Machine Learning is the technology of identifying the possibilities hidden in the data and turning them into fully-fledged opportunities. Coincidentally, opportunities are the things that fuel business operation and help stand out among competitors.
It is crucial to understand how machine learning algorithms are applied in various fields to get the kind of results that would lead to legitimate business advantages.
In our previous articles, we have covered different aspects of machine learning technology. Now is the time to look at major machine learning applications and the benefits it brings.
Understanding the big picture is a requirement for any company that wants to succeed in a chosen field. Data analytics is one of the preeminent tools that makes it possible. In essence, data analytics is a three-fold process. It involves:
- gathering data from different sources,
- extracting the valuable insights out of it
- presenting it in a comprehensive manner (i.e., visualizing).
Machine learning algorithms are applied at various stages to secure the efficiency and the accuracy of the process.
- The clustering algorithms are used to explore the data;
- The classification algorithms are used to group data, sift through it and get the gist of it;
- Dimensionality reduction algorithms are used to visualize data, i.e. show it in a coherent form.
Essentially, these data analytics algorithms construct a robust framework for quality decision making.
As such, data analytics is used practically in every business aspect of business operation.
Let’s run down the most common.
- Sales and operations planning tools - as a unified dashboard from which you can monitor the operation in general and in detail. In other words, it is a tight system that uses data analytics in full scale.
- Product Analytics - as a center for the information regarding the product use;
- Customer Modelling and Audience Segmentation - data analytics is used to identify relevant audience segments, and to define and describe subcategories of the customers. With predictive analytics - it is also capable of calculating possible courses of actions for different kinds of users in certain scenarios.
- Market Research / Content Research - as a set of tools to describe an environment around you and get to know better what the current market situation is and what kind of action should be taken to make the most out of it.
Read also: Machine Learning for Mobile Apps
Machine learning for Predictive Analytics - Stock Market Forecasting, Market Research, Fraud Prevention
When it comes to gaining a competitive advantage with machine learning - Data Analytics is one side of the coin. The other side of the coin is predictive analytics. That’s where machine learning comes in full swing.
You see - it is one thing to get the data from different sources in one place, to extract insights and show the thick of it. It is process automation with some fancy tricks. It is a completely different thing to look at what the future holds and plan your moves accordingly.
That is what predictive analytics is for. Here’s how it works.
- The prerequisite feature of data is patterns.
- There are sequences of patterns that can be explored if you have enough information about the behavior of the pattern. This information is often called “the historical data.”
- Based on extracted insights, the algorithm can build an assumption of what may come next and determine the probability of a certain turn of events.
For example, let’s take the stock price. The price of a particular stock is known to be volatile around certain mark due to a variety of factors. These influence of these factors over time are taken into consideration upon calculating the further volatility of the price and planning further actions.
Overall, predictive analytics is widely applied in the following fields:
- Supply Chain Management - used to control the product’s flow. Commonly used in retail commerce and eCommerce to handle the product inventory’s demand and supply routine intact.
- Stock Forecasting is one of the purest uses of predictive analytics. You have numbers, and you need to compute the volatility of a certain number figure in correlation with a variety of incoming factors.
- Recommender engines and content aggregators apply predictive analytics to make assumptions of the relevant content based on the preferences, intent, and behavior of the user. The content suggestion is one of the most basic types of service personalization on a consumer level.
- Fraud Prevention - machine learning and predictive analytics give the power of foresight that can assist in exposing the fraudulent activity. In addition to that, it can provide a complete picture of the perpetration. The thing is - the majority of online fraudulent activities are made with the assistance of automated algorithms. These algorithms work in patterns, which can be extracted and predicted from the data. Predictive machine learning algorithms are used to expose spam messages, account hijacking, fraud payments, bot traffic, and other types of digital fraudulence.
Every user loves when the service delivers exactly what the user wants and then some. That’s a foundational element of user engagement and a step towards building a strong relationship between the product and its user.
Things can get even better when the said service is tailor-made for the needs and the preferences of the end users. That’s personalization in action, and there is a lot of machine learning involved.
Personalization makes the most of available user data, calculate the possibilities, and turn them into a valuable asset of the business operation.
These days, personalization features are widely used in different services to
- increase user engagement to the service;
- make the whole user experience much more efficient and fulfilling.
To make that happen, regression machine learning algorithms are applied.
- The methodology is similar to predictive algorithms, but instead of building the assumptions of certain courses of action, personalization mechanisms rearrange the presentation of the service according to a particular user “state of things.”
Here’s how it works.
- While using the service, users are leaving a detailed history of preferences, actions, intents, comments, and other stuff. This information presents a user through the content he consumes and the way he consumes it.
- The content has certain features that describe its value - topic, category, type, color, weight, time of publication - the list goes on.
- The values of different pieces of content create a grid of relations between different types of content.
- Via machine learning - user information and content information are compared and matched together.
- The result of this operation is an assumption of what else the user might like in case if he likes or dislikes a particular piece of content. It is another grid of relations that overlaps with the content.
- As a result, the user gets the service that is designed according to his preferences.
Overall, personalization is used in the following fields:
- Content personalization - arranging the newsfeed or search results according to the preferences of the user and his interaction with the content.
- Product suggestions - based on the preferences and actions of the user combined with the relations of the piece of content to the suggestions (for example, products that go well combined - like socks and sneakers).
- Advertisement personalization - this one is a bit trickier as it involves dual content inventory. You have the website’s content inventory, and you have ad inventory. This enables the presentation of relevant ads throughout the session.
Natural Language Processing machine learning algorithms get into the nitty-gritty of the words and extract the stuff of value out of it. And since the text is a raw state of data - it is applied in one form or another practically everywhere.
To do that, NLP applies a wide scope of machine learning algorithms to enable its operation.
- Clustering algorithms are used to explore texts
- Classification algorithms are used to analyze its features
- Classification and clustering involve parsing, segmentation, and tagging to construct a model upon which further proceedings are handled.
- Regression algorithms are used to determine the relevant output sequence upon text generation.
As a result, the algorithm is capable of extracting insights out of the text and producing the original output.
Business wise, Natural Language Processing Machine Learning algorithms are used in the following fields:
- General Text analysis. NLP is applied for a wide array of content categorization, topic discovery, and modeling operation. This includes include parsing the text for key terms, studying semantics, and determining the context. (This technique is used by search engines like Google or DuckDuckGo and also by content marketing tools like Buzzsumo);
- Marketing Content Copywriting Plagiarism detection is similar to text analysis, except it specifically looks for anomalies. The text is broken down to key parts that are checked for matches elsewhere. Machine learning algorithm performs a comparative analysis of the text. It can be a direct comparison with a different document or web crawled multi-source comparative analysis. Copyscape is one of the tools that perform such operations.
- Text summarization for news digests, user profiles, banking information, and research summaries. In this case, NLP clustering and classification machine learning algorithms are used to explore the text’s semantics and context and determine the key points of the text. Then the dimensionality reduction ML algorithm reiterates the text into a condensed form.
- Text generation for conversational interfaces, automated reports, and content generation. In its core, the NLP model feeds on the knowledge base (i.e., its practical inventory) and uses it as a foundation for the creation of the custom texts. The knowledge base is mapped out by clustering and classification ML algorithms.
- In case conversational UI, the process involves input analysis and subsequent output generation (or performing the requested action).
- In the case of report generation, the model feeds off the analytics platform and visualizes in the text form via dimensionality reduction ML algorithm (such an approach can be seen in Salesforce).
- The automated content generation works similarly, except the form of presentation is adapted to the specific medium. A good example of this is automated emails and Twitter repost-updates;
- Legal / Medical Text translation applies general text analysis via classification and clustering algorithms and then comparative analysis in a different language to build a correlation map between languages. Then the entire thing is analyzed through corresponding reference bases in respective languages. As a result, the text’s context and semantics are transposed onto another language while retaining its essence. What is translated is the meaning of the words and not the words itself. The most prominent example is Google Translate;
- General Purpose and field-specific text correction is an expansion of text analysis. Just like plagiarism checker, text grammar correction applies an anomaly analysis of the text while referencing to the knowledge base, aka the grammar. The anomalies are then flagged or corrected. In the case of field-specific text correction, there is also an additional vocabulary of the relevant terms involved. The most prominent example of this is Grammarly.
Sentiment analysis is the next step in the evolution of data analytics platforms. It deals more directly with the way customers interact with the product and express opinions about it.
Sentiment analysis can be used to explore the variety of reactions from the interactions with different kinds of platforms. To do that, the system uses unsupervised machine learning on top of basic recognition procedure.
Here’s how it works:
- The words, in general, have a specific designation. To put it in broad terms, the word “good” is a positive term, while the word “bad” is negative.”
- Then you have certain words that describe the quality
As such, it is one of the major machine learning tools for the product- or service-based company that heavily relies on the power of the brand and its public perception.
Sentiment analysis algorithm is designed to get behind the words - into the mood, opinion, and, most importantly, an intent. This makes it a viable tool in the following fields:
The use cases for sentiment analysis include:
- Brand management - the most basic way of using sentiment analysis. Involves a web scraper framework and an exploratory algorithm that assesses the found mentions and the context in which the mention was made.
- Extended Product Analytics - sentiment analysis is used to explore and analyze the emotions around the product, including customer feedback, reviews, and also general brand mentions.
- Aspect-based sentiment analysis for Audience Research. Since Sentiment analysis digs up extra detail about the user’s attitude towards a certain product or theme in general - it can be used to expand the definition of the audience segments and develop more precise approaches to them. Such features are now tried out by Salesforce’s Einstein Platform services.
- Customer Support is another huge field for sentiment analysis. In this case, the sentiment analysis machine learning algorithm helps to navigate between the product’s knowledge base and customer’s issues. In addition to that, customer support with sentiment analysis can provide extended analytics regarding user’s recurring issues, their satisfaction of the service and general attitude towards the product;
- Sentiment Analysis is part of the personalization framework in Recommender engines. In this case, sentiment analysis is used to bring nuances and elaborate on the suggestions of the content. This boosts the efficiency and variety of suggestions. The best examples of this practice are Netflix’s“you might also like” section and Amazon’s “people also buy” subcategory.
Computer Vision is one of the most exciting fields of machine learning use. If text is a more or less raw state of data - images require a different approach.
- Computer vision algorithm describes image content via matching the features of the images with the features of available samples. The image is broken down to key credentials that are used as reference points.
- The process looks like this: a photo of a bicycle is recognized as such because the credentials of the sample photo on which the algorithm is trained and the credentials of the input photo correlate.
Computer Vision and image recognition, in particular, is widely used throughout different industries. Let’s round up the most relevant applications:
- Visual Search features are widely applied in Search Engines like Google and eCommerce marketplaces like Amazon and Ali Express. In essence, the visual search algorithm works similarly to the textual search. The image is broken down by ML algorithms to key credentials that are compared with the credentials of the sample base. In addition to that, NLP algorithm processes image metadata and other textual input (such as the context in which the image is placed).
- Face Detection is one of the cornerstones of social networks like Facebook and Instagram. The methodology behind face recognition is similar to visual search, except the process consists of two aspects:
- First, you have general image recognition of the shape of the face.
- Then classification algorithm matches the credentials of the available user base and finds the person whose appearance matches with the one on the particular photograph.
The key algorithms behind computer vision are a combination of unsupervised and supervised machine learning algorithms.
- First clustering algorithm explores features of the sample dataset and subsequently classifies it.
- The different samples of the object provide variables to the description of the feature and increase the accuracy of the recognition.
- Then the unsupervised clustering algorithm is used to explore an input image.
- After that supervised classification algorithm kicks in and matches the features and thus performs recognition of the image.
Speech recognition is something of a frontier these days. In a way, the technology is similar to computer vision; it just took more time to figure out how to analyze sound productively. With the emergence of conversational interfaces and mass adoption of virtual assistants - speech recognition turned into a viable business opportunity.
The major fields where speech recognition is applied include:
- AI Assistants / Personal Assistant apps uses natural language processing to recognize an input query, perform it and/or compose the output message. In addition to that, the assistant has a database of sound samples to perform the message. An excellent example of this is Google Assistant, Alexa or Siri;
- Sound-based Diagnosis is very similar to image recognition. The sound is broken down to credentials, which are then matched. There is a comparative database of sounds to detect anomalies and suggest a possible cause. This technology is used in healthcare and automobile industries to examine the patient or product and determine the root of the problem more efficiently.
- Text-to-speech / speech-to-text automatic captioning - this is basic speech recognition application. The technology is commonly used by specialized tech-to-speech services (like Transcribe) and also virtual assistants (Alexa, Siri, Cortana, et al.) In addition to that, this type of recognition can be used to augment audio-visual content with captions (for example, YouTube or Facebook automatic subtitling features).
Machine learning is one of those technologies that require a clear understanding of its capabilities to use to its maximum effect in the context of the specific business operation.
There are many ways you can apply machine learning for your purposes and our business analysts as well as ML specialists can help you identify all the ways machine learning can bring value-added to you as well as your customers.
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