Gaining a Competitive Edge With Machine Learning
Can machines think and act as humans can do? If this question fascinates you, this article is a good read for you. By now, most companies understand the value of machine learning in data processing. Those that have the means are already investing in technologies fueled by artificial intelligence.
In this post, you will become familiar with the basic concepts of machine learning and the scope of its application in business.
Machine Learning: Definitions and Core Concepts
In the modern world, datasets are constantly growing and data processing has become increasingly complex. Thus, more and more companies are looking at new approaches to manage data. Predictive analytics, along with machine learning, has become the most popular way to handle Big Data. AI-powered technologies allow getting more valuable insights that can help a business grow and develop effectively.
There are many definitions of machine learning containing similar terms and essence. Thus, we may define machine learning as a mechanism allowing machines to learn with the help of certain patterns. Patterns mean how machines comprehend the environment, categorize data, and make decisions. The main goal of ML technology is to provide more accuracy and avoid inappropriate decisions.
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Approaches to Handle Big Data
The key source of machine learning is data. It can be of any size, format, and structure. When it comes to managing Big Data, there are some advanced approaches that have proven to be workable for different business domains.
With the help of Classification, it is possible to group data according to a certain number of attributes. By classifying information, you’ll be able to explore different behavior patterns.
For example, a sales manager wants to find new prospects and understand if these prospects are worth spending time and efforts. In this case, the key input is customer data like total items bought and the money spent.
In each case, the manager will specify what customers are considered worthy and which ones – not worth spending the efforts. For example, the clients who spend more than 500 dollars per one purchase are classified as worthy. Classification is not always binary. Managers can specify any number of classifications for more precise results.
Unlike classification, Clustering doesn’t start with a particular target in mind. In clustering, analysts don’t specify what they are looking for. Clustering divides datasets into groups, each of which possesses similar data. Clusters are formed of objects with high similarity.
Clustering is used in many areas. For example, in marketing, it is used to explore customer segments. In insurance, it is used to identify fraud and acknowledge policies.
Forecasting is quite similar to classification as it also helps identify data behavior in the future. No matter which business you’re in, forecasting is ubiquitous. For example, Uber uses marketplace forecasting for predicting supply and demand in order to direct driver partners into high-demand areas that are likely to arise shortly. These include sporting events, concerts or high airport traffic.
In many situations, data may be inaccurate. For example, some Seattle software companies are able to estimate that a certain task can be completed in 2 hours. According to their experience, they can also specify the average cost and the number of employees needed to complete the task. Based on these initial Data, an analyst will be able to determine how much time will be spent to accomplish the project.
Addressing such challenges requires the simulation of various alternatives. Thus, data analysts can combine several techniques for accurate estimates.
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How to Apply Machine Learning in Real World?
In this section, we detail 6 areas in which machine learning impacts your everyday life:
Credit Card Fraud Detection
Recently, the number of credit card frauds have increased. Fraud detection can help companies identify transactions that are not made by a cardholder. Once a transaction is marked as fraudulent, managers start taking all the needed actions.
The capabilities of Siri are already known for every iPhone user. The essence of this functionality lies in speech recognition. A user request is interpreted by a machine and is mapped to a certain task.
Spam detection allows for finding spam e-mails. Based on certain rules, machine learning technology can detect spam content and move it to the spam folder.
This functionality is one of the key features of any eCommerce store. Having a customer purchase history, a machine can identify what products a consumer will likely buy. Based on this data, it sends special recommendations and suggestions to a customer to motivate him or her to buy the offered product.
Oftentimes, people make purchasing decisions after they read feedback or reviews written by other customers. Sentimental analysis is a mechanism that helps to identify and assess public opinion to properly build strategy and marketing campaigns.
Various social networks have begun offering a face detection feature that allows tagging a particular person on photos. This feature allows categorizing photos by a person.
How Machine Learning Can Be (And Is) Applied In Different Industries.
- Demand forecasting
- Process optimization
- Predictive inventory planning
- Recommendation notifications
- Market segmentation and targeting
- Real-time patients data
- Disease and risks identification
- Healthcare provider sentiment analysis
- Customer-specific pricing
- Demand and supply optimization
- Smart grid management
- Risk analysis
- Customer segmentation
How Can Small Business Use Machine Learning?
Not only large enterprises can take advantage of machine learning. Small and medium-sized companies can start benefiting from this technology. They’re actually already using ML without necessarily being aware of it. Below is a list of simple ML solutions.
SEO – When promoting any small business services, products, or brand, you use Google. In turn, Google leverages different AI-powered algorithms. Google Analytics also uses artificial intelligence to monitor new patterns and trends.
Automation – Even if your business is small, you surely have a bunch of tasks to do and papers to process. ML-fueled technologies like email automation or documentation automation can help you avoid redundancy and manual work as well as boost productivity.
Marketing – AI-powered tools provide deep insights into marketing decisions. With proper tools, you will be able to forecast revenue and develop relevant strategies. Another way to promote your services or goods is to use personalized suggestions for your customers, prospects and leads.
As you can see, machine learning can be applied in a wide range of industries and business domains. Providing customers with advanced functionalities and features, any company may improve marketing efforts, boost productivity and sales, therefore increasing revenue and gaining a competitive edge.
Before implementing any ML-powered features into your business environment, you should consult with experts and identify which technology is the best suited for your organization.
Over To You
Let us know what you think about machine learning. Write in the comments if you use it for your business and how. If you are simply thinking of introducing it into your business, what are your current considerations and concerns? Your thoughts might be helpful for other readers.
Christopher Nichols has always enjoyed using new technology advancements to scale marketing efforts. He believes in data-driven marketing and in practices it in his agency, Strictly Digital. You can tweet him @SurfsharkSecure
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