Yazi Product for HBS & Berkeley
Below is a summary of the core features and process of Yazi’s system. HBS / Berkeley team would be able to use in the following way:
Use Yazi webapp and Postman to create and make changes to structured questions and directive flows on the fly Use API endpoints to access User & Survey data Get GPT data from CSV or setup custom history in GPT codebase Monitor chats in Rather Chat app How the bot works
Signup and onboarding process which captures agree/disagree to data T&Cs and agree/disagree with opt-in’s to outbound communication (template messages). The following demographic questions are also asked Options in menu: Survey Menu, Opt-in / Opt-out, FAQs Announcements bar containing any information Yazi wants to share Shows surveys that participants qualify for Starter phrases are specific text that, if sent to Yazi’s bot, will take participants to a specific flow. I.e. Main menu jumps people to main menu. Starter phrases are used in buttons so that if a button is clicked, the starter phrase is sent and jumps a participant to a point in the flow. Survey access: Accessed in 2 ways Starter phrase: The title of the survey in the JSON Survey menu: Available to participants who qualify for the survey based on their demographic
Webapp
Yazi has a webapp that comprises of 4 key components:
Dashboard - see all survey campaigns created for a particular workspace Specify audience - Choose demographics of audience you’d like to target Number of participants/respondents Create survey - Similar interface to Google forms or SurveyMonkey. Types of questions: Single-select and multiselect multiple choice, Open text, Image/Video/Voice note ‘Other’ question toggle which automatically asks ‘Which other/s?’ if other is selected Logic: Can skip or jump participants to certain parts of the survey or flow using logic in single-select multiple choice questions Results - Bar graph of each response with ability to filter and select using both other questions and demographic information
Once the survey campaign has been launched in the webapp, the JSON generated by the webapp needs to be POSTed to a Rather Chat endpoint to be live on the bot.
Product notes:
Progress is not saved automatically, remember to save with the button The webapp does not currently support ‘multi-player’ teams. One set of login details can thus be shared and used.
To tryout webapp, use the following account details:
PASS: uUBD49*q5&PM
Connecting surveys to bot
Retool
Yazi uses Retool to send a survey from the webapp to the bot. Sharing user access to Retool with HBS & Berkeley is not ideal and so instead, postman can be used.
Connect or create launched survey to organisation in database Create survey name in database and connect to survey created by webapp. This automatically creates a survey ID and imports the survey JSON which contains: Post survey JSON to Rather Chat endpoint which automatically generates the bot ‘flow’. Postman
Postman can be used to retrieve (GET) the JSON generated by the webapp after it has been connected to a survey in Yazi’s database and it can be used to POST the JSON to Yazi’s bot.
Rather Chat app
Send template messages to participants by uploading a CSV of cell numbers to send to See chat history from last 24 hours (see below screenshot). A Rather Chat user can send a message to a participant as the bot in this chat See bots or flows running + Team members + Channels/Numbers (if different cell numbers connected to FB account)
Meta WhatsApp Manager
This is where template messages are submitted for approval. HBS & Berkeley would need to be invited as admins to Yazi’s Facebook business account to get access here.
Templates typically take 1-3 days to be approved.
It also shows usage, conversations and engagement with the bot in a dashboard
User Data
User data can be fetched using an API endpoint that has been setup to be accessed via a link in a web browser. The user data is in a JSON format like the below:
{
"id": 21937,
"phone_number": "2547118540***",
"created_at": "2023-05-12T12:45:29.117203+02:00",
"notify": true,
"ban": false,
"first_name": "Dominic",
"birthday": "1997-09-27",
"business_owner": false,
"income": 250,
"postal": "2201",
"region": "Suburb: None, City: Gatunga, Province: Tharaka-Nithi County",
"user": "whatsapp_aibochat:254711854***",
"gender": null,
"country": "kenya",
"preferred_language": "english",
"race": null,
"relationship": null,
"children": null,
"job": null,
"education": null,
"languages": []
},
URL:
The layout:
The count is the total number of profiles there are, these include completed & uncompleted profiles. The results is a paginated list of 100 profiles, once the count is more than 100 it will be paginated. You can use the next and previous links to offset the results. The content:
The fields are pretty self-explanatory, however, if a field is null it means the user has not yet provided that information The id is the "id" of the profile, not the user. The user's ID is stored in user. This is the same user ID used in the survey results CSV you receive. ban is the field for the "bad actor score", which is used to prevent the user from seeing surveys on the menu, this does not prevent them from doing the survey if you send them a template notify is the field that indicates whether the user has "opted-in/-out" for updates on new surveys. Additional uses:
You can view individual profiles by adding the profile "id" in the URL e.g. You can update a user's profile by calling a PATCH request to the above mentioned individual profile endpoint, this could be useful for banning / unbanning someone You can also delete a user's profile by calling a DELETE request to the individual profile endpoint Limitations:
It does NOT allow you to search according to a specific parameter (its possible, but would require additional work) URL with sort
Survey Data access
Each survey campaign created by the webapp has its own surveyID. An API endpoint has been created for survey data, which can also be accessed via a link in the browser. This data is a CSV file type and displays in the following format:
Yazi runs a script that transforms this data to have a single UserID per row and each question variable in a column. The data is joined with the User data and sent to a Google Sheet. It also checks for speeding and negligent open text characters.
responseQuestionId
The variable created and associated with a survey question. The variable’s response can be used in logic or in chains in the GPT codebase URL of raw CSV data:
How URL is set up:
GPT Pre-Pilot example with Python script
Python script formats the data and does the following:
Reformats so that UserIDs are in rows and questions in columns, sorted in ascending question order Joins signup data on survey data Adds columns for start / end time Calculates average time per response Speeding checker: under 7 seconds average per question = fail Negligent open text: single letter characters like ‘a’ or ‘c’ are flagged as negligent in open text questions Send all data to a specific Google Sheet tab Show proportions of responses and respondents in a different Google Sheets tab
GPT Engagement Data
GPT prompt and response data is sent in a CSV file to everyday at 00:00 and it contains the last 7 days of data. Yazi joins the CSVs together to the full time period of data.
GPT bot system
Each model (trained or vanilla GPT-3.5 or GPT-4) is connected to a Starter phrase. Once the starter phrase is sent to Yazi’s bot, the following is sent:
“Welcome to ChatGPT business advisor. You can ask me any question like, “What are your top 5 recommendations to boost your small business’s profits and sales in South Africa?”
To protect or restrict access to GPT, the following can be done:
Starter phrases can be updated on a regular basis Access to GPT could be restricted to specific user IDs
Rather Chat has agreed that HBS and Berkley can get full access to GPT code base and make changes and iterations to prompts and chains to enable frequent testing and updating.
Live view of Survey completions
You can access the data via this API
For example:
https://bot-yazi.comparisure.co.za/api/survey/83/
Will return:
{
"id": "83",
"title": "Project Fur Baby Visual Task Diary",
"created_at": "2023-03-09T10:24:13.234009Z",
"n_started": 13,
"n_completed": 2
}
Summary:
Well structured signup process that collects 10 demographic data points - easy to download demographic Easy download of survey response data, joined with signup data Easy download of GPT chat histories All navigation built around the main menu - the dashboard or home page of our bot which makes using the bot very simple for non-tech native participants Error messages for if required answers aren’t given in multiple choice or text specific question (postcode, cell number etc.) Starter phrases to jump participants to specific ‘flows’ Logic in surveys or questions to direct people to specific places based on both the previous question and data collected prior to that question. I.e. different paths for participants based on which treatment group the participant is part of Ability to go back to previous question (if made a mistake in response) Surveys or research tasks shown to participants based on specific characteristics in the ’Survey menu' Malleable bot design to create menus for diary studies or FAQs very easily - with ability to navigate to different flows or places in chat Template messages or notifications that allows Yazi to nudge participants to use chatbot Can add variables and reference them in chat or chain into GPT prompts Incentive tracking on bot menu to show participants how much they’re owed Full use of WhatsApp new features. The type of data Yazi collects Multiple choice - Buttons Multi-select multiple choice - Participants can indicate their selections by providing the associated answer letter (e.g., A, C, F), separating multiple choices with commas (e.g., A, C, F) Voice notes & transcriptions Webapp to be able to launch new flows or questions to specific audiences immediately
Product Spec for HBS
