Overlap Reporting And Advanced Attribution
In an effort to maximize conversion rates, today’s online marketers take advantage of myriad channels and ad types to reach their customers. They also employ a number of cross-channel strategies to reach users at more moments of purchase consideration, increasing awareness and the probability of conversion -- but that also makes managing and optimizing campaigns increasingly complex. This leads advertisers to wonder, “How much is too much?”
That question is answered by attribution-based overlap reporting. By understanding not only where and when one brand’s ads reach the same audiences, but how effective those ads are, marketers can avoid wasted spending, enhance performance across all campaigns and channels, and focus on activities that deliver increased lift and conversions.
There are two basic kinds of overlap in online marketing: overlap that works for you and overlap that works against you. A smart strategy can include infinite combinations, including overlapping demographics and psychographic parameters. Conversely, there is bad overlap that floods customers with an ineffective, wasteful and potentially brand-damaging combination of ads. While advertisers should diversify the methods and times they target prospective customers, attribution-based overlap reporting ensures advertisers make better decisions about optimizing in-flight campaigns.
Overlap reporting based on advanced attribution can identify both redundancy and lift, even when targeting the same audience. But not all marketers use advanced attribution this way. Some rely on ad servers for overlap data from a specific channel. However, this approach doesn’t paint a holistic picture. While traditional overlap reporting based on ad server data merely identifies overlap, advanced attribution goes farther, showing whether this overlap is good or bad by reporting the conversion rate lift of different characteristics -- sites, obviously, but also placements, campaigns, creatives and other factors.
Marketers must analyze and compare campaigns run across all channels to understand the difference – the kind of analysis that is offered through advanced attribution. Marketers may then integrate all channels to create a focused and targeted message, considering audience segments, funnel stage and other criteria.
With so many studies touting the beneficial effects of overlap (for example, display over paid search), it’s easy to mistakenly assume all overlap is positive. But there are often cases when overlap results in a negative customer experience, from information overload to inappropriate messaging or timing.
Sophisticated advertisers understand that diversifying marketing spend and monitoring and optimizing campaigns leads to success. However, with RTB and DSPs delivering so many advertising messages in real time, it’s increasingly difficult to control cross-channel overlap. And as marketers move away from last-click measurement, it’s quite complex and time-consuming to layer and analyze the siloed touchpoints that precede a conversion.
This is where advanced, data-driven attribution can help marketers. It can integrate data from multiple touchpoints and overlay it with other customer data to illustrate the whole customer experience. It measures the impact of overlap among any combination of elements. It can propose recommendations so marketers may eliminate sites with low overlap lift and low unique visitors, avoid saturating elements with positive lift, and understand how combinations of creative can maximize conversion rates. And it can layer in additional information like audience data or customer segmentation.
In an increasingly complex online marketing environment, it’s time to move past the ad server. While ad server data can identify overlap, only advanced attribution can extract the accurate insights needed to pinpoint where and when overlap works across channels and campaigns. So when it comes to overlap reporting, utilize advanced attribution to fully understand what is happening in today’s complex marketing ecosystem.