A Closer Look at the State of Predictive Analytics - eMarketer

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A Closer Look at the State of Predictive Analytics

Obstacles to implementing the technology remain

July 20, 2016

Predictive analytics, a discipline that helps marketers use customer data to provide improved personalization, understand user behavior across devices and anticipate consumer needs, is gaining plenty of traction with client-side marketers and agency-professionals alike.

Goals for Developing Predictive Analytics Capabilities According to Client-Side Marketers Worldwide, by Priority Level, May 2016 (% of respondents)

According to data on predictive analytics from eConsultancy and marketing automation firm RedEye, 40% or more of agency professionals and client-side marketers said they either currently use the technology or have set aside a budget to implement a predictive analytics program in the next 12 months .

The majority of marketers who turn to predictive analytics hope to use the technology to increase revenue and customer engagement. Among the respondents surveyed, increasing revenue and improving customer engagement were the two most frequently-mentioned goals, with 70% or more mentioning the two objectives as a high priority.

Obstacles to Investing in Predictive Analytics According to Client-Side Marketers Worldwide, May 2016 (% of respondents)

Among those marketers who already use or plan to use predictive analytics, the technique is also helping provide a variety of customer insights. Some 84% of marketers either already or plan to use it to generate insight around customer conversion, while another 83% said they used or planned to use predictive analytics to better understand customers.

Still, even as predictive analytics gains steam with more organizations, many companies continue to hold back due to factors like lack of budget, siloed teams and a lack of internal knowledge. Indeed, 40% of agency professionals said lack of budget was a key obstacle. On the client side, 32% of respondents said siloed organizations were holding back their predictive analytics strategies, while 30% mentioned the lack of internal knowledge.

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