Pace
B2B, Revenue Optimisation ML Software
Product Design & Management
After my design leadership and marketing work at Kinsu, I was brought in as the first in-house designer at Pace.

Pace is a fintech startup optimising revenue for the hospitality industry through in-depth machine learning capabilites.

As my involvement at Pace was across the teams from Engineering to Customer Success, I transitioned to Product Manager for the Customer Experience squad.
I have split the work for Pace into 2 portfolio sections. This section includes my work on product design and management, and concept development.

If you wish to see my work on website and marketing design at Pace: See this section.

Most of my work in this section was created using Sketch and the Adobe Suite.
Actions Table on the Pace App (QA Environment)
Creation of Atomic Design Library
A lot of the core features were in place when I joined, but the UI needed an overhaul.

This included analysing the current design and creating an appropriate Atomic Design System which could then be used to redo the screens and make all of the flows consistent.

I created a new Design System to maintain consistency and allow for rapid prototyping
Setup Design & Developer Process
I also setup all of the design infrastructure from scratch.

This included setting up Abstract to maintain version control of the design files, and then to also enable easy handover with developers using Abstract Collections.

These collections linked all the way through from user feedback entries on Notion to the Epics on Jira.

I also managed the Customer Experience squad to maintain agile and continuous delivery of awesome features to make sure the user always has a seamless and delightful experience.

I maintained the feature releases and press releases that make sure customers are always up to date.
Abstract Collections linked with our Jira ticket numbers
Iterating on Existing UI
Using the new Atomic Design Library, I dug into all of the user flows to see how the product fully worked as well as to see where UX improvements and UI consistency improvements could be made.

Navigation was a large area of this, for example settings were move out of a hidden dropdown and onto the relevant pages to edit in situ and context.

One of the core app pages is shown below with a before and after.
Design Wall to help get a better overview of the different areas of the product design
Before overhaul
After overhaul and testing
The overhaul made the app easier to navigate, allowed for collapsing of information when not needed, and was more on brand too.

From this solid design position, we then moved forward to continue iterating new features.
Feature Highlight | User Testing
One of the core features affecting the way the algorithm priced each night for a room in the future was the Price Settings.

Price Settings set boundaries for the algorithm to work within, and could be setup for all inventories.

User testing sessions revealed that users wanted to be able to setup different Price Settings for particular rooms for seasons or events that would occur.
Summarising and grouping the user testing feedback into themes
Defining the user problems and then ideating around them
Feature Highlight | Quick Prototype
Before implementing or testing anything on the Front-end, we planned an Admin-only feature on our Back-end Admin tool.

This allowed us to start setting up seasonal price settings for users through their requests, and to see what sort of inputs and customisability needed to be available.
Admin-only feature prototype
Feature Highlight | Iteration 1
Taking in feedback from users on the implementation in the admin-tool, as well as analysing the various setups we then implemented a barebones UI in the front-end.

This new front-end feature now allowed users to setup the price settings themselves.
Simple, searchable list view to create seasonal price settings
The Price Settings drawer opens ontop of the list to allow users to create specific boundaries for a date range
Feature Highlight | Iteration 2
After further feedback and ideation, I designed an improved layout to view the events and settings as they overlay with each other.

One big piece of feedback is that users wanted to have specific price settings that could override long-term seasonal price settings (which in turn override the default price settings for the rest of the nights).

The Price Settings drawer opens ontop of the list to allow users to create specific boundaries for a date range
The new Calendar view allows users to see which Price Settings are created on top of the Defaults, and to see where there are gaps for potential Event Settings to be placed.
The Price Settings drawer opens ontop of the list to allow users to create specific boundaries for a date range
The Price Settings drawer itself was extended as well to allow for specific day of the week patterns to be implemented.

This was based on observation that users were creating Price Settings for each day of the week, and so would end up with a large amount of very similar settings.
For convenience, the initial values are pulled from the Default values so that the user has some context for setting up new Price Settings for a season or event.
Designs synced to a feature-specific Abstract collection
The designs were then synced and committed to Abstract to create a collection to be used in feature grooming sessions with the developers.

This iteration is then tested on QA before releasing to production in specific pilot tests.
Overall, this feature has been extremely well received, and is one of Pace's most used features.
Feature Highlight 2
One smaller feature request from users was the ability to be able to dig into the pricing history for a particular night.

After user testings and iterations, we arrived at the Quick View which can be opened on the night and show any pricing history with the candlesticks.

The image below shows an example, it is quite a data-heavy screen, so a tooltip helps to describe the data on a focused night.

The Quick View can be toggled to a simpler mode with no candlesticks and reduced data
This feature was also very well received, particularly by the revenue managers that had strong data and analytics backgrounds.

Please get in touch for more details, full res images, or if you have any questions.