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New study reveals deep mistrust in data from retail marketers

A survey of marketing leaders has shown there are concerns about the quality of data available to them for making decisions about budget allocation. These concerns… View Article

RETAIL SOLUTIONS

New study reveals deep mistrust in data from retail marketers

A survey of marketing leaders has shown there are concerns about the quality of data available to them for making decisions about budget allocation.

These concerns include bias in ad-tech channel reporting, a belief that data to support cross-channel decision making is broken, and a lack of certainty about owned data.

Conducted by independent marketing agency, QueryClick, the survey of 150 of the UK’s top marketers provided evidence that current attribution data is ineffective for informing decisions about how best to drive ROI from marketing budgets.

– 80% of retail marketers worry about bias in Adtech channel reporting.
– Over 60% of retail marketers think data to support cross-channel decision-making is broken.
– 43% of Performance Marketing Directors describe their own data as “not very accurate”

Bias in Adtech Channel Reporting

The survey established that while 75% of retail marketers spend comparatively large amounts on Google and Facebook advertising, there is a concern about how to grow revenue from these channels going forward thanks to channel saturation.

Furthermore, there is a lack of certainty about the quality of this data, with 80% of retail marketers saying they were either somewhat or very concerned about bias in Adtech channel reporting.

Effectively, with huge amounts being spent on advertising with certain platforms, those responsible for the advertising budgets believe that the returns they are getting are falsely inflated by incorrect numbers provided by the biggest Adtech players.

Broken Cross Channel Data

The industry leaders that completed the survey were asked their views on the value of effective cross channel decision making from AdTech & MarTech providers. The results showed:

  • Over 40% said they would:
    • be able to capture new, incremental customers more easily
    • be able to confidently report on revenue and ROAS
    • spend more in ‘upper funnel’ activity
  • 24%, nearly a quarter of respondents, said they would eliminate spend on campaigns driven by Last-Click data

Although that sounds great, a significant majority of respondents (60%) think that data to support effective cross-channel analysis and decision making is simply not available to them.

They don’t have a single source of truth. This means the benefits that could be realised from effective cross-channel marketing simply can’t be – or not with enough confidence, which is likely to result in inefficient use of marketing budgets.

Lack of Accuracy in Owned Data

To compound the findings about untrustworthy data from 3rd parties, the ability for retail marketers to make the right decisions about campaign, channel and budget allocation is undermined by mistrust in their own data.

While 94% of respondents are at least somewhat confident that they understand their attribution models and 98% consider attribution technology to be at least ‘somewhat important’, 43% of Performance Marketing Directors believe their data is not very accurate.

This prevents fair and open scrutiny of media spends as well as accurate appraisal of the effectiveness of owned and earned marketing tactics.

Chris Liversidge, CEO and Founder or QueryClick, states

“Digging into the responses shows the attribution marketplace is fundamentally broken. The good news is that there are solutions to these challenges. As performance marketers ourselves we have recently brought our revolutionary attribution solution, Corvidae, to the market outside our existing customer base.”

“Corvidae appears to have all the most desirable characteristics for an attribution platform; this includes the ability to identify revenue growth opportunities with greater granularity, based on genuinely accurate data.”

To download the full survey click here.

 

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