B2C Marketers Show Appetite for AI-Driven Predictive Capabilities

November 16, 2022

This article is included in these additional categories:

Analytics, Automated & MarTech | Customer-Centric | Data-driven

B2C marketers are leveraging predictive analytics for various uses, but also face numerous obstacles to using it more often, according to a report [download page] from Pecan.

The study is based on a survey of 250 US marketing executives at B2C companies that use predictive analytics and have a minimum annual revenue of $100 million. Respondents reported that their most common uses of predictive analytics are for customer-level predictions of future behavior (51%) and to forecast customer trends (50%).

Other ways in which B2C marketing organizations are using predictive analytics include forecasting purchasing behavior for priority segments (46%), forecasting respondent-level purchasing behavior (44%), customer segmentation (44%), and modeling to uncover insights (40%).

Predictive analytics has led to a significant uplift in customer lifetime value (CLV) for some marketers, and CLV is one area for which marketers show interest in AI-powered capabilities. Indeed, when asked which of various AI-powered capabilities they wished they could access, 4 in 10 pointed to predictive customer lifetime value. Four in 10 also cited a desire for AI-fueled capabilities to identify upsell and cross-sell likelihood.

However, the most common AI-powered predictive capability that marketers want to access is predicting churn and retention on a customer level (46%).

There was consensus among those surveyed that AI-powered capabilities are in demand, as all respondents chose at least one of the capabilities provided as something they would like to access.

The outlook seems encouraging for AI-powered predictive analytics, as previous research has found that predictive analytics is the top way in which US CMOs are using AI in marketing.

Still, marketers have to navigate various obstacles to bring their goals to fruition. No single obstacle stood out as the top one; instead, similar percentages pointed to high costs of manual data science (40%), unorganized or unstructured customer data (40%), limited technical knowledge on the marketing & analytics team (39%), leadership not being convinced of the value (39%) and lack of internal data science resources (38%).

Perhaps as a result, predictive analytics remains one of the leading data capabilities that remain out of reach for marketers.

For more, download the report here.

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