Korey Thurber is a featured contributor to Predictive Analytics Deconstructed: A Super-Simple Recipe for Marketing Success report.
Here's the complete contribution:
Today’s marketers face, an overwhelming number of challenges when it comes to deploying truly personalized, choreographed and impactful communication streams across the customer journey. For every $1,000 spent, marketing activity can generate literally millions of data points; and the velocity and variety of data generated will not subside in the future.
The most significant evolution is in the field of machine learning and other solutions within the artificial intelligence (AI) family. We’ve been utilizing machine learning algorithms here at Harte Hanks for well over a decade – e.g. neural networks and support vector machines. But not until recently did marketers have access to the computing horsepower to allow these powerful algorithms to efficiently feed on the enormous amounts of data now being created, which in-turn find insights that would not have been found otherwise. There is little doubt that the AI tools and technologies required to analyze and derive insights from these massive pools of data will continue to evolve rapidly over the next few years. Well past traditional machine learning algorithms and into the realm of “Deep Learning” algorithms which are becoming all-the-rage.
Another trend gaining significant momentum is being able to truly get inside the head of consumers to understand what motivates them to make decisions. A deep understanding of your audience is critical to enable true “personalization”. The Aberdeen Group found that agencies best at personalization achieved up to a 36% higher conversion average and a 21% stronger lead acceptance rate. What will enable this personalization is the ability to predict someone’s personality, utilizing machine learning to derive insights from the vast amounts of data available. There is going to be a big push in this field in the next few years; evolving beyond predicting an individual’s personality, to predicting with accuracy how an individual’s personality evolves over time.
We will see an evolution past “predictive” analytics to “prescriptive” analytics. According to Gartner, approximately 10% of organizations currently use some form of prescriptive analytics but will grow to 35% by 2020. Traditional predictive analytics is based on probabilities, and simply estimates the likelihood that an event will occur or some specific action will take place. In short, predicting “what might happen.” Prescriptive analytics leverages the insights from numerous predictive models already in play, insights from machine learning algorithms, as well as common sense business rules, which in-turn enable the user to run what-if scenarios to estimate what will happen if they “prescribe” strategy X versus Y versus Z. Being able to evaluate the outcome of multiple scenarios simultaneously provides the marketer a view into not only what will happen, but why it will happen.
Data is going to rule the day and will become the critical product for marketers! Also, look for the rise of the “Data Strategist” and “Data Engineer” – roles that will evolve in importance beyond that of that of the “Data Scientist”. Data IS THE FUEL for everything mentioned previously. Very simply put, the more data we have the deeper and more precise the insights and predictive and prescriptive analytics can become. Those that understand the importance of data in the next few years will look to monetize it and will create a competitive advantage. Those that don’t, will fall behind.
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