Enterprise tool that improves KPIs for support and sales teams by automating responses and works across all helpdesks. (YC W’17)
In the summer of 2017, I helped the young team at BicycleAI in their effort to make support teams super productive. The brief was very open-ended and I was tasked with determining the right product for this enterprise solution.
The only constraint was to build a design strategy that fit within the current workflow of the agents. I had to work with their in-house support and product teams to analyse this workflow and build a point of view.
We identified that a Chrome extension was the best way to work across every helpdesk and integrate into every agent’s workflow seamlessly. Here are a few problems I helped solve via design in the course of learning this business.
#1 : Improve productivity
Since this was a core pitch we made to our customers we had to focus all our attention toward this problem. Here are some tenets that helped us reach the solution after multiple iterations.
- Reduce effort and errors in conversations with customers via automated responses
- Automate repetitive workflows like collecting CRM data, logging bugs, capturing leads etc
- Onboard teams with the least friction to collect data for our models
- Easily triggered and dismissed based on our confidence
A contextual tooltip interface
In order to work on all popular help-desk softwares our interface needed to adapt everywhere. A tooltip-based interface worked perfectly for this because of the following reasons.
- Our ML suggestions could appear contextually right next to the agent’s workspace
- We could choose to invoke it based on our confidence, thus becoming less obtrusive.
- It easily scaled to various agent workflows like repeated workflows, searching, viewing popular/recent suggestions etc.
#2 : Tracking and team management
Another reason customers needed a product like Bicycle was to eliminate manual quality checks on agents responses. These involved time-consuming and primitive random sampling of conversations by agents. Bicycle’s dashboard promised to eliminate this by —
- Collecting data about agent efficiency like ticket closure time, open conversations, response rates etc; crucial for support teams to track
- Showcase ML enabled metrics that would differentiate Bicycle like topics or issues discussed, grammatical errors, tone, customer sentiment etc.
- Showcase and bubble up anomalies in ticket closure by particular agents or overall performance of the team.
Colour palette for visualisation
We decided to use 2 palettes that complemented our brand blue colour for data visualisations. I chose these palettes based on extensive research around colours that are accessible and clearly distinguishable for colour-blind users as well.
The following image articulates what a typical dashboard looked like for any team manager on Bicycle AI.