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Report: 84% of marketing leaders use predictive analytics, but struggle with data-driven decisions


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Artificial intelligence (AI) holds great promise for businesses today, especially for marketing teams who must predict customer preferences and behaviors to achieve their goals. Despite the increasing availability of AI-powered technologies, many marketers are still in the early days of formulating their AI strategy.

There is a strong interest in the potential of AI-based predictive analytics, but marketing teams face various challenges in fully adopting this technology. No playbooks to integrate data science In the field of marketing, many different approaches have evolved, with varying degrees of success.

Pecan AI’s The Predictive Analytics report in Marketing Surveys reflects this complex situation and provides key insights for marketing teams and business leaders to tackle challenges with AI, regardless of their capabilities. can be anywhere on the way to apply.

Key finding – AI predictive analytics integration

While many companies emphasize the importance of consumer data across everything from predicting future purchases to customer acquisition, in fact more than four of 5 marketing executives report that they have trouble getting data-driven decision regardless of all consumer data at their disposal. A similar number of respondents (84%) say that predictability of consumer behavior is like a guess.

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The majority (95%) of companies have now integrated AI-powered predictive analytics into their marketing strategy, including 44% saying they have fully integrated it into their strategy. Among companies that have fully integrated AI predictive analytics into their marketing strategy, 90% report that it is difficult for them to make decisions based on daily data.

Marketing and data science face unique challenges when trying to collaborate. As a result, data projects stalled. Research that provides insight into their struggles includes:

  • 38% of respondents say that data is not updated fast enough to be valuable.
  • 35% say it takes too long to build models.
  • 42% say data scientists are overwhelmed and don’t have time to respond to requests.
  • 40% say modelers don’t understand marketing objectives.
  • 37% of respondents indicated that the data was wrong or partially used to build the model.
Image source: Pecan.

Methodology

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

Read Full report from Pecan.

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