In our recent survey, more than 60% of marketers revealed that they are planning to increase their usage of AI going forward — proof that more and more marketers are realizing AI’s potential to enable greater marketing success.
However, very few marketers are taking advantage of advanced AI capabilities that can not only enable them to do more than just acquire new prospects but also convert them into loyal and more valuable customers.
How marketers integrate AI into their strategies can determine their overall marketing success. They should consider AI as an integral component of their entire marketing game plan instead of treating it as a mere tool to bolt on when needed. Even before they consider specific AI techniques to implement, they should first look at their strategy for using AI by asking the following questions:
Here are our recommendations on using AI for these three key aspects of marketing.
Acquiring new prospects can be a very difficult endeavor no matter the size of the business. Increasing [brand] visibility and generating quality leads are two of the biggest marketing challenges businesses face.
Traditionally, marketers employ highly manual, time-consuming, and blunt approaches to win new customers. To get a decent list of prospects, for example, they use demographic data such as industry and job title to purchase lists, get referrals, and harvest their website visitors. After pulling this data into their systems, they then have to start engaging with these prospects to further narrow down this list to pinpoint and target the right customers.
AI can give marketers the capability to obtain relevant and useful information quickly using behavioral data. In fact, almost half of surveyed marketers (43%) use AI primarily to acquire new prospects using audience expansion techniques such as the following:
Look-alike audience expansion.
AI can be applied to the demographics, preferences and behavior of your existing customers to develop predictive scores of your best customers. You can then use this set as a seed list on a large network like Facebook to acquire similar audiences there.
Targeting and re-targeting users.
Almost 40% of surveyed marketers use AI techniques to better target audiences on large networks such as Facebook and Google. AI can be applied to prospect behavior to develop predictive scores that can then be used to re-target prospects with specific offers on the large networks. Similar techniques can be used to re-activate existing customers and prevent them from churning.
One of the biggest challenges for marketers today is around delivering personal and content-rich experiences at every touchpoint in the customer journey. AI has a lot to offer today to enable marketers to intelligently and insightfully engage their customers on a highly targeted basis, with recommendations and micro-segmentation.
Using AI-enabled technologies like collaborative filtering, for example, marketers can predict customers’ interests based on the preferences and behaviors of others similar to them. But despite the proven capabilities of collaborative filtering, only 6% of marketers are using it. Similarly, only 16% of marketers are using predictive techniques to learn user preferences or affinities for various products and services based on their behavior and to segment them using these affinities.
According to Harvard Business Review, “acquiring a new customer is anywhere from 5 to 25 times more expensive than retaining an existing one.” So if marketers want to save on costs while keeping their sales up, they should focus on creating more value out of their existing customers. To do so, they should harness both historical and real-time customer data—but herein lies the problem.
The majority of marketers (54%) are using less than half of the customer data they have. As a result, they are not taking advantage of the potential in this data to help them improve customer engagement and increase share-of-wallet. The study results also show that those marketers who have been able to get advanced access to their data are 2.4 to 2.8 times more likely to deployed AI techniques like predictive affinities and collaborative filtering.
