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Category: Conversation design

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Designing the Meta Verified chatbot to offer customers quicker resolution

KEY INFORMATION

  • I led a project to design and build the first Meta Verified customer support chatbot.
  • The project was a company priority.
  • There were 5 teams involved over multiple timezones.
  • Fast turnaround (3 weeks).
  • This project is covered by NDA so this is a high level summary.
  • I won an internal award for this project for moving metrics. 

THE PROBLEM

Meta Verified subscribers couldn’t find the answers to simple customer support queries, so were contacting customer support agents to resolve their problems.

The proposed solution was to rapidly design and build a chatbot to answer the most common questions.

Customers would still have human support one click away.

But if their questions were answered faster, it would be a good outcome for everyone.

THE PROCESS

Analysis
Identify
Design
Iteration
Handoff

ANALYSIS

This was a rapid turnaround project, so it was essential to quickly build a picture of the user with existing resources. 

To understand the users’ problems without conducting any fresh research there was one source of data: agent transcripts. Analysing these would be the key to understanding what the most common issues were and why.

A colleague had already shared a quantitative analysis but deep diving into the transcripts allowed me to understand:

  • user issues
  • what was causing them
  • and the resolution they sought.

IDENTIFY

  • The next step was to identify which user problems could be solved by a chatbot.
  • As the bot would be decision tree based, there would be a limited number of options available for the user to choose from.
  • I selected the issues that were
  • (a) most common
  • (b) easiest to resolve with the information a chatbot could offer.
  • Some issues are best handled by a human, and I did not attempt to tackle these, for example, complex integrity or security issues.

    • DESIGN

    I designed a simple chatbot flow by wireframing in a Figma flowchart. 

    I took into account:

    • Instagram style guidelines
    • Failure modes
    • Order of menu options
    • Ease of access to human handover
    • Providing links to longer help articles
    • UI choices - pros and cons of various options
    • UI limitations
    • Chunking for readability
    • Flesch reading score
    • Writing the correct responses based on user region

    Although the bot flow itself is quite simple, there were plenty of considerations to work through!

     

    MENU ITEMS

    Most common user issues first.

    UI

    Work within character limits, reword for localization.

    CHUNKING

    Separate long text into individual message bubbles.

    HELP OPTIONS

    Inline, help articles or human handover.

    ITERATE AND HANDOFF

  • There were many international stakeholders in this work.
  • I collected feedback from multiple teams to ensure people had a chance to improve the work or bring their expertise to bear.
  • The handoff to engineering and subsequent build was all managed in different timezones, mostly using Figma to communicate.

    • OUTCOME

    The chatbot was shipped and is currently shown to Meta Verified subscribers seeking support on Instagram.

    While I can't share details of the impact, my work on it led to me winning an internal award for moving metrics.

     

    If you'd like to discuss this or any other case study, I'd love to hear from you.

    Open post

    Creating individual chatbots for clients using a scalable system

    KEY INFORMATION

    • I led a project to create a system that would enable clients to customise their chatbots.
    • This had previously been done on a consultancy (one to one) basis and needed to be systematised.
    • I used user and competitive data to suggest a way to balance customisation needs and ability to scale.
    • I created a large set of utterances in two popular tones, while still allowing exceptions.
    • The ‘out of the box’ model was taken up by clients for 70%+ utterances and led to a more efficient use of samples for the training set.
    • In order to make the process even more efficient, I created a spreadsheet that generated the client file for the NLP model.

    THE PROBLEM

    HelloDone make chatbots powered by a proprietary natural language processing (NLP) model.

    The bots are used by retailers to help their customers track and manage their deliveries in WhatsApp and Messenger, along with answering any questions they might have about the order, the delivery, returns or shopping in general.

    The chatbots use NLP to understand what people are asking for and give pre-designed responses in return.

    The problem with pre-written responses is that clients all have different voices and tones but creating a individual bespoke bots for hundreds of clients is not scalable.

    The team needed a system for customisation.

     

     

    THE PROCESS

    Understand user needs
    Competitive analysis
    Design
    Create a system

    UNDERSTAND USER NEEDS

    We had a fantastic source of data about retailers' customers: their conversations. They were already telling us what they wanted to know. I could look both quantitatively and qualitatively at what the most common queries were and what people might then ask after they had the answer.

    I could also look at the language people used and how they asked questions or followed up with responses, so that sample design could reflect real users' phraseology.

    I'm a firm believer in not reinventing the wheel, our clients already had a deep knowledge of their customers. Working with stakeholders across multiple businesses was another route into understanding what their customers want to know in the "final mile" of the delivery process.

    COMPETITIVE ANALYSIS

    It wasn't enough to just look at our existing client base. If we were to expand, who would our dream clients be? How did they talk to their customers? What tone would their chatbot have?

    A deep dive into retailer help centres and customer support systems showed that while brands had their own voices and tones, if you had to narrow them down to two styles of communication, you could, at least for an MVP.

    These styles were really just degrees of formality. I classified them as "Polite and friendly" or "Chatty and informal".

    Polite and friendly

    Professional

    Clear

    Pleasant

    Straightforward

    Chatty and informal

    Upbeat

    Friendly

    Emojis

    Idiomatic

    DESIGN

    CREATE A SYSTEM

    Having two sets of "out of the box" utterances was the first part of the scalable system.

    The second part was to make it easy to set up a new bot.

    Before the team invested time in creating a user interface specifically for this job, we needed to test an MVP.

    Using macros and formulae in excel, I created a spreadsheet that generated the client file for the NLP model, meaning all the bot utterances could be defined and code-ready in one session just by choosing options in the sheet.

    OPT IN

    Choice of whether to include a question or not (for example an online business would not need to answer many questions about bricks and mortar stores).

    CHOICE OF TONE

    Polite or chatty pre-designed utterance, or client’s own customised response.

    LOCALISATION

    Change variables like phone numbers or spelling according to region.

    ADD URL

    Add URLs for more information or to take action (for example, return a product).

    OUTCOME

    New clients using the new system selected 75% of their answers from the pre-designed utterances.

    Onboarding time was dramatically reduced from weeks to hours.

    Training sets could be re-used, leading to greater efficiency in the code and all clients sharing the benefits of being in the same niche.

    WHAT I'D DO NEXT

    Develop a user interface for clients to set up their bot. 

    Check whether the two tones hypothesis works outside the UK for international expansion.

    Continuous research to check that bot is answering user needs.

    If you'd like to discuss this or any other case study, I'd love to hear from you.

    Open post

    Just a minute! Designing more accessible deliveries

    KEY INFORMATION

    • People with temporary or permanent disabilities sometimes have difficulties with home deliveries because the driver doesn’t wait long enough for them to get to the door.
    • I designed a chatbot flow that allows people to ask the driver to give them more time to answer the door, while being sensitive to courier companies’ need to deliver as many parcels as efficiently as possible.
    • This functionality was only offered with one delivery carrier, so I also had to design to handle expectations when a parcel was not eligible for the service.
    • This was the first time this service was offered for online deliveries.

    FULL CASE STUDY COMING SOON

    If you'd like to talk, I'd love to hear from you.

    Open post

    Planning a train journey with a chatbot (and other commuter delights)

    KEY INFORMATION

    • I worked in a two person design team on a chatbot using conversational AI to help commuters with their everyday transport needs.
    • We worked to understand commuter pain points. 
    • The most complex part of the design was a fully conversational journey planner.
    • The most delightful part of the design was a function that told passengers where to stand so that they could board the train near their seat.
    • I was responsible for writing the tests for a proprietary program to check we were providing the best possible customer service.
    • Commuters using the chatbot (on WhatsApp) expressed greater degrees of satisfaction with the train brand and perceived that they could carry out their desired action more quickly than using the phone or face to face contact.

    FULL CASE STUDY COMING SOON

    If you'd like to talk, I'd love to hear from you.

    Open post

    “Whatsappification”: 5 things I learned from migrating chatbot from Messenger to WhatsApp

    KEY INFORMATION

    • People with temporary or permanent disabilities sometimes have difficulties with home deliveries because the driver doesn’t wait long enough for them to get to the door.
    • I designed a chatbot flow that allows people to ask the driver to give them more time to answer the door, while being sensitive to courier companies’ need to deliver as many parcels as efficiently as possible.
    • This functionality was only offered with one delivery carrier, so I also had to design to handle expectations when a parcel was not eligible for the service.
    • This was the first time this service was offered for online deliveries.

    FULL CASE STUDY COMING SOON

    If you'd like to talk, I'd love to hear from you.

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