Category: Entrepreneurship

Over the last few years the number of startups pitching machine learning as part of their special sauce has increased dramatically. Just a few days ago Tomasz Tunguz asked the question Is Machine Learning Overhyped? and argued it wasn’t.

From Wikipedia:

Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed

So, if a computer is the figure out something without being told exactly what to do, there must be data with patterns that provides the basis for the learning. Here’s a Udacity video titled A Friendly Introduction to Machine Learning.

How does machine learning work?

Take a given set of data that’s known or correct (e.g. these are 10,000 pictures of cars) and have the software classify what it finds (e.g. size, shape, color, etc.) in this “seed” or “learning”, or “training” data (also called “supervised learning”). Now, give the software the overall data to analyze (e.g. 1 million pictures of anything) to find which ones have a high probability of being like what the classifier found (e.g. they have a car in the picture). Historically you’d have to “tell” the software what a car looks like in a photo. With machine learning, you give it examples to learn from for classification and then have it apply a probability of a match to each item in the overall data.

What’s a business example of machine learning in action?

Take SalesLoft and sales engagement software. In SalesLoft, sales reps are engaging with their leads via phone, email, social, etc. A common business use case is optimizing for when prospects are most likely to answer phone calls and then to optimize the engagement to coordinate the reps to make calls at those times. Using the SalesLoft data, machine learning would be trained with historical data of all phone calls that turned into conversations (as opposed to no answer, voicemail, etc.) with a variety of fields like time of day, day of week, day of month, day of quarter, timezone, job title, industry, etc. Next, the machine learning would trained on all the historical calls that weren’t answered using the same fields (the “error” data). Finally, the machine learning would process the “good results” and the “bad results” to find patterns and recommend the optimal times of day to make phone calls such that the quality and efficiency of time spent on the phone increased dramatically.

Why machine learning now and not 10 years ago?

Computing power has improved, data processing is better, proven open source machine learning libraries are available, and awareness has increased. Put another way, the challenge and complexity of doing machine learning has gone down 100x. With a lower barrier to entry and higher value, more startups are going to implement machine learning.

Machine learning is real and opens up a remarkable number of new opportunities to use computers to provide insights in a way that wasn’t possible before. Look for machine learning to grow in importance and the startup world to capitalize on it.

What else? What are some more thoughts on machine learning and the startup world?

Eight years ago I was pitching VCs trying to raise money for Pardot. At one of the pitches we were asked about potential acquirers and the VC said something to the effect of “you don’t want to be the last one at the party without a partner” meaning the major acquirers are going to buy a startup in this space (marketing automation), and if our major competitors are acquired, the chance of us getting acquired goes way down.

That got me thinking about some of the startup sayings I’ve heard over the years:

• He’s an angel investor with deep pockets and alligator arms (meaning, his short arms can’t reach into his own pockets so he’s never going to invest)
• Don’t trade your cat for his dog (meaning, don’t sell your company for stock in another private company)
• Watch out for sharp elbows (meaning, the person is known for pushing people out of their way to get what they want)
• We eat our own dog food (meaning, we use our own product internally)

Startups, like all industries, have their own jargon and sayings. These are just a few of the sayings that I’ve heard.

What else? What are some more startup sayings?

Entrepreneurs are constantly lamenting how hard it is to raise money, so I try and be helpful and point out the metrics to raise a modest Series A (often higher now). In addition, I explain that there are challenges with raising too much money, especially when it’s done early. Raising money isn’t a success — it’s just an optional milestone along the road to building a meaningful business. Now, here are some challenges with raising too much money:

• Need to Pivot Again – Even when product/market fit has been achieved in one direction it doesn’t mean that’s the best direction. Entrepreneurs are constantly finding new opportunities and sometimes a pivot is required even after finding product/market fit because there’s another opportunity that’s better. Once significant capital has been raised, changing directions is much harder.
• Limited Exit Opportunities – More money raised equals fewer exit opportunities as there are so few exits above $100 million (see less than 2% of venture-backed companies sell for $100 million or more). Raising money, especially large sums of money, reduces optionality (see Startup Funding and Optionality).
• Down Round Potential – More money raises the bar for a future round at a higher valuation which increases the potential for a down round if growth expectations aren’t met or the market turns. Down rounds can be devastating to a startup (see Startups are Broken After a Down Round).

Raising too much much too earlier can be a challenge. Entrepreneurs should evaluate the pros and cons knowing that more money isn’t always the answer.

What else? What are some more challenges with raising too much money?

Continuing with yesterday’s post on The Only 3 Things a CEO Does, Warren Buffet, in his book The Essays of Warren Buffet: Lessons for Corporate America also has strong ideas for CEOs. In this case, Buffet has three commands for CEOs:

1. They are its sole owner
2. It is the only asset they hold
3. They can never sell or merge it for a hundred years

Imagine the long-term, focused thinking when a CEO follows these commands, especially a true belief that “they can never sell or merge it for a hundred years.” Spend time contemplating these three commands and consider how it might change your own thoughts.

What else? What are some more ideas on the three commands for CEOs from Warren Buffet?

Recently I was reminded of the post on AVC titled What A CEO Does from a number of years ago. The question is simple: what does a CEO do? While there are a number of answers, I agree that there are only three things a CEO does (from the post):

1. Sets the overall vision and strategy of the company and communicates it to all stakeholders.
2. Recruits, hires, and retains the very best talent for the company.
3. Makes sure there is always enough cash in the bank.

Entrepreneurs would do well to keep this in mind, especially when they’re scaling post product/market fit. Getting these three things right is incredibly hard, but when it happens, it’s amazing the results.

What else? Is there anything more important than these three things for a CEO?

After several friends recommend Shoe Dog: A Memoir by the Creator of Nike, I had to check it out. Wow, it’s an awesome book. The writing is superb. The stories are excellent. And, the message is clear: it’s incredibly hard to build a business.

Here are a few notes from the book:

• Side Hustle – Imagine starting a shoe company in the 1960s with no money, no internet, and no place to begin. For 7+ years Nike was a side hustle while holding down a day job to pay the bills. Figure out how to make it work.
• Get on a Plane – With the initial shoe manufacturer in Japan, and very little credibility, major issues required a face-to-face. When issues arise, get on a plane.
• Sacrifices – Family was neglected. Friends were neglected. Nike constantly had major problems for years. Know that sacrifices are required.
• Dad – Talking to your dad (or a mentor) every night for years about the business is powerful. Find a sounding board that cares and talk regularly.
• Air – One day a crazy guy walks in off the street and says he figured out how to inject air into the sole of a shoe. A complete stranger. Then, only a few years later it’s the core of the most successful shoes in the world. Sometimes opportunity does knock on the door.
• Going Public – Fight it as long as possible. Only after every other option is exhausted consider the public markets. Maintain control as long as possible.

Looking for a great entrepreneurial book about grit and resilience? Read Shoe Dog: A Memoir by the Creator of Nike.

What else? What are some more takeaways from the book Shoe Dog?

As a follow up to the previous post Every Spreadsheet Shared Shared is Another SaaS App, there’s an important point that needs to be made: not all good ideas can be good companies. I’ve met with hundreds of entrepreneurs over the years and heard their pitch. Truthfully, on the surface, most of the ideas made sense (I don’t have the domain expertise to assess the quality of the ideas). Only, 99% didn’t become good companies.

Here are a few reasons why all good ideas can’t be good companies:

• Timing – Many ideas are too early such that that market isn’t ready yet. Some ideas are too late such that the market has already matured. My preference is to be slightly early to a market so that when the market crosses the chasm, the core foundation is already in place.
• Cost of Customer Acquisition – Some ideas don’t provide enough value relative to the cost of acquiring and onboarding a customer. In fact, this is often the case and a root cause of startup demise. And, it’s also a common indicator of a nice-to-have product (especially vs. a must-have).
• Competition – Competition is good. Markets are fairly efficient. Many ideas need some amount of scale to be a good company and most markets don’t support having a number of companies with scale.

Not all good ideas can be good companies, and very few ideas can be great companies. Consider these ideas and more when assessing a startup opportunity.

What else? What are some more reasons why not all good ideas can be good companies?