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Most Companies Are Struggling to Use AI-based Predictive Analytics Effectively
Large amounts of customer interaction data – gathered by the many platforms used in the call center and other customer support functions – can be both a blessing and a curse. Too much data can overwhelm if it’s not collected, interpreted and applied properly. There is evidence that few companies today know how to use this data effectively.
A new study conducted by Wakefield Research for Pecan AI, a provider of AI-based predictive analytics for business teams and the BI analysts who support them, has found that more than four out of five marketing executives report difficulty in making data-driven decisions despite all of the consumer data at their disposal.
Nearly all (95 percent) companies now integrate artificial intelligence-powered predictive analytics into their marketing strategies, including 44 percent that have integrated AI-powered predictive analytics into their strategy completely. Among the marketing executives whose companies have completely integrated AI predictive analytics into their marketing strategy, 90 percent say it is difficult for them to make day-to-day data-driven decisions. Every executive says they want to gain additional AI-powered capabilities and predictive insights for their teams, clearly indicating that current implementations of predictive analytics are poorly serving the needs of today’s marketing teams.
“With most companies today employing manual model building approaches, it's unfortunate, but not surprising, that the results are failing the needs of marketing teams,” said Zohar Bronfman, Co-Founder and CEO of Pecan. "While data scientists may be skilled in building the perfect software models, they are simply too far removed from the nuanced realities of the business to be effective.”
Other factors include heavy workloads, making it hard for data scientists to provide timely insight to allow marketing teams to respond to changing market conditions and consumer behavior effectively. Marketing experts have the skills to make use of predictive analytics – they just need to the right tools.
The most common obstacles to the effective use of data include data scientists not having the time to meet requests (42 percent), that those building the models don’t understand marketing goals (40 percent), data scientists not asking the right questions (38 percent) and using wrong or partial data to build models (37 percent).
The future is clearly AI driven. The question is how quickly will the tools be used effectively to allow companies to maximize on their investments? For now, it appears to be a struggle.
Edited by Erik Linask