All marketers have one thing in common — they want to know everything about their consumers. Consumer brands in retail, food, and personal care may have different business objectives, but they all want to create meaningful and authentic digital experiences for their customers.
Consumers have evolved at the same pace as technology in the last few years. Because we have so much data at our disposal, wouldn’t it be great to create real-time personalization for every individual consumer? Predictive personalization can enable this by helping you understand relevant consumer context and patterns like dynamic interests and behavior, events, time, and weather.
If you’re a marketer, you probably have a bunch of resources at your disposal, including:
- Market research firms like Nielsen that provide sales, industry insights, and demographic trends
- Tools like Google Analytics and Google Tag Manager that collect and make sense of digital data
- Marketing automation tools like Salesforce Marketing Cloud
Each of these tools helps identify and build customer segments, for which you can create different experiences. Unfortunately, however, there are a few limitations:
- While all this data is quite robust, it’s also static. It doesn’t take into account changing consumer behavior or dynamic preferences, and you are always looking at old data. In the past, this was a good enough way to plan for the future, but it’s not predictive or timely.
- Personalization through segmentation is time- and labor-intensive. The more you want to personalize for your consumers, the more time and resources it takes — and ultimately doesn’t allow for digital personalization at scale.
- The processes of data analysis and personalization are not smart. They are limited by the time and capabilities of humans and aren’t able to adapt in real time. AI-powered marketing solutions are able to find patterns and insights that humans can’t.
Using a predictive personalization tool as an add-on to existing tools can help you understand context like timing, external factors, and user behavior to predict buyer intent and create relevant experiences.
How to Understand Context in Digital Marketing
The best way to explain how this is possible is with an example. Around the time of the “Game of Thrones” finale, one of our partners BevMo!, an alcohol retailer that’s constantly running promotions and campaigns through its e-commerce website, was selling GoT-themed wine. Not everyone was obsessed with GoT (crazy, I know), and the marketing team didn’t have time to prioritize or plan a whole campaign, so the team turned to our AI marketing solution.
Our AI was smart enough to identify customers who would be interested in the GoT-themed wine and targeted these superfans with promotions around the same time as the series finale. These customers were able to make purchases in time for their GoT watch parties, and BevMo! captured an opportunity that otherwise might’ve been missed.
AI-driven personalization tools like Breinify are able to collect and process large amounts of data so you can understand individual customer profiles and use them to predict exactly what your customers need. As a marketer, this will not only save you time and provide quality insights, but also produce superior results.
Customers have so many circumstances that affect how they interact with your brand. You can use this context to combine them into a powerful and personalized experience!
To learn more about how Breinify can help you present meaningful, context-driven experiences to customers, get in touch with us!