As publishers rapidly introduce new campaign types, optimizations, ad formats, and targeting capabilities, advertisers have unprecedented opportunities to convey messages to their audiences. Yet with so many bright possibilities, advertisers face growing pressure to quickly identify the products and methods that provide the most valuable storytelling and business outcomes. Differing methods of attribution and measurement add an additional wrench to the equation—varying indicators of value and revenue can tell conflicting stories based on sourcing. How can brands make informed, data-driven decisions to forecast the impact of different advertising investments on their business?
While brands seek more precise methods of proving efficacy, incrementality has become one of the hottest topics in online advertising. In this blog series, the Kenshoo team will share insights gleaned from over five years worth of incrementality measurement across platforms!
A timely example of the issue facing marketers is when they have to distinguish the effect of retargeting strategies among programmatic display, RLSA, and DPA retargeting. Or how industry-wide buzz around video ads makes marketers vigilant to the revenue driven by their investment in video creation. Similarly, the ubiquity of mobile targeting has inspired skepticism that many attribution tools, in fact, misconstrue these investments. Advertisers are reasonably tasked with determining the incrementality of new and emerging channels against tried-and-true methods. Yet brands remain uncertain of true incrementality among disparate targeting strategies, channels, and ad formats.
Thus far the ad industry has been unable to provide clarity, and the problem is compounded by measurement challenges, walled-garden data sources, upper-funnel metrics, cross-device approximation, and online vs. offline activity. Even advanced attribution solutions struggle to isolate business impacts of specific tactics, and as with any correlation-based modeling, data quality can make or break results.
Enter Incrementality Testing! An incrementality test compares the revenue or relevant KPI generated between a test group and a control group. By exposing the test group to an advertising tactic versus the unexposed control group, marketers can easily isolate the affected variables, clearly, assess immediate business impact, and formulate next steps with confidence supported by data.
By cleanly dividing audiences and tactics into test cells and control groups, advertisers receive assurance that any business impacts are direct results of their own inputs rather than extraneous effects. Advanced AI technology allows for tests to achieve statistical significance even at low volume, small budgets, and within short windows. Also, a cross-channel testing methodology provides measurement and insights into indirect effects of the advertising tactic – the impact on other advertising tactics – what we refer to as the ‘halo effect’. Best of all, users can easily track business implications using consistent, comparable and coordinated data sources to eliminate vagueness among disparate modeling.
Having tested incrementality since 2012, Kenshoo’s data science team has developed methodology leveraging best practices for testing adapted to a variety of specific lift-measurement use cases.
Check out our incrementality case studies for more info:
During our annual K8 conference, we introduced Kenshoo Testing Services with eyes on helping marketers enter the world of incrementality supported by our experienced team of research marketers. Our team consists of data scientists knowledgeable across verticals, with experience scaling incrementality testing across platforms and strategies. If your team faces challenges related to testing, attribution, or measurement, we’re eager to help and would love to connect!
Stay tuned to the Kenshoo Blog for the next blog in the Measuring Up series about 5 Gaps in Attribution Measurement!