Forbes recently stated that 80% of enterprise companies are investing in artificial intelligence (AI) solutions today. AI is a machine’s ability to imitate intelligent human behavior by perceiving a set of inputs and processing that information in order to reach a desired outcome.

In the martech space, companies are utilizing AI to build customer profiles, resulting in more precise ad targeting as well as unprecedented customization. Below are three recent examples of how companies are using AI to build customer profiles and drive revenue.

1. Teleflora Uses AI to Deliver Personalized Product Recommendations

A recent article in Direct Marketing News details how Teleflora, a leading floral arrangements vendor with more than 15,000 member florists in the US and Canada, uses AI to build customer profiles and provide a personalized touch.

Teleflora uses AI to personalize product recommendations and build customer loyalty.


When Tommy Lamb, Teleflora’s new director of CRM and loyalty, joined the company, he immediately realized their marketing strategy was underdeveloped. Since customers typically only used Teleflora at spread-out points in the year, the company needed strong remarketing and customer service to build brand loyalty. But rather than providing personalized offers, they only utilized a few generic holiday email campaigns.

A retail marketing platform called Bluecore gave Lamb and Teleflora the AI capabilities they needed in order to execute a three-pronged personalization plan:

  1. Teleflora first created more comprehensive customer profiles by combining their product data with their individual customer data.
  2. Next, Teleflora paired Bluecore’s machine learning capabilities with these comprehensive profiles to anticipate the future purchases of various audience segments.
  3. Finally, Teleflora integrated advanced analytics, allowing them to identify best-selling items and other purchasing trends.

This AI strategy allows Teleflora to accurately anticipate customer needs. They can target customers who are ready to buy and make personalized recommendations, driving customer loyalty and ROI. Then, their analytics solution allows them to view the results and promote high- or low-performing products accordingly.

2. BMW Leverages AI to Personalize Ad Spend and Lower its Cost-Per-Acquisition

In order to execute a recent campaign, BMW Mini worked to connect and organize its data into an actionable format. Its goal: to target adults searching for a premium vehicle who had shown interest in the BMW brand.

BMW uses AI to target luxury vehicle shoppers who have shown an interest in their brand.
Source: BMW

By partnering with ad agency Universal McCann, BMW was able to leverage its first-party data—which included people who had visited the BMW website or were already in their CRM system. BMW used this data to enhance its existing search strategy, ensuring its ads delivered relevant messaging to interested car shoppers.

BMW then utilized an AI solution to optimize the efficiency of its targeted ads. Over time, this solution optimized BMW’s ad targeting so the messaging would reach the right person, based on factors like time of day, previous searches, and BMW website visits. As a result of this strategy, BMW Mini’s conversions tripled and their cost per acquisition declined by 75%.

3. Comfort Keepers Utilizes AI to Target Caller-Ready Audiences

Comfort Keepers, one of the nation’s leading providers of in-home care for seniors, uses AI-powered conversation analytics to understand what happens on calls to each of their 450+ franchisee locations. Since phone calls make up 70% of their marketing conversions, they analyze the calls to determine who each caller is, if they are a quality sales lead (vs. a non-sales call), and if they converted to an appointment or customer.

By using this conversation analytics data, Comfort Keepers is able to fully gauge the success of their marketing efforts and prove it to each of their franchisees. Not only are they able to identify the quantity of the calls their campaigns drove, but they can also understand the quality. For example, rather than simply saying “we drove 2,000 calls this week,” they’re able to identify how many of those calls are potential new customers versus current customers. This gives them a full picture of the ROI from each of their campaigns to each location.

As a next step, once Comfort Keepers understands who converted on their calls and who did not, they can use that same conversation analytics data from AI to retarget their prospects with search, social, and display ads and use good callers in their lookalike campaigns.