How to Apply Machine Learning to Business Problems: Part 2

By "Daniel Faggella - This article was originally listed on TechEmergence. This post breaks the original post into 2 Parts. Part 1 is here.

Pointers for Applying Machine Learning to Business Problems1– Begin with a priority problem, not a toy problem

In an off-mic conversation with Dr. Charles Martin (AI consultant in the Bay Area), he mentioned that many companies read about ML with enthusiasm and decide to “find some way to use it.” This leads to teams without the real motivation or gusto (or committed resources) to drive an actual result. Pick a business problem that matters immensely, and seems to have a high likelihood of being solved

UBER’s Danny Lange has mentioned from stage that there is one thought process that’s highly likely to yield fruitful machine learning use case ideas: “If we only knew ____.”

Ask yourself, what mission-critical business information are you dying to know, but can’t currently access? Maybe it’s understanding the lead sources most likely to yield the highest customer lifetime value, or the user behavior most indicative of expected churn.

2- You can give it data, but all of the context must come from you

Thinking through what information to “feed” your algorithm is not as easy as one might presume. While ML algorithms are adept in identifying correlations, they won’t understand the facts surrounding the data that might make it relevant or irrelevant. Here are some examples of how “context” could get in the way of developing an effective ML solution:

Predicting eCommerce customer lifetime value:

An algorithm could be given data about historical customer lifetime value, without taking into account that many of the customers with the highest lifetime value were contacted via a phone outreach program that ran for over two years but failed to break even, despite generating new sales. If such a telephone follow-up program will not be part of future eCommerce sales growth, then those sales shouldn’t have been fed to the machine.

Determining medical recovery time:

Data might be provided to a machine in order to determine treatment for people with first- or second-degree burns. The machine may predict that many second-degree burn victims will need only as much time as first-degree burn victims because it doesn’t take into account the faster and more intensive care that second-degree burn victims received historically. The context was not in the data itself, so the machine simply assumes that second-degree burns heal just as fast as first-degree.

Recommending related products:

A recommendation engine for an eCommerce retailer over-recommends a specific product. Researchers only discover later that this product was promoted heavily over a year ago, so historical data showed a large uptick in sales from existing buyers; however, these promotional purchases were sold more based on the “deal” and the low price, and less so by the actual related intent of the customer.

3– Expect to tinker, tweak, and adjust to find ROI

Building an ML solution requires careful thinking and testing in selecting algorithms, selecting data, cleaning data, and testing in a live environment. There are no “out-of-the-box” machine learning solutions for unique and complex business use cases. Even for extremely common use cases (recommendation engines, predicting customer churn), each application will vary widely and require iteration and adjustment. If a company goes into an ML project without resources committed to an extended period of tinkering, it may never achieve a useful result.

Quotes from the TechEmergence Network:

We again reached out to our network of TechEmergence interviewees and consensus respondents for opinions and tips on implementing machine learning in business. Below are a collection of quotes:

Dr. Ben Waber — PhD, MIT; CEO of Humanyze (AI-powered people analytics company)

“You cannot use ML to solve business problems in a vacuum. Make sure you get buy-in from business unit leaders to make concrete changes based on the analysis.”

Dr. Danko Nikolic — PhD, University of Oklahoma; Data Science and BD&A, Computer Sciences Corporation

“The most common mistake that businesses make when using ML is that they think that an ML solution is a one-shot process: They send data to data scientists, and data scientists send back THE model. In contrast to that, finding a good ML solution is an iterative process that involves research, trials, and errors, experimenting, talking to the business experts, etc.

ML cannot ever become a commodity. The success of ML depends strongly on the knowledge, skills, and dedication of the people who do it.”

Dr. Charles Martin — PhD, University of Chicago; CEO, Calculation Consulting

“Avoid setting up massive infrastructure until you have a handle on what you want to do. You can easily spend 6 months to a year setting up Hadoop and Spark and not see any ROI.

You will be lucky if 5% of your data is correct and useful. You need to design an experiment that can identify the low hanging fruit and ferret out the data you need. You can build an algorithm on a high memory AWS node.

Get the algorithm into a live environment and test it as early as you can. Don’t build try full production system. Remember, ML is about math, not coding! You want to test it small. Run enough examples to flush out the problems, but not so small that the statistics are meaningless.”

Ferris Jumah — Previously ML at LinkedIn; Bay Area ML Consultant

“Get data driven as soon as possible. Machine Learning doesn’t come for free. You need to build intuition around your data, how you measure the business and know your customers, link not just measurements but also insights to decision making. Log everything, build storage and processing systems, ensure they are accessible, conduct deep analysis and as many experiments as you can on your product, build in intelligence into as much as your product as possible.

At this point, consumers expect personalization and “smart” features. Build them in, learn from them, and ensure that you have a feedback mechanism in place. Finally, hire and invest in data people who are passionate about your problem and business.”

The consensus (in the limited number of quotes above, and from dozens of other conversations with business-minded data scientists) is that machine learning is not as much of a mere “tool” as, say, marketing automation software. Anyone with a good manager and a bachelors degree from a community college can pick up Constant Contact or even (with a bit of tinkering and calling the support line) Marketo or Hubspot and drive some company value.

There are no simple shortcuts to iterative, multi-faceted process of applying machine learning. image credit Microsoft’s CortanaIntelligence.

ML doesn’t yet show up in a neat box, and value is wrought by hard thinking, experimental design and – in some cases – hard mathematics. A little bit of time on Google and YouTube, and you can get a hang of how to set up DropBox for your business. Predicting churn rate across your customer segments with machine learning? Not the same game.

Preparing to derive business value from ML implies having trained talent, expert guidance, and an (often) tremendous “data cleansing” period – and none of it is guaranteed to be a win, as Dr. Martin states aptly above. If Google, Amazon, and Facebook could get their interns to set up ML systems, would they really be spending millions and millions of dollars to scoop the world’s top AI talent out of academics to work for them?

While machine learning isn’t an easy setup, it’s also not one that any future-minded business can leave off the table for too long. The efficiencies gained by the “rockstar” tech companies through machine learning are substantial, and startups here in the Bay Area aren’t just getting funded because “machine learning” is a buzzword. It’s also because many of them have a powerful and strong business case.

Interested readers might benefit from our recent consensus of 26 machine learning / AI researchers where we asked: “Where should machine learning be applied first in business?” The infographic featured drives home many of the same points highlighted in this article.

The ultimate question for executives remains: When can we have (a) the resources required to invest in machine learning seriously, and (b) a legitimate use case that started from trying to find real business value, not from “trying to find a way to kinda use machine learning.” That’s a thought process that can’t be done for you, but our hope is this article has helped to inform your perspective and give you resources to draw from in future.

A Thanks to Our Machine Learning Respondents

I’d like to extend a special thanks to our respondents for this extended article. Below you’ll see links to hear our full interviews with these researchers and businesspeople, as well as links to their respective organizations:

Charles Martin was previously interviewed about how ML is being adapted to business. Charles runs CalculationConsulting.com.

Ben Waber was previously interviewed about machine learning in HR. Ben runs Humanyze.

Danko Nikolic has spoken with us about his “AI Kindergarten” theory of machine intelligence. Previously at the Max Plank Institute, Danko is now a Senior Professional in Data Science and BD&A for CSC.

Peter Voss was interviewed on TechEmergence about his views on the future of artificial general intelligence. He is founder and CEO of AGI Innovations Inc.

Ferris Jumah works on a variety of ML consulting projects in San Francisco and can be found on LinkedIn

Ronen Meiri was interviewed about how businesses can get started in basic ML implementations. His company DMWay is a predictive analytics and AI company based in Israel.

Image credits: Extraordinary CEO and Tech Emergence

DanFaggella - I’m CEO / founder at Emerj (formerly TechEmergence), the only market research and company discovery platform focused exclusively on artificial intelligence and machine learning. I regularly speak for audiences of businesses and government leaders, with a focus on the critical near-term implications of artificial intelligence across major sectors – including presentations for the World Bank, the United Nations, INTERPOL, and global pharmaceutical and banking companies.

DanFaggella.com is where I keep a record of my press, interviews, and latest presentations – and where I explore ethical implications of AI (see essays), which is what I care most about. Feel free to be in touch on social, or through the “Contact Me” form on this page.