Although we are not aware of that, the future is already here. Self-driving cars, instant translations, personalized shopping suggestions, movie or series recommendations that might interest you. Thanks to Machine Learning (ML) our lives are changing in the last few years and what we envision in the next decade may change the future of business.
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Machine Learning is a discipline of Artificial Intelligence, which uses algorithms to find patterns among all the data provided to it. Its objective is to make predictions with the data provided, whether they are images, structured or unstructured data, etc.
When Machine Learning is used, it is not necessary to constantly program its evolution because it autonomously performs the work it has been asked to do. This discipline has gained relevance in recent years thanks to Big Data, but it was first used in .
In recent years, this technology has evolved by leaps and bounds. Machine Learning models are very versatile and useful for the business world. Their ability to adapt to changes (in data) and machine learning, allows forecasting future variables that have achieved accuracies of over 90%.
The use cases for machine learning are literally infinite, depending on the problem to be solved, the starting data and the data processing implemented. However, from Biuwer’s point of view, a business data analysis platform, we consider the following six cases to be very useful and applicable to a large number of companies.
Machine Learning helps deliver highly personalized experiences, which can translate into improvements in customer engagement, conversion, revenue and margins.
Examples include purchase suggestions or recommendations on e-commerce platforms. Initially, the interaction history of each user is analyzed (purchases, product views, searches, etc.) and compared with other similar users. In this way, the online store can propose content of interest to the user with greater accuracy, in order to achieve the objectives set (e.g. maximize sales, eliminate perishable stock, promote a product line, etc.).
Netflix is another example, the platform suggests series or movies that we might like based on what we have previously viewed. When it comes to buying or using a product or service, consumers expect a personalized and real-time experience. With Machine Learning we achieve this type of fully personalized experience, which will improve customer engagement.
Machine Learning can help turn a contact center ( or other channels, chat, , etc.) into a profit center by reducing waiting time for interactions (calls, messages,...), increasing agent productivity and satisfaction, reducing costs and identifying business improvement opportunities.
Applying Machine Learning in a Contact Center can improve your customers' experience, with continuous improvement through data analytics. The huge volume of data received by the Contact Center is an essential resource for the progressive adjustment and improvement of the implemented algorithms. These are some of the main benefits:
Worldwide, billions of US dollars are lost every year due to online fraud. Many of the applications that are designed to offer protection against potential online fraud rely on rules that do not keep pace with the ever-changing tactics of hackers, malware or malicious actors.
When it comes to SMEs lending, for example, where fraud detection is a key concern of the process. Predictive models are being trained to assess risks by analyzing financial fraud cases. To determine risks based on application characteristics, the models then analyze data such as the credit application process and the likelihood of fraud.
New antivirus and malware detection engines already make use of machine learning to boost scanning, speed up detection and improve the ability to recognize anomalies. Experts agree that fraud prevention is difficult because fraudsters are constantly changing and adapting. So, the best technology to combat fraud is one that can change and adapt as fast as the fraudster's tactics.
The field of modern entertainment includes an infinite number of resources (audio and video, among others), which are invaluable in providing the user with an enhanced experience. It is of vital importance to know the target audience to whom the content is addressed, which will have even greater value with improved personalization and monetization.
However, many companies fail to optimize their multimedia content to take full advantage of it. With Machine Learning you can reap benefits in four key areas:
Predicting what customers want, in what quantity and when they will want it is critical to the success of any organization.
Sales and finance departments depend on accurate demand metrics to meet customer demand, build inventory and optimize cash flow.
You can use Machine Learning to compare warehouse stocks between different reference periods to make product demand forecasts. Visually, data is often compared in time series graphs, allowing you to detect trends and play with data from the past, past events, the present, and estimates for the immediate future. Models and algorithms usually incorporate the necessary parameterizations and variables, such as product characteristics, geographic location, or meteorological estimates, to achieve more accurate predictions and greater final value.
As we say, even the weather forecast is a factor to take into account when it comes to a new launch or an event that takes place physically in one location (i.e., not online). Thanks to the application of Machine Learning, we are obtaining more reliable data every day, which can help us when making decisions, such as deciding the dates of certain events (store openings, VAT-free days, promotions and discounts in physical stores, etc.), depending on whether there will be weather imponderables with a certain probability in a specific location.
Companies generate huge amounts of documents that constitute a treasure trove of great utility for the organization. On the other hand, manually processing large volumes of data is a very cumbersome and time-consuming task.
Using Machine Learning, your company can access and use the data contained in the documents when needed. This allows you to obtain additional information on which you can base business decisions on a daily basis.
Biuwer is a data platform that has among its pillars the ease of use for the end user. Given the complexity and variety of possible applications in the field of Machine Learning, Biuwer's approach is to be eminently practical and provide elements of value with the least possible complexity for the user. We know that we will not reach all use cases, but we believe that the value provided to a wide variety of companies and businesses can be high.
Taken to the business environment, the basis is the use of time series for monitoring KPIs (Key Performance Indicators), for which an organization has historical data. Based on predefined mathematical models we propose a prediction of a configurable future interval.
This can be completed with further input parameters up to a "What-If Analysis", in which by modifying certain parameters, Biuwer automatically recalculates the data series, both in the past and in the future, by correlation with the rest of the data present in the dataset. This type of analysis is very visual and helps to understand very common business cases, for example:
In the following video, Alberto Morales (CEO of Biuwer) explains more details about how we approach the use of machine learning in Biuwer.
With that backdrop in mind, here are eight leading machine learning benefits for business.
The ability to cultivate customers ranks among the top reasons to deploy ML. Customer churn is a huge headache for enterprises. ML can help businesses identify which customers are likely to leave.
"This is absolutely the No. 1 problem we see with our clients -- whether it's a long-term contract or month-to-month, across different industries and company sizes," said Matt Mead, CTO at SPR, a technology modernization company in Chicago.
Customer retention is basically a classification problem. Mead said this ML task involves looking at the characteristics of a business' customers -- i.e., historical information on those who have left and those who have stayed, plus their different behaviors. Customers can use that analysis to establish "white-glove programs" for potentially at-risk customers, Mead noted. The business can try to boost customer satisfaction and create a stickier relationship, he added.
David Frigeri, a managing director who leads the East Coast AI strategy at Slalom, a business and technology consulting company, also cited customer retention as an ML benefit.
"We have found that the best return from a financial perspective is where the analytics capability is positioned as close to major revenue sources as possible," he said. "So, building a better customer experience, improving the retention, improving the lifetime value of the customers through better products or services is really the horizontal focus that crosses all the major verticals."
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Another in-demand ML application is predictive maintenance for fixed or long-term capital assets, Mead said. Here, ML identifies equipment likely to experience failure. Organizations can use that insight to schedule downtime and make repairs versus experiencing costly outages that disrupt clients, he said.
The global market for predictive maintenance is forecast to reach $19.3 billion by , growing at a compound annual growth rate of 30%, according to Vantage Market Research.
Netflix and Amazon offer high-profile examples of using ML to build recommender systems that suggest new products or services based on a customer's purchasing history.
"Those are interesting, very public implementations of ML in the spirit of personalization," Mead noted.
This ML use case creates greater value for customers -- and also opens upselling and cross-selling opportunities for enterprises. A recommender system can thus generate new revenue streams for businesses.
ML is all about making predictions, so the technology offers a natural platform for planning and forecasting activities.
ML can help businesses predict future costs, demand and price trends to facilitate budgeting and protect a business' financial prospects, Mead said. "That's a huge category of work we do for our customers," he noted.
Within enterprises, the corporate strategist role stands to benefit from greater ML uptake. The trends corporate strategists must consider -- and the pace at which they need to analyze them -- are fundamentally different in light of the COVID-19 pandemic, said David Akers, a research director in Gartner's strategy research group.
AI technologies can lend greater insight and efficiency to the process. But a Gartner study published in July found only 20% of the 200 corporate strategy leaders surveyed use tools such as ML. Adoption looks set to increase, however, as 51% of respondents said they are investigating ML.
ML's predictive modeling will bolster the foresight necessary for strategic decision-making, helping a business "see around the corners," Akers noted. He cited the importance of unsupervised ML and the ability to "identify new opportunities that we didn't see with traditional analytics."
Unsupervised learning models don't require humans to train data sets and can uncover patterns in unstructured data.
ML and its ability to identify patterns have found a home in fraud detection.
Mead said he sees customers deploy off-the-shelf fraud detection software, but he has also come across a fair amount of custom implementations. Fraud detection is often associated with financial services companies looking for anomalies in credit card transactions.
But Mead cited wider applicability.
"We've worked with customers to identify fraudulent accounts across all sorts of industries," he said. That includes helping e-commerce companies flag fraudulent orders.
While ML has considerable horizontal applicability, organizations can also marshal the technology to meet vertical market requirements. Here is a sampling of industries to consider:
Another benefit is the ability to generate multiple returns from an initial ML investment. For example, a retailer that creates a data set to forecast product demand has an opportunity to build upon that investment, Frigeri said. A company might not realize it, however.
"There is this kind of a soft barrier of low expectations around thinking, 'We've got really great demand forecasting -- now we're done,'" he said.
But the data set built for demand forecasting can also help retailers anticipate out-of-stock situations, Frigeri noted. And a retailer that can predict when it will lack a particular product can then build a recommender system for safety stock -- a replacement product it can tap as a just-in-case buffer. Other retailer groups, such as marketing, can also take advantage of the demand forecast data.
"You actually can get a lot done within that same dollar of investment, but you have to be really thoughtful," Frigeri said.
Automation through ML can trim an enterprise's expenses through labor reduction and improved efficiency.
Customer service is one area likely to see cost savings via machine learning. Gartner estimated conversational AI, which combines ML and natural language processing (NLP), will reduce contact centers' agent labor costs by $80 billion in .
Chatbots, getting an extra push from generative AI, have organizations questioning whether they can start to have fewer call center agents who are on the for less time, Mead said.
Replacing call center agents with chatbots is one possibility. But Mead said he views using chatbots to assist human agents and reduce call-handling time as the more creative use of the technology. The idea is to have chatbots listen to conversations, understand the context and assess customer sentiment. That insight, combined with NLP analysis of earlier call transcripts, lets a chatbot provide advice to agents while they are engaged with customers, Mead noted.
Generative AI, meanwhile, opens additional avenues for efficiency, said Zakir Hussain, Americas data leader at consultancy EY. He pointed to a 44% time savings in professional writing tasks and a 55% reduction in programming time, citing research from MIT and Microsoft, respectively.
The emergence of generative AI changes the nature of programming, he said.
"It's not about coding anymore," Hussain said. "We have moved on to an era where it is more about leveraging AI to do the coding, but then wrangling that [output] to make sure what it has generated is actually correct."
In that scenario, Hussain said he foresees many developers becoming "data wranglers."
But automation, while important, shouldn't outrank ML's ability to provide new customer experiences, according to Frigeri.
"Automation has had a huge impact for many organizations in terms of driving productivity, but No. 1 is your customer, first and foremost," he said.
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