5 Components That Usher In Modern, Predictive Marketing
In a world full of choices, we’re drawn to experiences that anticipate our needs and wants. Think about it - we finish binge watching our favorite TV series, and love when we’re served up the next show, or when our phones prompt us to leave for work earlier than normal because traffic is heavy.
Because these experiences are built to make our lives easier and more enjoyable, they can make the world feel like a pretty magical place. However, the capabilities needed to customize experiences for specific individuals is extremely complex. Despite the myriad of technological innovations, marketers struggle to figure out how to deliver these magical experiences. There’s intense pressure to make strides in personalized interactions, and marketers haven’t always been able to deliver that sense of awe.
If brands serve up a generic, one-size-fits-all experience, we’re left disappointed and frustrated. When marketing predicts a consumer’s need before it arises, and delivers content which then exceeds expectations, real magic can happen.
This is called predictive marketing, and requires marketers to rethink how they interact with desired customers, moment-to-moment. The new approach allows marketers to garner greater return on advertising spend, achieve greater depth of engagement, drive increased conversion and inspire greater consumer loyalty and evangelism. Dated strategies around assuming customer response, instead of anticipating it, are no longer effective. Here are five core components that make predictive marketing successful:
#1 Focus on the person, not the device
Device targeting should not be the end goal for modern marketers - if it is, you’re behind the curve. The key is in creating profiles from trillions of signals and matching people to devices through both probabilistic and deterministic methods. From this, marketers can develop a comprehensive idea about who consumers are, what they care about, and when they care about it. This data is bigger and more detailed than any human brain can grasp on its own, but with artificial intelligence we’re able to detect patterns and insights we might otherwise never see.
#2 Model moments, not segments
Marketers traditionally rely on static audience segments as the foundation for targeting efforts. Now, as predictive marketers, we use technology that optimizes media and creative in real-time, to reach a specific time, place and state of mind. We make decisions about the moment, in the moment, analyzing thousands of attributes that might influence the consumer’s experience. By focusing on the moments that matter most to consumers and brands together, marketers improve campaign performance, viewability, and brand safety.
#3 Own data, don’t rent it
Most marketers today buy the same data as everyone else, including their competitors. This isn’t the right path for a competitive advantage. Predictive marketers need to leverage first party behavioral data to generate unique, cost-effective models that are hyper-accurate. Data should be one of your largest assets, not an afterthought. A simple idea to start building owned data is to onboard consumer records and email data. This can lead to loyalty and cross/up-sell opportunities, while also improving prospecting by finding act-alike consumers based on specific models.
#4 AI paves the way for enhanced consumer journeys
The old marketing funnel tried to simplify consumer behavior, bucketing particular demographics of consumers together. However, with artificial intelligence, we don’t have to streamline consumer behavior to understand and respond to it. Instead, modern marketers adapt to a consumer’s actual journey by optimizing content and channel in real time, target individuals at the right point in their journey, and generate insights about what truly drives awareness and conversions.
#5 Go beyond big data, make big decisions
Until now, there’s been too much noise, too much latency, or too much difficulty processing and integrating vast amounts of data. We’re now in an era where machine learning smooths integration, speeds up processing, and extracts signal from noise. Data should be judged by the quality of the decisions it helps us make. That’s why it’s crucial to constantly test and tweak models, highlighting new attributes or assigning different weights to attributes. And after we run carefully chosen A/B tests on models over the course of hundreds of thousands of impressions - we use all of that rich data to make better marketing decisions.
Predictive marketers need to see the raw potential in moments, and the meaning in complexity. To anticipate and respond to the needs of consumers, predictive marketers must align data, technology, and people.