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Artificial intelligence (AI) holds great promise for today’s businesses, especially for marketing teams who need to predict customer interest and behavior in order to reach their goals. Despite the increasing availability of AI-powered technologies, many marketers are still in the early stages of developing an AI strategy.

Despite the strong interest in the potential of AI-based predictive analytics, marketing teams face various challenges in fully adopting this technology. With no universal playbook for integrating data science into marketing, different approaches have evolved with varying levels of success.

Pecan AI’s Predictive Analytics in Marketing Survey report reflects this complex landscape and provides key insights for marketing teams and business leaders tackling AI challenges, regardless of where they are on the adoption curve.

Key Findings — AI Predictive Analytics Integration

While many businesses emphasize the importance of consumer data in areas ranging from predicting future purchases to customer churn, the reality is that more than four out of five marketing executives believe that all consumption Despite having data at their disposal, they report difficulties in making data-driven decisions. The same number of respondents (84%) said the ability to predict consumer behavior feels like guesswork.


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The overwhelming majority of companies (95%) now integrate AI-powered predictive analytics into their marketing strategy, with 44% saying they have fully integrated it into their strategy. 90% of companies that have fully integrated AI predictive analytics into their marketing strategies report difficulty making decisions based on day-to-day data.

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

  • 38% of respondents say their data is not updated quickly enough to be valuable.
  • 35% say building models takes too long.
  • 42% say their data scientists are overwhelmed and don’t have time to respond to requests.
  • 40% say the people building the models don’t understand marketing goals.
  • 37% of respondents indicated that incorrect or partial data was used to build the model.
Image Source: Pecan.


The Pecan Predictive Analytics in Marketing Survey was conducted by Wakefield Research among 250 US marketing executives, with the minimum seniority being a director. These executives use predictive analytics and work for his $100 million minimum annual revenue B2C company. Participants responded to an email invitation and an online survey between September 13th and 21st, 2022.

Read the full report from Pecan.

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