Skip to main content

Marketers Crave Budget for Web Analytics Talent


Marketers everywhere seem to agree, it's important to have the full picture when evaluating digital marketing investments. That said, market studies indicate that marketers still consider the click-through their main form of measurement -- despite its flaws.

eMarketer reports that a 2010 survey performed by Web analytics service Omniture showed that marketers were unable to measure marketing effectiveness across the typical purchase life-cycle.

Asked which metrics would give them the most actionable insights, marketers said marketing cost, orders, average order size and conversion rate. However, they were only able to measure Web visits, page views, page views per visit and click-throughs.

The same measurement problems existed in mobile Web, social and video channels -- only 30 percent could measure mobile app or post-video conversions, and 41 percent could measure social marketing conversion.

Overall, 80 percent of respondents said it was important to measure ROI from online activities, but just 31 percent could effectively do so.

The biggest challenge was talent, or more specifically the lack thereof. Marketers indicated they did not have skilled staff with the expertise necessary to get the most out of the raw data. Available budget was the top reason why marketers lacked the talent they needed.

Mikel Chertudi, senior director of global media and demand marketing at Adobe Systems, Omniture's parent company, said many survey respondents did not seem to have a full-time staff member devoted to Web analytics.

As a result, about 60 percent of marketers said they spent less than 20 hours a week utilizing their online analytics data to extract content marketing performance insights.

Popular posts from this blog

Frontier AI Peaked. Here's What Comes Next

The prevailing narrative around artificial intelligence (AI) has been one of relentless scale. Bigger models, bigger clusters, bigger budgets. The assumption, largely unchallenged until recently, was that raw parameter count translated directly into competitive advantage. New research from Omdia suggests it's time to retire that assumption. According to the latest market study by Omdia, parameter growth in frontier AI models has slowed to around 5 percent annually since 2021, a stark contrast to the more than hundredfold expansion seen between 2019 and 2021. Enterprise AI Market Development For executives who have been making infrastructure and investment decisions based on the assumption that AI would keep demanding ever-larger, ever-more-expensive hardware, this finding deserves serious attention. The race to the top of the model size leaderboard has, at least for now, plateaued. Crucially, Omdia's analysts are not reading this as an AI winter. Alexander Harrowell, senior pri...