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Why is the industry still full of #clickheads?

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Why is the industry still full of #clickheads?


After years of condemning clicks and impressions, modern marketing should be less reliant on unclear metrics — if not fully proxy free. However, instead of fading into the background, notoriously misleading measures remain a major feature of performance analysis. This is a deep dive into “clickheads” by LoopMe’s senior sales director James Symonds.

Few marketers need reminding about why this lack of progress is so troubling. Having long recognised that proxies aren’t accurate enough indicators of real audience engagement, most know they have little scope to reliably assess or optimise advertising success. Yet even amid the drive to streamline efficiency, campaign effectiveness is often still judged in terms of click-through rates (CTRs) and basic ad delivery. 

According to the Internet Advertising Bureau (IAB), much of the issue is familiarity. Following its 2019 open letter bemoaning “clickhead” behaviour, the advertising body has released a new initiative highlighting the likelihood that lingering dependence stems from an inclination towards well-known and impressive-looking metrics, especially as global pressure to prove ROI intensifies. But more important is a repeated call to break the proxy habit. 

As the latest figures for wasted Aussie spend alone total more than $5 billion, sticking with what they know obviously isn’t paying off for marketers. To make better use of their budgets, big changes are needed now. It’s time to finally retire imprecise measures and start gauging ad impact against genuinely trackable goal outcomes, with help from smart technology. 

Clickheads need to shift to AI-powered outcomes

While linking performance assessment with outcome based KPIs may not be a new approach, ongoing proxy preoccupation means adoption isn’t as widespread as it ought to be. By basing campaign evaluation on whether or not defined goals have been reached, marketers can gain an exact view of the effect every message has on their target audience, not just an estimate. 

Additionally, setting objectives as tangible consumer actions will serve a double purpose. On top of making uplift in areas such as brand recall, affinity, consideration, and purchase intent easier to trace, persistent measurement will also track which messages, formats, and tactics are driving the best responses as campaigns roll: equipping marketers with insights they can harness to inform real-time adjustments to further bolster in-flight engagement and results. 

What recent technological advances have brought to the equation is improved data mastery. In brief, a combination of large-scale processing and in-depth analytical capacity means tools supported by artificial intelligence (AI) can fuel faster handling, analysis, and activation of incoming data that drives well-informed optimisation, both for live and future campaigns.

The longer explanation of practical benefits for marketers requires a closer look at the two key use cases: 

Refining targeting focus  

Leveraged as part of outcome-centric measurement, AI subsets such as reinforced learning automatically allow shrewd real-time decisions. Fed on a mixed input — including information about historic performance and data from current ad requests — sophisticated algorithms can be trained to figure out which advertising opportunities are most likely to drive desired actions in any given scenario, with models simultaneously factoring in the impacts of unique situational variables on how individuals interact with ads.

Armed with a continuous flow of live insights, marketers can then make swift pivots to direct spend at media placements with the highest probability of meeting campaign objectives, from purchases and video ad shares to product brochure downloads. As well as refining campaign targeting to make ad dollars work harder, this AI-assisted method significantly reduces the risk of wasting investment on ad space with a low probability of bolstering the bottom line. 

Augmenting long-term learning 

Similarly, implementing supervised learning paves the way for persistent incremental gains. Drawing on insights about previous hits and misses, AI engines powered by Bayesian Systems can determine how to effectively model multiple data sets, while adjusting model dynamics in line with fresh first-party data from audience interactions, including intelligence surveys. 

The greatest advantage of such nimble modelling is flexibility. With the ability to build near-unlimited predictive models, smart engines can run deep analysis across a range of data pools; unearthing patterns and forecasting how any number of specific audience segments might engage with certain ads. Or in simple terms, giving marketers the insight needed to guide future strategy, with accuracy only increasing as each campaign yields richer learnings. 

But it’s also worth mentioning that modelling driven by assessment of past performance and user-supplied data ensures zero dependence on third-party cookies. With no requirement for additional tracking data, marketers can maintain maximum privacy without compromising precision. 

The limitations and pitfalls of clicks have been well-known and accepted for far too long. There are alternative routes that provide a much clearer view of the precise impact that campaigns make and smarter tools to ensure the efficient use of data, with AI creating a closed and accurate optimisation loop. It’s about time to start leveraging these advances, rather than leaning on the familiar and wasting precious resources. 

It’s time to stop the clickheads.


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