5 Machine Learning Applications Being Used in Business

Business Technology ⯀  

The U.S.’s largest and most successful companies rely on machine learning algorithms that allow them to further understand clients and customers while identifying more clearly and even predicting revenue opportunities. 

While machine learning is not a solve-all solution, it is extremely powerful within the realms of B2C and B2B company requirements as it serves as an extension of human cognition.

Machine learning is especially useful given its identifying of patterns across big data and its application to create more influential content, more paid converters and reduced marketing costs.

Of course, nothing is simple in the world of artificial intelligence (AI), and there are hundreds of variant algorithms in the machine learning world. Let’s take a look at the most widely used machine learning applications for business.

1. Customer Lifetime Value Modeling

Arguably the most beneficial application for the commerce industry, customer lifetime value modeling is used to recognize, appreciate and keep hold of a business’s most valued customers. These can be those who spend the most within that company, are the most loyal or are the most active advocates of the brand.

This particular application of machine learning also predicts how much each individual customer will bring to the company in a certain period, allowing marketing teams to tailor campaigns and efforts in a more efficient and effective way, interacting in a more valuable way with customers.

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2. Customer Churn Modeling

More of an HR exit interview or analysis, customer churn modeling will notify the business of which customers could stop engagement with it as well as explaining why. Churn models can either provide risk scores for certain customers or highlight what factors motivate the churn in order of importance.

Customer churn modeling is exceptionally important as it can better target marketing initiatives such as discounted prices when they are most needed, i.e. before a customer leaves the business. This gives the business a chance to address the concern proactively.

3. Dynamic Pricing

Also referred to as demand pricing, this machine learning application allows a company to be flexible with its pricing given factors such as:

• The demand for the product by the customer in question
• Demand for the product by all customers at the time of interaction
• If any discounts need to be applied

Airlines and cab services such as Uber utilize price optimization applications that successfully assess how likely each individual customer will be to purchase goods and services, and how much they would be willing to pay. Of course, as a side note, while the rise of machine learning applications can give way to increased revenue for businesses, it also has led to growing IT needs that these companies must address.

4. Customer Segmentation

As opposed to solely relying on the marketing team’s ability to categorize customers for campaign targeting, customer segmentation is far more reliable and accurate, as it incorporates classification algorithms that group customers together depending on their identified personas.

These personas can differ from the other based on demographics, affinity and browsing tendencies, and linking up these factors allows businesses to personalize marketing initiatives. People respond better to marketing that speaks to something important to them.

5. Recommendation Engines

Netflix is an obvious user of recommendation engines, and they seriously boost a business’s value. Recommendation engines trawl through big data to forecast the probability of a customer buying goods or services and then go further to suggest these items to the customer.

In order for businesses to best facilitate all the benefits that arise from these well-placed algorithms, other areas of the business will also need to adapt and evolve. Bigger, savvier IT departments may be only one side effect.

By  Kayla Matthews	Embed
Kayla Matthews writes about marketing innovation and business solutions for Inc.com, Convince & Convert and WeWork. You can read more posts by Kayla on her blog, Productivity Theory.