Machine Learning (ML) and Artificial Intelligence (AI) are often considered the backbone of our future society. With the way these branches of data analysis and synthetic cognition engines are evolving, it is very probable that AI and ML will gain global dominance and serve and aid us in almost all facets of our lives- from business to healthcare. And while the road ahead is still long and littered with challenges, there are ways we can use ML and AI to our benefits, and today we will discuss how you can use ML to solve various business problems.
What is Machine Learning?
For the longest period of time, computers have been slaves to their rules. The if-else nature of programming made sure that computers did only what they were tasked to do but nothing more. However, ML strives to change that.
The foundation of machine learning are algorithms that let computers find these rules for themselves and constantly update and improve themselves according to uncertain or unplanned situations that they come across.
Instead of programming rules and giving a piece of software an identity, we work with algorithms such as neural networks and decision trees to teach computers how to make rules themselves.
Now the question that arises is, why now? Why are we able to do so much work in ML now when these algorithms have existed for a long time?
The answer is very simple: now we have data, in abundance. Thanks to growing internet connectivity, there is a trove of data available which can be used to train computers. With an abundance of data comes variety. As a result, ML devices can learn how to make better decisions according to specific situations.
Also Read: Machine Learning as a Service (MLaaS): Everything a Business Needs to Know
What Business Problems Can ML Solve?
ML can be a critical tool for your business. By using it, not only can you make the inner workings of your business more efficient and slash down a substantial amount of operational cost, but at the same time bring in more customers and improve and enhance your marketing strategy. Thanks to the massive pools of data of different shades, you can train your ML software to cater to the needs of different types of customers or make strategy according to their behaviour and nature.
Let’s see what business problems can ML solve in detail:
1. Data Entry
One of the most time-consuming and tedious tasks in the back-office is to enter customer or client data manually. An incredibly repetitive task, manual data entry requires zero human cognition, which means that employing someone to do data entry full time is not only a terrible waste of time, since humans are slower than computers and prone to make mistakes, but also a waste of manpower.
Instead of being involved in a task like this where his intelligence and decision making powers are not required, he can be put to someplace where he can make a difference.
This is where ML comes in.
Using predictive modelling algorithms and ML, the task of manual data entry can be automated. ML will also make sure that there is no data duplicacy or entry error and thus make your data pool more accurate
2. Financial Analysis
With so much data available to the businesses, it is hard to ensure its integrity. This is where ML can be of a lot of help. Using financial analysis, historical data can be checked for inconsistencies and made more accurate. Furthermore, ML is being used to detect insurance frauds, portfolio management, algorithmic trading, loan underwriting. Also, chatbots can be employed for customer service, care and sentiment improvement.
3. Predictive Maintenance
Manufacturers often invest in product maintenance and corrective measures. If their factories break down, they are looking at a complete or partial halt of production and at the same time a lot of time and financial overhead to get the problem fixed. This also means that the workers in the factory have nothing to do before the production resumes. Needless to say, this is a messy situation every manufacturer wants to avoid and here, ML can help.
Using predictive maintenance, you can analyse a factory’s historical data, workflows, feedback loop, etc. and find points of failure in the future. Now that you have this insight, you can get it fixed before it halts your production and get ahead of it. Also, you can study patterns in your factory. Using various visualization tools, you can see your entire workflow in front of you and see patterns that are hampering with your productivity.
4. Detecting Spams
As preventive measures evolve and improve, so do the problems. Spams have been around since forever and one of the most ancient uses of ML has been in detecting spams. Spam filters use neural networks, which are very much like human neural networks, and identify spams. Earlier, fixed rules were coded to identify spams but as we said, the problem evolved and now we have various types of junk mail, some of which are very harmful for the integrity of your system. Neural networks work to learn new spam types on a constant basis and filter them out from your regular mail.
5. Medical Analysis
ML is being used in the healthcare industry for some time now and its involvement is increasing as its sophistication level increases. ML can be used to detect brain anomalies and other such ailments in a patient with the help of various ML algorithms. It is also being used to predict the onset of cancers and other such diseases in patients by analysing his or her health record and comparing it with the vast health record data pool that hospitals have access to. ML is able to read patient symptoms with great accuracy and tell us what’s wrong with him. At the same time, it can propose a course of treatment and even medicines while keeping his allergies and other such bits of information in mind.
6. Improving Cyber Security
Integrity of a database, cloud storage, on-site server, or even a system can be improved with the involvement of ML. Things like data breach and phishing can be avoided with the help of ML’s constantly evolving nature. ML systems are particularly effective because they are designed in a way that they can detect the newest form of cyber security threats. In today’s time when data is our greatest commodity, it is essential to take every precaution to ensure its integrity and security. And ML is an essential part of the modern day IT security tool kit.
7. Product Suggestion
One of the most prominent uses of ML on the customer side is suggesting products to the customers. Using their activity online as a knowledge base, ML predicts what type of products a user might be interested in. But this is a tricky thing. An ML cannot suggest the same product that a customer just bought. No, the product that has to be suggested based on what the other customers who bought the same product bought along with it as well. Furthermore, if a user hasn’t bought something but just browsed, in that case, he should be suggested various options for that product, with various price-cuts. Companies like Amazon and Etsy are using ML algorithms actively to suggest to their users the products that they might be interested in according to their search and purchase history.
8. Image Recognition
Image recognition or better known as Computer Vision is the technique of mining numerical and symbolic data from images. Using this data, companies such as google can do browser searches, find people, etc. On the other hand, the healthcare industry can use it to find cancer tumors and other such problems. Furthermore, the automobile industry is using image recognition in self driving cars and to find errors on the production line. The process of image recognition involves using data mining, ML, pattern recognition, etc
9. Customer Lifetime Value Prediction
With the massive and constantly increasing pools of data, businesses now have the power to predict customer behaviour, analyze their shopping habits and then form personalized marketing plans for a certain group of customers. This involves sending them personal suggestions and deals, reaching out to them on social media, and more. Using ML and data mining, businesses have never been more equipped to bring in more customers while retaining the ones that they have.
10.Improving Customer Satisfaction
Chatbots, personalized emails, etc work great in today’s times when customers are looking for connection with their manufacturers. The time when you could sit behind a wall and yet earn money is gone. Nowadays, if you are not a part of a customer’s life, then you will be forgotten. So you need to make sure that you leave a lasting impact and reach out to your customers using every channel available to you.
How ML can be applied to solve business problems?
Now that we know how ML can help you in solving various business problems, let us see how you can apply it to your business to solve those problems.
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Data. Data. Data
The greatest requirement for a business looking to use ML is getting their hands on data. But once that is done, the other thing that needs to be taken care of is sorting that data into neat compartments.
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New Data
While getting your hands on data is not a difficult task anymore, the problem is almost ninety percent of it is outdated. This means no matter how much you use it to train your ML, the results will not be as good because those conditions will suit the older data and not the new ones.
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Clean Data
A clean data set means data without any errors. An unstructured and incorrect data pool can cause a lot of problems. Clean and accurate data makes sure that accurate predictions are made by the ML engine.
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Labeled Data
There are two ways to train a machine: Supervised training and unsupervised training. For a company that is just getting started on its ML journey, supervised training is probably a better approach. In it you deal with data that has been verified since the outcome will be known to the trainer.
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Enough Data
You need to accumulate data according to your goals. So telling how much data is enough data is next to impossible. It varies from situation to situation and can greatly affect the outcome.
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Picking the algorithm
There are tons of ML algorithms in open libraries on the internet such as scikit-learn. Some of the most popular algorithms are regression, classification, and model selection. It is going to be an essential part of ML, so make sure that you take your time and choose the algorithm that works for your business model.
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Feeding the data to the algorithm
Now is the time to expose the set of data to this algorithm. As a starting point, train your system on a labeled data set to make sure how effectively is working.
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Analyzing the results
The goal of this phase is not only to see how accurately your algorithm is working but also to see what type of mistakes are being made. Some mistakes are more dire than others. For instance, a mis-diagnosed cancer is still better than the cancer that wasn’t diagnosed.
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Improve the ML
Take your time and play around with the settings of your ML to get the desired results and increase the accuracy of those results. It is a time consuming process which needs to be done right.
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Infuse your ML to your business model
Now that your ML is giving you the desired results, you need to develop an app or a site so that it can be used by your customers or your employees. There can be a lot of use cases so make sure that you target the ones that bring out the best in your ML.
Things to keep in mind while implementing ML in your business
Some of the crucial things you need to keep in mind while you are bringing ML in your business have been given below:
- Be clear of your needs and the things that you want to automate using ML. A clear end goal will help you in making smarter decisions.
- Ask yourself the following questions:
- Calculate the Return on Investment (ROI). See if the cost of bringing an ML can be covered in the near future or not or if the benefits of ML outweigh the cost.
- Can I afford bringing an ML to my company?
- Is the data that I have clean and new enough to work with the current trends of my company?
- Are there biases in my data?
- Calculate the Return on Investment (ROI). See if the cost of bringing an ML can be covered in the near future or not or if the benefits of ML outweigh the cost.
Conclusion
ML is the future. Businesses in every industry can benefit by bringing ML into their folds. From being able to predict future market trends and helping designers in designing new automobiles to identifying tumors and suggesting courses of treatment, ML has many uses for every industry. It can also help you in making your business more efficient and reaching out to your customer in a more personalized manner. At the same time, by automating trivial tasks and freeing up humans who do them to do other things where they can use their intelligence, you are making your business model more efficient, faster, and cost-effective.
If you are looking for an ML-partner to team up with, Matellio has years of experience in developing ML and training them to create chatbots and trend analysis engines. Hit us up to get a quote!
