Marketers admit Big Data struggle/latest research from Warc

10 Feb

Major brand owners regard Big Data as a strategic imperative but around half admit they are struggling to cope with the sheer volume of information being generated and to deploy insights across their businesses.

The World Federation of Advertisers surveyed 47 members representing $35bn in annual marketing spend to assess the challenges and opportunities that leading marketers face in coping with Big Data, a term that many said they found unhelpful.

Despite that, 88% of respondents saw this area as vital for current and future business decision-making, with 93% agreeing that it will be “vital” within three years. But three quarters (74%) were currently unprepared to take advantage of the openings it presented.

In part this was due to the difficulty of managing the huge amount of data available – 54% cited this – and in part to problems in recruiting staff with the skills to produce truly actionable insights (49%).

And even when insights were gained, there was the need to organise their effective distribution across the business, which 49% had found problematic.

The survey identified the main benefit to marketers of leveraging Big Data as being an improved understanding of ROI. Fully 70% gave this as the primary reason.

Other factors included gaining a deeper understanding of customers (64%) and enabling relevant, timely, precision marketing (47%).

The survey concluded that Big Data efforts worked best when three key conditions were met. First and foremost, companies had to have a clarity of purpose around Big Data efforts and to know what they needed the data to achieve.

Secondly, they should ignore the hype around the subject and start small. Finally, all the tools to gather data were worthless without the right talent to produce truly actionable insights.

In the September 2013 issue of Admap, devoted to Big Data, Nick Barthram, the principal planner at engagement agency Indicia, warned against the “spurious correlations” that Big Data could throw up.

Let machines spot correlations, he argued, but then apply human understanding. “Although human inference brings errors to the table, it can also provide great context which cannot be quickly computed,” he said.

Data sourced from WFA; additional content by Warc staff


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