Report: 84% of marketers use predictive analytics, but struggle to make data-driven decisions

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Artificial intelligence (AI) holds great promise for businesses today, especially for marketing teams who need to anticipate customer interests and behavior to achieve their goals. Despite the growing availability of AI-based technologies, many marketers are still in the early days of formulating their AI strategies.

There is strong interest in the potential of AI predictive analytics, but marketing teams face a variety of challenges to fully embrace this technology. With no universal playbook available for integration data science in marketing, various approaches have evolved, with varying levels of success.

pecan ai The Predictive Analytics in Marketing Survey report reflects this complex picture and provides key insights for marketing teams and business leaders solving AI challenges, regardless of where they are on the adoption curve.

Key Findings — Integrating AI Predictive Analytics

While many companies tout the criticality of consumer data in everything from predicting future purchases to customer churn, the reality is that more than 4 in 5 marketers report struggling to do data driven decisions despite all the consumer data available to them. The same number of respondents (84%) say their ability to predict consumer behavior is guesswork.

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An overwhelming majority (95%) of businesses now integrate AI-based predictive analytics into their marketing strategy, including 44% who indicated that they have fully integrated it into their strategy. Of companies that have fully integrated AI predictive analytics into their marketing strategy, 90% say it’s difficult for them to make day-to-day data-driven decisions.

Marketing and data science face unique challenges when trying to collaborate. As a result, data projects stagnate. The study provides insight into their struggles, including:

  • 38% of respondents say data is not updated quickly enough to be useful.
  • 35% say building models takes too long.
  • 42% say data scientists are overwhelmed and don’t have time to respond to requests.
  • 40% say that those who build the models do not understand the marketing objectives.
  • 37% of respondents indicate that incorrect or partial data is used to build models.
Image source: pecans.

Methodology

The Pecan Predictive Analytics in Marketing Survey was conducted by Wakefield Research among 250 US marketing executives with at least director seniority. These executives work in B2C companies that use predictive analytics and have a minimum annual revenue of $100 million. Participants responded to an email invitation and an online survey between September 13 and September 21, 2022.

Read it full report of Pecan.

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