PopUpSales

PopUpSales is a side project in which I demonstrate similar skills as in one of the protected projects at work.

For emerging brands that don't have a big budget, pop-ups provide the environment to memorable experiences, making every minute count to create brand awareness. PopUpSales focuses on revealing insights into how your customers interact with your pop-up shops.

Inviting and Intuitive Analytics Experience

In order to help shop owners understand what is going on in their business and keep their fingers on the pulse, PopUpSales provide real-time data on how their performance is changing, actionable recommendations and alerts when something out of ordinary happens.

↑ User Journey

I started by grouping the data points into 4 buckets: Brand awareness, Performance, Customer Profile and Competition.

↑ Data Points Inventory

Real-time Dashboard at a Glance

By tracking performance in real-time, swift decisions can be made. For the homepage, the original experience was that users swipe through 5-8 hero cards which housing the primary metrics to see daily updates. But the main feedback was the relationship between the hero cards and the 3 main data points above was confusing and it was not digestible at a glance.

So I revised the dashboard structure to be more flat. After all, the main purpose is to provide a snapshot view of critical data at a glance.

"Hero cards" 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 interactivity. Interactions are added in PopUpSale such as: Change period filter using the date range control; Drill down on a particular bar of a chart; Choose a data point in a line chart and see the underlying data.

Change time frame filter using the date range control

Tap to see average transaction size of different age groups

Tap to see daily performance

Surface Opportunities to Maximize Revenue

Today, 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 can be 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, can be used to find best practices for turning pedestrians into shoppers.

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

All data points are grouped into 4 buckets

One of the detail pages describes best selling products.