Checkpoint 2 — Slide Deck
EXE101 · Group 01
Class SE1933-NJ
AI platform that simulates customer behaviour

MARKET
LAB

Turn scattered customer data into actionable insights — before you launch a product or campaign.

Instructor
Vương Tiểu Oanh
Mentor
Phạm Thanh Hương
Team
Group 01 · SE1933-NJ
Presentation Flow

AGENDA

Market Lab · Checkpoint 2
I
Slides 3–6

Market Analysis

  • Market Size & Growth
  • Customer Needs, Attitudes & Behaviour
  • Competitive Analysis
  • SWOT Analysis
II
Slides 7–14

Target Customers

  • Research Design & Survey Structure
  • Survey Analysis & In-depth Interviews
  • Key Customer Insights
  • Customer Personas & Target Priority
III
Slides 15–17

Value Proposition & USP

  • Value Proposition
  • Unique Selling Points
  • USP Statement & Positioning Map
Slide 18 · Closing

Thank you & Q&A.

EXE101 · Group 01 · SE1933-NJ
1.1–1.2 · Market Analysis

MARKET SIZE & GROWTH

Market definition — intersection of 3 segments
  • AI Market Research — Research powered by AI
  • AI-Powered Customer Insights — Real-time customer behaviour analysis
  • Synthetic Persona Simulation — Simulating customer reactions with AI before market entry
AI in Market Research34.2%
CAGR / yr
$4.6B · 2025$36.8B · 2032
Generative AI43.3%
CAGR / yr
$23.1B · 2024$90.6B · 2028
Overall AI Market×4
over 5 years
~$260B · 2025>$1,200B · 2030
Opportunity in Vietnam
82%
small SMEs reporting growth in 2024
44%
SMEs investing in AI 2024 — double 2023
78.8%
population using Internet, early 2025
95.4%
Internet users on ≥1 social network
$350M
Vietnam Digital Marketing Market
1.3 · Market Analysis

CUSTOMER NEEDS, ATTITUDES & BEHAVIOUR

Needs
  • Need to understand customers before deciding on product, price, campaign
  • 47% of researchers face budget constraints but must still ensure quality
  • Focus groups $4,000–5,000 → too high for SMEs & startups
  • Agencies need fast insights for pitches within a 5–7 day deadline
Attitudes & Values
  • Prioritise cost saving, shorter time, simpler analysis
  • 87% of users/providers worry about AI reliability
  • Trust factors: reliability, fairness, data protection, explainability
Purchasing Behaviour
  • SME/Startup: free Google Forms, existing data, rely on experience
  • Agency: online surveys, social listening, AI tools for qualitative analysis
  • Decision: cost · speed · data quality · ease of use · AI reliability
Trends

89% of researchers use AI tools regularly or experimentally · 69% have integrated synthetic data · strong rising demand to cut costs & shorten research cycles.

1.4 · Market Analysis

COMPETITIVE ANALYSIS

Selection criteria: same product segment (AI Persona / Synthetic Research) · same customer base (SMEs, agencies, research teams) · same price segment.
Synthetic Users
Yabble Virtual Audiences
Minds AI
Product
AI interviews, concept testing, problem discovery
Synthetic respondents; concept/pricing/messaging testing
Validated multi-persona panels for B2B teams
Customers
Researchers, PMs, agency owners
Research teams, agencies, large enterprises
B2B: product, marketing & research teams
Price
~$2–60 / interview
From ~$8,900 / year (high)
Quote-based, not public
Strengths
Fast, concept testing, integrates own data, 85–92% accuracy
Strong concept/pricing/messaging, backed by YouGov
Multi-persona, research-grade, 80–95% accuracy
Weaknesses
Not localized for VN, lacks VN behavioural data, hard to budget for SMEs
High price, enterprise-oriented, not localized for VN
Hidden pricing, focus on large B2B, not present in VN
Market Lab Opportunity
Localize VN + subscription + real-user validation
Cheaper, fits VN SMEs, adapts to VN behaviour
Target B2C SME VN, public pricing, VN personas
Biggest gap

No AI Persona platform is yet localized for Vietnamese SMEs at an affordable price with real-user validation.

1.5 · Market Analysis

SWOT ANALYSIS

Strengths
  • AI automation: build personas in minutes instead of weeks
  • Data-driven personas from real behavioural, demographic & psychographic data
  • Multi-Agent Architecture using the OCEAN model — consistent, psychologically deep
  • Strong localization: VN language, culture & consumer behaviour
Weaknesses
  • Depends on the quality of input source data
  • Only fits text-based research — not a replacement for usability testing
Opportunities
  • 44% of VN SMEs invested in AI in 2024 (2× vs 2023)
  • Strong rising demand for cost-effective research solutions
  • Few direct competitors localized for SMEs in VN
  • AI in Market Research CAGR 34.2% (2025–2032)
Threats
  • International rivals could localize for VN at any time
  • SMEs may think free ChatGPT/Gemini is enough
  • Data-privacy concerns when sharing CRM/internal data with AI
  • Trust in AI-generated insights is still low
2.1 · Target Customers

RESEARCH DESIGN

Quantitative Survey
  • Goal: measure trends & pain points at scale
  • Tool: Google Forms — branching by industry
  • Questions: multiple choice, Likert 1–5, open-ended
  • 17 responses (B2B target ≥30)
In-depth Interviews
  • Goal: deeply understand each individual's context, motivation & barriers
  • Format: semi-structured, open-ended · in person + video call
  • 4 in-depth interviews across agency, performance & F&B
Combining both methods
  • Cross-validate: quantitative figures + qualitative perspective
  • Analyse in parallel → derive shared insights
Sampling Criteria
Industry
Marketing, startup, agency, F&B, retail, education, SMEs in VN
Experience
Joined surveys, campaigns, product dev, insight analysis
Geography & Age
Living/working in VN (HCM, HN, ĐN) · 18+
DMU Role
User · Influencer · Decision Maker · Buyer
Sampling
Purposive + convenience · network, LinkedIn, MKT communities
2.2 · Target Customers

SURVEY STRUCTURE & SAMPLE

Questionnaire structure — 10 parts (branching by industry)
1
Screening
Filter the right respondents by industry & role
6
AI Acceptance
Trust & willingness to use AI for research
2
Business Background
Industry, type, size, research experience
7
Willingness to Pay
Suitable price for an AI research tool
3
DMU Role
Role in the buying decision (user/influencer/buyer)
8
Purchase Consideration
ROI, data security, ease of use, payment, VAT
4
Research Behaviour
How they collect insights today
9
Feature Preference
Persona, concept/price/message testing, export
5
Pain Points
Budget, time, respondents, analysis
10
Final Feedback
Overall rating, likelihood to use Market Lab
Sample breakdown — 17 responses
Marketing Agency64.7%
11 people · IMC / Digital / Branding · serving 5–10 clients · 10–50 employees · revenue 10–50 bn VND/yr
Retail / Fashion23.5%
4 people · 80% Marketing Exec · 60% with >20 locations · revenue 10–50 bn or >200 bn VND/yr
F&B~12%
2–3 people · from 1 location to chains >20 outlets · operating >5 years · mainly use sales data
2.3 · Target Customers

SURVEY ANALYSIS — PAIN POINTS

Agency · 11
Retail / Fashion · 4
F&B · 3
Hard to collect data
Slow data collection — 72.7%
Data scattered across sources — 100%
No dedicated research team — 100%
Hard to analyse
Hard to turn data → insight — 63.6%
Hard to predict customer trends — 80%
Online surveys are biased — 66.7%
Time pressure
Pitch deadlines only 5–7 days — 83.3%
Weekly/quarterly decisions — 40%
No systematic research process
Budget
Must use old reports (Nielsen/Kantar) — 50%
No budget to hire research agencies
Losses ~5–20M VND from wrong decisions
Open-ended

Decisions needing insight most: running ads (Agency 91.7% · Retail 100%) · designing promotions 83.3% · choosing messaging 83.3% · adjusting price & market expansion 75–80%. → Insight needs recur weekly, tied directly to revenue generation.

2.4 · Target Customers

IN-DEPTH INTERVIEW — WHO & WHAT

4 interviewees
DT
Dương Trần (Du Du)
Founder/CEO — Marketer Được Việc Agency
Cosmetics, F&B, beauty clinic, dental (Japanese/US/Korean brands entering VN)
MH
Vũ Minh Hoàng
CEO/Founder — CITO Agency
Performance marketing, e-commerce (mother & baby, beauty, edu)
VA
Vũ Thị Vân Anh
Strategic Planner — Ore IMC Agency
F&B, retail, beauty, lifestyle, education, service brands
ND
Hoàng Ngọc Diệp
Marketing Manager — Labooong
F&B (campaigns, product launch, competitor monitoring)
Key questions (semi-structured)
1
Current process
From brief → insight, how long? Which steps?
2
Tools & data sources
Which tools/methods in use? Main data sources?
3
Barriers with AI
Biggest barrier to using AI? Encountered AI errors?
4
Evaluating Market Lab
How should it be positioned? At which stage?
5
Suitable industries
Which industries best for Market Lab to add value now?
2.4 · Target Customers

INTERVIEW FINDINGS

Agency IMC / Branding
Du Du · Vân Anh

  • Need: fast insights for brief/pitch; brief→strategy takes 1 week research + 1 month positioning
  • Pain: data fragmented across sources, time-consuming to consolidate
  • AI barrier: hallucination — needs validation with real users
  • ML role: AI research co-pilot, strengthen proposals before pitching

"AI helps find data & build survey frameworks — but the big idea still has to come from people"

Performance Agency
Minh Hoàng

  • Need: data to define segments, tactics & conversion as soon as a client onboards
  • Pain: juniors depend on seniors; lack structured research tools to scale
  • AI barrier: general AI tends to agree with the user, lacks critical thinking; limited grasp of VN behaviour
  • ML role: reduce research time, test hypotheses, auto-segment

"If a tool creates customer data that feels 'human' and cuts cost vs an agency → businesses will try it"

F&B Brand
Ngọc Diệp

  • Need: consolidate data from sales + reviews + social to choose test directions before launch
  • Pain: AI creates generic personas not convincing enough for F&B
  • AI barrier: low reliability without real data; worried about data security
  • ML role: help choose test directions — NOT replace real testing

"If it only creates generic personas, it's not convincing enough for an F&B brand to pay for"

2.5 · Target Customers

FROM DATA TO ACTIONABLE INSIGHTS

1
Not short on data — short on synthesis
100% of agencies & retail have data but it's fragmented → ML must be a tool that consolidates + filters noise, not just creates personas
4
F&B is not the MVP main target
F&B depends on taste, location, in-store → real testing is faster. Focus on E-com/Retail & SaaS first
2
AI accepted as a support tool
87% worry about reliability; hallucination is the biggest barrier → need clear data sources, validation, explainable outputs
5
Industries with clear behavioural data fit better
E-com: views, add-to-cart, reviews · SaaS: trial, onboarding, churn → primary: E-com/Retail & SaaS
3
'AI Persona' isn't enough without real data
All 3 interviewees asked where the data comes from → shift messaging: 'AI turns data → insights' instead of 'AI creates personas'
6
Agency = secondary target & amplification channel
Agencies serve many clients → each agency is a multiplier for Market Lab (distribution channel)
2.6 · Target Customers

FINAL INSIGHT & TARGET PRIORITY

Final Insight Statement
Market Lab should be positioned as an AI tool that turns scattered customer data into actionable insights — rather than just a tool that creates AI personas. In the MVP: prioritise E-commerce/Retail & SaaS/Tech (clear behavioural data); Agencies act as a secondary target / distribution channel.
Target priority in the MVP
Primary Target

E-commerce / Retail & SaaS / Tech

Rich, clear behavioural data; AI can analyse and generate value from day one.

Secondary Target

Marketing Agencies

Frequent research needs, many clients → multiplier effect when using Market Lab.

Expanded Target

Retail / Fashion Brands

Real needs but dependent on real testing; suitable once case studies exist.

2.6 · Target Customers

CUSTOMER PERSONA — PRIMARY

K
Nguyen Minh Khoi
Marketing Manager · E-commerce
Age29 GenderMale EducationMMT University — Business Major LocationHanoi
Career

E-commerce Startup — Fashion & Lifestyle · income 25–30M VND / month

Tech stack
ChatGPT Meta Ads Google Analytics Canva Google Forms
"I need to know if a campaign will work BEFORE running it — not after losing the whole budget"
Pain points
  • Manual testing takes 3–5 days to gather enough responses
  • Survey results biased & not representative
  • ChatGPT output inconsistent & hard to report
  • CEO/Founder questions data reliability
  • No budget for a pro research agency
Desired gains
  • Campaign/post results within hours
  • Personas that reflect target customers
  • Visual dashboard for CEO/Founder reporting
  • Reuse old scenarios to compare strategy shifts
Decision-making behavior
  • Searches for tools when surveys are too slow
  • Watches demos & reviews before trying
  • Uses a free trial for 7–14 days
  • Upgrades if ROI is clear & cost < 5M/month
Buying factors
  • Questions AI-persona reliability
  • Compares with free ChatGPT usage
  • Concerns about CRM / data privacy
  • Needs proven ROI before reviewing
Budget & authority
  • Tool budget 3–5M VND/month
  • Self-approves spend < 5M/month
  • CEO/CFO approval above 5M VND
Buying cycle
  • Freemium → Trial → Subscription
  • Monthly subscription
  • Switches quickly if no ROI
3.1 · Value Proposition & USP

VALUE PROPOSITION

The Problem

Fragmented data

Teams need to understand customers faster before deciding. Data exists but is fragmented — time-consuming to consolidate, hard to turn into actionable insight.

→
The Solution

AI synthesis layer

An AI platform that synthesises data from many sources, analyses behaviour, builds personas by segment, and proposes actionable insights — not generic personas.

→
The Benefit

Faster, lower-risk decisions

Shorten research, reduce reliance on intuition, decide based on data. Agencies save pitch time; businesses reduce launch risk.

→
Differentiator

Data-first, AI-second

AI is a processing layer — it doesn't 'fabricate' personas. VN localization. Structured research vs ChatGPT; pricing fits SMEs better than Yabble.

Value Proposition Statement
Market Lab helps businesses and agencies turn scattered customer data into actionable insights — by using AI to analyse customer behaviour, identify segments, and test early assumptions before launching products or campaigns.
3.2 · Value Proposition & USP

UNIQUE SELLING POINTS

A USP sits at the intersection of: what you do well + what customers want + what competitors can't do yet.
1
Simulate Behaviour Before Launch
Create AI Customers that simulate how each group reacts to product, price & message before launch — reduce uncertainty, save real-test budget.
4
Structured Scenario Testing
Create personas, set up scenarios, save results, compare options, track — not just 'prompt & get answer' like ChatGPT.
2
Faster & Cheaper than Traditional Research
Focus groups $4,000–5,000 & take weeks. Market Lab: insights in hours, 3–15M VND/month — helps filter ideas.
5
Data Flywheel — smarter the more you use it
Accumulates scenarios & usage data → simulations grow more accurate. Creates natural switching cost, hard to replace.
3
Localization for the VN market
Understands VN language, culture, consumer behaviour & business context — what Synthetic Users, Yabble, Minds AI can't do yet.
3.2 · Value Proposition & USP

USP STATEMENT & POSITIONING

USP Statement

Market Lab creates AI Customers localized for the Vietnamese market, helping businesses simulate how customers react to product, price & message before real rollout — faster & cheaper than traditional research, more structured than ChatGPT.

Positioning: Market Lab vs. Alternatives
ChatGPT / Gemini
Synthetic Users / Yabble
Market Lab
Price
Free / low
$2–60/interview · $8,900/yr
Fits VN SMEs (subscription)
Localization VN
No
No
Yes
Structured research
Prompt-based
Yes but complex
Scenario system
Data accumulation
Not stored
Limited
Data flywheel
Real-user validation
No
Limited
Validation mechanism
Result explanation
Inconsistent
Limited
Explainable + sourced
Checkpoint 2 — Market Lab
EXE101 · Group 01
Class SE1933-NJ
Q&A

THANK YOU
FOR LISTENING

Instructor
Vương Tiểu Oanh
Mentor
Phạm Thanh Hương
Team
Group 01 · SE1933-NJ
Market Lab
← / → · space