Case Study: Popular Pizza Franchise Generates Online Orders and Identifies Considerable ROAS with


Brand Overview
•  Franchise of a popular pizza chain in the northwestern U.S.
•  Wanted to generate online orders and drive traffic to its six stores
•  Sought to identify a positive return on its advertising spend
Partner Overview
•  Full-service direct mail company in the Pacific Northwest
•  Year-long partnership with to provide programmatic advertising to its direct mail marketers
•  Wanted to utilize’s Transaction Value Reporting to attribute revenue to the digital advertising campaign
•  $21,319.52Revenue Generated in Nine Months
•  $2.02 Return on Advertising Spend
•  115% Increase in Incremental Store Visits
•  10,059 Physical Store Visits
•  $1.05 Cost Per Visit
•  910 Online Actions (Orders and Button Clicks)
•  $11.60 Cost Per Online Action




The amount that quick-service restaurants (QSRs) spend on local advertising continues to increase, and BIA Advisory Services predicts that this figure will hit almost $6 billion in 2024 (Radio and Television Business Report). Therefore, it’s more important than ever that QSRs have access not only to impactful targeting solutions, but also to valuable reporting metrics to make informed decisions about their advertising budgets.

A franchise of a popular pizza chain with six stores in the northwestern U.S. wanted to better understand how its marketing efforts were driving results for its stores. The franchise typically places a heavy emphasis on direct mail advertising, so it took advantage of its existing relationship with its direct mail provider to leverage’s advanced targeting and attribution solutions.

The advertiser wanted to promote special offers in six small towns in Idaho and Montana where it operates storefronts. It sought to drive physical visits to these locations, generate online orders, and achieve a positive Return on Advertising Spend (ROAS)., the
franchise, and the direct mail company developed a strategy to implement both location-based and online targeting tactics, including:

  1. Curating a relevant addressable audience that aligned with the chain’s target market—
     and leveraging first-party data—for household-level targeting.
  2. Conquesting competitor locations.
  3. Targeting consumers who were actively searching for, or reading about, pizza online.
  4. Retargeting the advertiser’s website visitors.

They planned to track the number of customers who received an ad and later visited the pizza chain in person, as well as measure the revenue generated from online orders.


The franchise wanted to promote its specials to “foodies” who were 25 years of age or older. Therefore, it took advantage of’s Addressable Audience Curation tool, which enables
advertisers to curate addressable audiences at the household-level in real-time, based on hundreds of demographic and location variables.

The team created separate audiences for each town featuring users who were 25+ years old and interested in food.’s platform identified 37,029 relevant households throughout the small towns. The advertiser also supplied its own address list of customers from its loyalty program, which it uploaded to the platform at an 87% match rate for an additional 18,907 households.

For all of the addressable audiences, the system pinpointed the exact shape and size of every address using publicly available plat line data.’s Addressable Geo-Fencing solution then automatically drew target fences around each property to reach relevant users at the household-level across all of their devices.

Furthermore, the advertiser wanted to conquest customers from its competitors to gain additional business, so it tapped into’s Geo-Fencing with Conversion Zones technology. Each store supplied lists of its top competitors for these efforts, including other pizza chains and local restaurants. drew target fences around these 68 locations to retarget users for up to 30 days after they visited a competitor.

With the addressable audiences and target fences in place, added Conversion Zones around each of the six pizza stores. This allowed to measure offline conversions on a store-by-store basis by identifying the number of users who were served ads and later visited the pizza chain. It also helped the advertiser determine the lift in foot traffic to each of its stores that could be attributed to the programmatic campaign.


In addition to targeting consumers based on their location, the franchise wanted to reach users who indicated that they were interested in ordering pizza. To do this, and the advertiser implemented Search Retargeting and Keyword Contextual targeting to reach users based on the keywords they searched and the content they read. deployed custom lists of 400+ keywords for each store, featuring terms such as “pizza shop,” “pizza takeout,” “order pizza,” and more.

Targeting consumers at the keyword-level rather than using pre-packaged segments such as “food intender” allowed the pizza chain to reach relevant users with greater transparency. With pre-packaged segments, advertisers cannot see who is included in the segment, when and why users are added to it, and more.’s keyword-level targeting, however, allowed the chain to see that it was reaching users who were interested in eating pizza without wasting impressions on users who were interested in another cuisine, such as seafood.

It also enabled to adjust the recency window—the time frame for how long a user can be retargeted after searching for a keyword—on a location-by-location basis. For example, found that three stores performed best when users were retargeted for up to 30 days following a search; one location performed best with a recency window of two weeks; and two locations performed best with a recency setting of one week.

Finally, to reach users who were already interested in the advertiser, implemented Site Retargeting. This enabled the franchise to retarget users who visited its website, thereby reminding customers to place their orders online and re-engaging them at the bottom of the sales funnel.


The franchise wanted to go beyond simply tracking the number of online orders placed on its website to also better understand how customers interacted with the site. Therefore, created multiple conversion audiences to measure the number of users who:

  • Completed online orders.
  • Clicked the “Get Directions” button.
  • Selected the “Order Carryout” button.
  • Clicked the “Order Delivery” button.

The direct mail provider and the advertiser then used’s Transaction Value Reporting to identify the total revenue that was generated from orders placed by targeted users. This ultimately helped the advertiser gauge the campaign’s ROAS and its effectiveness at generating online sales.


Targeting users based on their location data and online behavior, as well as optimizing campaign performance for each individual store, proved quite successful. Over the course of nine months, generated 10,059 physical visits to the franchise’s six stores for a Cost Per Visit of $1.05, as well as 910 combined online orders and button clicks for a Cost Per Action of $11.60.

Substantial Revenue Generated
Using’s Transaction Value Reporting, the franchise and direct mail provider determined that online orders placed by targeted users produced $21,319.52 in revenue for the six stores. They then used this figure to calculate a $2.02 ROAS. Given the relatively low profit margins that QSRs typically experience, this ROAS greatly exceeded the advertiser’s expectations.

Lift in Store Traffic
The franchise and direct mail provider also identified a 115% surge in incremental visits to the pizza chain’s six locations using’s Geo-Conversion Lift metrics. This figure reflects the percentage difference between customers who visited the restaurant after receiving an ad versus those who naturally visited the chain without receiving an ad.

Individual Store Performance
Furthermore,’s granular reporting provided the advertiser with unique insights at the individual store-level. For example, it identified that the store with the highest ROAS nearly doubled the store with the lowest ROAS. It also determined that the store that received the lowest number of physical visits was different from the store with the lowest ROAS, highlighting the fluctuations between online orders and physical visits that each store experienced. These data-driven insights will help the franchise properly allocate its advertising dollars across its six stores moving forward.

In the meantime, the advertiser has been so pleased with its advertising success that it keeps extending the campaign’s flight. will continue to optimize the campaign to further generate revenue and drive physical visits to the pizza chain.
Interested in utilizing’s Transaction Value Reporting to highlight the success of your e-commerce campaigns?
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