Project type: Group
Timeline: Eight + weeks
I served as a design consultant for two MBA students in CMU's business school's tech entrepreneurship track. This gave me the chance to lead the design efforts on a real life business project, making all the design decisions and spreading ideas about user-centered design and product strategy to the two MBA students heading up the project. Together, we took a nebulous concept about point of sale data and focused it into something viable using user research.
The origin of the project
Over the summer of 2014, one of my partners, Arjun Gopalratnam, worked with Clover, a company that makes a point of sale system for small businesses that runs on the Android operating system. Arjun felt that data in the point of sale system could be used to provide value to customers, thus driving business to the companies that use Clover's device.
Arjun felt that he could develop useful insights from the data inside the Clover machine, such as:
- whether the store/restaurant is currently very busy
- what the most popular items are
- whether the store is open on holidays
- whether a specific item is in stock
- which nearby small businesses offer specific products
They saw their challenge as figuring out how to deliver this information to customers. It's not currently available on Yelp, Urbanspoon, store/restaurant websites, Google Shopping, or any of the expected sites or apps. So, Arjun and his partner, Matthew Fei, decided to auto-generate websites for participating merchants, so that customers could access the information on the store's own website. (They hypothesized that these small businesses didn't already have websites.)
The value to the client would be the auto-generated website, and the increased traffic. The value to the customer would be the real-time data about product availability or wait time.
Backing up to do some user research
Before jumping in to do some user testing of Arjun and Matt's auto-generated website concept, I thought it best to not only figure out other possible ways to deliver the information to customers, but also to test whether insights like "the most popular item is X" or "the store is unusually busy this hour" even appeal to the target customer group.
We agreed that people who like shopping locally would be the best target market. Since the data source comes from small businesses, and won't include big box stores or online retailers like Amazon, therefore people who like to support local business would be our best early adopters.
An online survey with 91 responses gave us some useful data: Those who like shopping locally are not interested in the types of insights we thought we could provide.
Characteristics of people who like shopping locally:
Our survey showed, with statistical significance, that people who like supporting local business also:
- Have some desire to know more about the owner of a business
- Care more for American-made products
- Avoid big box stores
- Are less price-conscious compared to people who primarily shop online
- Don't mind visiting multiple stores before making a purchase
- More likely to be female
(Our survey also revealed insights about people who prefer to shop online--for example, they tend to dislike browsing, and care a lot about particular items being in stock. I believe we shouldn't target these customers.)
A necessary pivot
Because our target audience (people who like to shop locally) isn't interested in retrieving real-time data about local businesses via the web, we needed to find a new way to serve those customers using the technology we have. We came up with some ideas for how to proceed:
Needs and opportunities
- Don't focus on efficiency or optimization (e.g. lowest price, finding an item quickly).
- Provide a channel for small businesses to interact with customers who are already inclined to shop locally
- Businesses could give targeted deals or generate marketing content: our survey showed that people who like shopping locally may be interested in more information about the owners the stores
- Cater to hard-core local-lovers while providing resources to help those who'd like to shop locally more often--help tentative shoppers feel more comfortable
- mobile app or site that provides interesting content about local business
- social loyalty program-esque features, such as a Yelp-like forum for users to give tips and reviews, could encourage customers to do more local shopping
I believe that incorporating some loyalty program features that customers are used to engaging with at big companies (i.e. Starbucks Rewards, credit card points, frequent flyer programs) may help tentative shoppers feel more comfortable shopping in the more heterogenous environment that local stores provide.
One area I think will be important will be providing support for shoppers to buy big purchases from local companies. Items like washing machines are purchased infrequently, and when they're needed, there isn't much time to research options. Customers who like shopping locally would feel good about buying their big-ticket items from people in their own community, rather than Best Buy (for instance). I also wonder if digital goods such as MP3s could be purchased locally, and whether the Localist app could promote that.
Conclusion: Clarifying the concept
Conducting the user research and analyzing the results was crucial to Localist's formation. Some key assumptions, such as about customers' use of lists and local-shoppers' attitudes toward purchasing, were overturned. By analyzing the results of the survey, the team was able to think more creatively about the opportunities that technology can bring to this demographic of shoppers.
Icon credits: Laurent Canivet, Kwesi Phillips, & Emily van den Heever of The Noun Project