AI isn’t a magic bullet. It’s not enough to have the insights. Marketing teams have to have the ability to determine what the data means, and then seamlessly combine the data science with the art of marketing. The challenge is finding best practices in how to use the data, and making sure the data source is sufficient. What are the best options in terms of data modeling? How do they compare?
There are two main data models that marketers need to understand in order to gain insights from AI: look-alike modeling or real-time behavior modeling. Here’s what they do, how they compare, and how you can use each of them right away:
The premise of look-alike modeling is to find and target people who behave like your existing target market.
Marketers at one time relied on static demographic attributes. Who are the most recent visitors to your web site? Send marketing messages to that demographic. This is great for blanketing a certain age group or whatever the attribute might be. But is your message relevant to everyone in that demographic? No, so the data only takes your marketing strategy so far. AI is changing the game here.
Look-alike modeling uses machine learning to examine vast swaths of data to find patterns. It identifies characteristics of, say, the most recent visitors to your site. In looking at common demographics and behavioral similarities, you discover and target the people who “look” most like those who have already demonstrated to be your core audience.
How can you use look-alike data modeling in your work? I’m glad you asked. To find those ideal customers, who are behaving like your existing ideal customer, here’s how this could work for you: Demographic and behavioral data from a purchaser who follows the process to the confirmation page can be collected into a centralized platform. The data can then be analyzed to find common behaviors. With customer characteristics pinpointed, that data can be used to more accurately find and interact with a growing target market.
The couch you were just looking at on Wayfair appears top of screen when you head over to Mashable. Your Amazon search history shows up as ads in your Instagram feed. Do either of these scenarios sound familiar to you? Next-level predictive data modeling takes things several steps further using AI. You are then taking real-time data and targeting audiences in ways that make that Wayfair ad realize it was just scratching the surface.
Using AI for a deep dive into real-time behavior is an intent based predictive data model. Artificial intelligence is used to examine real-time behavior data. This allows you to customized messages and place them in front of the right audience in the moments when they are most likely to make a buying decision.
Regarding real-time behavioral data modeling, Nathan Pichette of Bilin Technology recently asked some excellent questions: “How would behavior data transform your marketing strategy? Have you ever published an article using a #hashtag? Think of what you could do if you could create predictive models using all relevant #hashtags to your business. Now, what would happen if that same model cross-referenced additional data sources and overlaid look-alike models to create an audience list who are your target market and are likely in the market to buy? Would that have an impact on who you present your message to and how you customize your strategy?” My answer: yes it would.
Privacy and data integrity are two issues that machine learning and AI-driven models will need to manage moving forward. Access to and use of data needs to be ethical, and the predictive nature of messaging needs to balance nearness to buying decision with just plain freaking people out with how much you know about them in this moment.
Look-alike and real-time behavioral data modelling are excellent targeting tools you can start using right away. AI is making it possible to find the right people at the right time who are ready to proceed to checkout.