Beyond CTR: Four Ways To Bridge The Creativity Chasm
Some creative concepts, versions, and offers resonate more with certain customers than others, but that doesn’t mean brands should simply leave creative success to chance.
In 2006, Project Apollo found that 65% of advertising sales lift came from creative. And over 10 years later, Nielsen research shows this figure is still sizable at 47%. Our research has found that creative has the greatest propensity to influence sales conversions and purchase intent, and when it comes to performance, the top-performing creatives are up to 7 times more impactful than the lowest.
The bottom line: creative matters -- and there is a vast gap between good and bad.
To truly understand which ads resonate best with audiences, marketers need an accurate and impartial view of the effect generated by specific creatives. To obtain this, there are four key steps marketers must follow.
1. Establish efficient metrics
Most marketers use click-through rates (CTR) to evaluate creative, but this metric produces potentially flawed insights. Firstly, it presumes that to make an impact, ads must drive clicks, ignoring the fact that creative can influence conversion in many ways such as by boosting brand awareness. Secondly, the metric doesn’t differentiate between types of clicks.
And with Google estimating that 50% of mobile ad clicks alone are accidental, CTR could be encouraging marketers to divert large swaths of budget to ineffective creative. Third, the percentage of users who click on ads is very small. A typical campaign might generate a CTR of 0.1%, equivalent to 1 in 1000 users. But discarding impressions based entirely on CTR means ignoring the preferences of the remaining 999 users, who may have engaged with your ads in a different way.
Consequently, creative assessment must include metrics that reflect actual conversion influence, as well as engagement. For example, brands might track purchases or measure indications of intention to buy, such as ad interactions where consumers customise car or clothing design.
2. Up-scale measurement models
When it comes to creative measurement, there is a growing recognition among marketers of the flaws in single touchpoint modelling such as last-touch — which gives sole credit for conversions to one interaction and creative, overlooking the effect of all others.
Instead there is a move toward adoption of multi-touch attribution (MTA) that uses algorithms to distribute credit across touchpoints. But standard MTA models aren’t perfect -- allocating pre-prescribed slices of credit, they don’t represent true creative impact or consumer journeys.
To increase accuracy, marketers should consider leveraging machine learning that is free from prescriptive distribution rules. This method assesses many digital channels to measure the impact generated by every touchpoint, in real-time. Plus, marketers can enhance assessment even further by running several algorithms at once and employing systems that can assess, and select, the most precise model.
3. Prevent bias-driven distortion
If bias leaks into creative analysis, it can significantly compromise the reliability of performance results. Generally speaking, there are two things to consider: audience opinion and media influence. The former mainly relates to brand favourability: ads are more likely to inspire a positive response when served to loyal customers or regular site visitors, no matter how engaging creative may be.
Media influence concerns the quality of the ad placement. For instance, creative placed at the bottom of an overcrowded page has minimal chances of success, while the opposite is true for ads served in prominent positions and surrounded by content that aligns with their creative message. Whichever way around, such bias shouldn’t mean the creative is unfairly amplified or penalised.
The solution to this problem is taking distorting factors out of the equation. And one of the best ways to achieve this is to use smart, unbiased technology. By setting machine-learning models to evaluate and segment audiences based on the complexity of their purchase path, marketers can pinpoint consumers with a simple or convoluted journey -- i.e., likely brand advocates and less enthused individuals.
This automatically mitigates both audience and media bias by removing underperforming media buys and segmenting users into unique clusters based on the complexity of their paths. Attribution models can then be used to calculate performance within individual clusters, generating an unbiased and representative set of consumers driven to conversion by ad creative.
4. Keep processes fresh
Last but not least, it’s important to avoid evaluation autopilot. The modern marketing mix covers an enormous range of channels and devices, not to mention measurement tools. As a result, brands are tempted to apply a technique continuously once they find one that works well. Yet doing so will diminish the accuracy of performance analysis.
Each campaign has its own set of variables that must be considered to eliminate bias and determine creative impact. Marketers therefore need intelligent systems that can harness insights from previous distribution of conversion credit, but will also accommodate elements that are unique to current campaigns and specific consumers.
There is a common misperception in advertising that the effect of creative is too subjective to be tangible, but statistics demonstrate this isn’t true. Creative makes as much difference to revenue as areas that often steal the measurement limelight such as media and audience.
So if advertisers want to receive real value from their investments and optimise campaign results, they can’t afford to overlook the chasm between effective and ineffective creative. They must prioritise objective, adaptable and precise creative measurement, to stand a chance of keeping audiences inspired.
by Peter Falcone, Director of Analytics
Originally posted on MediaPost