Your Delivery Platform Ads Are Driving In-Store Sales You're Not Measuring

The Measurement Gap in Delivery Platform Advertising

When a brand runs ads on Uber Eats or DoorDash, the platform dashboard shows one number: the return on ad spend within that platform. Click-throughs, orders placed, and cost per order - all measured in-app.

What it doesn’t show is what happens after someone sees your ad and closes the app.

For a national QSR franchise with hundreds of locations, a marketing mix model revealed something the platform dashboards had been missing entirely: brand ads on Uber Eats were correlating not just with Uber orders, but with total transaction volume across the business, including physical restaurant visits.

The platform was capturing a fraction of the real return.

What the Model Found

The franchise ran a combination of Uber Eats paid advertising and promotional offers. The paid ads were straightforward brand placements - elevating the brand’s position in search results on the app, increasing visibility to customers browsing nearby restaurants. The offers were promotional discounts available to customers ordering through the platform.

When the MMM analysed the data, a clear pattern emerged. Every time Uber Ads spend increased, the model showed a corresponding spike in total transactions, measured across all channels, not just Uber. The correlation was consistent and strong.

The interpretation: when customers see a brand ad on a delivery platform, they don’t all convert on the platform. Some go to the physical location. Some remember the brand and visit later in the week. The ad is building awareness and consideration that converts across multiple touchpoints.

This is the halo effect in action. An impression on Uber Eats doesn’t just drive Uber orders. It drives demand.

Why Platforms Undervalue Their Own Ads

Delivery platforms measure what they can observe: transactions completed within their system. An Uber Eats ad that drives 50 in-app orders gets credited with those 50 orders. The 30 people who saw the ad, didn’t order on Uber, and visited the restaurant on Friday are invisible to the platform’s attribution.

This creates a structural undervaluation problem. If you’re assessing Uber Eats ad performance purely on in-platform ROAS, you’re seeing a fraction of the impact. The brand awareness and consideration effect that drives offline behaviour doesn’t appear in any dashboard.

For categories where multi-channel behaviour is common - restaurants especially, where someone might browse delivery options, see a brand, and decide to visit in person - this gap between platform measurement and real-world impact can be significant.

Offers vs. Ads: A Different Dynamic

The same franchise was also running promotional offers on the platform: discount deals delivered to new customers. These offers are what most marketers think of first when they consider delivery platform investment. A compelling offer drives orders. The platform shows the uplift. The logic seems clean.

The model told a more complicated story.

The offers were more expensive in incremental terms than the brand ads. Many customers who redeemed a promotional offer on Uber Eats would have ordered anyway without the incentive. The discount was captured at checkout, but it wasn’t the reason for the transaction.

The brand ads, by contrast, were generating awareness that converted into genuine incremental transactions. Not just on the platform, but in physical locations that had no discount attached.

The higher-visibility metric (offer redemptions) was less efficient than the lower-visibility one (brand ad impressions driving total transactions). This is a pattern that appears repeatedly in MMM analysis: the channels that are easy to measure and easy to attribute tend to attract the most spend, while the channels with diffuse, hard-to-measure effects get underinvested.

The Spend Level Problem

There’s a complicating factor: the halo effect from Uber Ads was most visible at higher spend levels. When the franchise was investing around $20,000 per week in brand ads on the platform, the correlation with total transaction spikes was clear. When that spend dropped to $5,000 per week or below, the signal became much harder to detect.

This isn’t surprising. At low spend, the brand ads reach a small fraction of the potential audience. The awareness effect is real but too small to register in total transaction data. At scale, the cumulative impressions create a measurable lift.

It creates a threshold challenge for brands evaluating platform advertising. A small-scale test of brand ads on a delivery platform may not show a meaningful return - not because the channel doesn’t work, but because the awareness effect only becomes measurable above a certain spend level. Cutting a test before it reaches that threshold will produce a false negative.

What This Means for Budget Allocation

For any multi-location food brand or retailer selling through delivery platforms, there are practical implications here.

Platform dashboards are incomplete. A brand evaluating Uber Eats ad performance solely on in-app ROAS is missing the offline and cross-channel impact. The total return is higher than the platform shows.

Offers and ads serve different functions. Promotional offers drive on-platform conversions, but many of those conversions are from customers who would order regardless. Brand ads drive awareness that converts across channels. Both have a role, but treating them as equivalents and comparing their in-platform ROAS is a category error.

Spend level matters. Brand awareness advertising requires a minimum effective threshold to generate a measurable halo effect. Under-investing produces results that look weak not because the channel is ineffective, but because awareness needs scale to register.

Measuring total transaction correlation - not just platform-attributed sales - gives a more complete picture of what delivery platform advertising is actually doing for the business.

Key Takeaways

• Delivery platform brand ads drive transactions across channels, including physical locations - not just in-app orders
• Standard platform dashboards miss the offline halo effect, systematically undervaluing brand awareness advertising
• Promotional offers on delivery platforms can be less efficient than brand ads in incremental terms - many offer redemptions go to customers who would have ordered anyway
• The halo effect from brand ads becomes measurable above a minimum effective spend threshold - small-scale tests may underestimate performance
• Marketing mix modelling captures cross-channel and offline impact that platform attribution cannot