How to Use Machine Learning to Solve Business Problems

Deloitte predicts that in 2021 enterprises will spend $57.6 billion on machine learning. That’s 5 times more than in 2017. Why is this technology so attractive?

Companies applying machine learning in business say that a key benefit is the ability to gain a competitive edge. Also, machine learning is becoming much more accessible: not only IT giants but even startups can use it to solve business problems.

So, what’s the best way to start with ML?

List of contents

What is Machine Learning?

For a better understanding of how to apply machine learning to business, let’s speak briefly about the term itself. Until recently, computers could be used for solving business problems only if explicit rules were written for them. Сomplex “if-else” instructions development took time and significant effort. It was critical in rapidly changing fields: the rules became outdated before the computer system was ready to use.

Nowadays machine learning allows avoiding explicit programming and making the computers find out the rules by themselves from large datasets. Moreover, machine learning enables systems to improve all their life and adapt to the uncertain world.

The heart of machine learning systems is algorithms. Some of them are quite specific, while others, like decision trees and neural networks, are very general. The algorithms were developed a long time ago, but today’s gargantuan amounts of data – along with modern computer power – allows increasing the effectiveness of the algorithms significantly and applying them in business.

How to apply machine learning to business?

There are many problems that every company faces. Competing for the market and client loyalty, increasing performance, managing the risks and acting under uncertainty are real challenges for any entrepreneur. Let’s explore some ideas of applying machine learning to business.

Classify your data

Machine learning tools can quickly classify different types of data. One of the famous examples is Shazam, which automatically assigns tags and genres to songs. It may sound quite easy, but the issue is that every genre varies significantly among different styles – for example acid jazz, smooth jazz, big band, Dixieland, ragtime. Moreover, genre identification is sometimes very subjective for every listener.

To overcome this, Shazam’s developers worked out a model that is able to identify a genre by a song snippet. The model was trained on the songs separated by genres manually. The model found out the “heat map” of each music genre. The visualization is presented in a graph below.

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As a result, the model is able to define a genre with at least 90% accuracy by comparing a new song’s features to existing heat maps. It is clear why Shazam decided to apply machine learning in business: taking into account the millions of tracks in the Shazam library, it would be completely impossible to solve the task using only humans.

It’s easy to imagine more common machines learning use cases. For example, one of SoftMedialLab’s clients provides accounting services to a great number of grocery stores. He wanted to make an aggregated sales reports since he had some ideas about new analytical services for his customers. The problem was that every store has its own product grid, so the same item was named differently in each store.

To solve the task, SoftMediaLab developers prepared the data carefully, labeled part of it and fed it into an ML algorithm. After training, the system learned how to divide the products into the categories. Now, it works even when a store adds new products to its grid.

Other classification examples are medical diagnosis, image categorization, clients segmentation, and so on.

Predict the future

The one thing that’s always certain in business is uncertainty. But machines learning algorithms allow for the predicting of the future, within a given degree of probability, of course. For instance, a bank wants to know if a client is going to travel abroad or buy a car next month. In this case, client transactions are a very good source for a machine to find trends and predict future behavior.

Companies that know how to apply machine learning to business will share their experience from time to time. Uber uses ML algorithms to predict arrival times, pick-up locations, and food delivery times for UberEATS. Applying the machine’s learning models to data from millions of trips allowed them to increase customer satisfaction.

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Another example is house price prediction. Say, you run a real estate agency and you want to make pricing the houses an easier task. All information about the houses sold in your area over the last few years – the prices, number of bedrooms, shop accessibility, and so on – are a good “food” for a machine’s learning solution.

Predictions are also used in recommendation systems based on a client’s previous activity. For example, Google search engine analyzes previous user searches to offer recommendations and suggestions. Google’s algorithm, RankBrain, interprets the queries that the engine has never seen before, providing quick and accurate search results.

Retailers, like Amazon, and entertainment services, like Netflix, apply machine learning in their business processes to predict the purchases or the content a user would like. As a result, engagement and income increases considerably.

Find out similarities and anomalies

Insights are one of the most exciting benefits of using machine learning in business. Algorithms are able to find hidden patterns and detect anomalies that a human would never see.

For instance, a machine learning app can divide your customers into groups. It may look similar to normal classification, but in this case, you don’t label the data. This means that a system splits the clients without given classes. As a result, you may find out client-types you’ve never thought before. Todd Yellin, vice president of product innovation, says that Netflix’s viewers fit into “a couple of thousand” taste groups. Providing a highly-personalized service allows retaining the audience and attracting new subscribers.

This is how Netflix’s audience grows

Another kind of machine’s learning use case refers to fraud detection. PayPal implements deep-learning techniques to find anomalies in massive customer data sets. An algorithm understands the difference between friends buying tickets together and a thief making similar purchases with a list of stolen accounts. As a result, PayPal’s fraud rate is 4 times lower than the U.S. average.

Visualize your data

Visualizing data helps to make better decisions. Machine learning business applications are ideal for companies that have constant data streams. The real-time visualization lets you see what is happening exactly at all points of your production chain and understand how new factors affect existing processes. On top of that, you can see the anomalies straight away and react quickly.

There is a number of ready-to-use machine learning tools, like Oracle, Tableau, Google chart, and others. They allow you to visualize your business data and stay on top of all changes.

Oracle Data Visualization

Kinds of data you need

Machine learning depends not only on big data, but also on the right data. To get good results from a machine-learning business applications, it’s necessary to provide the system with a high-quality data set. So, what data can best work for you?

New data

You may have a large quantity of data, but 90 percent of it might be too old and useless. It is especially critical in rapidly changing fields. Say, you run a delivery service for clothes and your prices, product offerings and app have changed significantly over last year. You will need newer information than, say, a house insurance company.

If the data is not connected with current trends in your business, there is no sense using it in machine learning.

Clean data

“Clean data is better than big data”, machine learning experts often say. A great amount of unstructured data needs too much cleaning before a development process can be started.

Cleaning means finding and correcting errors in the data, e.g. incorrect values or missing entries. Accurate and complete data decreases the risk of invalid conclusions.

Labeled data

For companies that have never dealt with machine learning in business, it’s recommended to start with supervised learning. You’ll be sure that an algorithm works fine because it is easy to verify.

Enough data

How much data do you need? The correct answer is: it depends. It depends on the task, the desired performance, the input features, the complexity, and so on. To be honest, it is almost impossible to give an instant answer to this question; that’s why special methods should be used.

ML process stages

Once you have a relatively new and well-prepared data set, the machine learning process can begin.

1. Choose an algorithm. Machine learning algorithms can be found in open libraries. One of the most popular and qualitative libraries is scikit-learn. Even for professional developers, it takes some time to figure out which algorithm is best suited for your task.

2. Feed the data into the algorithm. A machine learning system trains on a labeled data sets. After that, you can check how successful it is.

3. Evaluate the results. There are several different evaluation methods, and which one you use entirely depends on the task. Sometimes we just want to see how accurate the model identifies the classes or predict the values.

In some machine learning use cases, the matter is not only the number of wrong answers but the types of mistakes. For example, it’s better for a cancer prediction algorithm to mistakenly diagnose cancer than to miss it – the consequences of the second mistake are much worse.

4. Improve the results. To apply machine learning in business successfully, it is obviously important to gain the desired quality of results. This means “playing” with the algorithm characteristics and the input data features. Usually this is quite a time-consuming stage of the development process.

5. Integrate the algorithm into business processes. Once the algorithm is accurate enough, it’s time to develop a user-friendly app. You may start from a minimum viable product (MVP) to test your ideas and estimate the benefits that machine learning can bring to the business.

More machine learning use cases
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Risks and limits

Like every complex technology, machine learning implies some risks and limits.

Can you afford an error?

The algorithms learn in the same way humans do. This means that a machine learning business application will be incorrect sometimes. If any mistake is critical for a particular task, it is better not to rely entirely on a computer.

For instance, if an application reads the amount of an invoice or bill and then pays it, an error is not allowed. It’s not necessary to refuse ML, but it’s reasonable to include humans in the loop.

Do you want an explanation?

ML systems often have a lack of “interpretability”, which means that an algorithm can’t explain its decision. You won’t know why this customer got a special offer and that one didn’t. Sometimes experienced developers can figure out the relationships, but some algorithms, like neural networks, are too complex to dive into.

Are there biases in your training data?

The algorithms may have hidden biases, derived from the training examples. For instance, if a machine trains on human HR decisions, it may learn to take into account gender or ethnicity. This is one more reason to take the initial data preparation seriously.

After all, we humans also make mistakes, have biases, and sometimes can’t explain our decisions. The advantage of machine learning algorithms is there ability to improve over time and give consistent answers when presented with the same data.

Check-list: how to apply machine learning to business

  • Think about a business problem you would like to solve.
  • Answer the questions:
  • Can I afford an error?
  • Do I need not only a result but an explanation also?
  • Is my data new and clean enough to correlate with current trends in my company?
  • Are there biases in my data?
  • Consult experienced data scientists to clarify the details and make an estimation of the development project.
  • Calculate ROI to understand the effect of using a machine learning system.
  • Decide if you want to:
  • Pay an outsource software development company and build an app on a turn-key basis.
  • Hire skilled dedicated developers and manage the process on your side.
  • Search for data scientists by yourself and hire them as staff members.
  • Build an MVP to find the best solution.
  • Integrate an ML algorithm into your business processes.
  • Enjoy the results and continue improving the app to get even more.

Conclusion

Early ML adopters are getting demonstrable return on their investment. If we look at the top largest companies, we see that all of them have used technology such as machine learning to transform their businesses.

After all, applying machine learning in business is not as difficult as it might seem. The key point is to think carefully about the problems and the data your business has. Once you understand it clearly, consult professional data scientists, and find the best way to integrate ML into your business.

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