Do You Even Need Predictive Marketing?
It comes down to insights versus actions.
Predictive Marketing is the umbrella term encompassing processes that rely on AI and machine learning to drive actions with accurate predictions. This is in contrast to traditional marketing, which has been built on descriptive and diagnostic processes.
Business questions are formulated and translated into requirements that include data collection, tooling, and governance. KPI’s are created and these leading and lagging indicators are put into reports and visualizations for statistical analysis and “data storytelling.” The results are insights—impressions of “what’s going on.”
There are two main limitations of this approach. First, statistical methods rely on guesses of what factors to consider. It is difficult or impossible to analyze every variable with respect to every other. Second, reports and visuals that demonstrate trends do not identify individual records that exhibit them. More steps need to take place to actually apply the insights.
Business questions are answered by the data itself. Machine learning is used to understand relationships within sets of known outcomes so that the patterns can be applied to never-before-seen data. Typical questions are “yes-no?” (binary), “which of three or more?” (multivariate), or “how much?” (forecast regression). No presumption of the causal factors is required. Simply by identifying which features (columns) in the known outcome data represent the answers, machine learning can fill the blanks for the new data. The result of AI-driven predictive analytics are data sets with record-by-record scores of which individuals are likely to exhibit the behaviors.
With predictive AI, knowledge of statistics, data science, and programming is unnecessary. Most importantly, marketers can instantly switch from insights to actions. For use cases such as lead scoring, conversion optimization, attribution, content targeting, cross-sell uplift, lifetime value forecasting, and churn reduction, actions always beat insights. Think of the value of simply examining data within your existing martech stack to produce ranked to-do lists for your most important marketing programs.
Sound like magic?
Predictive marketing is built upon sound AI science, with proven reliability and accuracy. In fact, the explanations of why machine learning algorithms made their predictions are tremendously valuable. By listing which variables are most predictive, AI reveals the most important elements for focus in marketing outreach—even ones you never suspected might be operative.
A famous movie director once opined that if film began as color, no one would have thought of inventing black-and-white. Try predictive marketing with Squark and see if your vision of the future is suddenly more colorful.