Mark Gamble is a featured contributor to Predictive Analytics Deconstructed: A Super-Simple Recipe for Marketing Success report.
Here's the complete contribution:
A major force driving evolution of predictive analytics in marketing is big data. Today's digital organizations have begun to collect massive amounts of customer information, so much so that its sheer volume and variety make it difficult to gain value from.
Marketers know the data is laden with insight to customer traits and behaviors that can help target ideal prospects, make recommendations and predict likely outcomes, but its size, diversity and and velocity make it unruly to work with for traditional predictive analysis tools. The data is comprised of more complex sources than traditional structured data, it comes in natural language, in the form of contracts, emails, text messages and tweets.
The means to extract the value is by leveraging Artificial Intelligence enhanced analytics, capabilities to apply Natural Language Processing to the data to extract context, meaning, intent and sentiment. AI-enhanced analytics allows organizations to leverage all the structured and unstructured information that represents the 'voice of the customer', identifying valuable insights into customers opinion toward your brand or service.
Algorithms and cognitive models help identify trends and patterns in the vast amounts of data and form coherent conclusions. Natural language processing and machine learning become more effective with higher volumes of data, meaning the more data that is analyzed, the better and more accurate the conclusions and recommendations become. As such AI-enhanced big data analytics represents the next evolution in marketing technology.
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