In the business world you cannot underestimate the power of information. Without appropriate knowledge about your target group, competition and market demand, marketing teams would not know where to begin as far as advertising is concerned. This is especially true nowadays, when clients’ attention has shifted to much more personalized shopping experience. Because of that, the marketing approach had to change – the company's traditional, internal information about customers is no longer sufficient to draw the right conclusions about their behavior and provide them with adequate customer service. Instead of treating clients as a group, you need to start treating them as individuals with different preferences. But how to avoid drowning in the sea of information? With a little help from machine learning systems.
Machine learning software, based on IT artificial intelligence, gives computers the ability to "learn" and allows process certain information in order to reach conclusions themselves. With time their performance on a specific task improves without the need for further programming. Instead of every action being pre-coded, the computer applies rules and data sets to perform complex calculations, organize and classify data, identify trends, discover hidden patterns, identify behavior and match data. These rules – algorithms – combine a statistical approach with the scale and speed of automated operation. The algorithms can be refined and taught in such a way that the categorization of statements is performed in a manner similar to an actual human drawing conclusions (but much faster). The main goal is the practical application of achievements to create an automatic system that can improve using the accumulated experience (data) and acquire new knowledge on that basis.
Machine learning emerged in the 1950s. One of the most interesting inventions during this time was Arthur Samuel’s machine learning program, which was able to play checkers. The software evolved and became capable of training chess players. IBM saw the potential in the program, and decided to improve it further. It was not until 1997 when they created the chess computer Deep Blue and challenged the chess world champion of that time, Garry Kasparov, to a match. He was defeated in a game that lasted just 19 moves. Kasparov accused IBM of cheating when the company refused him access to the history of previous Deep Blue games. Kasparov was not able to analyze his opponent's strategy, while the Deep Blue developers very carefully processed all previous Kasparov games and created the appropriate algorithms. The Russian chess player demanded a rematch, but IBM refused.
The company later faced accusations of promoting their own brand instead of a fair chess competition. Nevertheless, this experiment showed that an appropriate machine learning mechanism was able to perform on a previously unreachable scale. Now, let’s see how today’s version of this technology works in business.
How does machine learning work?
Machine learning systems allow computers to figure things out for themselves without being precisely coded. The software applies pre-configured rules to perform complex calculations based on data received. The analytical models are able to make decisions based on repeatable results, and find patterns hidden in data, which is extremely helpful to data researchers, engineers and scientist.
There are two main types of machine learning. The first one is unsupervised learning where the computer finds the hidden patterns in data or learns appropriate features. In this method, no labels are given to the learning algorithm, so the computer can find the structure on its own. It is used in business to group customers, for example, in analyzing purchasing behavior. The second type is supervised learning, where the computer is presented with input examples and the desired output. The task is to find a missing link between inputs and outputs. This is useful with handwriting/speech recognition, optical character recognition and spam detection, amongst others.
Machine learning in business
It is now time to consider the most important goals of machine learning. While some of them are useful for specific industries, the main features will help businesses in any sector:
- Personalized product recommendations based on shoppers’ browsing and purchasing history (introduced by Amazon, this function is becoming a standard for online shops)
- Control of medical equipment and recommendation of treatment
- Changing device parameters (e.g. equipment in factories can be automatically switched off when overheated)
- Message filtering (important in distinguishing legitimate emails vs. spam)
- Computer vision that reads images to visually impaired internet users (right now it is being researched by Facebook)
- Customer service chatbot
- Dynamic pricing based on lower/higher demand (services such as taxis, hotels and flights)
- Classifying pictures to given categories (e.g. interior, exterior, food and drinks photographs on Yelp)
- Fraud detection (separating money laundering from legitimate transactions)
- Facial recognition (already used in smartphones)
- Optical character recognition
- Detection of data breaches and cyber-attacks
- Error detection
- Search engines (analysis of massive databases)
- Accurate translations from one language to another
- Automatic navigation (intelligent cars, finding a way in an unknown place, controlling spacecraft).
You do not need to be tech-savvy to appreciate the benefits of machine learning in business. Let’s take a look at an example from everyday life. You register with a streaming service, such as Netflix, Spotify or similar. At the beginning, you will receive general recommendations about the most popular content available in the service. However, in time the recommendations will change, because the streaming service learns your preferences and suggests content to watch, listen or buy based on things you like.
The same rule applies to advertising. Companies adjust offers displayed online (social media, web browsers or just ordinary banners on websites) to your interests or past purchases and other Internet interactions. Machine learning systems are able to provide real-time monitoring and appropriate filtering from billions of social media entries, and to record specific trends in customer behavior in a specific business context. The most used function is the ability to identify the most commonly used words in a specific topic, which allows immediate response. Example? Post a status or comment about blue shoes or visit an online store, browse blue shoes, go to Facebook and you will not have to wait long before the same pair of blue shoes with matching bag show up on your timeline.
Machine learning makes human labor more effective. While machines automate tasks and reports the results, your employees can focus on creating content or other crucial marketing tasks that are often neglected. As far as creating ads is concerned, your company will stop overproducing marketing materials, thus saving lots of precious time. Using data processed by machine learning technology, you will target only the people whose behavior suggested they are interested in your offer, and are likely to be converted from shoppers to customers.
Last but not least, machine learning saves you money by reducing marketing expenses and cutting communication costs through automating emails, scheduling social media posts and ads.
With the increasing availability of relatively cheap and flexible computing power, machine learning technology is becoming far more accessible even to small and medium-sized enterprises. What is more, it reduces marketing expenses because it automates a lot of processes (data collecting, analysis, reporting, targeting ads, scheduling social media posts and ads, email automation, etc.). It also means that you will not be overproducing marketing materials, because ads will be seen only by the people you want to see them and who are most likely click on the targeted ad and buy your product.
In the future, machine learning systems will require less data to come up with satisfactory results, which means they will be able to learn even faster. Considering the ever-growing global data content this is a really optimistic prognosis.