In keeping with its mission to help brands connect with customers at every critical moment, Signal has distilled venture capitalist Mary Meeker’s recent 2017 review of internet trends into a few big ideas for marketers. You can read about our first big idea here: Customer Conversations Beyond the Keyboard. Our second’s here: The Intersection of Addressable Advertising and Customer Identity Ownership.
Our third big idea is: The Multiplication Effect: Context x Personalization x AI.
What’s happening? In their ongoing quest to improve both ROAS and relevance to their customers, marketers continue seeking more effective ways to deliver the ideal message to a specific person at critical moments in the brand-customer relationship, Meeker notes. This requires the use of more layers of data, both contextual (environmental variables such as day/time, geographic location, weather and content keywords) and personal (an individual’s browsing and purchasing history along with other personal attributes such as demographics).
For a growing number of marketers, 2017 is the year of embracing artificial intelligence (i.e., machines mimicking cognitive human functions like learning and problem solving) as an essential tool to manage these vast amounts of data for precise and automated targeting of all types of marketing, from display ads to emails.
Why is it big? There are several motivating factors to consider:
- Elevated consumer expectations for both contextually and personally relevant brand experiences. These expectations stem from consumer experiences with recommendation-driven brands like Amazon and Spotify.
- Growth of location-related targeting options, fueled by pressure to drive foot traffic to physical locations and made possible by ubiquitous smartphones with high-speed internet access and location-oriented apps (e.g., Google Maps, Uber). In fact, BIA/Kelsey predicts that 45% of mobile ad spending will be location-targeted by 2021, up from 38% in 2016.
- Rising awareness among marketers of not only the benefits of one-to-one personalization, but also the feasibility of doing it with AI. eBay, for example, has incorporated machine learning algorithms to personalize email messages and display ads. “We understood that we needed to reinvent our strategy to be more customer-focused and treat each customer as an individual, rather than a member of a group,” Alex Weinstein, direction of marketing technology and CRM at eBay, recently stated.
By way of illustration, let’s look at the evolution of location-based targeting. It started with targeting consumers by city or neighborhood, typically in attempts to drive traffic to local chains. As location precision improved, it became possible to target advertising to consumers as they approached a specific store (a.k.a. geofencing). The next step was triggered ads based on location-specific variables such as temperature, storm forecasts, UV index or pollen count. (Think: hot vs. cold drinks, sunscreen and allergy remedies.)
Since acquiring The Weather Company in 2015, IBM has expanded its weather-triggered ad targeting system and linked it with the Watson AI platform, enabling brands like Subaru to incorporate predictive weather modeling to determine which mobile ads to run alongside the weather forecast and to which audience segment. For example, one ad runs to the automotive brand’s outdoor-adventures segment when the weather is ideal for outdoor activities, while a security-seeking segment sees a safety message when driving conditions are poor.
Finally, when a brand can add its own customer knowledge to that context — for example, knowing that you’re a Starbucks rewards member and your favorite summer drink is an iced caramel macchiato — both targeting and messaging can be even more precise.
What should marketers do?
- Expand your identity graph (your database of addressable consumer profiles used for marketing activation and analysis) to include new personal data points on your customers, such as loyalty program status or self-reported preferences, to support more precise ad targeting and message personalization.
- Consider which contextual/environmental variables could best complement your first-party data, to further improve your ability to deliver the right ad to the right person in the right context.
- Talk to your analytics team about AI capabilities that you might already have in your martech stack and could put to greater use.
Everybody wants ROAS and customer engagement to improve. So, jump on the trend: multiply your marketing precision by using AI to process and filter more layers of contextual and personal data.
Originally published June 27, 2017