October 27, 2022 | In the News

Report: 84% of Marketing Leaders Use Predictive Analytics, but Struggle with Data-Driven Decisions

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

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

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

Key findings — integrating AI predictive analytics

While many companies tout the criticality of consumer data across areas, 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. The same number of respondents (84%) say their ability to predict consumer behavior feels like guesswork.

An overwhelming majority (95%) of companies now integrate AI-powered predictive analytics into their marketing strategy, including 44% who have indicated that they’ve integrated it into their strategy completely. Among companies that have completely integrated AI predictive analytics into their marketing strategy, 90% report that it is 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 stall. The study provides insight into their struggles including:

  • 38% of respondents say data isn’t updated quickly enough to be valuable.
  • 35% say it takes too long to build the models.
  • 42% say data scientists are overwhelmed and don’t have the time to meet requests.
  • 40% say those building the models don’t understand marketing goals.
  • 37% of respondents indicate that wrong or partial data is used to build models.

Source: Venture Beat

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