#sampling Archives - Marketing Systems Group

How MSG is Enhancing the Gold Standard in Survey Research

Address-Based Sampling (ABS) has been a trusted method in survey research for nearly 20 years. As technology and data sources have advanced, so has the power of ABS—solidifying its place as the industry standard for reaching virtually every residential household in the U.S.

At the forefront of this evolution is Marketing Systems Group (MSG). By taking the foundational USPS address file and transforming it into a dynamic, data-rich resource, we have turned ABS into much more than a list of addresses—it’s now a strategic tool for targeting even the most hard-to-reach populations.

Taking ABS to the Next Level

We have spent years refining and enhancing the ABS frame, and the result is a smarter, more powerful sampling solution. At the heart of this innovation is MSG’s consumer graph, which uses the ABS frame as its backbone while layering in multiple ancillary data sources. These enhancements enable researchers to build more precise and effective stratified sample designs.

Here’s What Sets Our Approach Apart

Linking Third-Party Consumer Data: By integrating data from reputable and commercially available data sources, we are able to enhance the vast majority of residential addresses with household-level insights. This includes details about household composition, lifestyle, and more—giving researchers a clearer picture of who lives where, and how best to reach them.

Cell Phone-to-Individual Linkage: In today’s multi-modal communication landscape, reaching respondents through various channels is key. We have made significant strides in accurately linking cell phones to individuals and their residential addresses, allowing researchers to reach participants via phone, text, email, digital ads, and traditional mail. This level of connectivity opens the door to more responsive, representative sampling.

Enhanced Geocoding Accuracy: Properly placing an address within the correct Census geography—such as blocks, tracts, or block groups—is crucial for aligning survey data with demographic benchmarks. Our improvements in geocoding precision ensure that researchers can confidently design geographically-stratified samples and accurately interpret population-level estimates.

Why It Matters

These innovations mean that ABS is no longer just a reliable starting point—it’s a highly refined engine driving smarter sampling strategies. Whether you’re aiming to engage niche populations, improve response rates, or design methodologically sound surveys, MSG’s enhanced ABS frame offers the flexibility and depth today’s research demands. Contact us here to book time with one of our ABS experts.

Looking for a Deeper Dive into ABS?

Grab your coffee and join us for a quick, insightful take on smarter sampling, in our latest Coffee Quip episode!

In a previous blog we introduced you to one of Marketing Systems Group’s newer products, Advanced Cellular Frame (ACF). This time, we will discuss how the frame is compiled and the key advantages it brings you compared to traditional RDD by itself.

Let’s start with a few key facts. When we do a traditional RDD sample of a county or group of counties, the telephone numbers within that frame are defined using rate center geography. The RDD frame has zero bearing on your ability to place numbers geographically outside of a rate center geography. It’s just not built for that capability.

With ACF, we have a better ability to actually place numbers within the correct geography, because we have more known information about more of the numbers (approximately 45% of them).

The Power of Split Frames

ACF can split an RDD frame into two pieces:

The listed / matched phone numbers we have information about for a particular geography
Everybody else, including unlisted / unmatched

The listed component we can put precisely within whatever target geography you’re doing, if it’s a ZIP Code, county or a CBSA.

The unlisted or unmatched numbers get pulled in using the old traditional rate center methodology, but we pull only the unlisted ones. All listed and matched numbers are excluded from this component.

A County-level Example

To illustrate how this all works, check out our Coffee Quip videos, specifically episode #3 featuring Subject Matter Expert David Malarek. There you will see him explain a case involving Multnomah County Oregon, home to the city of Portland.

The ACF frame does a better job targeting an RDD sample within Multnomah county. Dave shows how you can take the listed and unlisted portions and create a split frame, in which you can sample the listed’s independent of the unlisted’s or unmatched.

The ACF RDD universe is about 1.5 million numbers. About one-third of them are listed. We know they are in Multnomah county. The balance of the records (993,000) are coming from the rate centers that best fit Multnomah county geography.

But remember, rate center geography does NOT conform to any census geography we are accustomed to. It doesn’t conform to counties or city boundaries. Rate center geography is really just based on where the telephone companies run their wire lines.

This means you will have some over coverage and some under coverage, because you could miss a spot within the county or flow into adjacent counties outside the county.

With a split frame, you get the two components of ACF RDD that best fit Multnomah county.

How We Address the Migration Problem with ACF

Now we hit the biggest advantage of ACF: how it addresses migration — people who have a cell phone in one part of the country then move to another part of the country but keep the old cell number tied to the old rate center geography.

For the “known” listed cellular telephone numbers, ACF allows us to identify people who have migrated from other parts of the country into Multnomah County, Oregon. Conversely, we can exclude from the frame everybody who moved out of Multnomah County.

If you had defined the frame by rate center and just did a traditional cellular RDD sample, a huge chunk of those listed numbers would actually be outside the county. And you would be excluding 27% of the numbers inside Multnomah county from your sample frame. Not good.

Simply put, ACF does a better job in terms of getting the people who really live in your target geography into the sample frame and leaving out the people who’ve left.

What About Lower-level Geographies?

ACF is great for smaller geographies, especially city limits not easily defined by rate center, which tend to be fairly large. Rate centers might serve four or five dozen different communities; whereas with ACF (at least for the listed portion) you can pinpoint a town or city you want to sample and exclude the other towns around it.

You can see why we are so excited about Advanced Cellular Frame; it enriches the sample and empowers you to target smaller geographies with more precision.

For more details, check out Coffee Quip episode #3, with Subject Matter Expert David Malarek or click here.