As the OfferUp platform grew it played an increasingly important role for a wide range of sellers. What had begun as a tool to sell unwanted items and “clean out the garage” was now a platform for both casual and professional sellers. The time to monetize the selling motion had begun and ads would be a key part of ongoing revenue as well as seller satisfaction. Our customer segments were an important piece of information but we needed a more nuanced understanding of who would pay for ads, how much would they pay, and what additional requirements might exist to increase these sellers’ engagement on the platform. Sellers had different goals depending on what they were selling, a car vs. a dresser, and why they were engaged in sales at all. And their needs shifted with these goals. From this research, which I conducted in multiple interlocking phases, it became clear that the most important target customers were buying business speed, not simply the speed of selling an individual item. Speed of individual item sales was important, but those most likely to purchase ads were focused on maintaining an overall inventory turnover they could depend on.

Competitors at every level of cycling invest to improve performance but the Tour de France highlights the staggering gap between elite teams and other professionals with the so-called “super teams” spending as much as 3 times as lower tier teams. These teams are investing in technology, but they are paying for speed. Similarly, online sellers are paying for speed when they purchase advertising. They expect advertising to result in faster sales and a more predictable sales cycle. To meet their needs and support their sales goals we needed to understand the seller journeys, the unique friction experienced across types of sellers, and the items they were selling. This insight would inform who would buy our ads, what would drive their ad purchase decisions, and how much would they be willing to pay. Nuanced seller personas specific to ad purchase behavior were identified based on an extensive set of qualitative interviews across our current seller activity profiles. These personas made it clear who our ad purchasers were likely to be and the return they expected on their ad investments. For each seller persona, journey maps informed the role of paid advertisements in their overall selling experience, and highlighted additional unmet needs that could cancel out the speed advantages of paid advertisements.

Quantitative surveys delivered both the size of opportunity and strengthened our understanding of the differences between the qualitative persona characteristics. The Van Van Westendorp Pricing Model was used to determine specific pricing of individual ads and ad bundles. This model gauges optimal pricing based on 4 questions: When is a price too cheap, a bargain, expensive, and too expensive. The survey data also made it possible to predict which sellers would purchase ads and their unique purchase drivers. Triangulating with the qualitative data, I was able to determine the relative importance of the various seller characteristics and the relationships between purchase drivers across customer personas. The relationships between purchase drivers and the personas made it clear which customers were likely to purchase ads, their purchase drivers, and their price sensitivity. Who would pay for speed and how much would they pay?