Home AdExchanger Talks Inside T-Mobile’s Plan To Rideshare Its Way Into Ad Budgets

Inside T-Mobile’s Plan To Rideshare Its Way Into Ad Budgets

SHARE:
Cherian Thomas, head of marketing and go-to-market, T-Mobile Advertising Solution

Have you ever stepped into a taxi and suddenly you’re faced with Jimmy Fallon clips and random ads blaring from a tablet mounted to the back of the passenger seat?

The first thing I do is tap the mute button and settle in with my phone to check email for the duration of the ride.

But rideshare marketing is – or at least should be – more than just slapping a screen in the back of a car and running ads, says Cherian Thomas, head of marketing and go-to-market for T-Mobile Advertising Solutions, on this week’s episode of AdExchanger Talks.

The traditional experience that you see in a New York taxi served as a model for what not to do, says Thomas, who joined T-Mobile in January after the carrier acquired his rideshare advertising startup, Octopus Interactive.

T-Mobile, which now claims to reach more around 15 million riders per month through rideshare advertising, is going for what Thomas calls “a lean-in experience versus a lean-back experience.” Branded games with prizes and interactive content lead, rather than autoplay videos.

Games like trivia or photo hunt are a hook to grab a rider’s attention. Once a brand has it, there’s an opportunity to intersperse the content with an ad or a QR code to scan.

Still, getting people to put down their phones during a brief car ride isn’t an easy task. (The average ride time in a rideshare is between 13 and a half and 15 minutes.)

To lure people in, a trivia game might start out easy (“How many sides does an octagon have?”) and get progressively more difficult (“What’s the name of the butler on “The Fresh Prince of Bel-Air?”). But because riders know they can win money, Thomas says, they’re more likely to stick around.

For those that prefer following games to playing them, T-Mobile also has a new API so riders can check live scores for NFL games.

Content can also be targeted contextually based on a vehicle’s precise location, like when a car is entering an airport or driving through New York City’s financial district.

“We’re constantly doing things to encourage engagement,” Thomas says.

Also in this episode: What’s different about T-Mobile’s zig into ad tech when other carriers have been zagging away, fun facts about octopuses (they have three hearts!), post-pandemic rideshare behavior and how Thomas gets to the office from his home in Bethesda, Maryland. (Hint: It’s not in a rideshare car.)

For more articles featuring Cherian Thomas, click here.

Must Read

Who Will Stand Up For The Open Web?

The open web is done, stick a fork in it. Banner blindness is near universal, search traffic has run dry and publishers are struggling for oxygen. But what if that’s … not true?

A comic showing lab techs as stand-ins for legislators experimenting with provisions for US state privacy laws, including restrictions on collecting sensitive data.

What Publishers Don't Know About New Jersey’s Data Broker Law Could Cost Them

Attention, publishers: Although you might not think of yourself as a data broker, in the great state of New Jersey, that’s not really your call anymore.

Predict Bowl Icon. Magician Element, Forecasting Symbol – Vector.

Why This Marketing Measurement Company Just Open-Sourced Its Forecasting Engine

MMM can tell marketers what worked, but Lifesight’s open-sourced forecasting tool aims to tell them what to do next.

Privacy! Commerce! Connected TV! Read all about it. Subscribe to AdExchanger Newsletters

Podcasts Are Becoming More Programmatic. But Now Advertisers Have To Keep The Ad Load In Check

As programmatic buying becomes more common in audio, marketers and platforms fight the temptation to cram in as many placements as possible.

PubMatic Jumps On The Show-Level CTV Targeting Bandwagon

Connected TV advertisers are still pining after show-level control. And PubMatic announced contextual targeting at the episode level is available to media buyers accessing CTV inventory through its platform.

SQREEM Touts The Large Behavioral Model – Not The LLM – As The Winning Predictive Engine

Rather than relying on machine learning, SQREEM uses a mathematical AI model to track how systems change over time and predict audience behavior.