AI in Advertising

AI is Transforming Advertising — and the Buyer’s Journey

Nudging Customers Along Their Journey

A group of young women are walking together along a San Francisco street, passing restaurants and storefronts. It’s mid-morning, about 10:00 a.m. The women approach a digital display outside of a restaurant, and a video begins to play. It shows a group of young women sipping mimosas, then flashes an ad for brunch specials. One of the women in the group passing by says, “Ohhh, Mimosas!” and convinces her friends to stop for a bite to eat. The ad is successful because its content is specifically targeting the viewers. Coincidence? Unlikely.

Photo credit: Volta Charging

Chances are AI is crunching data behind the scenes. Precision advertising such as this digital-out-of-home (DOOH) example is becoming more common, as marketers learn about the vast capabilities AI can offer for creating targeted, compelling advertisements across channels — from social media to digital signage. AI and specifically machine learning (ML) algorithms are transforming the way businesses connect, engage and market to consumers, delivering ROI to marketing teams while also providing some surprising benefits to consumers along the buying journey.

The Use of AI for Advertising Has Evolved Over Time

AI’s usefulness for marketers has evolved and expanded since its humble beginnings, when collaborative filtering enabled simple recommendations engines made popular by Amazon and Netflix back in the late 1990s. In 2014, programmatic ad buying became popular, automating the process of researching target markets and enabling better metrics tracking for analyzing effectiveness. This helped marketers fine-tune ad campaigns to increase conversions.

And in recent years, Google’s complex, AI-powered search algorithms have made search ranking an art that marketers strive to master, providing consumers with more and more relevant answers to queries despite the vast and ever-growing volume of data available across the web. In-home digital assistants have become eerily accurate at producing viable matches to complex search queries based on very specific, individual preferences and behaviors.

But these innovations only scratch the surface when it comes to the potential for AI to transform how marketers attract, convert and retain customers. Here are some of the most common ways AI is being used to optimize marketing spend and deliver greater value to customers and prospects today:

• Campaign and Advertising Optimization: Modern marketing teams leverage machine learning algorithms to process data about a target’s location, past purchasing behavior, brand perception and more. AI can act on this information to constantly refine and tweak content and messaging, increasing conversions and reducing spend over time. Knowing what activities, content and messages have the most impact guides budget allocation decisions and helps teams avoid wasting dollars on campaigns that yield little return. Some types of modeling involved in campaign optimization include:

1. Visitor behavior modeling algorithms that analyze the behavior of website visitors, allowing for real-time campaign optimizations that target audiences most likely to convert.
2. Content optimization algorithms that help determine the best layout and copywriting choices so that deliverables are relevant to specific groups of recipients.
3. Campaign effectiveness analytics that help identify what customer segments to include or exclude from a campaign, as well as which campaigns deliver the most bang for the buck.

• Email Marketing: AI significantly reduces the time it takes to create, deploy, test and optimize personalized email campaigns. Using ML algorithms, marketers can analyze millions of data points about the consumer to determine the best times to contact them, how often to contact them, and what content is most relevant, given their interests, preferences and behavior. AI can surface which subject lines are most effective in encouraging click-throughs, as well as optimal email length for various target groups and what content works best in different phases of the customer journey. For example, clustering algorithms can help teams identify segments of its audience that read blog posts on specific topics, then personalize email communications with similar or related content.
• Digital OOH Displays (DOOH): According to a new report by Allied Market Research, the global DOOH market was valued at $3.6 billion in 2016 and is projected to reach $8.3 billion by 2023. DOOH is the evolution of the “poster,” and replaces static billboards and other OOH displays with dynamic, interactive digital advertising that can react to viewers in real time. DOOH displays can use sound sensors and cameras to collect data that feeds into AI algorithms that enable advertisers to tailor messages to passers-by. For example, cameras can be used to identify demographic characteristics of groups of people looking at the display, and serve up relevant ads and content. Or a sensor can detect changes in the weather, and trigger the display to serve up ads targeting weather-related purchases, such as umbrellas or sunscreen. A DOOH display at a gas station or electric vehicle charging station can tailor ads to the type of car parked in front of it. By enabling this level of personalization, AI helps to increase conversions, while collecting valuable data about the target audience that can be used to refine and perfect other marketing outreach.

Nudging Customers Along their Journey

AI will continue to usher in marketing innovations that nudge consumers along their buying journey at every touchpoint, and provide opportunities for upselling and cross-selling along the way. One example is the in-store experience. Strategically placed sensors can feed geo-spacial data through AI algorithms, enabling advertisers to target consumers with location-based ads and displays as they walk though malls and department stores. Advertisers can send department-specific offers directly to their mobile phones, or change display ads based on recent google searches the shopper has performed.

Some retailers are even experimenting with facial recognition software embedded in DOOH displays, which can collect data that changes elements of an ad in real time. And machine learning algorithms will get really good at learning how to connect with individual consumers at very specific times and places, delivering maximum value to both the business and the consumer.

Advertisers won’t be the only ones who benefit — this level of personalization and targeting improves customer experience. Today’s consumers are bombarded with messages and ads that don’t pertain to them, which is causing them to tune out and miss opportunities to take advantage of new products and services that might enrich their lives.

With AI doing the filtering and targeting, the guesswork is removed, and marketers can whittle down their audiences to the people most likely to complete — and benefit from — a purchase. In this way, AI actually enables merchants to be more helpful in meeting consumers’ needs, faster. The end result is less money spent creating more satisfied, loyal customers.