• by Ray Schultz , October 28, 2022

Marketers have tons of data at their disposal. But 80% say they have trouble making data-driven decisions, according to a study from Pecan AI, conducted by Wakefield Research. 

Worse, 90% of those with AI-powered predictive analytics have trouble with decision-making. Yet 95% of companies now integrate that capability into their marketing strategy, and 44% have done so completely. 

 Why are there so many problems?

“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 AI.

Bronfman adds: "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.”

With that in mind, all 250 respondents seek to gain additional AI-powered capabilities and predictive insights. 

• Data scientists don’t have the time to meet requests — 42%
• Those building the models don’t understand marketing goals — 40%
• Data scientists don’t ask the right questions — 38%
• Wrong or partial data is used to build models — 37%

However, 93% also feel data scientists could solve more complex problems if they had low/no code AI predictive-modeling tools for metrics that could be automated such as future churn and lifetime value.

Bronfman concludes: "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.”