FAQ    Channel Incrementality     Can I Measure Incrementality for Google?

Can I Measure Incrementality for Google?

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:

    1. 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.

 

    1. 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.

 

    1. 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.

 

  1. 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.

Author

Madan Bharadwaj - Cofounder & CTO

Expert in advertising measurement, attribution and analytics

 

Multi-touch attribution is more challenging today due to limited tracking options, identity and cross-device resolution hurdles, data leakage and the massive amount of time it takes to implement.

 

What is cross-platform attribution (or cross-channel attribution) and why is it difficult?

The goal of cross-platform attribution in marketing is to gain clarity on the interplay and contribution of influence that each channel/tactic/campaign has on driving conversions over and above baseline sales.

It’s a task that has proved to be very difficult for many reasons including but not limited to:

  • Walled gardens are typically inaccessible to third-party tracking of impressions
  • Identity resolution across media platforms is quite low
  • Cross-device tracking is difficult and match rates are extremely low
  • Instrumenting a tracking infrastructure by a third party measurement provider has proved to be fraught with breakage and data leakage
  • It is extremely time consuming to implement without the help of a partner

Video: Landing a source of truth cross-channel media reporting dashboard

 

 

What are some cross-channel attribution tools?

MTA – collects individual, or user-level data, for trackable addressable media and conversion events in order to determine the impact of each media event to the desired conversion at the customer level. By summing the impact of each addressable media touchpoint on each customers’ likelihood to convert, MTA quantifies the total media channel lift provided by addressable media. MTA does not account for the impact of non-addressable media, and furthermore much addressable media is either non-trackable or lost due to the innumerable challenges of tracking data at the user level.

Incrementality Measurement – Incrementality in marketing refers to the incremental benefit produced per unit of input stimulation. Incrementality is the lift in desired outcome (awareness, web visits, conversion, subscriptions, revenue, profitability) provided by marketing activity.

Incrementality in marketing is especially needed for channels where ad impressions such as display, Facebook, social, or even TV are hard to measure. To measure incrementality, the audience is broken out into test groups (exposed to the ads) and a control group (suppressed from seeing the ads).

MMM – MMM is a top down (aggregate marketing data) and very artistic statistical exercise where one or more models (e.g. econometric, multi-linear regression) are leveraged to extract key information and insights by deriving information from multiple sources of marketing, economic, weather and financial data. MMM is also a high-touch consultative approach that is very manual with little to no automated data inputs, whereas MTA and Incrementality, when deployed properly, is a very automated approach leveraging preconfigured connectors that extract the required marketing data, across many channels, on regular cadence. (It’s important to note that MTA can take 6 months or more to deploy, whereas Incrementality can be up and running with reporting in 4-6 weeks.) See this article for more on why always-on automated experimentation is the future of marketing measurement.

Author

Trevor Testwuide - CEO

Expert in business strategy and marketing measurement.

 

Multi-touch attribution is more challenging today due to limited tracking options, identity and cross-device resolution hurdles, data leakage and the massive amount of time it takes to implement.