in a second.

If you’re wondering whether to go with A/B testing or Multivariate testing, then go with Multi-Variate. For A/B testing, you need a lot of traffic for it make sense. For example, Google used A/B testing to figure out which share of blue worked best for them – but for Google that makes sense, because of the avalanche of traffic that they get.

If you’re a early stage startup though, go with Multivariate testing. You’ll be able to learn a lot more.

Back to cohorts.

If you just look at the first table below, the average revenue for each month seem to be stagnating.

But look at the second table and you start to notice that the average revenue per cohort is actually getting better much better each month. This is a classic example of how a startup’s number should look like.

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Startups are not about business plans. They’re about business models. In fact, if you’re doing it right, you’re creating a new business model or company each month. And the second table above is a great example of how the startup is almost reinventing/iterating itself each month.

Exercise for Lecture 2: Good Metrics vs Bad Metrics

Questions:

  • Check the metrics you wrote down before — How many do you think are still good?
  • Which ones are genuinely actionable?
  • Are any of them correlated to other things?

Here’s the metrics that I’d written down before (for the the Lecture 1 Exercise):

  • % of “active” users
  • % of the charity goal achieved

As a refresher, for a metric to be good, we’ll need to make sure:

  • Its Understandable
  • Its Comparative
  • Its a Ratio/Rate, and that
  • Its Behavior Changing

So, how good are my metrics based on these points?

“% of Active Users”:

  • Its not comparative. So this is better: “% of active users per month”
  • Its somewhat understandable, but this is better:
    • “% of active users per month” – where “Active” means those who create exercise entries in the app at least 3 times a week.
    • This is of course arbitrary – but as you’ll see further down in Lecture 5 – the idea is to “first draw a line in the sand”.
  • Its not behavior changing – at least the original version wasn’t.
    • By clearly defining “Active”, its better – since we have more clarity as to whether the app is performing well enough or not.
    • But we can do even better: by drawing a line in the sand for the % itself. But we’ll come back to this after “Lecture 5” below.
  • Thankfully, its at least a ratio. So, 1 out of 4 🙂

So here’s the revised version of the first metric:

  • “% of active users per month” – where “Active” means those who create exercise entries in the app at least 3 times a week.

Let’s look at the second one:

% of the charity goal achieved:

  • Its not comparative. So this is better: “% of the charity goal per month”
  • Its understandable. No changes needed here, I think.
  • Its a ratio. So we’re fine there as well.
  • Its not behavior changing. The following is better:
    • % of the charity goal per month (where 80% or above is acceptable)

Lecture 3: Lean Analytics Framework

We’re first introduced to two popular frameworks – one from Eric Ries (“Engines of Growth” – from the Lean Startup) and the second one from Dave McClure (called the “AARRR” framework).

Engines of Growth

There are 3 fundamental way to grow your business – in the Lean Startup world. They are Stickiness, Virality and Paid (shown as “Price” below).

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“AARRR” Framework (also called Pirate Metrics)

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Lean Analytics Framework

Here’s the Lean Analytics framework:

Screen-Shot-2015-05-20-at-5.49.38-am.png

Many startups end up making the mistake that they’re already at the Virality or Revenue stage, when they’re yet to cross the Empathy or Stickiness stage.

Don’t jump ahead. As a startup, it’s important to go through the various steps in sequential order.

NOTE: The Revenue stage does not mean that you have paying customers. It means you’ve reached a stage where you have enough traction to start investing in your growth using your revenues.

Case Study: Static Pixels

Static Pixels is a product that allows you to print pictures from Instagram.

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The InstaOrder feature was specifically meant to help repeat purchases easier (see slide below). But they found that almost no one was using their system.

They eventually removed the InstaOrder feature completely and made the whole process much simpler to optimize for one time purchasers instead.

Screen-Shot-2015-05-21-at-3.16.18-am.png

At the Empathy/Stickiness stage, when you’re not sure whether customers will buy from you once, it doesn’t make much sense to optimize for repeat purchases.

Remember: Its important to go sequentially down the Lean Analytics stages.

Exercise for Lecture 3: Lean Analytics Framework

Question: What stage are you at? (Be Honest!)

To review, here are the various stages again:

  • Empathy: I’ve found a real, poorly-met need that a reachable market faces [Problem Fit]
  • Stickiness: I’ve figured out how to solve the problem in a way they will adopt and pay for [Solution Fit]
  • Virality: I’ve built the right product/features/functionality that keeps users around
  • Revenue: The users and features fuel growth organically and artificially
  • Scale:  You’ve found a sustainable, scalable business with the right margins in a healthy eco-system

So where am I with the MoveIt product?

  • This is a bit complicated. I think we’re at the Empathy/Stickiness stage.
  • We’re now dogfooding the product internally at my company and also doing customer development interviews to test the demand in the market outside.
  • Internally:
    • I run a consulting company and I know we have the challenges of measuring and growing employee engagement on a consistent basis.
    • We’ve also built the initial version of the product and have seen some initial engagement in the product.
    • So, from that perspective, I think MoveIt is at the Pre-Stickiness stage. Of course, the pricing part of the product cannot be tested internally.

 

  • Externally:
    • We’re just starting out with customer development interviews. So, from that perspective, we’re at the Pre-Empathy stage.

Lecture 4: The One Metric That Matters

We’ve seen the various stages that your business will go through in the section above. At each of these stages, there’s only one thing that you should be measuring to keep track of your business.

That one thing could change – as you move down the stages, but at a certain stage, make sure to focus on just the one thing that matters the most, at that stage.

So how do you know what to measure?

There are broadly one of six business models that would be characteristic of your business:

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NOTE: Most of the time you’re gonna be combining one or more of the above business models:

  • You’re a two sided marketplace – but on a Mobile Platform (Mobile Advertising)
  • You’re an e-commerce site – along with a subscription model (Audible Subscription)

First figure out which type of business model matches your startup best and then combine that info with the stage of the business you’re in – to find the One Metric That Matters:

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As an example, Alistair and Ben talk about the SaaS business model.

Finding the OMTM for a SaaS business

The following diagram shows describes the lifecycle of a typical users and customers as they interact with your product or service – over time, at each stage of your business.

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So, for example if you’re in the SaaS business, but at the empathy stage, the metric you’re concerned about are:

How many visitors to the landing page signed up for the Free Trial Offer?

As you move from the Empathy to the Stickiness stage, questions such as the following are more important:

How many of those users enrolled or registered themselves in the product?

How many of those users are engaged with the product?

And so on..

So what’s your OMTM?

The book has diagrams for each of the six business models. But here’s a quick reference you can use for any business model:

Screen-Shot-2015-05-21-at-4.19.17-am.png

At the empathy stage, track qualitative data.

  • “Have I talked to enough people?”
  • Don’t start counting things too soon. You’ll miss the open ended exploration – that will tell you where your business opportunity is or where your  differentiation is.

After that, it depends on what type of business model you’re and what stage you’re at. The above slide gives some indicators on what to track at each stage of the business.

Key Point: Once you’ve decided on what metric you wanna measure, you also need to decide on what the value for that metric should be, before you can move to the next metric. The next Lecture (“What Growth Rate is Healthy”) talks a lot more about this.

The Importance of Focus

Let’s take the example of a car. There’s a lot of metrics you could track – RPM, Fuel, Tyre Pressure etc.

But what if you’re just backing up your car? What matters at that specific moment is your distance from the object behind you.

Screen-Shot-2015-05-21-at-4.07.22-am.png

Focus is so important to march a startup forward. The one metric allows you to do that.

Of course, you still have to make sure that other things are good, but that’s “Management by Exception”. You’re managing by intention on the metric that matters at that time.

How do you know you’re ready to go to the next stage?

You’ll just know. At any time you’re ready to move on to another metric, that will jump right at you. Think of a squeeze toy – when you squeeze one part, another part just jumps out. Its not very different with your startup and the OMTM’s that you should be looking at.

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Exercise for Lecture 4: The One Metric that Matters

Question: What’s the one metric that matters right now?

We’ve already determined that we’re looking at MoveIt from both an Internal and External angle (Refer to the answers from Lecture 3 Exercises above):

  • External (Figuring out whether the problem of engagement is significant enough in other organizations)
  • Internal (Dog Fooding the app within our consulting organization)

The OMTM is a factor of two things: The Business Model and the Stage of the Business.

  • The Business Model
    • Here’s the 6 Business Models again:
      • SaaS
      • Mobile (for example, Gaming)
      • E-Commerce
      • User Generated Content (or UGC)
      • Multi-Sided Marketplace
      • Media
    • Since MoveIt requires its users (the employees within the organization) to share their physical exercise details on a regular basis – with other employees, this is best bucketed as a UGC business model.
  • The Stage of the Business
    • We’ve already discussed the stages of the business in the previous exercise and came up with Pre-Empathy and Stickiness for the External and Internal routes respectively.

Moving on, for the two sides of the business (External and Internal – Refer to the earlier exercises for context), here are the OMTM’s that I came up:

  • External: Since we’re at the Pre-Empathy stage, the number of Customer Development interviews is the metric that we should be tracking.
  • Internal: Here’s the “Business Model and Stage intersection” slide again:

Based on the slide below, the suggested OMTM should be related to “Content”:

So, here’s the OMTM:

I’m keeping it the same as what I started with:

  • “% Active users per month”
  • But based on the above slide, I’m defining “Active” as a user who uploads at least 30 words of content and 1 image per week.

NOTE: I also had a metric with respect to charity at the beginning – but I’m now dropping that based on the learnings from this course. This is a UGC product – and charity doesn’t have much to do with content creation.

Lecture 5: What Growth Rate is Healthy?

Once you’ve chosen your OMTM (based on your Business Model and the Stage you’re in), its time to draw a line in the sand.

That means you need a goal for that metric. But its a line in the sand, not in concrete – so yes, it can change. This is hard – there’s some guidelines in the book.

Aim for something and work toward it.

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Here’s some slides that are good “market” indicators of what you should be looking at, when drawing that line in the sand:

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Here’s some numbers for a social app that relies on the User Generated Content model:

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Of course, these are numbers that you should target beyond the Stickiness stage.

Case Study: Ask for a Credit Card or Not

Assuming you’re in the SaaS business, should you or should you not ask for a Credit Card upfront? What kind of line in the sand should you draw, for conversions – to make up your mind?

As in the case of everything, its a bit err..complicated :).

Here’s a study that was done by this company called Totango that had 5000 users try out both types of systems:

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As you can see in the above table, not asking for the credit card seems to be the clear winner.

But the question is this: Can anything be done to increase conversions even more? And it turns out, the answer is yes.

This is where segmenting your answers (we spoke about Segmenting and Cohorts in Lecture 2) and re-targeting them with specific messages based on their segment, helps.

There are 3 segments of users:

  • Serious: They come in and they try it. You know they’re going to convert. Try to get them on the annual plan.
    • Who’re on the fence: Try to convince them to start using the product.
    • Who’re just kicking the tyres: Ask them to tell them friends using email marketing – and try to use word of mouth to get other paying customers through them

Focus just on the users who are serious (using user profiling) – and then encourage them to sign up for a paid account.

Here’s the results after doing that:

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For example, if you’re selling a project management tool:

    • Serious Users: These folks have already made a decision to invest in a project management tool. They’re now looking at various options seriously. Get them on board as paying customers quickly as possible – with proactive, specifically targeted offers.
      • Who’re on the fence:  These folks are considering investing in a project management tool. They’re not aware of the value of such a tool. For these users, getting them educated on the value of such a tool would be better. So, the content in the email newsletter (for example), would be general benefits of a project management tool, rather than something specific to your product.
      • Who’re kicking the tyres (Casual Users): Use these folks as a marketing resource.

So the goal is:

    • Casual users become the source of leads
      • Convert the on-the-fence users into serious users
      • Convert the serious users into your customers

Exercise for Lecture 5: What Growth Rate is Healthy?

Question: What’s your current line in the sand for your OMTM?

External:

  • Metric: Number of Customer Development interviews
  • Line in the sand: I remember Ben or Alistair mentioning a number of “10” during the course. So, I think I’ll go with that: “10”

Internal

  • Metric: % Active users per month:
  • Line in the sand:
    • Since we’re dog fooding this and we have more influence on our own employees, I’m going to make this number higher than what is suggested in the course (see Fred Wilson’s “Number of Engaged Visitors” slide above).
    • For now, I’m going with 70% Active Users per month

Lecture 6: Lean Analytics Cycle

Take a look at the following diagram. This is basically the Lean Startup Build/Measure/Learn cycle with more specifics.

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Identify a key business problem, identify the OMTM, draw a line in the sand and get started

If you don’t have data, make a guess. Just do something – do anything to move the needle.

  • Generally, speaking, don’t add features. The cycle time for learning is just too long.
    • Find a few of your best customers – whatever best means for you – and find out what’s common about them.

Giving Up or Pivoting:

  • Giving up: Of course, this is the worst case scenario. Hopefully that does not happen.
    • Pivoting: Don’t do the “lazy pivot”. Pivoting is about validated learning using a more rigorous process.
      • You can still use qualitative methods though, and perhaps you move onto the next OMTM – if you’re speaking enough to customers and it looks good enough.

Five Lessons

Alistair and Ben share five important lessons before ending the course:

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We’ll look at each of these in more details.

NOTE: For some reason, there’s no mention of Lesson 3 (Analysing for Cognitive Overhead) in the course.

Lesson 1: Using data to find a hypothesis

There’s two case studies here, one from AirBnB and another one from Circle of Friends

Case Study 1: AirBnB

Some folks at AirBnB asked this question: “Is it possible that improving listings with better quality pictures (and taken with a professional camera) would increase bookings”?

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To confirm this suspicion, a “concierge MVP” was created – with a small sample of existing customers. Professional photographers were hired to reshoot quality pictures for their listings.

And it turns out, their intuition was right. These updated listings with better pics, had 2-3 times more bookings that the others.

AirBnB now has full time photographers on staff – who help rental owners improve their listings with better pics.

Case Study 2: Circle of Friends

Circle of Friends started out as a forum for people to create groups and interact online. This is circa 2007 – before Facebook had the groups feature. So, it was still a new idea then.

They had 10 Million registered users at one point. But they had a horrible problem – with engagement:

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When they dug into the data however, they realized that there was a certain group of users who were engaged much more than the others – Moms.

They renamed “Circle of Friends” to “Circle of Moms” – and rebooted the product with a sole focus on Moms. They lost most of the original users – but the focus on Moms clicked. They eventually grew the Mom user base from 1 Million to more than 4 Million – and eventually got acquired by Sugar Inc.

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Comparison of the Case Studies

In both of the above cases, significant improvements were made by both AirBnB and Circle of Friends. But there was something different in the methods they used to determine what hypothesis to test.

AirBnB used intuition. But Circle of Friends used data. The latter approach is much more recommended.

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Lesson 2: Picking the Right Experiments

Not all experiments are created equal. Its important that you choose the experiments that are high value for low effort/investment.

Case Study: Houston Airport

The Houston Airport did a customer feedback survey. The number one complaint from passengers at the airport? “The wait is too long at the baggage carousel”.

A lot of time and money was spent on retooling the airport to make the whole baggage handling process more efficient. The result? After millions of dollars of investment and many months of work, they brought down the total time for the bags to move from the planes to the carousel. Down from 8 minutes to ….. 6 minutes.

Another survey was conducted some months later. The number one complaint? You guessed it. “The wait is too long at the baggage carousel”

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These are the times when subversive thinking helps. Someone on the airport staff realized that the problem was not that the bags were taking too long to arrive. It was the wait at the carousel. This could be solved potentially be solved by improving even something WiFi capability at the airport, so that the wait is less boring.

The final solution they came up with? Asking the planes to park further away from the airport. It took longer for the passengers to arrive at the baggage carousel – but the bags would arrive sooner. Result? Problem Solved!

Which of the following is easier to experiment with?

  • I could spend millions of dollars to retool my airport or
    • I could ask the planes to park further away from us

Think Subversive: 

  • Which experiment will give me quicker results and more accuracy?
    • Remember, both solutions could solve the problem

Lesson 3: Analyze for Cognitive Overhead

As noted above, this was not covered in the course.

But this probably refers to reduce the number of “brain cycles” required for the user to perform an action on your product. Stick to the “Don’t Make Me Think” principle while designing your product – and you’ll find it easier to keep users engaged.

Lesson 4: Growth Hacking

“Growth hacking is what Marketing should have been – but fell in love with Don Draper and Opinions along the way”

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Growth Hacking is about getting at the intersection of Guerrilla Marketing, Data Driven Learning and Subversive Thinking.

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We’ve already spoken a bit about Subversiveness (Refer to the Houston Airport case study above). Let’s now look at Guerrilla Marketing and Data Driven Learning. Let’s look at Guerrilla Marketing now (“Data Driven Learning” is covered as part of Lesson 5 below):

Growth Hacking – guerrilla Marketing (4 STEPS):

Follow these steps:

  1. Get your users’ attention
    1. Create an element of trust
    2. Appeal to Made, Paid, Laid or Afraid, and then use this in your product design or user interface
    3. Grow one of the 5 “mores” [see below]

Here’s more info on each of those steps:

1. Attention:

We live in an “attention” economy. Economies are not defined by what’s abundant, but by what’s scarce.

Screen-Shot-2015-05-21-at-7.45.25-am.png

2. Building Trust

Screen-Shot-2015-05-21-at-7.52.18-am.png

3. Appeal to Made, Paid, Laid or Afraid

Screen-Shot-2015-05-21-at-7.50.20-am.png

4. Grow one of the 5 “mores”

Screen-Shot-2015-05-21-at-7.39.43-am.png

Lesson 5: Data Driven on a Daily Basis

“Your goal is to make faster, more intellectually honest decisions and empower your organization to do the same.”

But how do you make “being data driven” a habit?

You may have seen Ash Maurya’s Lean Canvas:

Screen-Shot-2015-05-21-at-8.10.29-am.png

The important thing to keep in mind is that the above canvas has to be seen as a “Living Document”. Because in an early stage startup, at least some of those boxes are going to be changing quite oftem

Ben and Alistair suggest the following model instead to drive your weekly plans within your team:

  1. Its key to keep in mind that the “Goal is to Learn”.
    1. First document the current status of your OMTM and what lessons you learned last week.
    2. Then write down your top 3 (business) problems that you have right now. Make sure to right no more than 3.
    3. Then for each of the those 3 problems, come up with Hypothesised Solutions and how you’ll measure the success of those solutions.
    4. Repeat steps 1-4 once a week – and you have your weekly iteration plan :).

Steps 1-3 are covered in the first screenshot below. Step 4 is covered in the next screenshot.

Screen-Shot-2015-05-21-at-8.11.38-am.png

Screen-Shot-2015-05-21-at-8.14.05-am.png

Exercise for Lecture 6: Lean Analytics Cycle

There are no exercises for this lecture.

Conclusion

Thank you for reading. I hope you’ve found this summary useful.

A critical skill you’ll need to build as a leader is to ask the right questions. Make your startup metrics driven. Learning how to analyse the data. Be bold and subversive when you need to be.

“Once,  a leader convinced others in the absence of data.

Now, a leader knows what questions to ask”

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