PopUpSales app aims towards more lightweight use cases such as monitoring and reporting, rather than exploration and data mining. I focused on creating an intuitive experience for small merchants to guide them to the right digital solutions. This is a side project in which I demonstrated similar skills as in one of my protected projects.

My Role

In the protected project that this case study is based on, I was the product designer at Intersect working alongside the UX director. Additionally, I worked closely with our product strategy team and developed design solutions based on insights derived from user needs and testing.

Generative Research

Who PopUpSales is built for

Our persona Colin and his partner has an online boutique, selling must-have summer items such as floats. He wants to expand his business and reaches more customers. But he doesnt' know what campaigns will be most effective to increase their brand awareness. He is also not comfortable to use those advanced analytics tools with hundreds of different features.

↑ Persona

Key insights about small merchants

Before, in order to uncover the important insights from noise, small merchants needed to consume the PDF format reports. We found out through user interviews that:

  • "Small business owners need to keep their finger on the pulse."

    Key Takeaway - Providing merchants with real-time data and engaging merchants by driving actions and letting them understand the results of those actions.

  • "Small business owners don't have the time to be trained on a complicated platform, or to experiment with hundreds of different features."

    Key Takeaway - Making data something anyone could work with. The dashboard should be able to answer the most frequently asked business questions at a glance. The chosen data visualizations should correctly represent the data and the information the user would expect to extract from it.

User goal

As a busy small business owner, I want to find the critical things immediately and interact directly with my data, so that I can spot trends and figure out how my business is performing.

↑ User Journey

Data points inventory

Part of the research was to figure out a logical way to group and present those data elements. I started looking at what data merchants care about the most, and how those data could start to be broken down and grouped. The categories the data started falling into: Brand awareness, Performance, Typical Customer Profile, New Customers and Competition.

↑ Data Points Inventory WIP

My Approches

Real-time dashboard at a glance

The main purpose of the dashboard was to provide a snapshot view of critical data at a glance.

The initial experience was that users could view the 3 main data points they cared about the most and then swipe through 5-8 hero cards which present the primary metrics. But the main feedback from a hallway testing was "What is the relationship between the data points at the top and the hero card carousel? Are they the same data?"

After about 4 iterations, the final result was a visually driven summary with a flat structure that showed only the most important insights and allowed merchants to dive in deeper if required.

Iteration 1 (Original homepage experience)

Iteration 2 (Revised homepage experience) - All critical metrics are visible with a quick glance

Revised homepage mockup

Drill down interactions

Compared with static reports, the primary value of a real-time dashboard should be interactive. Interactions were added in PopUpSale such as the abilities to: 1-Change period filter using the date range control; 2-Drill down on a particular bar of a chart; 3-Choose a data point in a line chart and see the underlying data.

One thing to consider about the date range control was that it would only affect the dimension it filters, not globally. Otherwise it may result in an empty view with no data on some dimension and cause unnecessary confusion.

1-Change time frame filter using the date range control

2-Tap to see average transaction size of different age groups

3-Tap to see daily performance

Actionable recommendations

Advanced data science techniques can sift through raw data to unearth valuable insights in places shop owners might not have thought to look such as identifying peaks and valleys in pedestrian activity to optimize store hours.

In the example below, sidewalk traffic metric was used to uncover the pedestrian activity pattern. By shifting store hours one hour later on weekdays and one hour earlier on weekends, the store could increase the number of pedestrians passing the open store. Walk-in rate, or street-to-store conversion rate, on the other hand, was used to find best practices for turning pedestrians into shoppers.

↑ Data Points Inventory WIP

↑ Progressive Onboarding

"Try Our Demo" allows people to experience the app without an account

All data points are grouped into 5 buckets

One of the detail pages describes best selling products.