3 Ways AI Can Make The Most Of Mobile Data

AI has the ability to help companies understand their customers’ mobile behaviors to enrich the customer experience they have on their devices. For digital leaders, that value cannot be overstated.

This article is part of CMO.com’s February series about mobile. Click here for more.

There’s no arguing that smartphones are firmly entrenched in our everyday lives. In fact, recent Adobe research found that consumers spend the same amount of time sleeping as they do consuming content on their smartphone devices.

And while the phone provides a means of connecting with consumers wherever they are, customers have always been one step ahead of brands. But as the adoption of artificial intelligence accelerates, brands may be able to catch up, experts say. AI is making it much easier for them to deliver vastly improved experiences to today’s consumers.

“The widespread adoption of mobile phones and then smartphones completely changed the consumer experience,” said Sheryl Kingstone, research director, customer experience and commerce, at 451 Research. “Add intelligence to the mix, and the future is all about aggregated learning regarding behaviors and preferences that can help ensure relevant customer interactions.”

For digital leaders, that value cannot be overstated. AI has the ability to help companies understand their customers’ mobile behaviors and enrich the experience they have on their devices.

“Data has become the new currency of marketing, and marketers now have access to more data than ever,” said Frank Palermo, EVP of IT consultancy Virtusa. “The question comes down to how to harness the value from that data.”

Three ways AI is helping companies better leverage their mobile data to provide meaningful experiences are personalization, predictive analytics, and content generation.

Personalization

According to 451 Research’s “Corporate Mobility and Digital Transformation” study, 82% of businesses said machine learning for automated contextual recommendations is important to creating personalized customer experiences.

“Key advancements [in machine-learning intelligence] include data governance, synthesis, and identity, which power a dynamic customer graph to fulfill the vision of contextual experiences,” Kingstone said. “Automated reasoning helps to make inferences and enrichments on each customer profile; it provides a deeper understanding of individual customer journeys and unique interactions, combined with transactions, to accurately understand and improve customer experience.”

Used well, AI can help companies deliver that highly personalized experience based on a deep understanding of factors, such as what motivates a consumer and that person’s past behavior. This intelligence can fuel the ability to offer relevant recommendations—“one of the most popular and effective forms of personalization used in digital consumer experiences,” said Kevin Lindsay, director, product marketing, at Adobe, who noted that Amazon’s recommendations reportedly drive 30% of its revenue.

Brands such as eyeglass maker Warby Parker have also tried their hand at personalization on mobile. The brand’s iPhone app uses Apple’s face-mapping technology to precisely measure users’ faces and recommends the best glasses for every individual’s face shape. And Starbucks’ mobile app uses information it already has on its users, such as the coffee they drink or the time of day they usually enter one of its branches to craft offers, discounts, and coupons.

“By embedding intelligent personalization into customer-facing processes, businesses can build deeper connections, recommend next best actions, and create more contextually driven interactions,” Kingstone said.

Predictive Analytics

Predictive analytics are intelligent statistical tools that pull insights from big data to help forecast customer behavior accurately.

“AI is now enabling a degree of intelligent automation that the world of digital marketing has never seen before. AI and predictive analytics can be used to optimize the end-to-end customer journey,” Virtusa’s Palermo said.

For many brands, data is their most important asset.

“eBay manages 1 billion live listings and 164 million buyers daily, with about 10 million new listings from its mobile channel every week,” Palermo explained. “Processing that volume of data at scale requires sophisticated data platforms that go beyond basic digital marketing platforms.”

Predictive analytics are used within mobile applications in a number of ways. For example, mobile dating apps such as Plenty Of Fish and Tinder use predictive analytics to recommend other members who are likely to be a good match. Google uses predictive analytics for mobile searches and makes recommendations. And financial conglomerate Scottrade uses predictive analytics to forecast whether a client will add or withdraw money from an account.

Content Generation

Automating content generation has created a specialized AI field called natural language generation (NLG), which translates data and insights into useful narratives.

“Organizations are leveraging platforms such as Wordsmith to generate daily news feeds and to perform targeted marketing messages,” Palermo noted. “For example, the NBA’s Orlando Magic used Wordsmith to generate custom in-app and personalized email messaging to every one of their fans.”

The Associated Press also uses an AI-based automated system to produce many of its financial stories, he added. “This system produces millions of articles a week for the likes of AP, Samsung, Comcast, and Yahoo, and is capable of producing 2,000 articles per second,” Palermo said.

Automatic content generation can also be useful for mobile advertising. Jordan Kretchmer, a senior director at Adobe, told MediaPost in August that the biggest opportunity lies within automated creative.

“If you know that you have to create 150 banner ads, and you have to create 20 e-mail campaigns and Facebook and Instagram posts, why do I have to create every one of those manually?” he said. “The opportunity is to create systems that automatically generate headlines and automatically morph them into the different [formats] for different media, and on the analytics side, as we’re collecting information based on the performance of those ads, to automatically change the headlines to match those that are performing best. Content automation and automatic optimization is a huge opportunity.”