Celebrating the human mind, with a unique brand experience that doubled sales
NSW Department of Finance, Services and Innovation
Using artificial intelligence to better guide users and lighten the load on a call centre under pressure
The NSW Department of Fair Trading is a sub-division of DFSI responsible for the administration of consumer protection laws in NSW. They aim to ensure consumers and business alike are treated fairly and equitably in transacting between themselves. As such, a large component of what they do involves informing consumers of their rights and facilitating dispute resolution between the parties.
The Department was interested to explore the use of artificial intelligence (A.I) e.g. chatbots, to ease the burden on its call-centre and facilitate deeper browsing of its website, more relevant content and resources. After running three years of call transcripts through IBM Watson’s AI Suite, we settled on ‘Rental Bond’ disagreements as a topic area to test the use of such technologies. We set ourselves a goal of rapidly iterating and having a prototype in market within 3-4 weeks.
The volume and quality of work that we’ve collectively delivered, in such a short time, is incredible. Why aren’t we working in this manner all the time?
We began with stakeholder workshops involving both internal and external users groups. We explored a number of key areas, including anticipated use cases, implications these would have on key features of the chatbot and particular areas to watch out for – for example, where the legislation is not clear and the content does not provide clear remediation for a particular situation.
This translated directly into a detailed conversation map, where we clearly curated conversations to lead specific users to useful information. This conversation map fed straight into the widely used Facebook chat framework and we ideated improvements over a three week period. We involved both consistent and new user testing cohorts each week to ensure we weren’t ignoring intuitive design requirements. The resulting chatbot was launched within the four-week deadline and is now in market for full testing and rollout across a number of key department content areas.
Meet Holly, the Rental Helper
A lot of work went into defining a detailed persona for the Chatbot with us ultimately landing at ‘Holly - The Rental Helper.’ Everything from Holly’s design style to the tone of voice was thoughtfully considered in order to present a more useful and pragmatic Bot.
Framework Determined by User Familiarity
We assessed a number of chatbot frameworks, but ultimately settled on the Facebook Messenger framework (as people already have a certain level of comfort with the way it works).
Immersions in 3D
Lookback User Testing
IBM Watson used to assess 3 years of Call Transcripts
IBM’s Watson was used to process 3 years of call centre data and assisted in choosing rental bond disagreements as an area where AI could drive significant efficiencies, especially when encouraging more people to source information online.
Content Structure Informed by Data
By analysing breakpoints in our user testing, we realised that the personalised tone of voice we had developed was encouraging users to thank the Bot for its utility. This was in turn, broke content flow and was quickly rectified.
In-market in 3 weeks
It might have been ambitious, but we went from concept to in-market testing of a prototype in just 3 weeks!
We created a more positive experience
Over 85% of test users said that the prototype contributed to a “more positive experience” of the Department of Fair Trading website
Call centre reduction
We are now in extensive conversation mapping across a number of key DFT content areas, with an overarching goal of reducing the load on the call centre