It’s true that more and more industries rely on automation to boost efficiency and productivity, and marketing is no exception to the trend. Although marketing as a function still exists as a people-focused one, technology helps marketers reach their desired audiences faster and at a more accurate rate.
Targeting the right customers will always be at the heart of successful marketing, and machine learning shortens the path for marketers to achieve their desired campaign results by analyzing customer data such as previous purchase histories, demographics, and lifestyle habits. In 2022 and beyond, machine learning will continue to play a major role in targeted advertising and will eventually replace traditional methods of contact sourcing and customer data analysis. Although companies will be slow to accept new advanced automation technologies at first, once they find one that works for them, it’ll be of great benefit to their productivity and results.
Three Ways Machine Learning Helps Marketers Reach the Right Audience
Nobody likes being flooded with out of the blue ads that don’t pertain to their interests and needs, resulting in deleted emails and overlooked calls to actions. With machine learning, marketers can learn not only the types of customers to target, but also how and when to target those customers. Here are three examples where machine learning comes into play:
1. Profile Building – Before blasting out promotional emails or sending registration links to your organization’s latest webinar, you must first know who’s most likely to respond to those forms of communication. Instead of spending hours on ZoomInfo looking for people with a particular title to put on the webinar email list, machine learning simplifies the process tenfold by creating an algorithm that analyzes existing data for you. Marketers can then build profiles of ideal customers based on real, hard data rather than going through a process of trial and error with prospects.
2. Ad Placement – If data shows that a certain group of customers prefer to make purchases while scrolling through social media, marketers can take note of that and only target them through Instagram, Facebook, or Snapchat ads made to look like regular posts. Just by knowing where customers are most receptive to ads, companies can save money on producing and placing ads in other places that aren’t customers’ main shopping sites. Machine learning helps narrow down the channels of focus so marketers can do a better job of customizing the ads themselves and perfecting their placements where customers are most likely to see and respond positively to them.
3. Timing of Product Placement – By analyzing customers’ purchase histories and interests, machine learning lets marketers know exactly when to show certain products to their ideal audience. For example, a stationery brand wants to know who’s most likely to purchase their newest Christmas card. After running an algorithm to search for customers who purchased Christmas cards from them in the last two years, they notice a pattern of people only purchasing cards three weeks before the holiday. So, the brand decides to increase promotional sales around Black Friday to attract the newest batch of customers, knowing that their highest volume of sales is most likely to occur around that time. Without machine learning to bring this personalized data to light, marketers need to play a guessing game of not only who, but when to launch their campaigns.
Machine learning in marketing is a relatively new concept, but it’s one that will continue to spread as more companies realize how personalized data positively impacts the speed of decision making and the ROI of marketing campaigns.
Although some might think that machine learning means machines taking over marketers’ current jobs, the function is actually the exact opposite of that line of thinking. Machine learning is a helpful tool that makes marketers’ jobs easier and garners a higher rate of success than the “old” way of simply blasting out ad campaigns, newsletters and emails to a list of potential prospects found through a contact database.
As more people grasp the true concept of machine learning and how it benefits companies, they will embrace machine learning as a key driver of marketing strategies in the future. That will lead to better targeting, successful campaigns and more revenue.
The author, David Finkelstein, is CEO of BDEX.
