Compare Local Marketing Changes Across Test and Control Locations

A business with several branches may roll out a new landing page, campaign, or follow-up process in one group of locations first. Comparing those branches with others can be informative, but only when the groups are selected thoughtfully and their differences are documented. A raw before-and-after increase at the test locations may reflect seasonality or local demand rather than the intervention. This guide explains a practical comparison structure and its limits.

Choose comparison locations for a reason

Select locations with similar operating hours, service availability, historical outcome patterns, and customer mix where possible. Do not choose the control group after seeing which branches performed worst or best. Record why each location was included and what makes it imperfect as a comparison. If branches serve very different markets, use separate strata rather than forcing a single combined estimate.

Record the pre-change period

Collect the same outcome definition for test and comparison locations over a suitable baseline. Note closures, staffing changes, price changes, promotions, and data-quality differences. Check whether the groups already moved in different directions before the intervention. Similar baseline levels are useful but not sufficient; the trend and operating context also matter. If tracking differs by branch, resolve it or treat the comparison as limited.

Calculate the contrast carefully

A simple difference-in-differences description compares the test group's change with the comparison group's change over the same periods. For example, if one group rises while the other remains stable, the contrast is the difference between those changes—not merely the test group's raw increase. This interpretation relies on assumptions, including that absent the intervention the groups would have followed reasonably comparable trends. Explain those assumptions and avoid treating the arithmetic as automatic causal proof.

Monitor spillovers and uneven implementation

A change may affect the control group through shared staff, cross-location customers, central advertising, or common website templates. Some test locations may also implement the change later or incompletely. Record compliance and exposure by location. If the intended contrast is weak, the analysis may understate or misrepresent the effect. Do not hide operational deviations to preserve a clean-looking result.

Translate the estimate into an operational choice

Report the estimated contrast alongside raw counts, baseline trends, sample size, and known limitations. If outcomes are sparse, use cautious language and avoid exaggerated percentage changes from a tiny denominator. Consider whether the result is practically important, whether costs differ by branch, and whether the implementation can be repeated. A pilot can inform a decision without delivering a universal rule for every location.

Worked example

Hypothetical example: four branches receive a revised appointment page while four comparable branches keep the previous version for a defined period. The analysis compares each group's change in qualified bookings and documents whether shared campaigns reached both groups.

Check whether locations are truly independent

Branches may share a website, central phone team, inventory, staff, or advertising campaign. Such links can allow the intervention to affect comparison locations too. List the shared systems before launch and decide which spillovers can be measured, prevented, or simply acknowledged.

Avoid comparing unlike reporting periods

Branch opening hours, public holidays, closures, and booking lead times can create unequal exposure. Align windows and record any period when a location could not serve customers. If exclusions are needed, apply them transparently and show how the result changes under the chosen rule.

Frequently asked questions

Is this method always a randomized experiment?

No. Locations may not be randomly assigned, so differences between groups can bias the estimate.

What if the control locations also change?

Record the contamination and interpret the contrast cautiously; the comparison may no longer isolate the intervention.

Can I use revenue instead of leads?

Yes, if revenue is recorded consistently and the observation period allows enough time for outcomes to mature.

Decision rule

Decision rule: make a causal claim only when the assignment and assumptions justify it; otherwise describe the result as a controlled observational comparison.

## Inspect pre-existing trends before interpreting the contrast

If test locations were already growing faster than control locations, a later gap may not be caused by the intervention. Plot the historical series for each group and discuss whether their movement was reasonably comparable before launch. A few matching points do not prove that the assumption will continue, especially when branches differ in size or local demand. If trends differ materially, consider a different comparison group or report the result as descriptive rather than causal.

Evidence to retain

For the location comparison, retain each branch's raw count and implementation status alongside the combined estimate so local differences remain visible.

If the groups cannot be made reasonably comparable, publish branch-level observations and explain the limitation instead of forcing a single estimate that implies more certainty than the design provides.