Optimizing Your Optimization: A Jumpshot Story of Redemption
The Old Way
First, a vignette from my days as an operations analyst:
Me: Are people really going to click on this ad, then surf around the different pages of visitorlando.com?
Team Lead: Dude, I don’t know… This is what the agency asked for.
Me: So I need to set up a conversion pixel for each of their subpages because THEY don’t want to use Google Analytics?
Team Lead: Yeah . . .
Me: And that won’t slow down the site for users?
Team Lead: No, it probably will
Me: And there will be enough visits to these pages for us to make educated optimizations?
Team Lead: I doubt it . . .
Me: I guess I’ll get started.
This exchange took place during my first month on the job. A tourism board was running a campaign using clickthrough rate and conversion pixel fires on visits to six different category pages (because people NEED to know about the city’s burgeoning restaurant scene). That was the best they could do to measure success for a campaign with a goal of driving tourists to Florida’s third-largest metropolitan area.
This strategy didn’t really measure the campaign’s impact, and required the placement six different conversion pixels on their site for each partner.
Granted, this was July 2012. “Call Me Maybe” was giving way to “Gangnam Style” in the battle for song of the summer, and the average rent for a 2-bedroom apartment in San Francisco was a paltry $3,600. My point is, a lot has changed since then.
New Data, New Techniques in Optimization
In my work at Jumpshot, I’ve worked to establish new metrics and, in the case of Condé Nast, find new opportunities to understand the impact of campaigns and better allocate resources to make them work with efficiency. My absolute favorite use-case for the optimization feed thus far was not too different from the one I complained about as a dorky 22-year-old, but the tools available to gain actionable insights have drastically improved.
Using Jumpshot data, Condé Nast was able to help a tourism board for a tourist destination in the Southeast measure lifts in a variety of behaviors. In addition to visits to their cities tourism website, we also provided valuable information on search engine searches and hotel searches and conversions on OTAs like Expedia and Priceline. In addition, we were able to show a comparison of that city’s results to nearby competitor cities, like Austin, New Orleans, and Nashville.
All of this led to a more informed picture of Conde’s impact on potential travelers, and allowed them to optimize towards the content that was most effective at driving product views and conversions.
Because Jumpshot’s 100-million member consumer panel reveals internet behavior on any domain, advertisers aren’t limited to their own websites when it comes to calculating results. Advertising brands and agencies, as well as publishers like Condé Nast, can gain insights beyond their internal data.
Tourism campaigns are just the tip of the iceberg. Retail brands can peer into walled garden data from Amazon and Walmart, auto manufacturers can see users shift focus away from the competition, and any advertisers can see impact on search engine results. These actionable results can help change the trajectory of a display campaign mid-flight, which is ultimately their goal in running ads.
I’ll write a bit more in the coming months about the strategies we’ve developed to understand the influence of ad campaigns themselves and control for other factors, like publicity or seasonality, that influence consumer behavior. I’ll also share more results from our work as everything—rents, monster pop jams and ad metrics alike—continues to change.