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A market research platform that allows you to survey a targeted audience segment that is typically hard to reach digitally by utilising our research panel. Surveys run entirely through WhatsApp (app with highest penetration rate in SA), allowing access to a much larger segment whilst also being faster, more cost-effective and highly personalised.

Roles & responsibilities

1
Team member
Role
Responsibilities
2
Louis Buys
Founder/CEO
Fundraising & strategy
3
Tim Treagus
Founder/Product
Growth, product decisions, customer feedback, strategy
4
Bruce Elliot
Backend engineer
Database design, product architecture
5
Alex Mathews
Product Lead
Strategy, data architecture
6
Jono Hart
Part-time growth
Growth
7
Paul
Database designer
Database architecture and assessment
There are no rows in this table
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🌅 Early Days

AI-BO is owned by Old Mutual and was founded as as a solution to future financial security (AKA a pension), aimed at the mass market. The core mandate of AI-BO from OM was to provide income earning opportunities to people who struggle to earn any income. An extension of this was to organically create a savings behaviour by incentivising people to save their income by giving them access to more work if they do so.
AI-BO started out as a data labelling and acquisition business for the purposes of building vernacular language models. Our mechanism for collecting/labelling data was WhatsApp as it allowed us wide and seamless access to people. We thus developed the capability internally to build WhatsApp bots for the purpose of handling data.
In April 2021, after months of trying to land customers from varying industries with no success. We decided to pivot to utilising AI-BO as a market research tool.
Conducting market research in low-mid income markets can be expensive and inaccessible, especially with resistance to data usage. With AI-BO, you can access faster, more cost-effective and scalable consumer insights.

Research & Competitors
⁠

The online market research industry is a whopping $23 billion dollars whilst only 5% of that coming from Africa. Currently, only 13% of research is done online in Africa vs 40% globally.
As Africa catches up to the rest of the world there is a massive growth opportunity to capitalise on the shift to digital research methods for the informal market
Particularly because our product is borderless and allows us to work across Africa whilst also appealing to international investors looking to enter the African market
⁠
Screenshot 2021-08-26 at 11.43.52.png
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Using WhatsApp

WhatsApp is the most downloaded and used app in South Africa. It uses very little data to send and receive messages. To access insights from people, we were not forcing them to change their behavior in any way: Don’t download a new app, don’t learn a new system, don’t go anywhere online you’ve never been. Thus, we hypothesised that it would give us access to a segment of people that were previously inaccessible through digital channels.
We will be able to access people through WhatsApp that are otherwise digitally inaccessible because of phone storage and data usage constraints
We will have higher response rates, engagement and completion of surveys because of how comfortably people use WhatsApp (most popular, downloaded and use app in SA)
We will able to be cheaper than existing low LSM research methods

🕵️ Validating Assumptions: Market Research Businesses

We hypothesised that the best customer segment to go after for market validation and to get insights from was the market research industry. In South Africa alone there are over 200 market research businesses. Most of these businesses have been around for a long time and their employees are hard to reach digitally (LinkedIn or email)
How work typically comes in:
Corporate → Marketing/advertising → Market research
Normally have to do in-person field research to mass-market
Some see us as a competitor
Wouldn't use 3rd party like AI-BO to get research (segment 1)
Want to use their own research panel
How do we verify accuracy?

What we found out is that the decision to use a different market research tool for acquiring their insights is a big decision that has direct consequences to their reputation as they will be presenting the insights that we give back to their client.
Competitor pricing
1
Type
Field Agent
African Response
Qualquarter
Survey54
Survey Monkey
1
Users
153,000
8,000
​
500,000
​
2
Setup
R3,200.00
​
​
​
​
3
Reporting
R3,200.00
​
​
​
​
4
Per survey of 10-15 questions
R250.00
R175.00
R300.00
R105.00
R86.00
5
5 Questions
​
R125.00
​
R42.00
​
6
Subscription 1
​
​
​
​
R540.00
There are no rows in this table
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🔨 MVP 1 (May → Aug)

WhatsApp Bot-builder platform

Cognigy

To build a WhatsApp bot for this MVP using code with developers was unnecessary from a budgetary point of view and would result in significant delays in building new survey flows. Thus the decision was made to utilise a low/no-code bot-building platform that could be fully managed by a product manager. Cognigy was selected as the platform of choice. The core reason for choosing them was the free 3 month trial that they offered us as well as their platform being really good.

Twilio

The WhatsApp API and cell number was issued by Twilio. Messages from Cognigy are routed through Twilio’s platform.
Pricing structure: $0.005 per message received or sent.

Landbot

AI-BO moved to Landbot in May for the following reasons
Our Twilio bill was $870 for the month of May. Once the Cognigy cost gets added to this and we start growing, it is likely that monthly subscription fees will be north of $4,000
Landbot’s pricing structure is per active user in a month period: if 1 person does 3 surveys, you pay the same. Twilio’s pricing structure is counterintuitive because it charges per message.
Compared to Landbot, the NLU flow builder design takes longer to setup new flows/surveys
No menu functionality in Cognigy (because of the NLU design) making it really difficult to direct users to new surveys
Cant send template messages through Cognigy (easily)
Still haven’t got the balance store proc function working (Alex has access so it must clearly be very hard)
How it works
User signs up on WhatsApp on a signup flow
User arrives at a menu with: Surveys, Income Balance, Refer a friend, Help as options
When the user does a survey they are directed to a new flow to complete it and then directed back the menu on the signup flow
Variables are setup for the survey responses that map to the database through a webhook

Database Design

MS SQL database setup. What to say?
@Alexandra Matthews
⁠
Tables
Users

Database Webhooks from Landbot
Setup the following store procs to handle different scenarios
Create_user → Mobile, Landbot ID, Name, Referrer mobile
Survey_response → Save survey response data
Transaction → Add income to a participant’s income balance
Income → fetch a participant’s income balance for the period

Retool

Retool is used to streamline the process of creating new variables for response data from surveys to map into the DB.
Create an organisation
Create a new survey that sits inside the organisation
Create survey questions and add variable names that correspond with the survey response
As a result, we can see which survey and organisation every variable comes from. The difficulty with this is that if we do 2 surveys asking the same question our responses will not relate with one another in the database

Webflow Landing Page

AI-BO decided to completely rebuild the website by purchasing a template and adapting it to what we needed. This decision gave us flexibility and autonomy to make changes in a streamlined and agile way. The ultimate goal was to be able to direct customers to a place that encompasses what we do, how it works, how much it would cost etc. to remove friction of businesses not wanting to use us because of having a lack of clarity on what we do + lack of credibility.

Analytics Tools

Google Tag Manager → Manage all web integrations
Heap → Event-based analytics tracker to see user journeys and button clicks etc
Google Analytics → Free analytics tool to track web traffic and sources of traffic
Hotjar → Heatmaps and user journey videos of how users interacted with site

Customer Order Forms: Typeform & Google Forms

I created a price estimation and question submission typeform that effectively acts as an order form for customers. The form is dynamic in that it calculates the order price based on your different order requirements and quantities.

Google Sheets

The responses for a survey and demographic attributes of the respondents are sent to a Google Sheet (setup in Landbot). This allows the client to see the results live as they come in and has been used for internal pilots. It also allows a simple connection to Data Studio.

Results/Analytics

Tableau workbooks are generated once the survey has been completed. The downside with Tableau is that
It depends on a BI resource to build the dashboard
A new Tableau license needs to be issued each time a report is shared with an external business - roughly R3,000

Data Studio was trialed as an alternative to Tableau for the following reasons
It’s free to use and can be shared with anyone
It can be embedded into Webflow
A BI resource is not relied upon to build dashboards for clients

Recruiting Participants

Facebook
Our WhatsApp account is connected to our Facebook business account which enables us to do ads where the CTA is a WhatsApp message to our account
Referrals
Participants are incentivised to refer their friends to sign up

🔬 Experiments

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Interviews
⁠

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LinkedIn Post
⁠

⁠
Calendly Sales Meetings
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⁠
Internal Pilots
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🤯 Risks
⁠

💄 Product Overview

AI-BO’s product success is driven by our ability to deliver quality insights to customers. This is based on the following factors
Responsiveness of participants
Efficient access to our customer’s target audience - having the right people
Accuracy and reliability of our results
Reporting, visualisation and guidance to actionable next steps to our customers
Customer experience of using AI-BO

Backend Rationale

Responsiveness of Participants

Efficient access to our customer’s target audience (Size of user base panel)

Size of panel
Ability to recruit the right people
Rich data on our participants that goes beyond high-level demographics

Accuracy of results

Frontend Rationale

Customer experience of using AI-BO

Interface to make an order for new insights. How much can they self-service?
Turnaround time of AI-BO
The interface that clients will be dealing with contributes to the credibility of AI-BO.
As a concept that is unfamiliar to most people above 40, we need to ensure that we demonstrate how AI-BO works in a very clear and obvious way. The mechanism to achieve this is by taking the prospective customer through an example of past work to show them exactly how to setup a campaign and the results of the demo campaign.
Client ordering workflow
Audience identifier/database structure
Tagging system for a variable to add to off the shelf attributes (enabling us to target people based on that variable that was acquired in the survey)
Map same/related question IDs into a single tag or category
When setting up a new survey, the creator can view all the related questions
Question success rate system that enables clients and survey creators to view different clients
Profiling/segmenting people (maybe using tag system)

Participants

The research/survey participants are the most crucial resource to our product. We want our participants to be happy for the following reasons
They refer their friends and we grow exponentially grow our panel without much effort
They produce accurate answers and put effort into their responses so that our clients get better insights. The opposing outcome is that they feel mistreated and so try to game AI-BO by producing rubbish results.
They are highly responsive giving us the ability to offer fast turnaround times to our clients.

Currently, our participant panel is made up of low-income people. Everything below describes the product for this segment of people
😁 The factors that contribute to our participants upholding these things are as follows
[Push] The frequency and quantity of surveys available for them to complete
[Push] Being paid on time
[Push] Being paid the right amount
[Pull] Being incentivised to produce better responses, respond faster and refer more people by getting paid more through a rating system
Paid more per survey completed
Priority on completing surveys (early batch access)
Priority notice through a template message an hour before others
[Pull] Being told when new surveys are out to remove uncertainty of when surveys are and allow participants to have a consistent expectation for new surveys

🔍 Recruiting

Facebook

AI-BO recruits using FB

Standard Referrals

Participants are incentivise to refer their friends and network

Agents

AI-BO pays certain people to post in related Facebook groups

Moya


⚒ 🔩 Product Build

Different Users/Roles

⁠
Client Ordering System
⁠
: Web Dashboard

Primary User
Can Register Company/Main account
Can view demo campaign of setting up and results
Can create Research Campaigns
Can setup research objectives and goals
Can choose demographic attributes of audience to send survey to
Can edit audience selection after choosing
Can create Survey within Campaigns (See below)
Can select survey from template and edit
Can select from audience that they previously surveyed
Can create screener question with reject/accept criteria for the difference MCQ answers
Can add users / roles and assign to campaigns
Can comment on questions and tag people in survey questions
Can get a cost estimate for the survey campaign in the dashboard
Can submit the campaign for approval
Can be sent proposed changes to survey or campaign
Can resubmit campaign with changes
Can get an invoice for the amount they owe according to their order
Results
Can view results in real time
Can mark people they want to interview by
Secondary Users (for MVPv.2 can leave out now?)
Invited by Primary User
Can create Campaigns?
Can create surveys?
Guest User? (for MVPv.2 can leave out now?)
Can request access to view results

⁠
Survey Participant on WhatsApp
⁠

Sign-up
Check runs that participant is above 18
Participant is asked to confirm privacy policy
Participant is asked whether they agree to receiving notifications about payments and new surveys. This enables us to send outbound messages to participants who have not chatted to use within the last 24 hours.
Participant gives demographic information
Quality checks survey that adds a point to their
Bad Actor Score
⁠
if they submit the incorrect answer
Can not see other surveys until Quality Checks and Signup Survey is finished
Can see available surveys (only available to their demographic)
Can complete surveys and earn airtime
Can see income balance for the current period (after last payout). Payout happens every Thursday
Can get the referral link and message to send to their friends
Can view FAQs about payment, survey frequency

⁠
Database/Participant Management
⁠
: AI-BO employee

Can clean data to remove non-sensical data (after surveys and throughout)
Can group open text responses into categories
Can communicate with product team to advise on how to enrich data or better construct questions
Can add tags to variables to associate them with various categories. Variables can have many associated tags (e.g. Finance → lending)
Should we have tag hierarchy?
Variables can be mapped into one another so that the answers from the same question from different surveys can be matched.
Survey answer checks
Bad Actor Score
⁠
system
Once a survey has been completed, the following checks occur which impact the participant’s
Bad Actor Score
⁠
(see how it works in the page)
Scramble checking survey responses
Manual checks of answers
Database automatically soft deletes user if there is a a duplicate of the same profile
@Paul Brittan
⁠

Paul Brittan
11:28 AM
⁠
⁠
The best option is to take the data from your App DB (marketing) and run it through ELT packages to clean the data into a new DB. This new DB with clean data is what we will then use to report off of.
We then have control of what info we are displaying to the reporting layer as well as keep our source data intacted

Questions

If we use Data Studio as our analytics tool, which DB should we use?
Which ETL system

⁠
Retool Views
⁠
: For AI-BO employee

Finance sheet
Can view how much revenue is earned per month in a bar chart
Can view how much was paid out to participants for surveys and referrals in a month in a bar chart
Participant overview
Can see an overview of the demographics of participants (how many of x income etc.)
Can see a breakdown of the highest earning participants and where they earned their income from (total referral vs survey income)
Breakdown of where people came from (acquisition) between referred and not referred
Can view a record of the weekly payout and source of income for payout
⁠
Screenshot 2021-08-23 at 09.09.49.png
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⁠

Quality/verifiability
Exclude or flag people who lie.
Manually look through people’s answers. If we find answers that are obviously not accurate send them a suspension message to say that they will be suspended from surveys for 3 weeks
Add a rating system to people based on responses. This will be visible to them and motivate them to give good responses. Rating based on:
Length of answers in open text questions
Speed to complete survey after receiving template message notifying them that they are eligible to do a survey
How many people they refer
Initial quality screener survey
Uses a red herring technique to assess whether participants are trying to game the system
Automatic truth checker
Social penalisation
Query that finds all people that user has referred and been referred
⁠
https://blog.feedbackloop.com/feedback-loop-comprehensive-data-quality
⁠
⁠

⁠
Insights Dashboard/Reports
⁠

Analytics/insights
Currently being manually created using Tableau and Data Studio
Builds automatic reports based on question types.
Responses can viewed cross-sectionally against the demographic attributes of the respondents OR the MCQ answers of other questions

⁠
Survey Builder
⁠

For User (AI-BO customer)
User can add a new question
User can choose what type of question they’d like it to be
MCQ
Open text
Number
Picture
Voice note
Video
User can add media to their question so that the survey participant receives the media (voice note, picture, video)
MCQ
User can add a new MCQ answer for an MCQ question
User can add and edit text for MCQ answers
User can create skip/conditional logic by specifying which question the participant goes to based on a particular MCQ answer
Multiple users can edit questions
AI-BO Backend
Each question created by the AI-BO customer generates a variable name
Question type gets transformed and mapped into bot-builder

Product Role Out

Features roll-out
3
⁠
Database
Phase 1 Features roll-out
3
Database/Participant Management
1
Website
0
Client ordering system
1
Retool views
0
Understanding/planning where all activities take
Planning
ELT layer
Phase 1
Wireframe/plan order journey
Planning
Mapping out user journey
Phase 1
⁠
Phase 2 Features roll-out
3
Database/Participant Management
3
Website
3
Client ordering system
5
Retool views
1
Scramble checking survey responses
Building
ELT layer
Bad Actor
Phase 2
Manual check of answers
Building
ELT layer
Bad Actor
Phase 2
Quality Check Surveys
Building
ELT layer
Bad Actor
Phase 2
Pricing page
Building
Phase 2
Participants page to sign up
Building
Phase 2
Case study
Building
Phase 2
Create campaign
Building
Phase 2
Setup campaign objectives
Building
Phase 2
Specify audience
Building
Phase 2
See estimate pricing
Building
Phase 2
Submit for approval
Building
Phase 2
Participants view (acquisition, demographics etc.)
Building
Phase 2
⁠
Phase 3 Features roll-out
3
Database/Participant Management
2
Website
0
Client ordering system
2
Retool views
1
Sample Checks for data accuracy and identity verification
Building
ELT layer & Bad Actor
Phase 3
group open text responses into categories
Building
ELT layer
Phase 3
Edit/make changes
Building
Phase 3
Make comments/collaborate
Building
Phase 3
Finance view (exp, rev etc.)
Building
Phase 3
⁠

💸 Revenue Model

For more about the finances and business model, go to
Business Model & Finances
⁠

Pricing

⁠
Group 5687 (1).png
⁠
⁠

📈 Go-To-Market

Continue to be active on LinkedIn and Twitter

Sales Funnel

⁠
Screenshot 2021-08-26 at 12.52.02.png
⁠
⁠
Screenshot 2021-08-26 at 12.52.21.png
⁠
⁠

Inbound

SEO

Organic site traffic will lead to conversions.
We can dip back into referring domains for at least 3 backlinks.
AI-BO will benefit from blog content relating to search queries that AI-BO solves for
Research
Concept validation, testing ideas
Recruiting, accessing people
Getting early customers
SEO will bring 10x more organic traffic than organic social traffic.
There will be more desktop traffic than mobile
Other content
Case studies on website

Social Media Marketing

LinkedIn
Leads will come through LinkedIn.
LinkedIn will be used to assess brand credibility by prospective leads.
LinkedIn will attract: entrepreneurs > small businesses > corporates.
Posts
Market updates - news and info
Customer case studies and use cases
Product updates

Communities

Slack, WhatsApp, Facebook, LinkedIn and other communities
Thought leaders in the startup research space (Ryan Glasgow from Userleap e.g.)

Outbound

Paid Media

Social Ads
We can get leads from advertising.
LinkedIn will be a better source of leads, particularly with the filtering.
The best formats for LinkedIn are single post ads or carousel ads.
Paid Search
We can get leads from advertising.
Search ads will be a good source of leads, particularly with the advertising to specific searches.
The best formats for Google are search bar ads.

Direct Sales

Alex, Tim, Louis,
@Bruce Elliott
⁠
hustling

Pilot

Extra questions post-pilot
Column 1
Per Respondent
Question fees
Setup Fee
Total Cost
1
100 Total Responses
R35.00
R1,750.00
R2,000.00
R3,750.00
2
150 Total Responses
R35.00
R3,500.00
R1,500.00
R5,000.00
3
200 Total Responses
R35.00
R5,250.00
R1,000.00
R6,250.00
4
250 Total Responses
R35.00
R7,000.00
R500.00
R7,500.00
5
300 Total Responses
R35.00
R8,750.00
R0.00
R8,750.00
6
400 Total Responses
R35.00
R12,250.00
R0.00
R12,250.00
There are no rows in this table
⁠

💰 Required Funding

Product Development

Project management
Role
Hours
Rate
Total
Description
Select list
1
Product Manager/Owner
80
675
54000
Manages development team
​
2
Marketing/Growth Manager
60
675
40500
Creates marketing content and drives growth
Building bot flows in botbuilder
Creating social content
Creating blogs/articles
creating case studies
Updating website
User interviews
Usability testing
managing invoices and finances
3
Backend engineer
3
675
2025
​
​
4
Customer support
60
475
28500
Monitors what participants are saying on Landbot
​
5
Data scientist/engineer
40
975
39000
Creates relationships with variables - Builds audiences based on responses to variables - Analyses result/responses to better understand how to build insights - Adds NLP capability to open text responses
Fixing database bugs
6
No-code engineer
80
475
38000
Builds Retool views
Building retool views
7
Data analyst / BI
​
​
​
​
Creating analytics dashboards
Reviewing responses for non-sensical results
There are no rows in this table
323
Sum
658.33
Average
202025
Sum
⁠


🔥 Progress to Date

1
Signed up users
6,000
2
Survey pilots to date
12
3
Paid to participants
R12,000
4
Responsiveness of panel
80%
5
Leads in pipeline
9
6
Web Traffic
558 visits in last 30 days
7
Fb Page follows
93
8
​
​
There are no rows in this table
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