Starbucks Table Story is a feature added to Starbucks app. By scanning QR code on the coffee table, customers can read stories shared by people who sat at this table before. At the same time, they can share their own stories through the same channel. This project starts from a hypothesis. I conducted user research and verified it.
Some Starbucks customers are interested in knowing more about other patrons and stories around Starbucks community.
I had this hypothesis because in many cafe or small restaurants, there're many sticker notes posted on walls. Customers wrote down their stories and others love to read it. So I wonder if in Starbucks there are sticker notes walls, there would be full of all kinds of stories.
Anyone who sits on a table in Starbucks' store with smart phone or tablet.
Trigger: When customers see the QR label on table, or posters, or ads on social media/cups/sleeves, they may want to check it out.
• Scan QR code on table
• Discover stories from people who sat on this table before
• Share your own stories
Effort: For marketing, Starbucks has its own well-developed marketing platform and App. Add several new pages to its App and send out ads on its social media is easy to achieve. For QR code on each table, printed out stickers is ideal choice with low cost.
Impact: This feature is a great extension of Starbucks' mission to reinforce connection within Starbucks community. At the same time, it can improve retention, and add positive interactions to customers' experiences and provide data to better understand customers.
Verify the Hypothesis
In order to verify the hypothesis, I conducted design research and usability testing in a Starbuck store at Chicago magnificent mile. It's a store with 24 seats, about 400 square feet.
I observed for an hour from 14:06 to 15:06, Mar 3rd, 2019, captured 64 customers' activities in total and categorized them into 5 types:
1. Buy & Go: Stay in store for 1 to 5 minutes. Observed 23 people;
2. Buy & Short Stay & Go: Stay in store for 6 to 22 minutes. Usually eating or go to restroom or short rest in seat. They all took a seat for some time. Observed 27 people;
3. Meet & Talk: Sit in store for 14 to 40 minutes. Observed 10 people;
4. Working: Sit in store for more than 30 minutes. Observed 2 people, who also claimed that they would stay for more than 4 hours;
5. Stay Alone & Killing Time: Sit in store for more than 25 minutes. Observed 2 people, who claimed that they would stay for more than 2 hours.
Target Customer: 64.1% customers who take seats in store.
Interview and Usability Testing
After previous observation, I conducted interview and usability testing by first asking interviewees if they were interested in knowing stories of other patrons. If they said yes, I did usability testing by showing them the prototype; if they said no, I continued to ask them questions related to their experiences in Starbucks.
Results: I set a goal to get 5 Yes answers from target customers. When I got my 5th Yes, I have interviewed 14 people in total. All of the people who said YES are young adults or tourists.
YES: "I think it's an amazing thing, get you know people to each other, know this community, it's really a great idea" "Yeah it's pretty cool, I never knew it's a thing, but I think if I had the app, I'll take a look at it" "It's so cool, it's so awesome"
NO: "Not really interested. I met many people in my work, when I come to Starbucks I want to focus on my work" "No that much, it's good to know more people, but not here, because I come here to get things done, to drink more coffee"
To summarize, 64.1% * (5/14) = 22.9% customers would like to use this feature.
Starbuck has 15.3 million active members in Starbucks Rewards, its loyalty program, which means, 3.5 million loyal customers might want to use this feature. This is a huge market.
Base on the Effort-Impact Matrix in previous hypothesis, Starbucks could make the assumption that these customers might become more retentive users, who would support the business.
• Number of people successfully scanned QR code
• Number of stories created on each table
• Number of interactions among users