Sramana Mitra: Let’s take maybe three customer use cases and talk us through how you have optimized their campaigns.
Ryan Golden: We work with a lot of QSRs. As opposed to traditional ways of just drawing the radius around their stores, we focus on taking a macro location in finding pockets or areas where lift is occurring. Based on time, location, and other data points, we’re able to make adjustments in real-time to then course-correct the campaign in-flight to increase performance. McDonalds, Burger King, or any QSR can say, “My customers are all over San Francisco. When is the right time to speak to these people and I need to talk to them now.” That makes it dynamic fundamentally.
Sramana Mitra: What is the answer to that question if I’m McDonald’s and my customer is all over San Francisco? What am I optimizing for?
Sramana Mitra: In the morning, there’s morning breakfast commute that aligns very well with some of these strategies. When you look at location and patterns of the behaviour and the patterns with regard to location near stores or the activities that push them to the store, that is some of the magic of optimization. It makes better sense to talk to all those people who pass McDonald’s about breakfast versus talking to them at the Mountain View train station where there is no McDonald’s. It’s not even top of mind or there’s no opportunity to react from the location.
Sramana Mitra: Give me a couple of other examples of use cases like this.
Ryan Golden: There’s a savings perspective that a lot of clients are liking such as where they could target anywhere from national down to zip codes. We’re able to save them money. Let’s take BMW. BMW wants to reach their consumers. We’re going to be working with data. We’re going to be looking at location. With our optimization technology, you don’t have to be serving ads in Tenderloin.
We’re then able to course-correct that in-flight saying, “Wait a second. Nothing is happening here. There’s no reason to target that area. Turn it up on areas such as Pacific Heights.” It’s that kind of real-time shifting of location while the campaign is in-flight when some of the magic is occurring. A lot of times what happens is you run a campaign, everything is post-flight. You look at all the historical data and then you figure out how to make adjustments going forward.
Sramana Mitra: I have a question here though. If you’re talking about mobile, people are in transit. Just because they’re in Tenderloin, it doesn’t mean that it is not a segment that doesn’t match up to a BMW customer base. To go to Pacific Heights, you have to sometimes go through Tenderloin. That’s not necessarily a mismatch.
Ryan Golden: It’s not a mismatch. That’s why I said turn down versus turn off. All you’re doing is adjusting budgets and adjusting different levers. Let’s say there are a couple of offices there and maybe it’s 50 people. Out of that 50 people, based on data, it’s now down to 10 people. There may be 10 people in Tenderloin that you want to speak to, but 10 people is not a scalable solution for advertising for BMW.
Being able to work anywhere and everywhere within a macro location such as in all of San Francisco gives them the type of reach and scalability they need. A lot of marketers are attracted to device ID targeting. Through a lot of conversation, you can’t ignore the base of the mountain. If you want to scale, you have to get the people that are one-to-one, one-to-few, and one -to-many, and you have to work within that funnel.