Lifelogging – recording every single minute of your life – is quickly moving from science fiction fantasy to real life phenomenon.  Of course to truly document every minute of your life today is still a daunting task.  Although some people are already doing it, full lifelogging is time consuming, expensive, and limited in quality.  But logging every single minute of your life is not the only way to lifelog.  Each video, each picture, each email we create everyday is another piece of the puzzle that documents our lives.  If you are like me, you are accumulating more and more of these digital pieces each year.  This trend is accelerating, and within ten years or less it will be cheap and easy for most of us to record nearly every moment of our lives if we choose to do so.  How much of your life will you choose to record, and how much of your life will be recorded by others?

Technology has already made it so easy to record our lives that many of us might not even appreciate how much is already being recorded.  Gmail, for example, has a record of every single email I have ever sent or received for many years now.  Those of us that maintain online calendars can tell who we were with and what we were doing on any given day for the last several years.  I use my smartphone to capture several photos and videos per day without even thinking about it.  Most chat programs allow you to record and archive every single online chat you have ever had.  And these are just some of the more ubiquitous examples.

For those that want to be more ambitious about their lifelogging, there is a device you can wear  around your neck that will take photos automatically around the clock.  Ucorder makes a video camera that you can snap onto your clothing.  And taking things to the ultimate limit is Justin Kan, founder of Justin.tv, who was originally famous years ago for broadcasting his life 24/7 to the internet for anyone to see.

Being able to explore our recorded past opens the door to a whole new world of possibilities.  Arguments could be resolved by going back in time and seeing who really said what, rather than relying on flawed recollections of what we think we said.  Crime could be revolutionized with recordings of who was doing what and where.  Our entire past could be made accessible – from meaningful conversations with a loved one to trivial encounters with random people or places.  We would rarely forget anything ever again.  When we lose the keys, fail to recollect a desired piece of information, or lose some clarity on what something looked like – all of this can be fixed by looking back into our recorded past.

Our recorded past could be the best (or worst) movie we have ever watched.  We could watch ourselves go to school for the first time, go on our first date with our future spouse, or go to our first job interview.  It is a window into our past, with all it’s glory and blemishes.  Some of these recorded moments won’t be our proudest, but ultimately I think we will gain great value from being able to access our recorded past.

Recording our lives is one thing, but being able to find a particular conversation or image of interest from thousands of hours of footage is a daunting task.  Indeed, our ability to record information today is far ahead of our ability to organize and dig through that information.

The tools for searching through our lifelog are getting better though.  Video and images almost always have a timestamp associated with them, so programs like Google’s Picasa are easily able to sort your lifelog by date.  Software has recently made impressive strides in being able to automatically recognize and tag faces within images, making it possible to search for all images with a particular person in them.  Devices with a GPS such as the iphone are now able to associate a location with your images and videos.  This means it is now trivial to sort those recordings by location and then view them on a map.  See below for an example of how an iphone does this:

 

Map where pictures/videos were taken on an iphone

But ultimately what we really need is something much more sophisticated than “sort by date” or “sort by location”.  What you want to do is say “computer, please pull up the conversation I had with Dr. James about longevity a few years ago.”  Such a capability is not here yet, but the foundation for its creation is already in the works.  When you consider voice recognition technology and automatic speech to text translation that exists today, it is not unreasonable to expect Star Trek quality access to digital records in 10-20 years.

Even though the tools to search our lifelog are still lacking, this does not mean that those who are interested in lifelogging should stop their efforts.  Lifelogging can begin now, and we can count on the ability to effectively dig through that lifelog in the future.  Already I am lifelogging more each year.  At my current rate, I am easily capturing several photos and videos per day.  Even with this incomplete lifelog, when you stitch it all together it allows me to showcase an unprecedented digital diary of a single person’s life.

For my children, this lifelog is growing into an invaluble record of what they looked like, what they said, where they went, and what they were capable of during any day of their youth.  I am not just recording the “big moments” like their first steps or their first words.  Conversations, playing games, jokes, falls, fails, arguments, discoveries, crying fits…the minutae of daily life are all being recorded for my children and their loved ones to look back upon when they are older.

Many will say that such a massive lifelog of information is a useless pile of data.  But this is very shortsighted thinking.  When given the chance to view a video record covering years or even decades of our lives, I am pretty sure we all would find plenty of reasons to want to look back.  Besides, given how cheap and easy it is becoming to record a lifelog, the debate about the usefulness of a lifelog can mostly be ignored.  If you use the lifelog – great.  If you don’t use it, then it didn’t cost you much to record it anyway.

Although lifelogging unleashes fascinating ways for us to explore our past, it also exposes us to possible embarrassment, or even worse – trouble with the law.  It is a double edged sword, yet this is no different than with our actions that take place in the present time frame.

We all accept that actions we take in the present time frame are subject to negative consequences, but this doesn’t make us not want to live in the present.  For most of us, the same will be true for recording our past – the benefits we gain from being able to retrieve our past will far outweigh the risk we face for our past actions.  This is especially true if we consider that we will have full control over who can and cannot access our recorded lives.  That is, unless our hand is forced by the real risk of a search warrant or a security breach.  We will each have to decide for ourselves how to balance those risks as we record more and more of our lives.

 

The future of first dates?

As lifelogging becomes more prevalent, we will see that the book is yet to be written on the etiquette and law surrounding when we can and cannot record things.  Is it appropriate to record your first date with someone?  Would the two of you both be comfortable with having such a moment on record?  That choice will be up to the two of you.  Would a corporation really allow you to record your job interview with them?  Probably not, and the law will likely support the corporation on that choice.  It will be interesting to see how different individuals and entire cultures evolve to accept or deny the invasion of video recording into our daily lives.

As recording our lives continues to get easier and cheaper to perform, most of us find ourselves lifelogging more and more – taking more photos, recording more videos, sending more emails.  The trend is unstoppable.  Some of us will embrace lifelogging more quickly and more openly than others, but ultimately we are all moving in a similar direction.  The gains to be had from these detailed records of our lives are fascinating, but the threat to our privacy is very real.  I for one, tend to embrace inevitability rather than fight it, so I will continue to increase my lifelogging as technology makes it easier and cheaper for me to do so.  If you ever see me in person beware – it will probably be recorded.

First, what is an actionable metric?

An actionable metric is one that ties specific and repeatable actions to observed results.

The opposite of actionable metrics are vanity metrics (like web hits or number of downloads) which only serve to document the current state of the product but offer no insight into how we got here or what to do next.

In my last post, I highlighted the importance of thinking in terms of Pivots versus Optimizations before product/market fit.

Pivots are characterized by maximizing learning while Optimizations are characterized by maximizing efficiency.

This distinction carries over to metrics too. As we’ll see some metrics matter more than others depending on the stage of the company but more importantly, it’s how these metrics are measured that make them actionable versus not. I’ll share my 3 rules to actionable metrics, derived from Lean Startup principles, and specifically focus on what metrics I measure and how I measure them.

Rule 1: Measure the “Right” Macro

Eric Ries recommends focussing on the macro effect of an experiment (such as sign-ups versus button clicks) but it’s just as important to focus on the right macro. For example, spending a ton of effort to drive sign-ups for a product with very low retention is a form of waste.

Identify Key Metrics

The good news is that there are only a handful of macro metrics that really matter and Dave McClure has distilled them down to 5 key metrics. Of the 5, only 2 matter before Product/Market Fit – Activation and Retention.

Before Product/Market Fit, the goal is validating that you have built something people want. You don’t need lots of traffic sources to support learning and people don’t usually refer a product unless they have used and like the service. So both Acquisition and Referral can be tabled for now. What does correlate with building something people want is providing a great first experience (Activation) and most important of all, that they come back (Retention).

Note: Some of you might have noticed that I swapped Revenue with Referral from Dave’s version. This is because I believe in charging from day 1 which more naturally aligns (but does not replace) Revenue with Retention.

Map Metrics to Actions

The next step is to map specific actions in your product to Activation and Retention.

Activation Actions
Activation actions typically start with your sign-up process and need to end with the key activities that define your product’s unique value proposition.

Note: The “Tell Friends” here is used to publicize shared galleries and I don’t count it towards “Referral”. I view “Referral” actions as being more deliberate endorsements of the product such as through an affiliate program.

Retention Actions
There are a number of ways to define retention and Andrew Chen even draws a distinction between retention and engagement. Personally, I prefer to tie my retention action to the key activity that maps to the UVP.

Rule 2: Create Simple Reports

Reports that are hard to understand simply won’t get used. Similarly, reports that are spread across pages and pages of numbers (ahem Google Analytics) won’t be actionable. I am a big fan of simple 1-page reports and funnels are a great format for that.

Funnel Reports – The Good, The Bad and The Ugly

Funnels are a great way to summarize key metrics: They are simple, visual, and map well to the Activation flow (and Dave’s AARRR startup metrics in general). Here is an example of a funnel for a service that is offered under a 14 day free trial.

But funnel analysis, as implemented by analytics tools today, have several shortcomings:

Tracking long life-cycle events
For one it is hard to accurately track long lifecycle events. Almost all funnel analysis tools use a reporting period where events generated in that period are aggregated across all users. This skews numbers at the boundaries of the funnel. But more importantly, because you are constantly changing the product, it is impossible to tie back observed results to specific actions you might have taken a month ago.

Tracking split tests
A more serious manifestation of the same problem is tracking split-tests for a macro metric like Revenue which also has a long life-cycle. An example of an experiment I am currently running is studying the long-term consequences of offering a Freemium plan alongside a Free Trial plan. I believe that a properly modeled Freemium plan should behave like a Free Trial. The only difference is that Free Trials have a set expiration while with Freemium, a user outgrows the service after some time X. I can guess with reasonable certainly that I will get more sign-ups with Freemium, but the bigger questions are whether that will also translate to more Retention/Revenue. If so, what is the average time to conversion (time period X)? I can’t answer these types of questions with the current funnel tools.

Measuring Retention
And finally, funnel tools don’t provide a way to track retention which by definition needs to track user activity over long periods of time.

Funnels Alone Are Not Enough. Say Hello to the Cohort.

So while funnels are a great visualization tool, funnels alone are not enough. The analytics tools today work well for micro-optimization experiments (such as landing page conversion) but fall short for macro-pivot experiments.

The answer is to couple funnels with cohorts.

Cohort Analysis is very popular in medicine where it is used to study the long term effects of drugs and vaccines:

A cohort is a group of people who share a common characteristic or experience within a defined period (e.g., are born, are exposed to a drug or a vaccine, etc.). Thus a group of people who were born on a day or in a particular period, say 1948, form a birth cohort. The comparison group may be the general population from which the cohort is drawn, or it may be another cohort of persons thought to have had little or no exposure to the substance under investigation, but otherwise similar. Alternatively, subgroups within the cohort may be compared with each other.

Source: Wikipedia

We can apply the same concept of the cohort or group to users and track their usage lifecycle over time. For our purposes, a cohort is any property attributed to a user that we wish to track. The most common cohort used is “join date” but as we’ll see this could just as easily be the user’s “plan type”, “operating system”, ‘sex”, etc.

Lets see how to apply cohorts to overcome the shortcomings with funnels we covered up above.

Tracking Long LifeCycle Events

The first report I recommend implementing is a “Weekly Cohort Report by Join Date”. This report functions like a canary in the coal mine and is a great alerting tool for picking up on actions that had overall positive or negative impact.

You group users by the week in the year they signed-up and track all their events over time. This report was generated from the same data used in the funnel up above (which I’ve shown again for easy comparison). The key difference from the funnel report is that other than the join date, all other user events don’t need to have taken place within the reporting period. You’ll notice immediately that a lot of the conversion numbers (especially Purchased) are quite different because a cohort report doesn’t suffer from the boundary issues with simple funnel reports.

More importantly though, the weekly cohort report more visibly highlights significant changes in the metrics which can then be tied back to specific activities done in a particular week.

Tracking Split Tests

Apart from reactive monitoring of the funnel, cohorts can also be used to proactively measure split-test experiments. Here is a report that uses the “plan type” as a cohort for the “Freemium” versus “Free Trial” experiment I described up above.

Disclaimer: My Freemium versus Free Trial experiment is still underway and these results are made up.

You can see that while activations are higher with the Freemium plan, Revenue (so far) is lower. That may change over time and it’s important to know the average time to conversion so the Freemium plan can be modeled accordingly.

You can create a cohort out of any user property you collect and run reports to uncover questions like:

1. Do mac users convert better than windows users?
2. Do certain search keywords convert better than others?
3. Do female users convert better than male users?
etc.

Tracking Retention

Now for the most important metric that matters before Product/Market Fit – Retention. This report is also generated using a weekly cohort by join date but instead of tracking conversion, it tracks the key activity over time.

We only track “Activated” users which is why all the Month 1 retention values are 100%. A Retention report can quickly tell you if you are moving in the right direction towards building a product people want or simply spinning cycles.

Rule 3: Metrics are People too

Metrics can only tell you what your users did. They can’t tell you why. A key requirement for making metrics actionable is that you should be able to tie them to actual people. This is not only useful is locating your most active users but more importantly for troubleshooting when things go wrong.

This last part is particularly important before product/market fit when you don’t have huge numbers of users and need rely more on qualitative versus quantitative validation.

Here is an example where I am able to extract the list of people that failed to complete the download step of my funnel. Armed with this list, I don’t have to guess what could have gone wrong. I can pick up the phone or send out an email and simply ask the user.

How do I Create these Reports?

I alluded before that most analytics tools are better suited for micro-optimization experiments versus macro-pivot experiments which actually makes sense because optimization is (typically) a post product/market fit activity and where the money is at.

I have been an early user of both KISSmetrics and Mixpanel and while both of these tools are good at Funnels, they fall short when it comes to Cohort Analysis. Mixpanel does currently support retention cohort reports but not funnel cohorts, and I know Hiten from KISSmetrics is definitely thinking hard about cohorts. So hopefully we’ll see something soon there.

That said, I was really struggling with tracking my Freemium versus Free Trial split test so as an experiment, I decided to spend an afternoon building my own homegrown cohort analysis tool based on the conceptual People/Events/Properties model I learned from using KISSmetrics. All the reports you see here were generated using that.

I was going to detail exactly how I did this but I have reached my word limit for this post and you probably need to get back to your startup too. If there is enough interest on how I did this in the comments section, I’ll cover it next time.

Related posts:

1. 3 Rules for Building Features in a Lean Startup
2. A First Look at Some Metrics Numbers
3. How I am Measuring Product/Market Fit

When Phil Libin set out to develop the Next Big App, he put forth one cardinal rule: "I didn't want a clever business model." Come again?

"Data analysis, referrals, advertising -- it was all kind of sleazy," says Evernote CEO Libin. "I wanted a clever product. I wanted that product to reach hundreds of millions of people. And I wanted 99% of them to be using it for free."

The "freemium" model -- giving away service to users and making money when some opt to pay for additional features -- has become wildly popular among Silicon Valley entrepreneurs, all banking on reaching the critical mass of a Facebook or Twitter (or being acquired). The competitive advantage is tantalizing. A product that's largely free can't be edged out by something cheaper. "But for most startups, freemium is a cop-out," says Lincoln Murphy, managing director of Sixteen Ventures, a Dallas-based strategy consulting firm. Even if a company can eventually persuade users to shell out for something they've been getting for free, it's extremely difficult to deliver freemium's other requirements: a massive potential audience, next-to-nothing operating costs, a gotta-have-it value to consumers, and a sky-high retention rate. Ning, for example, abandoned the model because its few free-to-paid conversions weren't generating enough revenue, and Flickr hedged its bet by folding premium subscriptions into a larger moneymaking strategy that includes merchandise and advertising. "Freemium is a numbers game," says Murphy. "And most companies lose."

Evernote is a rare exception, remarkable for both the devotion of its users and the slope of its revenue curve. Libin's two-year-old Web and mobile app bills itself as "your external brain," letting its 3 million -- plus users clip Web articles, take photos, record voice or text notes -- and store everything in the cloud. During users' first 30 days, 0.5% convert to its paid version ($5 a month or $45 a year), which offers perks such as added storage space and offline access. At the six-month mark, the rate has gone up to 1%. After two years, almost 6% of the initial group have started shelling out -- and one-third of them are still storing content on a monthly basis.

Those numbers are a testament to the success of Evernote's engagement strategy, which hinges on maintaining a high-quality free product. "The easiest way to get 1 million people paying is to get 1 billion people using," says Libin. Periodically, Evernote unveils features that save users time or make their stored information safer; some updates are free, while others are available only to paid users, such as the ability to revert to previous versions of notes. The add-ons trigger conversions and turn customers into advocates. "The more memories users store in Evernote, the more invested they become," says Murphy. "Whether they're paying for premium access or telling their friends how great it is, they're willing to do their part to keep Evernote around."

From the start, Libin modeled Evernote to be profitable at a 1% conversion rate. It helps that the service relies on word of mouth for customer acquisition, and that most users write their notes on their own smartphones or computers, then sync to Evernote's servers. Right now, roughly 2% of all Evernoters are premium customers, which is good for business. As the service adds more users, both free and paid, Libin wants to maintain that rate at 5% or less. If people start converting en masse, "that means our free product isn't good enough," he says. "And if our free product isn't good enough, what's the point of being freemium?"

"The Odds Are Increasing That Microsoft's Business Will Collapse"

That's a pretty good title if you (like Henry Blodget from Silicon Alley Insider, the writer of the article) are trying to grab eyeballs. It also provides a useful introduction to what I call the "Innovator's Paradox."

Blodget's article was provocative. He argued that Microsoft is in a no-win situation. It isn't sitting on any idea that is on the cusp of turning into a multi-billion dollar business. The personal computer is losing its dominance to mobile devices and tablets. The company's core profit drivers (Windows and Office) are under disruptive assault from Google's freely available applications and operating system. At best, Microsoft will respond with its own free products and erode its profit margin.

The most telling thing in Blodget's post was a chart that showed the sources of Microsoft's profits over the past few years. Microsoft's core business has continued — despite continued proclamations of the company's coming demise — to throw off cash and to grow. But new growth businesses that were specks in 2006 (entertainment and devices and online services) remain tiny, and Microsoft hasn't created any material new businesses over the past few years.

So the real problem isn't what Microsoft is doing today. It's what Microsoft did, or didn't do, five, or even 10 years ago. At the time, its base business was a bastion of strength. Today's threats were in their infancies. It would have been the perfect time to plant seeds that today would be blooming profit generators.

Why didn't it? It's The Innovator's Paradox: When you don't need the growth, you act in ways that lead to you not getting the growth you will need. And when you do need the growth, you can't act in ways that deliver it.

Got that?

I use this chart to illustrate this point. It's helpful to stimulate discussion among a company's leadership team.

As the chart shows, when times are going great ("exploitation"), companies have ample resources to invest in growth. But because times are going great, innovation efforts tend to be undisciplined. New growth efforts get flooded with cash. Companies don't worry about iterating to find a successful business model. Overpriced acquisitions occur.
Then, inevitably, maturation sets in. The need for new growth intensifies. But resources are harder to come by. Acquisition targets would rather join forces with the new kid on the block whose rapidly appreciating stock acts as a powerful acquisition currency. The acute need for growth gives an almost gravitational pull to large, existing markets. After all, that's where the money is. But large, existing markets are an innovator's fool's gold; glimmery, shiny, but not of much value.

So what do you do about the Innovator's Paradox? Consider what my colleague Clark Gilbert found in his doctoral research. He looked at how newspaper companies responded to the Internet, intersecting the disruptive innovation research of Clayton Christensen and the famous behavioral research of Amos Tversky and Daniel Kahneman.

Some companies in Gilbert's study viewed the Internet as a threat. That led to them committing resources to innovation efforts. However, they responded in very rigid ways (typically creating replicas of the newspapers online). Other companies viewed the Internet as an opportunity. That framing allowed for more expansive response strategies, but made it difficult to support serious resource allocation. Gilbert summarized this challenge by saying, "Absent a sense of threat, response to disruptive opportunities is inadequate; but with threat, the fully funded response is too rigid."

The trick, Gilbert argued, was to decouple the allocation of resources from the use of the resources. Breaking the Innovator's Paradox requires similar logic. Companies have to recognize that their core business, no matter how great it seems today, has a limit. That recognition should drive consistent allocation of resources to new growth efforts, especially when times are good. But those resources can't be burdened with a "bunker" mentality, or else they will struggle. As one friend told me, "No great growth business was built from a defensive crouch." Further, companies have to make sure they avoid the "curse of abundance" that can lead overly resourced growth efforts to struggle.

So, all you have to do is invest when you don't need to, break psychological barriers that might unintentionally inhibit creativity, and somehow avoid the abundant resources that would seem to be your primary source of competitive advantage.

No one said this was going to be easy.