Description: Businesses are adopting Machine Learning so that tasks can become automated and do not require humans' intervention, thereby leading to efficient results. Read everything you need to know on the topic!
Many people today still associate artificial intelligence and machine learning as something that has sprung out of a science-fiction dystopia. However, this misconception is slowly fading away, as machine learning is developing and taking a more important role in our daily lives. Today, machine learning and artificial intelligence are becoming widely used in the household – for example, smart home appliances and voice-activated assistants like Alexa.
While the acceptance of machine learning in the mainstream society might seem recent, it is not a new concept. The first sign of machine learning appeared around the 1950s. However, it took decades of research and work to make significant progress towards its development and turning this science fiction into reality.
As has already been indicated, the widespread adoption of the Internet only means that people expect a fast response. In the modern business era, we now have taxis that arrive at your location at the touch of a button and order a king-size bed within seconds. The amount of data that exists today is enormous. However, today's machines also have a greater capacity to go through these terabytes of data better than a human analyst.
When it comes to business, machine learning can have a lot of different uses. Machine learning is virtually taking over almost all business processes today. As the world moves towards a technological future, businesses need to understand machine learning uses to stay ahead of their respective competitions.
What Is Machine Learning?
Machine learning, or ML, is a subfield of computer science that uses training algorithms and large data sets to give the machines (computers) the ability to learn, without explicit programming. They can also be described as tools that can facilitate decision-making and predictions based on data. It means that you can generalize patterns from previous data and make the decisions accordingly, especially when you do not know the value of what you want to predict. In terms of business, machine learning can make predictive analytics and use cases like email spam, credit card fraud, and customer churn.
How does machine learning work? Machine learning functions through the data available that was made while under observation, through instructions or experience. It results in better learning. Overall, the goal of ML is to improve the learning process so that it becomes automatic and does not require human intervention.
Why Machine Learning Matters Now?
The rise of the hype for machine learning for finance and business is due to three main factors. All these developments have minimized the barrier that stops companies and industries from different sectors to apply machine learning within their organizations. These factors include:
1. Cheaper Storage and More Data
There is a rise in cloud-based tools, and the cost of storing data is dipping through Amazon Redshift and other similar services. This means that business-critical applications can generate additional data. The same can be said about storage.
2. Open-Source Libraries
Google’s TensorFlow and other available machine learning libraries have made cutting-edge progress in creating machine learning libraries. All these machine learning algorithms are now available to a wide range of audiences. It includes generalist software developers/engineers and data scientists.
3. More Horsepower
Cloud-based platforms are developing, and custom hardware is being optimized for machine learning. It means that these applications will have a lower cost and run better and faster. Due to this, the suitability for different types of business needs will increase.
How Can Machine Learning Be Used in Business?
From the above, it is understandable that you might feel compelled to invest in ML since machine learning is the future. However, is it true that business organizations use this technology? The answer is yes – here are some aspects of a business that makes use of machine learning. For instance, games such as Wild Jack might be developed using patterns to understand what gamblers are really looking for when it comes to casino titles.
1. User Acquisition
The user acquisition for an enterprise consists of three stages. First, you need to segment your customer base to understand and address their requirements/needs. Next, you need to attract new customers and engage the old ones with the right messages at the right time and then converting them into users for your product/services.
For user acquisition, ML is widely used by both startups and major companies alike. For instance, let us consider Amazon as an example. In 2017, CEO Jeff Bezos remarked that machine learning could significantly contribute to the experience of Amazon.
This could be done by powering deal and product recommendations based entirely on the user's preferences. However, this was only the first step. Today, many organizations around the world use machine learning to adjust several aspects, like adjusting promotional pricing and branding, to maximize the chances of a sale.
Recently, Salesforce has launched Einstein for enterprises. This product helps examine the CRM data and provide tailored recommendations that can increase the chances of converting a user into a buyer. It can even send an email at the right time to get the job done. It is almost similar to how machine learning can predict football scores.
2. Customer Support
Apart from acquiring new customers, it is equally important to retain them, whether it is a new e-commerce site or a full-fledged business enterprise. Providing effective and timely customer support can also limit churn. Today, several companies use machine learning to improve their customer care support.
For instance, Ocado, an online supermarket from Brazil, used machine learning APIs by Google to construct a customized customer support system. This algorithm moved negative responses to the top of the list. It allowed the Ocado support team to respond to these messages at least four times faster. The process created a valuable opportunity to win back customers that they were going to lose.
Additionally, conversational ‘bots’ allocating support requests work without the action of a human operator. Machine learning has enabled these bots to use natural human speech to provide the first response to fulfill routine requests. This step reduced the support costs by 30% and improved customer satisfaction levels thanks to the faster response time.
Many companies and brands have started using machine learning in the back office to build accurate, granular, and robust forecasting models. Walmart ran a competition in 2016 on Kaggle, a data science recruiting platform. The applicants were asked to use the historical data of 45 stores and built a model that could forecast the sales on each store, department-wise. Similarly, AIG, one of the giants in the insurance sector, used a data science team to build and enable similar machine learning models that could improve its ability to antedate claims and foresee the outcomes.
4. Prevention of Fraud
Typically, an average e-commerce retailer can spend 7% of the total revenue on fraud. Legitimate transactions, chargebacks, and the salaries' fraud management employees are denied due to the false positives contributing to this expense. Machine learning applications have the potential to be utilized as powerful tools to monitor thousands and millions of ongoing transactions in real-time.
It decreases the chances of fraud. The best example of this aspect would be PayPal. The financial transaction company uses its transaction data and open-source tools to create an AI system.
It only has one job to do – reduce the number of frauds produced by the older fraud models. While this machine learning tutorial system was still very new, it delivered astounding initial results. As soon as the bugs were fixed, PayPal reduced the fraud rate by 50%.
5. People Management
For a business to succeed, hiring, managing, and retaining talents is very important. One of the most painstaking tasks is to filter out thousands of resumes to shortlist some people for the interviews. According to more than half the recruiters worldwide, this is the most difficult part of their job. This problem has been addressed by Restless Bandit, a startup that developed a candidate management system. The machine learning for beginners system is so successful that big brands like Macy's and Adidas often use it to filter resumes.
The algorithm has been trained to ignore and even flag human biases in the job description. It means that the ML has been trained to identify diverse and high-performing candidates that might get overlooked by human recruiters. When it comes to retention, ML can help by augmenting the mentorship to great managers and even help employees perform better by providing unbiased and specific career advice, based on previous employees with similar profiles.
From the above, it is clear that machine learning horse racing is being used in business. Companies that do not act now to prepare for ML will be left behind. Machine learning will ensure that everything becomes automated and will deliver on-demand customer service.
The goal of machine learning is to independently adapt to new data and make the right recommendations and decisions, based on thousands of analyses and decisions. The possibility of human error is removed. If you have any questions about this article, feel free to drop your inquiries in the comment section below.
Alex Norwood is an experienced traveller and an online entrepreneur. He runs a successful eCommerce business and is always on the lookout for new lucrative ways to make money online, and currently trying to be a part-time ghostwriter at Assignyourwriter.co.uk. Travelling is Alex’s passion, and he has visited over 20 countries in the last 5 years.