4 Ways Machine Learning Can Make Marketing Smarter

CMOs are looking to machine learning to provide the intelligence, scalability, and experimentation to ensure their marketing efforts are continually optimizing customer lifetime value.

Machine learning, a field traditionally dominated by data scientists, has become a marketer’s best friend, especially for industries that rely on continually earning the customer’s share of wallet.

As more and more companies overhaul their business models to evolve from having a transactional relationship with their customers to a service relationship, CMOs are looking to machine learning to provide the intelligence, scalability, and experimentation to ensure their marketing efforts are continually optimizing customer lifetime value. This approach is becoming so popular that industry analyst David Raab has begun to map out the landscape for “machine learning marketing platforms.”

Here are a few ways machine learning can help companies evaluate the effectiveness of their marketing campaigns.

Machine Learning Assumes A Long-Term Look

Most marketers measure success with short-term metrics, such as response rate, clicks, and open rates. Meaning they measure how many customers clicked on or accepted an offer. While click and response rates indicate some level of interest, it represents only an initial interaction.

Instead, the information marketers need to know is if the 10% coupon they offered actually kept a customer from churning or increased the company’s monthly revenue. With machine learning, marketers can customize each customer interaction and measure it against long-term metrics, such as 14-day revenue and 90-day retention. This long-term customer view nets a customer experience that is highly optimized, resulting in the business becoming more profitable.

Machine Learning Analyzes Any And All Interactions

Most marketers would agree that traditional A/B testing does not maximize revenue lift. This conventional approach is a very manual process that requires complex control group management and attribution and that ultimately limits the number of marketing interactions that can be tested at any given time.

It’s like if a convenience store owner is trying to grow his business and bring in more revenue, but is limited to selling only soda and air fresheners or is only allowed to sell between 8 p.m. and 9 p.m.; he would never achieve growth goals because of such strict limitations.

In the same vein, marketers can use machine learning to increase their options at a massive scale. It can test literally thousands of marketing interactions on millions of customers at once and discover the conditions and contexts for optimal targeting.

Machine Learning Considers Cannibalization

It’s a fact that not all marketing initiatives are effective. Far too often, marketing is highly cannibalistic. Marketers frequently give away too much value for a given call to action.

For example, with the traditional way of testing, if you run an offer that results in a 16% response rate, you may continue to run it because that is a good response. However, if you are able to harness the testing and learning capabilities of machine learning to look at the profitability of that high response rate and see a negative lift, you may make a different decision and readjust your strategy accordingly.

In order to optimize future marketing strategies and maximize the revenue it generates, marketers need to look differently at performance measurement—over a long-term horizon that continually focuses on customer lifetime value impact, not based on a single campaign performance.

Machine Learning Balances Multiple Objectives

Marketers need the ability to weigh the impact on one metric versus another so that potential tradeoffs are understood and optimized.

Machine learning allows you to measure impact on more than one objective at a time, such as the impact on quarterly revenue versus customer retention, depending on the goal you need to meet. With a machine learning-based approach to marketing, it’s possible to measure the impact of taking no action so you can measure against a control group.

As consumers have become accustomed to and now expect to receive personalized communications from preferred subscriptions and service providers, marketers are faced with the daunting task of achieving nano-level targeting.

They must employ a hyper-personalized customer experience, repeated at scale, and then measure whether it positively affected their companies’ bottom line. Even with an army of data scientists and marketing experts, succeeding at marketing effectiveness and efficiency can benefit from using a data-driven technology that is powered by machine learning.