Manufactured inventory

Deal density is a big deal for a hyper-local marketplace. On a map view especially, it becomes even more clear where sales are a challenge and consumers are not incentivized. For a quick read, we decided to anchor our consumers in intent-fulfillment regardless of any discount or platformization and put more merchants on our map. We used Google Places API to boost category results and we highlighted decision-making factors such as location and ratings.

We mapped the optimal flow utilizing existing pages though which we could simply repurpose with new data.

We had new challenges. 

  • What data do we actually get from Google Places?
  • Will that data be enough to incentivize consumers?
  • What data do we actually get from Google Places? 
  • What are the legal requirements for representing non-platformed businesses as Groupon inventory?
  • Does that data align with our existing design components? 
  • How do we differentiate this “inventory” on maps and in deal feeds?
  • How do we incentivize clicks while also clarifying there is no affiliation with Groupon, or no deals?
  • Can we incorporate a brand expansion strategy?
  • How do we market this feature?
  • We’ll need coach marks/feature awareness.
  • Can we rank deal merchants first in comingled feeds?
  • How far will customers go to earn Groupon Bucks (historically not a driver)?
  • How quickly can we launch a proof-of-concept by repurposing existing templates?

Customer’s always expect deals on Groupon. Without deals, we would need a serious brand strategy. In user research, the first consumer question was always “Why!?” The same went for my product design team. For board presentations, I worked with our CEO to highlight our brand position as a local marketplace platform that could support and serve hyper-local economies and further boost Groupon’s brand strength regardless of deals.

I worked with Product and Engineering teams to use our existing exposed filter toggle to create design options for surfacing non-deal inventory with landing pages. We presented options with various levels-of-effort to match degrees of tolerance for timing from our our executive teams who were matching feature releases to quarterly shareholder meetings.

With high fidelity mocks, we began iterations on our new visual cues and filter functions.

In the end, we launched our end-to-end MVP experiments in limited markets with an aggressive line-up of fast follows and research studies to make quick adjustments and evolve the product.

Using wireframes, we plotted out our design needs and technical pain points.