Business/Customer Intelligence & Data Science

Data Science is Not Benefiting Marketers: Survey

Data Science

While companies tout the criticality of consumer data on a variety of fronts, from predicting future purchases to customer churn, the reality is that more than 4 out of 5 marketing executives report difficulty in making data-driven decisions despite all of the consumer data at their disposal, according to a new study of senior marketing executives by Wakefield Research for Pecan AI, the leader in AI-based predictive analytics for business teams and the BI analysts who support them. Surprisingly, the study also found the same number of respondents (84%) saying their ability to predict consumer behavior feels like guesswork.

Among respondents, most or all (95 percent) companies now integrate AI-powered predictive analytics into their marketing strategy, including 44 percent who have indicated that they’ve 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 report that it is difficult for them to make day-to-day data-driven decisions. All 250 respondents have specified that they wanted 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. In addition, given their workloads they are too slow to respond when considering the rapidly changing market conditions and consumer behavior. Marketers and marketing analysts are more than capable of handling predictive analytics responsibilities if provided with the right tools.”

When asked about the top obstacles in keeping data projects from progressing:

  • 42 percent say data scientists don’t have the time to meet requests
  • 40 percent say those building the models don’t understand marketing goals
  • 38 percent of respondents say data scientists don’t ask the right questions
  • 37 percent of respondents indicate that wrong or partial data is used to build models

The study also found that nearly all (93 percent) of marketing executives polled agree that data scientists could solve more complex problems if they were able to use low/no code AI predictive modeling tools for automatable metrics as future churn and lifetime value.

Among other findings, the study also revealed that today’s marketing executives need more than generalized actionable insights from their data investments. 61 percent are aiming to empower their teams to extract the most impactful analysis from our data. And importantly, not only should that information be impactful, it should also be specific to key team KPIs and readily surfaced: 60% of marketing leaders said they wanted to “uncover specific KPIs from my data instead of scouring for potentially useful insights.”

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