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MARKETING ANALYTICS

Better analytics will not necessarily make for smarter decisions

By our News Team | 2022

CMOs must push for a data-informed organisational culture and reduce internal cognitive biases to get maximum benefit from their data.

Marketing analytics are responsible for influencing just over half (53%) of marketing decisions, according to a survey by international research and consulting firm, Gartner. 

During May and June this year, the company surveyed 377 users of marketing analytics to explore the role of analytics in organisational decision-making.

Marketing Analytics

Photo by Tima Miroshnichenko from Pexels

“CMOs often believe that achieving marketing data integration goals will lead to greater influence and increased value of marketing analytics,” said Joseph Enever, Senior Director Analyst in the Gartner Marketing practice. 

“The reality is that better data won’t increase marketing analytics’ decision influence alone. CMOs must address the real challenges – cognitive biases and the need for a data-informed culture.”

The survey found that the quantity of marketing decisions that analytics influences does matter: Organisations that report marketing analytics influence fewer than 50% of decisions are more likely to agree that they are unable to prove the value of marketing. 

Once marketing analytics teams cross that 50% threshold, there are likely diminishing returns to striving to increase the quantity of decisions influenced.

“By 2023, Gartner expects 60% of CMOs will slash the size of their marketing analytics department in half because of failed promised improvements,” said Enever.

Top barriers to marketing analytics’ influence

According to Gartner, consumers of marketing analytics continue to cite evergreen data-management challenges as the top reason analytics are not used when making decisions. The challenges of “data are inconsistent across sources” and “data are difficult to access” rose to the top in this year’s survey.

Marketing organisations regularly respond to these challenges by integrating more data or acquiring different technology seen as a fix-all approach to marketing data management — yet they fail to realise tangible impacts on key outcomes. For example, marketers experience diminishing marginal returns on data integration when pursuing a 360-degree view of the customer.

The researchers found that barriers to the use of marketing analytics in decision-making are not always caused by data integration challenges unique to marketing. Rather, much of this boils down to people and/or process problems.

“For instance, key cognitive biases are at the root of marketing analytics’ influence plateau. One-third of respondents reported that decision-makers cherry-pick data to try to tell a story that aligns with their preconceived decision or opinion,” the research team noted.

In addition, roughly a quarter of respondents said that decision-makers do not review the information provided by the marketing analytics team (26%). Decision-makers also reject their recommendations (24%), or rely on gut instincts to ultimately make their choice (24%).

Gartner recommends that CMOs address these challenges by:

  • Tracking the decisions that are made based on analytics, [in order to] to provide a current state of view and areas to improve. Identify examples of marketing analytics work that provided actionable recommendations to a marketing campaign or programme. Marketing leaders should encourage their team to look for patterns in decision-making habits and to document the types of decisions they influence.
  • Combatting cherry-picking. Set KPIs and metrics before launching a new campaign or marketing strategy, not after the data has already started to come in.
  • Encouraging senior leaders to set an example. Avoid being a HiPPO (Highest Paid Person’s Opinion) and actually allow data to inform, or change, decisions.
  • Establish analytics upskilling programmes that account for differing workflows and resource constraints across the marketing organisation. Build personas that detail how different employees need to use data in their roles and prioritise training sessions that best enable participants to learn the skills they need to perform their job.
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