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
🌅 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.
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
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.
🔨 MVP 1 (May → Aug)
WhatsApp Bot-builder platform
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.
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.
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
MS SQL database setup. What to say?
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 is used to streamline the process of creating new variables for response data from surveys to map into the DB.
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.
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.
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.
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
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
Participants are incentivised to refer their friends to sign up
💄 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
Responsiveness of Participants
Efficient access to our customer’s target audience (Size of user base panel)
Ability to recruit the right people Rich data on our participants that goes beyond high-level demographics
Accuracy of results
Customer experience of using AI-BO
Interface to make an order for new insights. How much can they self-service?
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)
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
AI-BO recruits using FB
Participants are incentivise to refer their friends and network
AI-BO pays certain people to post in related Facebook groups
⚒ 🔩 Product Build
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
If we use Data Studio as our analytics tool, which DB should we use? 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 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 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 Query that finds all people that user has referred and been referred
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 For User (AI-BO customer) User can add a new question User can choose what type of question they’d like it to be User can add media to their question so that the survey participant receives the media (voice note, picture, video) 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 Each question created by the AI-BO customer generates a variable name Question type gets transformed and mapped into bot-builder
Product Role Out
Phase 1 Features roll-out
Phase 2 Features roll-out
Phase 3 Features roll-out
💸 Revenue Model
For more about the finances and business model, go to
Continue to be active on LinkedIn and Twitter
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 Concept validation, testing ideas Recruiting, accessing people SEO will bring 10x more organic traffic than organic social traffic. There will be more desktop traffic than mobile
Social Media Marketing
Leads will come through LinkedIn. LinkedIn will be used to assess brand credibility by prospective leads. LinkedIn will attract: entrepreneurs > small businesses > corporates. Market updates - news and info Customer case studies and use cases
Slack, WhatsApp, Facebook, LinkedIn and other communities