From predicting future purchases to customer churn, marketers have strategized around the criticality of consumer data insights for years—but the reality is that more than four out of five marketing execs report difficulty in making data-driven decisions despite all of the consumer data at their disposal, according to a new study from predictive analytics firm Pecan AI. In fact, the same number of respondents (84 percent) saying their ability to predict consumer behavior feels like guesswork.
Among respondents, virtually all (95 percent) companies in the survey, conducted by Wakefield Research, now integrate AI-powered predictive analytics into their marketing strategy, including 44 percent who have indicated that they’ve integrated those analytics into their strategy completely. Among the execs whose companies have completely integrated them into their marketing strategy, 90 percent report that it is difficult for them to make day-to-day data-driven decisions. All 250 survey respondents specified that 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, in a news release. “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) marketing execs 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 execs 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 percent of marketing leaders said they wanted to “uncover specific KPIs from my data instead of scouring for potentially useful insights.”
The Wakefield study polled 250 senior marketing executives with the title of director and above at B2C companies that use predictive analytics with a minimum annual revenue of $100 million.