Research / Product Strategy / UX / UI
Sketch, Zeplin, Trello


"The Market @ Macy’s" is a program that provides pods to selective D2C brands on Macy’s space to sell products. Research revealed that Macy's faced difficulties in exploring and evaluating the right brands for the program.

The following questions helped drive the design process.

How can we utilize our datasets on D2C brands to help Macy’s?
What are the current challenges in the program?
What data will help Macy's to decide the right brand?

Structure of The Market @ Macy's Program


The location Strategy map is a web application that provides research-backed data to decide the right brands for the "The Market @ Macy's" program. Insights such as physical store presence, pop-up store presence, and an interest score help in deciding a brand's potential to be a part of the program.


In addition to solving the problem for Macy's, the solution proved to help retail space owners find brands for their empty storefronts, thus identifying new opportunities for business. Macy's signed a one-year contract that increased the MRR by 30% in 2019.


Since I did not have the ability to interview people at Macy's and understand the problem deeper, I decided to an exploratory research. Initial research on the internet revealed pop-up stores are helping retail space owners tackle the empty storefront problem. Since pop-up stores are connected to Macy's, I interviewed 4 retail space owners to identify their process in finding the right brands for their business.


Key findings of the research revealed that the process to find a brand that is interested in setting up a pop-store takes a lot of time and the data needed to judge a brand is scattered across the internet. The interview also uncovered categories that retail space owner weigh to judge a brand.


My findings were based on interview from retail space owners and could not simply be applied for Macy's. Brainstorming session with my product manager revealed a hypothesis that both Macy's and Retail space owners needed a system to explore and research brands that set up pop-up stores.


The categories that retail space owners look at to judge a brand were drawn from the user research and matched with the available data points. For instance, a brand's ability to pay the rent is judged from their revenue. Five such data were identified to help Macy's with decision making.

Dissecting UI


Initial Design

The objective of the initial version of the design was to rather test the functionality and focus less on the look and feel. Considering technical feasibilities and tight deadline in mind, I decided to go with the below design as it helped showing concurrent information at the same time.


I performed an internal guerilla usability testing to get feedback from the team. I received a lot of feedback that were relevant to product and usability. Keeping the hard deadline in mind, I only considered feedback from that were critical to ship a minimum viable product.

Information Architecture

The information architecture is structured into 3 levels.

Level 1
Retail Locations: Explore brands by location.

Brands: Different brands for research.

Level 2
The second level contains different locations and brands to choose from.

Level 3
The third level shows information that helps with research.

Design revisions

  1. Only data that helped with decision-making for Macy's were included.
  2. 100% of the participants in the internal usability testing preferred location-first approach. Hence an interactive map was included to help with information consumption for users.
  3. Since a lot of emphasis were made on location, the iteration was inspired from Google Maps.
  4. Several other cosmetic changes were made to aesthetically please the users.

Final designs

By location

Map shows all the pop-up and physical stores of D2C brands at a location.


Map shows a brand's pop-up and physical store presence.

Interest score

PipeCandy's algorithm predicts brands' interest level to expand to a location out of 100. The interest level scored helps predict the location where a brand is likely to expand. This helps in saving time spent on researching about the brand on web and social media. Higher interest score means there is a more chance for opening a store in that location. This will help users approach brands with high confidence.

Business goal

One of the main business objectives of this web application is to plug and highlight our flagship product. We wanted to hook users with experience and cross-sell our other applications. Restricting data offerings is one way to make users try our flagship product. I conducted observation study with retail space owners as participants and identified data that will be the minimum requirement to carry out a basic research on a brand. Additional data like a brand's financials, social media engagement can be accessed by upgrading the pricing plan to get a premium access to PipeCandy's market intelligence platform.


The design hand-off was done using Zeplin. Zeplin helps developers to identify components that are reusable and aids them in creating a pre-defined CSS and JS.


Working on this project taught me to adapt to difficult situations. I learned that choosing the right fidelity to work with during the design process helps in saving time. Since we had a hard deadline, I allocated most of my time to user research and ideation. I gathered mood boards for layouts and quickly explored designs using Hi-Fi prototypes to make a decision. I learnt not to be married to the design process, and being flexible depending on the project's severity is much more important.

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