Machine Learning with Transparency Changes the Art of the Possible in Marketing
Most marketers are just beginning to explore machine learning applications. Machine learning is already providing tremendous analytic efficiency gains and increased precision. One of the biggest challenges though is knowing what is possible with machine learning, and aligning its capabilities to the broad range of insight generation, campaigns, interactions and measurement activities that occur every day. Here are a few of the ways that marketers can improve each of these areas with machine learning methods that provide the required level of transparency to take effective action.
Customer Lifecycle Modeling – When Transparency is Critical
The degree of speed and efficiency gains that can be applied to predicting customer behavior is in the 80%+ range coupled with a lift in predictive performance of 20% to 100%. However, churn, next best action, payment or collection risk, path to purchase and conversion models need the Why factors to understand, personalize and take effective action. Without knowing Why an algorithm is predicting an outcome, it’s very difficult to craft a relevant offer message and treatment to the customer.
Dynamic Predictive Segmentation – Predictive, Granular and Relevant
Most marketers use static customer segmentation that can’t continuously process and factor in contextually relevant customer data. Dynamic predictive segmentation segments customers based on propensities, with important shared predictive characteristics revealed for each segment. Marketing executives can now serve relevant content to their customers in real time and understand the key drivers of customer behavior when using dynamic predictive segments vs. static segments.
Explainable Forecasting – Understanding the Drivers of Future Forecasts
Transparent machine learning applications are capable of using massive amounts of historical data to predict future sales, product demand, inventory, media consumption, spending patterns, and campaign response, just to name a few. By understanding the drivers of forecasts, marketers can provide much greater insight into expected future outcomes and adjust strategies to optimize results.
Trending & Pattern Detection – Uncovering Hidden Trends & Patterns
Machine learning software can sift through enormous amounts of data quickly. But without transparency, hidden trends and patterns can’t be uncovered, only predictions can be made. With transparent machine learning, granular level factor analysis reveals changes over time that can be continuously tracked, measured and monitored. Patterns are reflected through time-based comparisons to uncover new insights about buying, consumption, spending, defection, usage, etc.
Contextual Customer Experience – Self Adjusting Recommendations
Being able to predict, using up to the moment interaction data across touchpoints, what a customer wants, requires machine learning technology. Understanding then the context of how best to engage with the right message requires transparency into Why they are predicted to exhibit a particular behavior. Finally, being able to make personalized recommendations that automatically adjust during the interaction enables the highest level of contextual engagement. This is now possible with transparent machine learning methods.