AI and Machine Learning are Becoming the MVPs of Marketing

In the past few years, AI and machine learning have paved their way deeper into marketing. Marketing is at a new phase of sophistication where Big Data, AI and machine learning are factors in strategy development and decision making. Both AI and machine learning are tied closely to Big Data and Analytics. Due to the increased availability of large data sets as well as new computing technologies and vastly improved algorithms, both AI and machine learning have evolved.

One of the common uses for AI is to help organizations get better insights into large data sets and use these insights for marketing intelligence to improve their marketing research, forecasting and campaign experiences to give them better competitive advantages and bottom lines.

A survey by Weber-Shandwick and KRC Research of 150 Chief Marketing Officers from companies in the US, UK and China with more than $500 million in revenue found two thirds of them believe AI will be a significant factor in their future marketing operations. Another survey of B2B marketing executives at companies with 250+ employees believed the best use of AI was for marketing.

The survey also found that nearly seven in 10 CMOs (68%) reported that their company is currently selling, using or planning for business in the AI era, and nearly six in 10 (58%) believe that within the next five years, companies will need to compete in the AI space to succeed. As for their own roles, 55% of CMOs expect AI to have an even greater impact on marketing and communications in the future than social media did.  AI tools previously available to enterprise level businesses are now more accessible and affordable to small and medium sized companies.


Among the unique uses of AI for marketers are:

  • Chatbots. Chatbots are computer programs that can mimic conversations with consumers. The AI now is so advanced that many chatbots are virtually indistinguishable from an actual person talking. Chatbots are used in a variety of marketing and advertising functions—anything from automated-customer service robots to bots that reach out to customers to ask survey questions or gather other feedback.
  • Generating content. For certain functions, AI content writing programs are capable of extracting elements from a dataset and creating an article. An AI writing program called “WordSmith” produced an astounding 1.5 billion pieces of content in 2016, and is expected to grow in popularity in the coming years.\
  • Content curation. Content curation generated by AI allow marketers to show visitors the content or products they would most likely be interested in. Amazon is an example of a site that shows “customers who bought A also bought B.” And Netflix recommends programming to its individual subscribers based on their previous viewing habits of types of programs, genres, etc.


Without machine learning, it would be too difficult to compile and process the enormous amounts of data coming from multiple sources such as website visits, online purchases, mobile app usage, etc. required to predict what marketing offers and incentives, etc. will be the most effective for each customer.

Pattern recognition in machine learning can also help marketers in numerous ways with generating insights about customer behavior, including:


  • Customer churn prediction. Churn prediction is one of the most popular uses of Big Data in marketing/business. It consists of detecting customers who are likely to cancel a subscription to a service. By discovering patterns in the data generated by customers who churned in the past, machine learning forecasting can accurately predict which current customers are at a high risk of churning and implement procedures to hopefully discourage the churn.
  • Customer segmentation. Machine learning customer segmentation models are extremely effective at finding and extracting homogeneous groups of customers with similar behaviors and preferences. Successful segmentation helps to drive dynamic marketing content and personalization tactics for relevant and more effective marketing communications. It is estimated out of 30,000 new consumer products launches each year, 95% will fail because of ineffective market segmentation as the wrong customers are targeted.
  • Customer lifetime value forecasting. CRM machine learning systems are an excellent way to predict the customer lifetime value (LTV) of existing customers.  LTV aims to find, nurture and retain the customers who create the most value for a company in the long term. When a company is able to predict LTV, it can then better manage customer relationships, maximize the effectiveness of marketing and retention initiatives, optimize the resources invested in retaining each customer and generate more accurate customer metrics.


Great marketing has always been data driven. With AI and machine learning driven by Big Data, marketers may well be entering a golden age of better targeting, decision making and customer satisfaction, leading to greater ROI and bottom lines.


Manohar Vellala is Groupware Technology’s Practice Manager for Big Data & Analytics.