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What Is a Design of Experiments (DOE) with Respect to Marketing?

Design of Experiments (DOE) in marketing is a systematic method to design experiments to measure the impact of marketing campaigns. DoE is a method to ensure that variables are properly controlled, the lift measurement at the end of the experiment is properly assessed and the sample size requirements are properly estimated.

DoEs can be designed for most major types of media, like Facebook, Search, TV, display, etc.

How do you design an experiment for marketing campaigns?

Experiments are usually designed to understand the impact of a marketing campaign on desired marketing objectives. A simplistic design to measure certain marketing stimuli like a TV campaign or a Facebook campaign is a 2-cell experiment, where the marketing campaign is published to a certain group of users and held out to another group of users. The response behaviors of the two user groups are then observed over a period of time. The impact of the marketing campaign is then assessed as the difference in response rates between those two user groups.

The science of experimental design applied to marketing is about carefully selecting and controlling the variables that affect outcomes, designing the approach for sample size sufficiency, and tailoring the overall design to have enough power to read the phenomenon being observed.

What are factors in experimental design for marketing campaigns?

The factors to be controlled depend on the phenomenon being measured. But in general, some of the factors that play a critical role in marketing that are candidates to be controlled are: marketing spend, campaign reach, impression frequency, audience quality, audience type, conversion rates, seasonality, collinearity and interaction effects.

Each marketing channel, like Facebook or TV or Google Search, each have their own unique campaign management levers to control audience reach, spend, frequency, etc. The challenge designing proper experiments is to apply experimental design principles to the specific channels and how they are typically operated by marketers.

Basic Principles of Experimental Design in marketing measurement

Learning objectives: The first and foremost thing is to identify objectives that are meaningful to measure. Typically, these are sales and other business outcomes that marketing campaigns are looking to drive.

Audiences and Platforms: Each marketing platform like Facebook and Google have very specific ways to activate audiences and market to them. Experiments have to be designed around these campaign specific levers to control the factors relevant for the marketing experiments.

Decisions: Marketers make specific decisions around campaigns, like campaign budgets, campaign bids, creative choices, audience choices etc., Experiments have to be designed to inform the specific choices at the level of granularity that is meaningful for marketers.

How does experimental design differ from A/B testing?
Design of Experiments is a formal method for designing tests. A/B tests are a simple form of a two-cell experiment. Typically industrial scale experiments are generally multivariate in nature, maybe 2-cells or more, and designed carefully to control for various factors to enable flighting the experiment and collecting data in very specific ways to enable getting a clean usable read.

Design of Experiments (DoE) Examples

Many marketing platforms enable experimentation deliberately or coincidentally. In platforms like Facebook it is possible to select and target audiences in randomized ways but target them differentially. This enables marketers to design experiments and test audiences for different marketing treatments. Similar approaches are taken in tactics like site retargeting where audiences are split into segments and various segments are offered differential treatments, like retargeting some segments, and holding out other segments from retargeting, and observe the behavior of response over a period of time.

How Does MTA Attribution & DOE Experiments Work Together?

MTA and DoE are complementary because incrementality testing addresses many of the data and data tracking gaps that currently serve as severe limitations to MTA’s ability to measure marketing contribution across all addressable marketing channels.

Currently MTA has a major data gap in the so-called walled gardens (Facebook, AdWords,Instagram, Pinterest, YouTube etc.) in which no customer level data gathering is permitted. MTA has no answers for these channels with no clear avenues for improvement short of a 180 degree reverse of course on data sharing by the likes of Facebook (don’t hold your breath). Even in trackable addressable media channels, pixel related data loss can be severe, ranging from 5% in paid search to as much as 80% in channels like online video. While cookie level data tracking has lower rates of data loss, it’s ongoing viability is in question after Google recently announced the discontinued sharing of Google User IDs that this approach relies upon beginning in Q1 2020. DoE can both fill the gaps created by the so-called “walled garden” media channels as well as validate and inform media channels suffering from pixel related data loss. As the market continues to evolve, and legislation to address privacy concerns like GDPR & CCPA proliferate, MTA measurement unsupported by DoE will likely become obsolete.


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.


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.