“Investing in Machine Learning Is like Investing in Mobile 10 Years Ago — It Can Transform Your Business”
What is Machine Learning?
A lot of people have probably heard of Machine Learning, but do not really know what exactly it is, what business-related problems it can solve, or the value it can add to their business.
Machine learning is the science of designing and applying algorithms that are able to learn things from historical data. It was born from the aspects of pattern recognition ML explores the study And construction of algorithms that can learn from and make predictions on data. This allows ML programs to respond to different situations even though not being explicitly programmed.
ML has given us ample amounts of use cases like self-driving cars, product recommendation engines, predictive analytics, speech recognition to name a few. Increasing reduction of human effort is the main aim of data scientists with implementing ML. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. It wants to bring down the time that humans take to read, understand, analyze the big data to a few seconds.
The process of applying a machine learning method can be broken into 5 main steps :
ML algorithms work differently. At a high level, they make decisions/predictions by ingesting large quantities of historical data and using that knowledge to guide their results. Some examples of ML currently being used in businesses include:
- Email filters marking messages as either spam or not spam
- Netflix recommending what movies/shows you are likely to enjoy
- Google maps predicting how difficult parking will be at your destination
- Facebook’s facial recognition identifying people in photos
- Anomaly detection algorithms that can identify fraudulent purchases
In short, ML will connect intelligently people, business, and things. It will enable completely new interaction scenarios between customers and companies and eventually allow a true intelligent enterprise. To realize the applications that are possible due to ML fully, we need to build a modern business environment.
ML Technology and Applications for Business Development
Business processes will become automated and evolve with the increasing use of ML due to the benefits associated with it. Customers can use the technology to pick the best results and thus, reach decisions faster. ML will also help businesses arrive at innovations and keep growing by providing the right kind of business products/services and basing their decisions on a business model with the best outcome.
As a result, businesses would be able to act at the right time and take advantage of sales opportunities, converting them into closed deals. With the whole operation optimized and automated, the rate at which a business grows will accelerate. Moreover, the business process will achieve more at a lesser cost. ML will lead businesses into environs with minimal human error and stronger cybersecurity.
ML Use Cases
The following 15 examples show that how ML can be applied to an enterprise model :
Finding possible clients – also called prospecting – is extremely important. If you don’t target the right people, all your marketing strategies will be pointless.Finding the best prospects and classifying them into categories can be a tough task, but it’s necessary. If you send long-time customers the same email you send your leads, it will look like you’re sending them spam and they won’t be happy with it.With machine learning APIs, you can improve your prospecting and classification of leads and clients. This will improve your marketing efficiency and can save you a lot of money!
Prediction APIs make use of advanced data science algorithms to find patterns. You can use these machine learning APIs for forecasting, detecting frauds, predicting maintenance, and more.Small businesses usually struggle to be ahead of the market. They usually follow the changes implemented by large companies. Isn’t this the case for your business? With machine learning APIs that predict the evolution of the market, you can decide when, where, and how much you should invest.
3. Supply Chain Optimization
Another high-value application area of Machine Learning is found in applying algorithms that continually analyze the state of your supply chain and recommend or automatically execute changes to meet customer requirements while maximizing company objectives. Optimization driven by algorithmic planning has also been in practical use for many years. Supply chain optimization relies on a set of provided information (supply chain facilities and capacities, transportation lanes and capacities, customer service requirements, profit requirements, etc.) and real-time operational updates (orders, shipments, unplanned events, etc.) to suggest optimal responses to planned and unplanned events.
4. Product Recommendation System
Each person’s preferences are different, and preferences change over time. Companies like Amazon, Netflix, and Spotify use ratings and engagement from a huge volume of items (products, songs, etc) to predict what any given user might want to buy, watch, or listen to next.
5. Multi-Echelon Inventory Optimization
One powerful application of Machine Learning is Multi-Echelon Inventory Optimization (MEIO), which automatically adjusts inventory parameters to meet stated customer service requirements while minimizing inventory investment. Using the latest demand and inventory projections and variabilities, MEIO seeks the optimal balance of component, Work In Process and finished goods inventory at the right locations. Embracing MEIO can reduce total inventories by upwards of 30% while maintaining or improving customer fill rates.
The job of prioritizing incoming applications for positions with hundreds of applicants can also be slow and time-consuming. If automated via ML, the HR can let the machine predict candidate suitability by providing it with a job description and the candidate’s CV. A definite pattern would be visible in the CVs of suitable candidates, such as the right length, experience, absence of typos, etc. Automation of the process will be more likely to provide the right candidate for the job.
7. Support Ticket Classification
Consider the case where tickets from different media channels (email, social websites etc.) need to be forwarded to the right specialist for the topic. The immense volume of support tickets makes the task lengthy and time-consuming. If ML were to be applied to this situation, it could be useful in classifying them into different categories.
API and micro-service integration could mean that the ticket could be automatically categorized. If the number of correctly categorized tickets is high enough, an ML algorithm can route the ticket directly to the next service agent without the need of a support agent.
8. Real-time bidding (online advertising)
Facebook and Google could never write specific “rules” to determine which ads a given type of user is most likely to click on. Machine learnings help to identify patterns of user behavior and determine which individual advertisements are most likely to be relevant to which individual user.
9. Monitor customer behavior and act based on the behavior
Most B2B companies are actively monitoring web analytics, but that’s just the very minimum a company needs to do today. With the power of machine learning, the more effectively you use big data to learn about your customers and respond to what they need when they need it, the more successful you will be. Machines can and already are being programmed to respond immediately to the input from customers. Machines are better able to efficiently consolidate data points and analyze the data for meaning; something that would take humans exponentially more time. This has wide applications from improving content marketing to customer service to upsell opportunities.
10. Sentiment Analysis
What if you could know what your customers, users or even investors felt about your company? While the ability to read minds is still the stuff of science fiction, technology has made it easier to discern and track what people think about you.Sentiment analysis, also called opinion mining, is the process of studying words and determining their emotional meaning. In other words, you track what people say about you online and determine if those statements are positive, negative or neutral.
Now that people post almost everything on social media, review sites, forums and blogs, those opinions can be analyzed and tracked. CloudFactory’s does sentiment analysis by combining automation with our on-demand workforce to help brands track online feedback, gain insights and improve their experiences.
11. Fraud detection
Fraud detection process using machine learning starts with gathering and segmenting the data into three different segments. The machine learning model is fed with training sets to predict the probability of fraud. These datasets are found from historical data. Lastly, we build models as an essential step in predicting the fraud or anomaly in the data sets. By comparing each transaction against account history, machine learning algorithms are able to assess the likelihood of the transaction being fraudulent.
12. Inbound Logistics Planning
Logistics planning ensures the right person receives the right number of supplies at the right place at the right time. Inbound logistics focuses on the management of suppliers and the goods they send into a business. It’s a complex process of managing orders, shipping, warehousing, inventory control, and utilization. By gathering and feeding data on existing planning into an ML model, businesses can predict and recommend future processes.Consumer retailer Walmart uses ML to optimize business efficiency. Its Retail Link 2.0 system uses information that flows throughout the supply chain to identify deviations from its process so it can make adjustments in real time.Automotive manufacturer Honda uses machine learning to detect quality issues beyond the assembly line by identifying patterns in the free-text fields of warranty return notes and in reports from mechanics.
13. Engaging with customers
You may have noticed “contact us” forms getting leaner in recent years. That’s another place where machine learning has helped streamline business processes. Instead of having users self-select an issue and fill out endless form fields, machine learning can look at the substance of a request and route it to the right place.Big companies are investing in machine learning … because they’ve seen positive ROI.
That seems like a small thing, but ticket tagging and routing can be a massive expense for big businesses. Having a sales inquiry end up with the sales team or a complaint end up instantly in the customer service department’s queue saves companies significant time and money, all while making sure issues get prioritized and solved as fast as possible.
14. Data visualization and KPI tracking
Visualizing data helps everyone make better decisions. “90 percent of information transmitted to the brain is visual, and visuals are processed 60,000X faster in the brain than text.” So, the more we focus on making data analysis accessible to every member of the team, the more reliable organizations will become at hitting KPI’s.Business leaders should focus a portion of their team’s energy on getting comfortable with visual data. Give every member of the organization access to the information that they need to self-assess their effectiveness — even if it isn’t fully optimized.
We are already seeing machine learning platforms evolve that automate critical reporting. Sisense Pulse, for example, is a platform that simplifies the process of reviewing business intelligence, automating the creation of visual reports and improving the chances that an organization can successfully track and exceed their key metrics.Through active monitoring, the platform can immediately notify key personnel when data anomalies occur that either negatively or positively impact key performance metrics. This helps “corporate first responders” jump into action to solve problems before they become red ink, or to double down on initiatives that are yielding fast returns.
15. Better insights into consumer behavior
The cycle of report generation and KPI analysis will continue to become increasingly automated. And, just as the effectiveness of your team will begin to be monitored by computers, so will the analysis of consumer behavior.
Generating actionable business insights with AI is becoming much easier, thanks to cloud-based platforms that can mine existing data to predict future consumer trends. And consumers are happy to fork over personal information in return for a personalized shopping experience that predicts their needs and wants.
This is exciting because it empowers businesses to provide a more customized experience — speaking specifically to their future needs. When companies can predict future needs, they can better position themselves to meet them more effectively than the competition.
Machine learning is transforming all industries with tools that become more powerful as more data is fed into them. Every business can benefit from jumping onboard the machine learning bandwagon.
I’m excited to see where the trend in Automated Business Intelligence heads through Machine learning in the coming years. And, it’s my hope that making BI more accessible leads to another surge in entrepreneurship — as employees gain the confidence to strike out on their own and disrupt stagnant industries.
The uses of Machine Learning are almost infinite (as long as there’s data to assess) and the benefits of having an automatically improving model are too good to pass up. Plus, the longer we use machine learning in business, the more accurate any application of it will become. Aside from the data and power requirements, the only way is up!
What do you think about machine learning? How do you think it could be used? I’d love to hear from you in the comments below!