Marketing professionals today are inundated from all sides with hype about the future of big data, AI, and machine learning.
Whether the takeaway is better reporting, concern for job security, or mathemagical seeming insights, everything boils down to big data and the mysterious “black box” of AI and machine learning.
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First in a series on how and why conDati choses the prediction models we use to improve your marketing performance.
Marketers have had to rely on instinct and experiential knowledge alone to produce winning, hopefully profitable campaigns. In addition, months would go by without any solid information and when they did get numbers, accuracy was always a problem.
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Hoping to Leapfrog the Competition by Connecting Siloed Data and Implementing Machine Learning and Predictive Analytics
The typical large enterprise uses 90 or more marketing and analytics tools, while SMBs and Mid-Market companies employ between 10 and 25 martech tools to manage marketing operations and drive revenue. That’s a lot of systems for any company to manage. The results are dreaded data silos, which at best provide partial insights, and in the worst case overlook critical business
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In 2011, the inaugural MarTech Stack Landscape identified about 150 companies servicing marketers. Even then, one would have expected all those vendors to consolidate as the industry grew and matured. Instead, the martech landscape has fractured into 50 or more categories, populated by more than 5,000 vendors, fully half of which are brand-new venture-backed start-ups. Every marketer now needs to evaluate hundreds of tools, representing every new approach to customer acquisition, to find those
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