The marginal incremental contribution of Google products (Search/PLA, Display, Video) on business outcomes can absolutely be measured. There are multiple methodologies to measure its contribution to conversions, revenue, LTV, etc.
Measuring incrementality on Google can be accomplished in any of the following ways:
- Design of Experiments (DoE): Carefully designed experiments that control for targeted audiences, overlap, campaign structures and optimization algorithms are the most transparent and granular way to measure impact on your business metrics. Experiments have to be designed around these campaign specific levers to control the factors relevant for the marketing experiments. Typically channels within Google’s Adwords platform are tested via a geo-matched market approach. A handful of small markets are identified as representative of the national market for a brand. On these “test markets” the desired media treatments on the Google channels are executed. The results from these markets are then compared to other larger markets – the difference in performance metrics (like conversion rates, revenue per user) between the test and control markets are then interpreted to inform incrementality.
- Lift Study: In some cases, if the advertiser is running enough spend through a channel, Google may offer to run a lift study for the brand. The study is run as a managed services offering where the design and execution is taken on by the Google account team. Google may use a ghost ads approach or a geo approach in the background to run the study and report back results to the brand.
- Marketing Mix Models (MMM): This approach uses aggregate marketing data rolled up at a week, or month-level, into a time series which is then fed to a regression model for estimating the impact of Google on business metrics. This is a top down approach and results tend to be very macro in nature, providing an average impact of Google investments over a quarter, or longer.MMM is not useful in breaking down the impact estimation by campaign or tactic, so it’s less appropriate for short-term tactical planning. Also in practice, these models take a while to build and stabilize, which could mean 6-12 weeks of lag from end of a quarter to results reporting.
- Multi-Touch Attribution (MTA): This approach ingests user-level data collected, or leveraging other third party tracking technologies, on all ad exposures to construct consumer journeys which are then fed into a machine learning algorithm to decompose the impact of each ad exposure and its effectiveness in driving a business result. The strength of this approach is extreme granularity of the reporting and the insight into customer journeys. More recently with the advent of privacy regulation and Google outlawing user-level third party tracking, the collection of this kind of data has been nearly eliminated except in very special cases. Even when this data was being collected, the measurement would only be correlational out-of-the-box.
For tactical and timely measurement, DoE is the primary approach preferred by marketers to inform incrementality, especially for performance driven acquisition marketers. In some cases, marketers employ Google’s lift studies to get another read. When available, multiple incrementality reads are beneficial as they provide different perspectives on the impact of Google’s advertising on their business.
DoE – Pros & Cons:
DoE is typically executed by either the brand or by a third party vendor like Measured. DoEs can be designed to be very tactical and shaped to meet a diverse set of learning objectives for marketers. It can be executed independent of Google, and hence offers the highest levels of control and transparency in executing experiments that match marketers’ learning objectives. All of the observations are captured through normal campaign reporting methods, leaving marketers to make inferences about campaign performance without any opaqueness to the methods of data collection. It’s strengths therefore lie in being fully transparent and neutral, while preserving tactical granularity of measurement.
Google Lift studies – Pros & Cons:
Lift studies are typically conducted by the platforms, in this case Google, and are typically executed via a ghost ads counterfactual approach or a geo-based approach. In the ghost ads approach, the ad delivery systems within Google implement a version of what’s called the ghost ads framework to collect data about audiences who matched a campaign criteria but were not served an ad because of other constraints, like budgets and competitive bids, in the auction. These audiences are then placed into a control audience whose performance is reported alongside the audiences who were exposed to campaign creatives. This allows marketers to read the lift of a campaign without actually selecting control audiences and executing a control treatment. The geo-based approach, similar to the geo-matched market test, is the preferred method when a clean audience split test is not available. Audiences are split by geo and a strong read can be attained.
The primary advantage of using a platform lift study to get a read on the platform’s contribution is that sometimes they are offered at no cost to the brand, whereas there is a cost associated with running a PSA ad (another tactic for measuring ad effectiveness). For many marketers, the primary objection of any publisher led counterfactual study is neutrality: having the publisher grade its homework. This has led many marketers to seek a non-biased publisher agnostic advanced analytics partner, like Measured.