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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.

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