QuickLaunch

QuickLaunch

QuickLaunch

machine learning driven
action centric launcher

machine learning driven
action centric launcher

machine learning driven
action centric launcher

#team lead

#team lead

#team lead

#ui design

#ui design

#ui design

#ux research

#ux research

#ux research

#product analytics

#product analytics

#product analytics

#figma

#figma

#figma

Background

The search bar is the the most common single action after logging in, and the feature boasts 6.23m clicks in 30 days.

Through user recordings obtained through our product analytics tool, Pendo, I identified an common flow that was unoptimized, and slow. Often a customer will perform a search, realize what they’re searching for doesn’t exist, and navigate away to a different part of the application to create it.

This single flow, repeated hundreds of thousands of times a day, was the inspiration behind QuickLaunch - an action centric, machine learning driven launcher.

Research and Process

As the leader of the QuickLaunch project from start to finish, I led the design and a team of developers towards an innovative solution to enhance our searching experience.

I performed user interviews where the user would guide me through their workflow, and some common pain points were quickly identified.

Product

Product

Simpro Premium [Cloud Based Desktop Software]

My Role

My Role

Team Lead [5 Developers]

User Research

UI Design

Timeline

Timeline

1 Sprint [Two Weeks]

Industry

Industry

Field Service Management

Pain Points

“I’ve just created or edited something, and I’ve forgotten where it is.”

“I have to scroll for miles to find a job that I access all the time.”

“If I can’t find [what I need] I have to navigate somewhere else in the app and create it.”

Our identified pain points were validated and supported through secondary research.

Technical Inspiration

A lot of my initial inspiration for the predictive behaviors present in the final product was driven by the concept of word trees.

A word tree depicts multiple parallel sequences of words. It could be used to show which words most often follow or precede a target word (e.g., "Cats are...").


This concept was extended upon to show likely suggestions for the next word in a users command. The data was sourced from all our users across Simpro - if after a user types “Create”, “Job” is the highest scoring hit then “Job” will be suggested in the search bar, and the user can simply press tab to autofill the command “Create Job”.

Word Tree Example

Designs

After our research concluded, the scope of QuickLaunch included several key features which I developed into high-fidelity designs to test with our userbase.

Simplified mini-workflows

Step-by-step processes for creating jobs, quotes, and invoices, with prefilled data.

Natural language processing

Ability to interpret commands like "Run X report on Y customer from dates X to Y on this cost center."

Show unpaid invoices for DSM Offices

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Invoices

DSM Offices

Status: Unpaid

Press Enter to finish

Predictive action suggestions

QuickLaunch identifies patterns, such as addresses, and suggests actions like creating a site.

123 Main St

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Recent item access

Quick access to recently opened items within the application. We enabled this as part of our default open state, and this was a big timesaver for our users.

Previously there was nowhere in our application to access recently opened items.

Search or select action...

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Billy Brown

Recent Customer 2

Recent Invoice

Machine learning

Enhancing result accuracy based on user activity appwide.

Results

In less than a single sprint, we were able to achieve:

The development of a scalable neural network to enable deep learning to accelerate user experience.

Quick access to recently opened items.

Action based commands.

Machine learning to increase the accuracy of results based on global user activity.

In less than a single sprint, we were able to achieve:

The development of a scalable neural network to enable deep learning to accelerate user experience.

Quick access to recently opened items.

Action based commands.

Machine learning to increase the accuracy of results based on global user activity.