Predictive Analytics Proving Most Effective For Conversions, Most Difficult For Customer Insights

July 11, 2016

EconsultancyRedEye-Predictive-Analytics-Areas-Jul2016Roughly 4 in 10 in-house marketers and agencies are either using, implementing, or budgeting for predictive analytics, a new study [download page] from Econsultancy and RedEye has found. For company respondents to the survey, which was fielded among about 400 digital and e-commerce professionals with a heavy UK skew, increasing revenue (73%) and improving customer engagement (70%) are the highest priorities for developing predictive analytics capabilities.

While the report points to the “increased accessibility” of predictive analytics today, this is still an area where there is much room for growth. To wit, while almost 8 in 10 company marketers rated their organizations as at least competent in descriptive analytics (who, what when, how, where?), only 23% could say the same about their competency in predictive analytics.

Additionally, some of the areas where marketers most wish to use predictive analytics also turn out to be the most difficult areas in which to implement the technology. For example, 82% are either using or planning to use predictive analytics to further their understanding of their customers, but 45% (the most of any area) rate this as a highly difficult area in which to implement predictive analytics. The same goes for personalization, which is another top use case, but which seems to be one of the more difficult areas in which to use predictive analytics.

The sweet spot, at least right now, seems to be in boosting conversions. Fully 84% of company marketers surveyed are using or planning to use predictive analytics for conversions – and respondents are experiencing the most success and virtually the most ease of implementation in this area.

Fast Facts

The following are some other key takeaways from the Econsultancy and RedEye report.

  • By far the most critical skill for effective use of predictive analytics is understanding data in the context of the organization, with 59% of company respondents naming this their top choice (of 5 possibilities). While few (14%) deem their organizations excellent in this regard, an additional 67% rate their ability as “good” or “very good.” Disparate tech platforms and data sources are perceived to be the biggest barrier to using predictive analytics more effectively.
  • A plurality (42%) of company marketers use a mixture of in-house solutions, third-party solutions and outsourcing for predictive analytics. In-house developed solutions are more popular than third-party vendor solutions by almost a 2:1 margin (33% vs. 18%).
  • The tools most commonly used by company marketers for predictive analytics are R (12%) and SAP (10%).
  • A majority (54%) of company marketers agree that they’re yet to realize the benefits of predictive analytics. Only 21% are able to agree that the ROI on their predictive analytics activity is positive, with a strong majority (69%) neutral on the topic.
  • Roughly 2 in 3 company and agency respondents predict an increase in budget for predictive analytics over the coming year. Earlier this year, a report from the DMA and Winterberry Group found that predictive analytics was one of the most popular data-driven activities set for budget increases this year in the US.
  • Some 61% of company respondents feel that their organization is significantly under-resourced for predictive analytics.
  • The main focus areas for company respondents in relation to predictive analytics over the next 12 months will be using data to provide strategic insight (23% share) and getting internal buy-in (22%).

About the Data: The Econsultancy and RedEye report is based on a survey of almost 400 digital and e-commerce professionals from the client-side (59%) and supply-side (41%). About half (51%) of the respondents are based in the UK, with another 22% from Europe (non-UK – no need to make that distinction soon…) and 23% from North America. The financial services and insurance (15%) and retail (12%) sectors were the most heavily represented.

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