As the prize was sponsored by the Laudato Si’ Challenge, the theme of the Hackathon was “Chasing a Billion Dreams — Building uncommon apps for common people” and the guidance I gave at the beginning of the Hackaton was the seven categories of the Laudato Si’ Challenge.
Explaining the Laudato Si
After we did the opening and set the ground rules, e-Zest CEO Devendra and I had to go and personally sign the 250 certificates of completion for the attendees. It took a while for us to sign all the certificates, but we had a lot of fun doing it. We joked and said this is what it must be like when the president takes office and immediately has to sit down and sign a bunch of stuff.
No event in India is complete without the certificate! 😉
As the day started to turn to night, I went to visit as many of the 100+ teams as I could. The standout teams to me where:
Emergency response: increase ambulance and police response time via location services, GPS, etc (the goal was to get an ambulance faster than an Uber)
Job and skill learning/improvement using machine learning (ML)
Augmented reality social media like Pokemon Go for social activists
Crop health app via image analysis of drone photos
Car lane sensor and software for the masses
A Firefighting robot
Wheelchair automation and navigation
Governmental services for the masses
Solar power installation chatbot
Farming automation and instrumentation via Industrial IoT
As you can see there was a healthy mix of software and hardware solutions. The trends were around some of the major problems in India: traffic, farm yields, competitive job market, and governmental services. It was great to see the Laudato Si’ values being implemented at a Hackathon in India.
We have a problem with not enough women in IT jobs in the United States. That is not the case in India: about half of the Hackathon participants were women. 50% of the winners at the Hackathon were women as well.
About 12 hours after we started, the developers started to get tired. That is when we took a break and had the live band come out and play. We temporarily turned the office into a nightclub.
Developers getting down
After midnight the sleeping bags came out and some people crashed for an hour or two. I was struggling with jet lag and cheated and went back to the hotel for about 4 hours of sleep.
Its not a hackathon without sleeping bags
At 6:30am the judges started to arrive and talk to each team, taking a few hours to narrow the field down from 100+ teams to a short list of about 10.
Final presentations
The final teams made their presentations and we chose the top three winners. While there was a lot of impressive hardware, all the shortlisted apps were software apps with the themes of: farming, social activism, governmental and emergency services, and traffic.
Besides having a lot of fun, we have three goals for organizing another Hackathon in India:
· Engaging with the developer ecosystem
· Learning about new technologies
· Seeing the Laudato Si’ in action
Engaging with the Developer Ecosystem
Spending a weekend side by side with the most motivated developers in an ecosystem for a weekend is best way to engage. We did the same last year and learned things that you can only learn by hanging out with the developers (what technologies they like, what technologies their customers like, how Agile is embraced, what companies are the best to work for, what companies are based in an area that is hard to get to, etc.) Looking forward to the full emersion experience again.
Learning about new technologies
Last year, we learned a lot about the bot market and hope to learn just as much as last year. This year the tech theme is a little more focused on Cloud, so we are excited about what kind of applications 150+ Indian developers will unleash. I usually find the most creativity at Hackathons and expect the same here.
For the last 200-plus years, capitalism has essentially been guided by one central tenet: delivering the most value to shareholders as possible.
Capitalism, of course, is not perfect.
So while companies scrambled to increase value for their shareholders, they did all sorts of atrocious things — forcing the government (or unions) to intervene on behalf of the citizenry.
In 1938, for example, the Fair Labor Standards Act became the law of the land, ostensibly outlawing child labor. In 1970, along came the Occupational Safety and Health Act (OSHA), which was designed to improve workplace conditions. The Family and Medical Leave Act became law in 1993, enabling workers to take extended breaks from their jobs for medical and family reasons without having to worry about becoming unemployed. There have also been laws passed to regulate the environment and prevent securities fraud.
The list goes on and on.
Focusing on the Wrong Thing
When companies are guided solely by maximizing shareholder value, management tends to focus on the wrong thing.
Capitalism, as it’s currently conceived, is far from perfect. Something needs to change — and everybody understands this.
This is why Bernie Sanders and Donald Trump — who actually had very similar messages — were so popular last election cycle. Both were a reaction to what is broken with the traditional model.
Capitalism is great — don’t get me wrong. But the misguidedness of focusing exclusively on maximizing shareholder value is what is broken.
Here’s where Bernie and others at war with capitalism get it wrong: Instead of focusing on the entire philosophy, they should be focusing on the shareholder value part of the equation.
Capitalism 2.0
I believe we’re in the middle of a defining moment. Capitalism is evolving into what I call Capitalism 2.0.
Capitalism 2.0 is straight up capitalism — but with a twist. Instead of focusing solely on shareholder value, companies operating under this model will prioritize the customer experience first and their contributions to society second.
By focusing on these two areas, shareholder value will automatically increase.
Thomas Malthus was the original proponent of the notion that there will be no jobs and no food in the future — and he made this prediction all the way back in the late 1700’s.
However, his wasn’t a prophecy of intelligent robots stomping out the little guy, but instead a commentary on the impending economic juggernaut that was the machine age.
“The industrial revolution will automate all the jobs and nobody will have anything to do!” he said. And really, can you knock the man for his anxious foretelling?
Back in the 1700’s, farming was virtually the entire economy. If they calculated GDP, farming would have been like 90% of GDP. Our friend Thomas was just reading the writing on the wall. His only mistake was being epically, embarrassingly wrong.
The industrial revolution was the greatest job creation engine of all time.
It created jobs that were unimaginable only 30 years earlier — much in the same way that Web Designers, Growth Marketers, and Data Scientists, were all unimaginable occupations 30 years ago. In fact, so many people all over the world switched from farm work to industrial work that today, farming isn’t a factor in a developed nation’s GDP and is left out of unemployment statistics. It literally got written out of the books.
Time marches on though.
Now we are exiting the Industrial Revolution and teetering on the cusp of truly entering the Information Age, a brand new era powered by connectivity and this big group of little people you keep reading about called Millennials.
Now sure, self-driving cars will eliminate the taxi (and Uber) driver, and machine learning will take away software developer jobs. Robots will serve coffee, iPads will teach our children, and I’ll be able to use a tricorder instead of going to the doctor. These are the things we’ve been promised.
So nobody will have a job in the future, right?
Well, hang on.
Just as our farmer from the 1800’s couldn’t have imagined the factory that he’d be working at in the next decade, or how someone from the early 1900’s couldn’t have imagined how the auto and aviation industries would dramatically shrink the world, or even how someone from 2000 couldn’t have imagined the digital marketing jobs that exist today, we similarly can’t imagine the jobs that will exist in 25 or 50 years.
The Information Age will usher in more jobs than humanity has ever seen before. We just don’t know what they will be yet.
Keep in mind though, that these jobs will come in a cycle — as they have for centuries. First jobs will be lost, and then they will be replaced a few times over. The magnitude of this progression is dependent on how disruptive the technology is — and AI, robotics, machine learning — these things are going to be exceptionally disruptive.
So sure, in the short term we will witness tremendous pain. We already are to some extent, and it’s causing a great divide to open within our country, and around the world.
Just as a laid off coal miner in Pennsylvania or an unemployed auto worker in Detroit can’t move to Silicon Valley and fill the open engineering roles out here, unskilled labor in central Britain can’t move to fill the tech jobs in London.
What do these things (partially) lead to? One has orange hair and the other has made it way cheaper to travel to England.
So yes, there will be tremendous upheaval of the status quo as we transition away from jobs that have sustained many over the past century — and those people will need assistance, not just financially, but also emotionally. Furthermore, they will need a new education to bestow them with the skills required to be competitive in the new economy.
Nevertheless, in the long term — say 50 years from now — we will look back and see that the Information Age was not the harbinger of humanity’s gainfully-employed doom, but instead a revolution that went on to become the new most powerful job creation engine of all time.
A lot of people misuse the term “MVP” or Minimum Viable Product. To be clear an MVP is not a beta, not a prototype, but rather an experiment designed to test your value proposition’s assumptions by measuring a behavior and learning from the results.
Back in the day, Dropbox did an MVP as just a video and Buffer was just a landing page. Both were experiments to determine if Dropbox or Buffer should even exist. Instead of guessing and building prototypes, they built the simplest of things in order to measure a user’s behavior. Today startups are building functional prototypes and calling them MVPs. They are better off building something they can learn from. Typically the first MVP doesn’t even have to be anything on a device or computer. For example, I once advised a new travel startup that wanted to give you one click access to a daily itinerary based on a map. They assumed that people wanted a map with pin points on it and times to follow. I told them to go to tourist spots and give people real maps with real pin points circled and an analog itinerary to follow. That was an MVP, it was an experiment (map) that measured (how many as a percent of total) a user behavior (did they use the map or not). Let’s take a look at how to build a better MVP.
Getting Started: Customer Segment and Value Proposition
The whole idea of an MVP is to measure an actual result against your expected result to prove or disprove your assumption. In order to do that you need data. The first place to start is to think about is your customer segment; you have to know who your target customers are going to be. Without knowing your exact segment (22–34 year old professional, urban women, single, living alone, earning over $75k), you won’t be able get the correct pool of users to test on.
After you define your customer segment, you define your value proposition. Too many people think that their value proposition is just the solution to the problem they are solving. That is incorrect: your value proposition is the delta between the current solution or workaround to the problem people are currently using and your solution. You measure your value proposition in terms of how much better your solution is compared to the solutions that exist today.
Let’s say you are solving a problem for buying movie tickets. Several solutions already exist; there are lots of web sites, apps, etc. Maybe your solution involves buying the tickets via SMS. Regardless, you have to think about what the alternatives to your solution are and compare them against that. One is simply buying the ticket at the box office. Here your alternative has value, but not tremendous value. Alternatively, let’s say you are developing a life saving cancer drug. The alternative without your solution could be death. In this case your solution would be incredibly valuable.
The Assumptions That Fuel Your Value Proposition
Underpinning your value proposition are your core assumptions. These are the things that would compel someone to buy your product or service. The job of the MVP is to test those underlying assumptions. The only way to successfully test those assumptions is by making a prediction of the result and comparing the behaviors that you measured up against your predictions. Your predictions should be based in fact, facts that would determine if you have a viable business or not. If you don’t make a prediction, then you will not have a way to determine success or failure of the MVP test.
Let’s say you are building a landing page, Buffer style. Your MVP will be to measure how many people give you their email address after your landing page described your product. You will have to drive traffic to your landing page, most likely by taking out some Facebook or Google AdWords ads. You want to measure the conversion rate of people who clicked on the ad (since you pay for click) to providing their email addresses. For example, if 100 people clicked on the ad and came to your page, but only 4 provided their email address, your conversion rate is 4%. (Not bad actually in e-commerce.)
Should 4% be your target? No. You need to determine your prediction based on facts and your business model. Let’s say you estimate spending $100 on Google AdWords to drive traffic to your MVP. If you have a conversion rate of 4%, it will then cost you $25 to acquire each customer. $25 is your CAC or customer acquisition cost. You need to estimate what your Customer Lifetime Value (CLV), or the amount of profit you expect to get out of each customer over the course of their relationship with you, is. At this stage it will be fairly inaccurate, but you need to ground your assumption in reality. (Future MVPs can test pricing.) Let’s say you make the CLV to be $21, based on a lot of factors in your business model. (I talk more about your CLV and CVC here.)
With a a CLV of $21 and a CAC of $25, you will lose $4 on each new customer you acquire. Or CLV ($21) — CAC ($25) = -$4.
For your MVP test, you will need a higher conversion rate/lower CAC rate in order to make a profit. For the first MVP test make a prediction that the conversion rate will be 5%, bringing your CAC down to $20. Or CLV ($21) — CAC ($20) = $1.
Interpreting The Results
Now with your assumptions based in some business reality, it is time to run the test. Typically the results are one of the three following numbers (remember you are aiming for 5% conversion):
0.021%
4.28%
17%
Let’s take 0.021%. This is an absolute failure, you can safely assume that your assumption is invalidated. Safest thing to do is declare the assumption invalid and go back to your value proposition and rethink it. If you have other assumptions associated with your value proposition, you can do some more MVP tests to determine if the entire value proposition is invalid or not. Chances are you will have to iterate your idea and value proposition some more.
What to do if you are at 4.28%? Technically it is invalid since you need 5% conversion rate in order to make any money. Should you just give up and go home? No. You should try some new UX and new design or different language and run the test again. Don’t run the test without changing anything! If your future tests with minor changes are at or over 5%, then you can declare your assumptions valid and move on to test the next one.
Let’s look at 17%. Woo-hoo, your assumptions are more than valid, you blew away your predictions. Verify that your test was fair and then declare your assumption valid and move on to test the next assumption.
Thats all there is to it! Only by clearly defining what success is and basing those numbers in a business reality is an MVP useful. Anything else is just a beta.
