Speed to Insight – How Marketers Can Achieve Long Term Competitive Advantage

Speed to Insight – How Marketers Can Achieve Long Term Competitive Advantage

Machine learning helps level the playing field for all marketers if applied effectively, because it can quickly be deployed at speed and scale within critical areas of the business – such as preventing high value customers from leaving or more effectively attracting and delighting high value customers through more relevant dialogs.  But it all starts with insights around where the threats and opportunities are, so that machine learning’s capabilities in upgrading speed, accuracy and automation – what we refer to as “speed to insight,” can be focused in the right areas of the business.

So how does machine learning reveal key threats and opportunities?  First, it helps to use a machine learning technology that provides granular level transparency into what is driving customer behavior.  A good classifier will accurately predict high value customers likely to leave or likely to want to purchase a specific product or upgrade, however, understanding Why an algorithm predicts that a customer will exhibit a certain behavior, is critical.  The Why provides the insights needed to make the right decisions across the entire customer lifecycle.

Similarity based machine learning provides the Why.  And as a method, when operating at the speed and scale required to integrate vast volumes of data continuously into explainable predictions in real-time, it can reveal the factors driving a predicted outcome by individual customer.  Similarity is going to compare the distance of a customer to the most similar customers in your database that have previously exhibited a behavior.  It is then going to reveal the factors associated with those customers that were the most important in influencing them to act a certain way.  Dynamic factor analysis vs. traditional k-means will further weight each factor for each prediction based on that specific customer’s “distance” from the most similar objects.  This yields a highly accurate prediction and depth of insight into Why.

Furthermore, by grouping individual predictions together that share common characteristics, a group of consumers predicted to exhibit a particular behavior can be analyzed, evaluated, monitored and treated in a similar manner. Trends and patterns can be revealed to uncover critical insights into likely defection and its causes, new sales opportunities and what’s driving them, and how the factors affecting behavior are changing over time.  Knowing the Why provides not just data, but critical insights at speed and scale.

Similarity based machine learning can uncover new insights at granular levels of detail.  This is one of the first steps to using machine learning to become an insights driven business.

A critical place to start is to look at your existing customers through the lens of a machine learning model that provides insight into the sub-segments of customers. Similarity based machine learning can uncover new insights at granular levels of detail.  This is one of the first steps to using machine learning to become an insights driven business.