Machine learning is undoubtedly sophisticated and advanced technology. For someone who isn’t a data scientist, machine learning specialist, or has nothing to do with artificial intelligence, it may even sound like witchcraft. However, from time to time, there is a need to explain exactly what machine learning is about and why it is beneficial to a company. For instance, if you talk with your prospect, you have to explain to them why they ought to invest in machine learning consulting. In order to do that convincingly, you have to master the entire idea of machine learning and be able to talk about it in plain, understandable language. This is exactly what we will do in this article. We will discuss the machine learning basics with product and project managers in mind.
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If you work in an AI company and you are not a data scientist or a machine learning specialist, but you do communicate with clients or aforementioned machine learning specialists, you have to know this discipline inside out. Not to work on it in the future, but to explain to your client or co-worker what they are spending money on or working on. And, even more importantly, why it is worth their investment. It is particularly essential if you are a product manager or project manager.
Basics of Machine Learning
So, machine learning for project managers. Firstly, you have to understand thoroughly what is the general idea behind machine learning. We’ve been working on the ML projects for many years now, and we can simplify this matter to one, maybe not so short sentence:
Machine learning focuses on the development of AI programs and applications that can access data, learn it themselves, and use it to speed up or facilitate human work.
In other words, the machine learning algorithms process your data and give you an answer or a solution with no human assistance needed. It happens much quicker compared to human labor and, in numerous cases, the result is also more accurate. The key benefit that machine learning offers is saving time. And it goes further–many machine learning projects could have never been executed without this technology! Therefore, it’s so important to be able to explain the machine learning basics to your potential client. To make them understand that ML can really introduce their company to an entirely new level of development.
Examples of how Machine Learning is applied to business
Let’s examine a couple of examples from various industries and disciplines:
- Machine learning is extremely helpful in the drug design process. The algorithms search for the perfect chemical combination to create an effective drug for a given disease. It happens much faster and requires just a little human time and attention.
- Machine learning can help you in finding the most suitable candidate for the opening in your company. It can scan hundreds of resumes within seconds and provide you with a short list of the best offers. Isn’t that a huge time saving for your HR department?
- ML can be equally as helpful in marketing. It can help you predict sales and income in the following month, or help you in establishing which marketing strategy is the most efficient one.
- If you have a video surveillance system in your company, the machine learning applications can help you noticeably in scanning the recordings in order to find any abnormalities, just to mention an intruder or a security breach.
In all of these cases, human workers could do the same or similar jobs. But the computer works much more quickly, never gets tired, never asks for a break, never goes on sick leave and willingly works 24-7. If you interested in topic further reading can be found in our article Machine Learning in manufacturing
Where does machine learning find an application?
Nowadays, machine learning finds applications in numerous fields, just to name HR, marketing, finances, business development, healthcare, pharmacy, gaming industry, motor industry, etc. Over time and improvements in this technology, machine learning finds more and more applications. How can it be used? As always, we have a couple of examples in store:
- Business: video surveillance, virtual assistants, e-mail filtering, online customer service, chatbots, face recognition, resume selection
- Marketing: predicting market trends, product development, and promotion, marketing strategy assessment
- Motor industry: traffic predictions, license plate recognition, pedestrians’ security
- Medicine: medical image analysis, drug development
- Finances: fraud detection, loan debtor assessment, financial forecasts, predicting economic trends/crisis
And the list goes on. You have to think of ways to present machine learning to the project managers, so they understand the benefits behind it. This is particularly important when you talk to product managers interested in machine learning. They are interested in solid data and improvements which machine learning offers. No wishy-washy will do. What you need to do is to prepare yourself. Find out as much data and information relevant to managers as you can. Select the crucial parts and think about how to present them. This way, you will get the most of presenting the basics of machine learning.
How is machine learning applied to business?
While we are at the basics of machine learning for project managers, it is valid to understand how it can be applied to business. There are lots of possible applications. Consider a few of them.
THE BIG DATA CONSULTING SERVICES
The big data consulting is a sophisticated service based on the process of examining vast amounts of data. Every large company has to deal with it! This service uncovers useful business-wise information and hidden correlations and connections within it. It has been designed to help organizations in making more apt decisions. So, if you are talking to a manager who’s interested in machine learning, you can say that big data analytics consulting helps their company in dealing with data they possess, and therefore grow faster and improve their decision-making process considerably.
The text extraction and enhancement methods are applied with the help of machine learning algorithms. The extracted text is collected from the image and transferred to the given application or a specific file type. For instance, it can be used for a car license plate recognition. This has a lot of possible applications, from police databases (data obtained from speed cameras) to private parking lots that open the barrier after a license plate is verified.
It is a set of tools and techniques used to communicate data by presenting it as visual objects (e.g., points, lines, or bars) contained in graphics. Every diagram is a data visualization tool. So is every infographic, chart, and dashboard. There is plenty of data visualization almost everywhere you go and what you watch. In a shop, on an airport or train station, on a news report, in a corporate periodical or operational report, in a prospect. Everywhere. Data visualization brings big data to ordinary people and helps to understand trends and correlations within it.
CUSTOMER CHURN PREDICTION AND SALES PREDICTION
Today every business utilizes a lot of data about its customers. The real value is to use it in a way that is beneficial to the company. The key is to find the most important factors and correlations amongst this vast amount of data. In order to do so, you can transform data, create algorithms, and automate flow, which will give you favorable results. These techniques could be used in an up-selling system, CRM, or marketing automation software. If you cooperate with the project managers, you could show them that machine learning predictive systems can help in estimating the level of customer churn, but also the sales level for the coming month or year. All of that is possible on the basis of previous sales and customers’ behavior on the website.
Read also our article: “Customer Churn Prediction using Machine Learning (How To)“.
This is another outstanding machine learning application, particularly for eCommerce companies. In general, it’s the practice of flexible pricing products based on factors like the level of interest, demand at the time of purchase, time of the year, and many more.
This requires a lot of data about customers’ behavior, at least from the past year, but once you possess such knowledge, you can harness machine learning algorithms to implement dynamic pricing to your company and, therefore, maximize your revenue.
What problems does machine learning solve?
As you know from one of our previous articles, machine learning can be divided into two main categories:
Both of these categories are for different purposes. The supervised machine learning methods are used when you want to predict or explain the data you possess. For instance, if you want to predict future churn prediction levels–you ought to use the supervised learning method. On the other hand, the unsupervised machine learning methods find hidden patterns or intestine structures in data. These methods can be used for customers’ segmentation or product structurization.
To sum up, If you want to explain the basics of machine learning to the managers, you ought to concentrate primarily on the benefits that this modern technology entails. It really is a real game-changer and can significantly improve the way a company works. One of the core problems with machine learning is that it’s still a new technology, not yet implemented in the majority of corporations and institutions. Therefore, lots of people don’t understand what does this technology entails and how it can help them.
This is the point where we come in. Addepto is an AI consulting company. Our role is to bring modern and sophisticated technologies, such as business intelligence, AI, ML, and data engineering services to the business world. We act either as a counselor and as a technological partner. Cooperation with us means that you get everything that’s needed to take every possible advantage of machine learning.
As always, we encourage you to get in touch with us! We are always keen to talk with you about modern technologies that change the way your company works!