How to Apply Machine Learning to Business Problems: Part 1

By “Daniel Faggella – This article was originally listed on TechEmergence. This post breaks the original post into 2 Parts.  Stay tuned for Part 2 in upcoming weeks.

It’s easy to see the massive rise in popularity for venture investment, conferences, and business-related queries for “machine learning” since 2012 – but most technology executives often have trouble identifying where their business might actually apply machine learning (ML) to business problems.

With new AI buzzwords being created weekly, it can seem difficult to get ahold of what applications are viable, and which are hype, hyperbole or hoax.

In this article, we’ll break dow categories of business problems that are commonly handled by ML, and we’ll also provide actionable advice to begin an ML initiative with the right approach and perspective (even it’s the first such project you’ve undertaken at your company).

Best of all, we’ll reference real business use cases, along with quotes and perspectives about “how to solve business problems with ML” from our network of AI researchers and executives. By the end of this article, you’ll have a good idea as to whether any of your present business challenges might be handled well with ML.

Note: At the bottom of this article, I’ve listed a basic glossary of ML terms in simple language. If you find a phrase or term in this article that you don’t understand, see the glossary below, or contact us ( if you’d like us to be more clear about a concept in this piece.

What Kinds of Business Problems Can Machine Learning Handle

1 – Is the prediction you’re trying to make (or decision you’re trying to make) complex enough to warrant ML in the first place?

If it’s possible to structure a set of rules or “if-then scenarios” to handle your problem entirely, then there may be no need for ML at all. Also, if there is no precedent for any successful outcome applying machine learning to the specific problem to which you’re developing, it may not be the best foray into the ML world.

For illustrative purposes, it will be helpful to list a number of well-established business use-cases for machine learning so that you (the reader) can churn up your own application ideas:

  • Face detection: It’s incredibly difficult to write a set of “rules” to allow machines to detect faces (consider all the different skin colors, angles of view, hair / facial hair, etc), but an algorithm can be trained to detect faces, like those used at Facebook. Many tools for facial detection and recognition are open source. Below is a video of facial recognition using MATLAB:
  • Email spam filters – Some spam filtering can be done by rules (IE: by overtly blocking IP addresses known explicitly for spam), but much of the filtering is contextual based on the inbox content relevant for each specific user. Lots of email volume and lots of user’s marking “spam” (labeling the data) makes for a good supervised learning problem.
  • Product/music/movie recommendations – Each person’s preferences are different, and preferences change over time. Companies like Amazon, Netflix, and Spotify use ratings and engagement from a huge volume of items (products, songs, etc) to predict what any given user might want to buy, watch, or listen to next.
  • Speech recognition – There is no single combination of sounds to specifically signal human speech, and individual pronunciations differ widely – machine learning can identify patterns of speech and help to convert speech to text. Nuance Communications (maker of Dragon Dictation) is among the better known speech recognition companies today.
  • Real-time bidding (online advertising) – Facebook and Google could never write specific “rules” to determine which ads a given type of user is most likely to click on. Machine learnings help to identify patterns in user behavior and determine which individual advertisements are most likely to be relevant to which individual user.
  • Credit card purchase fraud detection – Like email spam filters, only a small portion of fraud detection can be done using concrete rules. New fraud methods are constantly being used, and systems must adapt to detect these patterns in real time, coaxing out the common signals associated with fraud.

(For lists of specific industry applications, explore our other articles about ML in marketing, ML in healthcare, ML in robotics, and ML in finance)

2 – Do you have new data and clean data?

“Clean data is better than big data” is a common phrase among experienced data science professionals. If you have reams of business data from years ago, it may have no relevance today, particularly in fields where the basic business processes change drastically year-over-year, such as mobile eCommerce). If you have reams of unstructured and disjointed data, you may have too much “cleaning” to do before you can ever get around to learning from the information collected.

UBER’s Head of Machine Learning Danny Lange once recommended that companies just starting out in machine learning should begin by applying supervised machine learning to historical data. Find data that’s already clean and relatively recent, and use labeled training data to start finding insights.

Note that in a rapidly-changing field, newer data is positively required. For example, if you run a door delivery service for pet supplies, and your app, prices, product offerings, and service areas have changed significantly over the last six months, you will need much more recent data to learn from than, say, a company selling homeowners’ insurance in Montana. If data is not related to the relevant trends and nuances of your current business, it is unlikely to glean predictive value.

3 – Does your data have existing labels to help a machine make sense of it?

While unsupervised learning (see glossary below) allows for a wide degree of applications in making sense of data without labels, it’s usually not advised for companies to “jump into” ML with a first application in unsupervised learning. The low-hanging fruit for an ML use case is likely to spawn from its historical, labeled data. Below are some examples that might help a reader garner new ideas:

  • Facebook had millions and millions of tagged human faces on its platform, faces that were already associated with an individual person. This gave Facebook the ability to train algorithms on a tremendous volume of labeled data, with millions of faces in all kinds of lighting conditions and from various angles, allowing the algorithms to be highly refined and attuned to identifying specific human faces.
  • Google serves billions and billions on search results and can gauge the usefulness and relevance of its search results based on click-through rate of its top lists, page -load time, time-on-page from a specific visitor, and many other factors. It would be impossible to find a set of hard and fast rules for showing the right search results, so Google’s algorithms learn what the best options will be based on real-time engagement from billions of daily searches.
  • Credit card companies like CapitalOne are faced with a huge volume of chargebacks and reportedly fraudulent purchases each day. By finding connections and patterns across types of purchases, locations of purchases, and types of customers, CapitalOne can use the “labeled” instances of fraud to predict other transactions that are most likely to be fraud. Anomaly detection plays an important role in various security and fraud applications; below is a short “anomaly detection primer” video from Numenta’s YouTube channel:
  • An eCommerce company with a massive volume of customer support emails will have a lengthy record of support tickets that were labeled “refund requests”, “technical issues”, “delivery issues”, among others. The company may choose to develop an ML system to instantly label incoming emails, transcribed phone calls, and chat requests with the proper support issue “type.”

(See the comments from the TechEmergence network for more ideas about using labeled data)

4 – Can your solution to this problem afford for some allowance of error?

ML might be thought of as a kind of “skill”, in the same sense that one might apply the word to human beings. A skill that’s alive, adapting, growing and informed by experience. For this reason, an ML solution will often be incorrect a certain percentage of the time, especially when it’s informed by new or varied stimuli. If your task absolutely cannot allow for any error, ML is likely to be the wrong tool for the job.

An example of an application that cannot allow for error might be an application that aims to read the amount of an invoice or bill and then pay that invoice or bill. One letter difference or one number difference could mean overpaying your bill by 10x the original amount (if the decimal was interpreted to be in the wrong place), or sending money to the wrong company (if an invoicing company name isn’t registered exactly).

In a case like above, some degree of ML might help with “bucketing” different types of bills or invoices, but the final decision to enter the payment amount and send a payment would likely require an accountable human.

As an interesting caveat, there is a San Francisco-based startup called which is aiming to use natural language processing and machine vision for real and pay bills, albeit it pulls humans into the loop before sending funds.

Quotes from the TechEmergence Network:

In order to gain additional perspective on the issue of “picking a business problem for machine learning”, we decided to reach out to our network of previous AI podcast interview guests for additional guidance for our business readers:

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

“Any business problem where you have hard data, variability, and a large number of examples.”

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

“1. There is a lot of savings to be made by companies.”                                                                                                      “2.  Predictions require many variables, complicated nonlinear relationships between them and in some cases are highly stochastic. So it is often only algorithms that can learn those relationships. Humans alone would have a hard time.”

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

“The best problems are those in which there is a very large, historical data set that includes both rich features and some kind of direct feedback that can be used to build and algorithm that can be implemented and tested easily and will either decrease operational costs and /or increase revenue immediately.“

Dr. Ronen Meiri — PhD, Tel Aviv University: CTO & Founder, DMWay

“Most business problems can be appropriately addressed using two machine learning methods:

1st: ‘What will likely happen?’ and                                                                                                                                      2nd: ‘What is the future expected value of …?’.

The first is a classification type problem that includes classifying who is likely to Churn, Default, Buy, Sell among many others use-cases.

The second question is an Expected value problem that is solved by regression and gives accurate predictions for a variety of use cases like Pricing Optimization and predicting Life Time Value.”

Peter Voss — CEO, AGI Innovations Inc

(To begin, Peter quotes Dr. Robin Hanson, Professor at George Mason University: “Good CS expert says: Most firms that think they want advanced AI/ML really just need linear regression on cleaned-up data.”)

“I think that most businesses don’t justify the investment in ML/DL (of course, ML means many things). Cutting edge stuff that everyone is talking about requires a lot of data and expertise, and is static – i.e. it needs to be retrained when data or categories change.”

Linear regression is one of the oldest, simplest, and widely used machine learning models. Some researchers contend that many intermediate prediction problems may need little more than this basic approach, at least initially.

Image courtesy of MathWorks.

Peter’s comment is apt, and shouldn’t be taken lightly. When ML is sought out because it’s hip and popular, it’s unlikely to yield significant results. Find the tool that best suits the needs of your bottom line; there’s a high likelihood that ML may not be the solution you need to meet your business or growth goals.

This concludes Part 1.  Part 2 is the second major section of this guide.  Stay tuned for Part 2 coming in the next few weeks.

Glossary of Machine Learning Terms

Unless otherwise noted, definitions have been sourced from

  • Machine Learning (ML) – Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions. (definition taken from our “What is Machine Learning?” guide)
  • Supervised Learning – The machine learning task of inferring a function from labeled training data.
  • Unsupervised Learning – The machine learning task of inferring a function to describe hidden structure from unlabeled data.
  • Classification – Identifying to which of a set of categories a new observation belongs, on the basis of a training set of data
  • Regression – A statistical technique for estimating the relationships among variables (includes linear regression, logistic regression, and other approaches)
  • Algorithm – A self-contained step-by-step set of operations to be performed. Algorithms perform calculation, data processing, and/or automated reasoning tasks.
  • Natural Language Processing (NLP) – A field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages

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:

Image credits: ExtraordinaryCEO 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. 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.

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