FAQ    Marketing Measurement     Can I Measure Incrementality on Direct Mail and Catalog?

Can I Measure Incrementality on Direct Mail and Catalog?

Yes, incrementality can be measured on Direct Mail and Catalog. It is in fact among the more measurable channels because of the strength of identity resolution between audiences who received a mailer and customers transacting online, or in store.

The classic approach to incrementality measurement on Direct Mail and Catalog is to use systematic holdouts and compare the response rate and revenue per piece from users in the holdout group versus the users in the mailed group. This will get you the true measure of Direct Mail and Catalog impact to the business. You can read more on incrementality testing here.

The most basic form of performance reporting on Direct Mail and Catalog is through what is called matchback reporting. The list of households that were mailed a piece is matched to the list of customers who transacted, to identify how many of those in the mailed cohort eventually made a purchase. Brands who are more analytical holdout a cohort of users from the households selected to be mailed. Metrics from the matchback reporting on mailed users is compared to the same metrics on held-out users to calculate the incremental impact of the Direct mail or Catalog campaign.

There are two major types of Direct mail and Catalog campaigns.

  1. Housefile campaigns are programs that target households who are typically customers of the brand, and the brand has both the household address and consent from the user to market to them.
  2. Rental campaigns are programs that target households who are typically prospects of the brand. The names and households targeted are typically rented from a co-op that has the household address and has obtained consent from the user to be marketed to.

The audience selection, response rates, mail merge process, data collection process, data formats, data quality, match back process and other associated norms vary significantly between Housefile and Rental campaigns. Hence, experimental design, holdout selection and data processing is tailored very carefully to align with the process behind how lists are created and households are mailed for housefile and rental campaigns.

For Direct Mail and Catalog, the incrementality reads vary significantly by several audience dimensions – Housefile vs Rental, recency of engagement with brand, frequency of engagement with brand etc., Also, it is reasonably typical to find that response rates and incrementality are inversely correlated.

graph showing the inverse relationship between response rates and incrementality by recency


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.