Artificial intelligence (AI), perhaps the hottest buzzword of the current century, means different things to different people. For some, it’s an over-hyped tech trend that still has a long way to go before maturity. For others, it’s the force that drives so many established and upcoming innovations, including big data and the Internet of Things (IoT).
Either way, there’s no denying the huge impact that AI has had on traditional industries. For digital marketers, AI offers an incredibly useful set of tools that enable marketers to analyze massive troves of customer data, giving them a cleaner and more accurate look into customers’ needs.
Plus, with mobile usage rates going through the roof in recent years, AI and mobile marketing are crossing paths more frequently. Mobile platforms have always been an essential component of digital marketing, especially now that customers and businesses are using these platforms to their advantage.
Consequently, AI is playing a significant role in helping marketers make sense of the ever-increasing troves of data as more users spend longer hours on their mobile devices. Here are some of the ways AI is helping disrupt digital marketing on mobile devices.
1. Mobile Ads
There’s been no question about the potential impact of AI on ads, especially on mobile. The bigger question perhaps had been when that revolution was going to take place. And when Forrester published its report, “Predictions 2017: Mobile is the Face of Digital,” at the end of 2016, it was evident that time had come.
The Forrester report found that advertisers and marketers were increasingly using mobile platforms to link brands with their customers, with many of them using AI-powered platforms and innovations such as chatbots, messaging apps, and the likes of Siri and Alexa in mobile ad campaigns.
Unlike desktop platforms, mobile platforms provide unique, location-based data that, when analyzed using machine learning algorithms, can help marketers determine what customers like and when they like it, sometimes even before they walk into a store to buy it.
Coupons aggregator RetailMeNot, for instance, has partnered with multiple companies to help them make sense of customer data. The aggregator’s mobile app sends push notifications to customers when they are in close proximity to a store, informing them of special offers and deals associated with that store.
2. Advanced mobile analytics
Marketers have always had a difficult time measuring the effectiveness of mobile marketing campaigns, especially when it comes to tracking the buyer journey across multiple touch points. Click-through rates, which are often used to measure the effectiveness of ad campaigns, are usually ineffective when determining ad ROI. This often denies marketers an unobstructed view of the entire buying journey, which then makes it difficult when justifying the mobile spend.
With AI-powered tools, however, marketers have a significantly higher chance of making sense of the buyer journey by analyzing real-time behavioral data from potential customers across multiple channels, including mobile. Multi-touch attribution (MTA) models, which have become increasingly popular in recent years, often ride on AI-powered algorithms to help analyze the mountains of user-level data.
The results of such analyses, while not perfect, are helping shed light on a user’s journey to purchase as LiveRamp illustrates in this cross-channel attribution guide.
3. Personalized customer journeys
In a perfect world, marketers would have no problem fitting customers into neat, convenient personas with specific guidance on when, where, and how to target those customers. In the real world, however, this is not always the case.
Priorities and situations are always changing. Consequently, your customers’ needs will always be in a state of flux, which makes it difficult for you to offer personalized and relevant content on different channels.
With finite resources and limited time, AI can help mobile marketers create authentic and personalized customer experiences beyond basic segmentation.
AI is helping companies with huge amounts of mobile customer data to break down customers’ wants, needs, and personalized preferences across different channels, which enable these companies to deliver contextualized messages to the right customer at the right time.
Through machine learning, these algorithms can pick up on patterns on mobile platforms and, with time, generate the perfect communication interface for keeping customers engaged.