19 October 2022
Uber rolls out advertising in ride-hailing app
The company is also standing up a new advertising division as it expands retail media offerings.
Ads are coming to this app. (Photo by Priscilla Du Preez on Unsplash)
The company is also standing up a new advertising division as it expands retail media offerings.
Uber is joining the ranks of platforms expanding retail media offerings.
The mobility company announced on Wednesday that it will stand up a new advertising arm, and bring advertising to its ride experience.
Key details of Monday’s announcement:
Uber now has a dedicated advertising division. It will be led by Dr. Mark Grether, who was previously with Amazon Advertising and led the company’s ad-serving platform Sizmek.
Journey Ads are a new ad type being launched by the company that are designed to reach users during the ride. This expands in-app advertising across both Uber's ride and delivery services. Through this format, users will see ads in their Uber ride-hailing app while ordering a ride, and during the trip. The company said 40 brands have already run these ads, including NBCUniversal, Heineken and United Artists Releasing.
What it offers: Uber said it has an audience of 122 million monthly active users. With the Journey Ads, Uber said brands have 100% share of voice during a trip. Early results showed that riders were exposed to two minutes of ad content during trips. Between the lines, Uber ads come at a time when people are waiting, and checking the app. That’s a moment when a customer is “uniquely attentive,” Dr. Grether said.
”We have a global audience of valuable, purchase-minded consumers who, as part of our core business, tell us where they want to go and what they want to get,” said Dr. Grether, in a statement. “While these consumers are making purchase decisions and waiting for their destination or delivery we can engage them with messages from brands that are relevant to their purchase journeys. And with 1.87 billion trips last quarter, that means we can connect advertisers to consumers on average five times per month across rides and delivery.
A growing menu: The new format will be added to a network that also includes a variety of ads that are served in Uber Eats, which is the company’s delivery service. These include sponsored listings within the Uber Eats marketplace that elevate a brand’s placement on menus, checkout or the homepage.
Reaching across these two modes, Uber is in position to combine purchase and location data as it seeks to serve relevant ads for users. That’s the kind of first-party data that is making retail media a compelling offering for many marketplaces.
The advertising division is launching with a number of ad formats in its arsenal. These include sponsored emails for exclusive offers across both the ride and delivery sides, storefront ads that place CPG brands atop a virtual storefront and out-of-home Car Top Ads that reach users through screens on a driver’s roof based on location and time of day in several US cities. Uber is also set to pilot ads on in-car tablets in LA and San Francisco.
Uber joins DoorDash, Pinterest, YouTube and Quora among platforms that are rolling out new ad offerings this week. The announcements come as Advertising Week New York is being held.
Here are a several more updates announced this week:
Here's one more update from TikTok World: The platform is rolling out a new automated performance marketing solution, as Social Media Today reported.
TikTok describes Smart Performance Campaign as its "first end to end automation solution that leverages machine learning to optimize for best performance and marketing goals. In order to reach the right people and maximize results, Smart Performance Campaign is designed to run performance campaigns at scale, while reducing the number of manual steps to drive results."
TikTok said all that's required to get started is a marketing objective, budget, country and creative assets.
Among the top potential beneficiaries: Advertisers who are new to TikTok, highly performance-oriented or businesses that don't have hands-on campaign management resources.
As flagged by Search Engine Journal, Google has new seasonally themed ad templates that have audio and video for specific moments, including Black Friday-Cyber Monday, Hanukkah, Christmas and Diwali. Google will take creative assets such as images, logo and brand colors, then put together a video for the moment.
Why last touch attributed sales hide opportunity.
US retail media spend crossed $40B last year, with Amazon taking the lion’s share. eMarketer forecasts that by 2024, this will account for nearly 1 in 5 digital advertising dollars. While the scale of this spend is staggering, what has been truly mind-boggling is the speed at which retail media has reached these levels: search needed a full 14 years to reach $30B in spend, social took 11 years, and retail media has achieved this feat in a mere 5 years.
Retail media has grown under a confluence of factors that make it very difficult to measure effectively. The biggest being that both, the advertising and the sale, take place within walled gardens; and, due to the shifting privacy landscape, there are limited outside options for tracking the customer journey to purchase.
The net result of these conflicting trends toward rapid growth and limited data availability have contributed to widespread adoption of “ad-attributed” sales as the de facto measured outcome of retail media. This metric is widely available and often the default metric across Amazon Ads' own platform as well as the numerous retail media buying platforms. Underpinning “ad-attributed sales” is a very simplistic approach to attribution. If a user saw or clicked an ad and subsequently purchased within the look-back window, the retail media ad gets 100% credit for driving the sale.
When viewed more broadly among all factors which influence a purchase, the rather blunt nature of this approach becomes obvious.
While those users are certainly exposed to an ad, does that ad deserve 100% credit for driving that sale or are there other factors and touchpoints which should be receiving credit as well?
This type of single-source or single-touch attribution has widely been considered inaccurate in almost every other form of advertising and has driven brands and agencies to more nuanced approaches to attribution.
Those of us who have lived through the previous boom cycles of digital media and the catchup game attribution plays will be quick to see the inherent dangers in such an approach to measuring the performance of retail media. Given how close retail media sits to the point of purchase, it can easily take credit for sales being influenced by upstream advertising or external factors like seasonality. There is an analogous set of learnings that came from the early days of digital media. At the time, search and retargeting sat closest to the point of purchase, and because credit was attributed based on the last touch, their performance looked stellar. These channels almost always were the last touch before a user converted. As brands shifted to multi-touch attribution, they came to learn that these channels and tactics were being massively over-credited by last touch attribution.
There was a harder lesson still, learned in the early days of digital which is relevant here as well. Ads on many of these channels were being purchased programmatically using bidding algorithms optimizing toward last touch attributed sales. The result was that the algorithms were optimizing toward users that were the most likely to convert organically so they could touch the users before they converted and steal the conversion credit. The result was huge sums of advertising dollars being directed toward users that actually didn’t need advertising to convert.
Retail media looks to be in a strikingly similar position, with effective measurement lagging behind. The last touch attribution behind “ad-attributed” sales certainly obfuscates what is actually driving sales and just like we saw in the early days of digital advertising, the first wave of platforms providing measurement is usually the same platforms buying or selling the media which introduces questions of misaligned interests and neutrality.
There are a number of alternative approaches to attribution, each with its own tradeoffs. The approach used here draws from multivariate time series models. These types of time series models have a long history in attribution and are the underpinnings of most marketing mix models today. They attribute sales based on a multitude of factors which influence the outcome, including other media channels like search and social, promotions, and even seasonality and holidays. The results below are an aggregate of these models across a broad set of brands and retail media campaigns.
Before we dive into the results, let’s establish a few operational definitions to ensure we are all speaking the same language:
While there are some other interesting insights that have come out of this work (more on those in a later post) I’ll focus on only two here:
Now there is a silver lining to the second point – this is a huge optimization opportunity. Across the industry, that 33% would add up to over $10 billion worth of investment that could be optimized. If those poor-performing campaigns can be identified quickly and that budget reallocated to stronger-performing retail media campaigns, brands can generate significant additional sales with the same working retail media budget.
Now, not all campaigns with iROI of < 1x should be seen as a “bad” investment. Some may serve a strategic value like bidding on branded keywords to protect the brand from competitive conquesting but there is certainly some additional juice that can be squeezed out of retail media with some more robust measurement.
This comes down to last-touch attribution obfuscating the true value of retail media. When looked at under the lens of a more nuanced approach to attribution, there were plenty of retail media campaigns which drove fantastic ROI but last touch attribution blurs those campaigns with ones that are not producing strong returns.
To illustrate this, we compared ROAS and iROI at a campaign-level. One might expect a generally positive relationship between the two. The top performing on one side likely being the top performing on the other.
The results are Jackson Pollock-esque to say the least:
The campaigns that last touch attribution suggested were top performers were not the ones to which our attribution model gave the most credit. Last touch attribution simply is not effective at identifying the actual winners and losers at a campaign level. The upside here is that a more nuanced attribution approach, can become a source of competitive advantage for brands that move first. They should be able to optimize their retail media investments far more effectively than their peers still using last touch attribution.
There is absolutely a maturity curve to climb in measuring retail media and we are still in the early days as an industry. The good news is that there is a wealth of alternative approaches to measuring retail media other than the last touch attribution being used today and there is a lot we can learn from the hard-won yards in other media channels that we can apply here to avoid making the same mistakes twice.