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Machine learning (ML) is transforming the global economy on every conceivable level. Increasingly advanced artificial intelligence (AI) algorithms are improving production and fulfillment, and ML-driven data analytics are helping businesses make their operations more efficient and profitable.
With so many aspects of business benefiting from the steady advancement of machine learning technologies, identifying the most ideal applications of machine learning development services can be a challenge. In this guide, we'll identify the three main machine learning models that are most applicable to operating a modern business and explain why custom machine learning development is essential.
While online shopping has made transactions smoother for both brands and consumers, the process of exchanging services or goods for currency online is still far from perfect. Additionally, you can't stop customers from deciding not to shop with your company or canceling an existing subscription, which is why predicting customer retention is essential to generating long-term profitability models.
The process of losing customers is called "customer churn," and customer churn can occur for a variety of different reasons:
Contractual: Customer churn can occur when a customer decides to cancel an existing contract. Contractual churn occurs when customers cancel fixed-term contracts or fail to renew contracts that do not have predetermined term durations.
Non-contractual: This type of customer churn occurs most commonly when a customer fails to finalize a purchase. For online retailers, non-contractual churn usually occurs when a customer adds an item to their cart without checking out.
Involuntary: This form of customer churn occurs when payment for a service or product cannot be completed. Involuntary churn may occur due to a customer's inability to pay or due to an issue with the credit card company or payment service provider handling the transaction.
Regardless of the fact that customers are inevitably lost due to customer churn, it's cheaper to retain existing customers than it is to gain new ones. Therefore, developing accurate models of customer churn and predicting overall customer retention is an essential part of operating your business efficiently.
By observing trends within your customer data, machine learning tools can use models like logic regression to determine which factors affect customer churn. From there, a support vector machine (SVM) can be used to determine whether customers are likely to be affected by these identified factors. Further machines, such as random forest classifiers, can be used to increase the accuracy of predicted customer churn models.
Knowing the likelihood that you'll retain a given customer is essential to maximizing your profits, but it's equally essential to determine how much revenue each of your customers will generate. The amount of business a customer does with your company is referred to as that customer's lifetime value (LTV), and calculating a customer's LTV can help you understand the degree to which customer churn will affect your bottom line.
According to the Pareto principle, roughly 20 percent of your customers will invariably generate around 80 percent of your revenue. If you calculate the LTV of each of your customers effectively, you'll be able to centralize your efforts around revenue-generating customers and reallocate resources away from customers with reduced lifetime values. In the past, it was necessary to rely on guesswork or calculate LTV manually, but the latest machine learning tools can predict LTV with startling accuracy.
Machine learning tools can be used to determine the number of purchases a customer is likely to make, the expected lifetime of the customer's relationship with your company, and the total monetary value that the customer is likely to generate. If a customer has already purchased products or services from your company, it is a simple task for machine learning tools to determine the LTV of that customer using historical data.
Predictive machine learning tools can also determine the expected LTV of a first-time customer who has never interacted with your company before. These ML tools use data from other customers to predict the likelihood that a new customer will churn along with the total monetary value that your new customer is likely to generate. Using machine learning to determine the LTV of your customers will help you allocate resources properly even if you've never worked with a particular customer before.
Regardless of how many customers you generate, you'll need human resources to keep your operation running. Employees can fall by the wayside for a wide variety of different reasons, which is why it pays to use machine learning to determine the likelihood that you will retain a particular employee.
Also known as employee attrition, employee turnover can significantly impact your organization's bottom line, but you can use ML tools to predict the performance of new hires and determine which variables are linked to employee attrition throughout your company. For instance, it's possible to use machine learning to determine whether the amount of time you train an employee impacts their attrition likelihood, and you can also identify particular job roles that are more susceptible to employee turnover.
Additional factors, such as overtime, can affect the likelihood that an employee will stay with your organization. Once you understand the factors that affect employee attrition, you can take proactive measures to prevent critical assets from leaving your company.
If an excellent employee is currently working within a set of factors that your ML tools have identified as leading to turnover, for instance, you can adjust that employee's work environment to counter these factors. Also, based on the attributes of a new hire, you can use ML tools to ensure that your new human resource will not enter a "perfect storm" of attrition-influencing factors that will negatively impact their particular mindset or work ethic.
Machine learning is no longer a fringe topic, and applying machine learning models is becoming increasingly essential to efficient business operations in the 21st century. Regardless, grasping all the benefits that machine learning insights can grant your business is complicated, which is why all businesses can benefit from custom machine learning development services.
Leveraging the latest breakthroughs in artificial intelligence, ML software developers unlock your company's ability to predict customer retention, lifetime customer value, and employee retention. That's only the tip of the iceberg of what ML can accomplish in business, so contact a machine learning development provider today to unleash your true profit potential.
Chetu, Inc. does not affect the opinion of this article. Any mention of specific names for software, companies or individuals does not constitute an endorsement from either party unless otherwise specified. All case studies and blogs are written with the full cooperation, knowledge and participation of the individuals mentioned. This blog should not be construed as legal advice.
Chetu was incorporated in 2000 and is headquartered in Florida. We deliver World-Class Software Development Solutions serving entrepreneurs to Fortune 500 clients. Our services include process and systems design, package implementation, custom development, business intelligence and reporting, systems integration, as well as testing, maintenance and support. Chetu's expertise spans across the entire IT spectrum.
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