QSR Case Study – Unlocking Meta Insights

Problem & Result

A Fortune 500 QSR wanted ongoing consulting to improve their digital marketing efforts on Meta. Their performance had plateaued and did not see improvement in iROAS over some time. The major QSR valued consistent lift testing to improve their digital marketing efforts but was not seeing consistent results and iROAS was typically under $1. With a 7 figure weekly spend, any results, good or bad, would compound greatly and their current strategy was costing them money every week.

Goal

The goal was to improve incremental returns for both returning customers and new customer acquisition;

Improve iROAS to consistently be above $1 on a weekly basis for returning customers and above $0.40 for new ones.
Determine if there were any differences in the approach or strategy for new vs returning customers

Our Approach

Our process was to systematically measure changes through weekly lift testing, testing a single isolated variable each week until we were able to understand improvements through a champion vs challenger approach. We wanted to cover the entire gamut on Meta to fully understand the system and how to optimize the algorithm.

Results/Findings

After working with the company we were able to achieve an iROAS of $3-4 for returning customers and consistently over $1 for new – Far surpassing the original goals! We also unlocked insights on how audiences responded differently to understand how to effectively target them in ongoing campaigns. What we found;

New vs returning audiences responded differently and at different times

New customers needed a price point to know what they are getting and how much they are getting it for. Returning customers responded equally well to price point and no price point creatives.
New customers should only be targeted during store open hours so they can buy immediately. Returning customers could be targeted at any time of day and would consider purchase within the week.
Returning customers responded well to organic type creatives and more fun brand content whereas new customers responded to traditional offer and ad-like content more

Objectives matter

New customers were better acquired using a conversion objective. This raises CPMs however Meta’s system was successful in finding high intent users through thor algorithm.
Returning customers were better targeted with a reach objective which lowered CPMs but conversion rates were much higher since we were targeting an already qualified audience

Certain side items performed well on paid media

We found certain side items in rotation improved performance for both audiences instead of just highlighting their core menu items

Consolidating ad sets and campaigns is key

Instead of having multiple ad sets and campaigns for products, keeping it simple and consolidating to as little ad sets and campaigns consistently proved to have the best outcome. This gives Meta’s algorithm more signals while allowing it to work smarter. For setup this also helps reduce errors and time it takes to set up campaigns.