How to Leverage the Power of Machine Learning for Customer Success

machine learning and customer success

April 19, 2019

Reliable forecasting of customer subscription renewals is essential to your company’s short- and long-term success. Discover how your subscription-based business can leverage the power of machine learning and customer 360-degree insights for customer success to boost renewals, increase upsells, drive more revenue and so much more.

Machine Learning vs AI: The Basics

Before you can reap the many benefits these incredible technologies have to offer, you need to understand what they are, and perhaps more importantly—what they are not.

What is AI?

After Alan Turing conceptualized AI in 1950, John McCarthy was the first to use the term “artificial intelligence” to describe machines that could think autonomously. By his definition, AI meant, “Getting a computer to do things which, when done by people, are said to involve intelligence.”

When a machine (or software) is programmed to perform a single, repetitive motion or task, it is not considered intelligent. The AI component comes in when the software is able to adapt appropriately as it encounters different situations.

AI is a broad concept that encompasses many things, including machine learning.

So, What is Machine Learning?

Machine learning is a branch of AI and is a method of training algorithms such that they can learn how to make decisions. When he coined the term in 1959, Arthur Samuel defined machine learning as, “A field of study that gives computers the capability to learn without being explicitly programmed.”

A lot has happened in the world of technology and innovation over the past 60 years. The definition and use of machine learning—which is a technique used in predictive analytics—has evolved right along with it.

Starting with the outcome rather than an assumption of cause and effect, machine learning uncovers what is driving a particular outcome by evaluating thousands of factors (including user interactions) to make accurate predictions.

3 Reasons to Use Machine Learning for Customer Success

Now that we understand the concept of machine learning, it’s easy to imagine the powerful impact it would have on any customer success strategy. This is especially true in the subscription industry, which is prone to higher churn rates and is driven by constant user interaction. In this context, machine learning can be applied to predict subscriptions wins and losses, make specific recommendations on how to improve those outcomes, and gain critical insights into the levels of customer engagement and product value attainment.

It’s time to turn that knowledge into believing, with three reasons why using machine learning for customer success can be so powerful:

1. Bring unprecedented accuracy to your lead scoring

Do you really know which customers and leads are in good shape and which need corrective actions to turn-around? Or more importantly, does your sales team know? As you know, customer acquisition costs aren’t cheap. If the customer doesn’t stick around longer than one or two billing cycles, the cost of acquisition will far outweigh the generated revenue.

By analyzing historical wins and losses and the customer’s information over time, machine learning can micro-segment your customers and identify attributes associated with churners and those associated with long-term subscribers, and give uncannily accurate predictions on the likelihood of winning each deal. This type of customer segmentation and win prediction scoring provides customer success and sales teams with valuable insights into who they should be spending their time on for a greater probability of closing a sale.

2. Know your customers’ habits better than they do

A machine learning-powered approach to customer success gives you revolutionary insight into engagement scoring metrics vital to your unique business. By interpreting and analyzing thousands of data sets, machine learning can study user habits and determine what every action (or inaction) means.

If a user begins utilizing certain features of the platform more extensively, for example, machine learning might infer that that particular person is ready to upgrade to a premium version of your service. Having this knowledge will enable your sales team to effectively market to the right users at the exact right time.

3. Take proactive actions to improve outcomes

You may not like it, but some customers are always less likely to renew their subscriptions than others based on the amount of value they are getting. And when a customer pauses or terminates their subscription, lifetime value (LTV) is reduced (right along with your revenue).

But machine learning can give your company an edge by accurately predicting renewals, and more importantly giving you timely and very specific recommended actions to increase the amount of value they are getting. This allows your internal teams to take a proactive—not reactive—approach to customer success and renewals.

The Future for Machine Learning and Customer Experience

The International Data Corporation (IDC) predicts that by 2021, worldwide spending on cognitive and AI systems will reach $57.6 billion. Platforms that support machine learning are expected to grow at a 13% CAGR through 2021.

These stats aren’t specific to the subscription software industry, but they do represent a monumental shift in the future use of machine learning and AI. As indicated earlier on in this article, subscription-based companies and their customers stand to benefit tremendously from the adoption of these technology systems.

As more businesses continue to invest in machine learning to enhance the customer experience, the field will evolve to levels that we, in 2019, can only begin to imagine.

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