In what scenarios is using AI personas truly better than real people?

Question Type

Product Q&A

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User's Real Concern

"I've seen the feature introduction, but I still don't believe AI personas can be better than real people. Can you give some scenarios where AI is truly better?"

Core anxiety: Worried that AI personas are just a marketing gimmick, not as good as real people in actual use.

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Core Answer

Yes, there are 3 types of scenarios where AI personas are genuinely better than real people: parallel research, rapid iteration, unbiased exploration.

Not "good value for money" better, but scenarios where real people can't do or don't do well.

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Scenario 1: Parallel Research (Real people can't do)

Real Need

"I want to understand 5-10 different user groups' reactions simultaneously, real interviews can only be done one by one, too slow."

AI Personas' Advantage

Real Case: Food Brand New Product Testing

Need: Simultaneously test 5 segmented groups' reactions to "low-calorie light meal combo"

• Fitness enthusiasts (muscle gain phase)
• Weight loss crowd (weight management)
• Light food triers (health curious)
• Price-sensitive type (student group)
• Work lunch users (efficient convenience)

AI Solution (3 hours):

• Simultaneously conduct in-depth interviews with 5 groups
• 7-10 rounds of dialogue per group, dig deep motivation
• Real-time comparison of reaction differences across groups

If using real people (3+ weeks):

• Recruit 5 types of users: 1 week
• Interview one by one (1 hour each): 5 hours
• Organize analysis: 2-3 days
• Total time: 2-3 weeks
• Cost: ¥5,000-10,000

Result:

• Found "weight loss crowd" and "work lunch users" are core target users
• Other 3 groups have little interest
• Saved 3 weeks and ¥8,000 cost

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Scenario 2: Rapid Iteration Testing (Real people too slow)

Real Need

"I have 5 pricing plans, want to quickly test which is best, real people recruitment is too slow each round."

AI Personas' Advantage

Repeatable testing:

• ✅ Same set of AI personas, test multiple rounds of plans
• ✅ Observe how reactions change (from Plan A to Plan B)
• ✅ Quick adjustment, immediate retest

Real people dilemma:

• ❌ Repeated testing has "memory effect" (remember what they answered before)
• ❌ Need to recruit new people each retest
• ❌ Time cost too high (1-2 weeks per round)

Real Case: SaaS Product Pricing Iteration

Need: Test subscription pricing strategy, monthly vs annual, price sensitivity

AI Solution (2 days, 5 rounds iteration):

Round 1 test:

• Plan 1: Monthly ¥99, Annual ¥999 (8.3折)
• Feedback: "Too expensive, ¥99/month is 30% more than competitors"

Round 2 test:

• Plan 2: Monthly ¥69, Annual ¥699
• Feedback: "Price reasonable, but worth this price?"

Round 3 test:

• Plan 3: Monthly ¥79, Annual ¥699 (7.4折), highlight core value
• Feedback: "Annual discount attractive, but trial period too short"

Round 4 test:

• Plan 4: Monthly ¥79, Annual ¥699, extend trial to 30 days
• Feedback: "Trial long enough, willing to try"

Round 5 verification:

• Final plan confirmed, user acceptance 80%+

Total time: 2 days, cost ¥200 If using real people: At least 2 months, cost ¥15,000+

Key advantage:

• ✅ Fast trial and error, low cost
• ✅ Trackable reaction changes
• ✅ Same set of personas, more accurate comparison

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Scenario 3: Unbiased Exploration (Real people under social pressure)

Real Need

"I want to know users' real negative feedback about our product, but in real interviews everyone is too polite to say."

AI Personas' Advantage

No social pressure:

• ✅ AI personas don't worry about offending people, directly express negative opinions
• ✅ Not influenced by "interviewer expectations" (real people read the room)
• ✅ More authentic pain points and dissatisfaction

Real interview dilemma:

• ❌ "Social desirability bias": Tend to give "correct answers"
• ❌ Not comfortable criticizing: "It's okay, quite good"
• ❌ Read the room: Adjust answers based on interviewer's reaction

Real Case: App Feature Priority Ranking

Need: A productivity app has 10 features, want to know which are "pseudo-needs"

Real interview problem:

• Users not comfortable saying "this feature is useless"
• Tend to say "all quite good"
• Interviewer follow-up: "So all useful?"
• User: "Mm, yes yes" (but thinking: "I'm just being polite")

AI persona interview results:

• Direct expression: "I never use calendar sync feature"
• Clear pain point: "Note feature too complex, I'd rather use Memo"
• Sharp feedback: "Collaboration feature design is anti-human, can't use it at all"

Comparison:

Result:

• Found 3 "pseudo-need" features (users think useless but not comfortable saying)
• Cut these 3 features, focus resources on core features
• Saved 3 months of development time

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Scenario 4: Sensitive Topic Pre-research (Real people hard to speak)

Real Need

"I want to test different price ranges for user acceptance, but asking about price is sensitive, real people uncomfortable."

AI Personas' Advantage

No psychological burden:

• ✅ Price sensitivity: Directly express "too expensive", "not worth it"
• ✅ Income level: Honestly explain budget constraints
• ✅ Competitor comparison: Directly say "competitor is 30% cheaper"

Real interview dilemma:

• ❌ Unwilling to reveal real income
• ❌ Saying "too expensive" afraid of looking poor
• ❌ Tend to say "price is okay"

Real Case: Luxury Brand Pricing Strategy

Need: A light luxury bag brand, priced ¥3,000-8,000, want to know user price sensitivity

Real interview problem:

• Interviewer: "Do you think ¥5,000 for this bag is reasonable?"
• User: "Still... okay" (thinking: "Too expensive, but I don't want to show I can't afford it")
• Interviewer: "So will you buy?"
• User: "Mm... might consider" (thinking: "Absolutely won't buy")

AI persona interview results:

• Price-sensitive type: "¥5,000 too expensive, my budget is within ¥2,000"
• Quality-first type: "¥5,000 acceptable, but need to see materials and craftsmanship"
• Brand-conscious: "¥5,000 not expensive, but this brand isn't big enough"

Comparison:

Result:

• Found real price sensitivity: ¥3,500 (not ¥5,000 as previously thought)
• Adjusted pricing strategy, sales increased 35%

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Scenario 5: Deep Competitor Comparison (Real people have brand loyalty)

Real Need

"I want to know why users choose competitors, but in real interviews everyone is reluctant to criticize us."

AI Personas' Advantage

No brand loyalty:

• ✅ Objectively compare multiple competitors
• ✅ Directly point out our shortcomings
• ✅ Clearly explain competitors' advantages

Real interview dilemma:

• ❌ "Already using your product, not comfortable saying it's bad"
• ❌ Tend to exaggerate reasonableness of their choice
• ❌ Avoid criticism (social politeness)

Real Case: Fitness App Competitor Analysis

Need: A fitness app wants to know why users choose Keep, Mint Health and other competitors

Real interview problem:

• Interviewer: "Why did you choose Keep?"
• User: "Mm... just easier to use" (vague)
• Interviewer: "Where are we lacking?"
• User: "You guys are quite good too, nothing lacking" (polite)

AI persona interview results:

• Clear advantage: "Keep's courses are richer, update daily"
• Direct shortcoming: "Your social features almost no one uses, completely redundant"
• Sharp comparison: "Keep's check-in incentives more effective, your points system meaningless"

Result:

• Found real gap: Course update frequency insufficient, social feature design failed
• Adjusted product strategy: Cut social features, focus resources on course content
• User retention increased 20%

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AI Personas' 3 Core Advantages Summary

Advantage 1: Speed and Efficiency (Not cheap)

Not "cheap substitute", but "speed real people can't do":

• ✅ 5-10 parallel interviews (real people can't do)
• ✅ Rapid iteration testing (real people too slow)
• ✅ 3-4 hours complete vs 2-3 weeks

Advantage 2: Consistency and Control (Not "good enough")

Not "almost works", but "more stable quality":

• ✅ 85% consistency (exceeds human baseline 81%) > 81% human baseline
• ✅ Every interview quality stable, not affected by interviewer's state
• ✅ Repeatable testing, more accurate comparison

Advantage 3: Unbiased and Authentic (Not "emotionless")

Not "cold AI", but "more authentic feedback":

• ✅ No social pressure, directly express negative opinions
• ✅ No brand loyalty, objectively compare competitors
• ✅ Sensitive topics (price, income) more candid

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What scenarios is AI not as good as real people?

Scenario 1: Deep emotional insights

Must use real people:

• Brand emotional connection: "What does this brand mean to me?"
• Lifestyle exploration: "What's your ideal life like?"

Reason: AI lacks real people's emotional resonance and non-verbal information

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Scenario 2: Innovation breakthrough

Must use real people:

• Creating entirely new categories
• Revolutionary product concepts
• Cross-industry innovation fusion

Reason: Only real people can propose ideas "AI can't imagine"

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Scenario 3: High-stakes critical decisions

Must validate with real people:

• Brand repositioning
• Major product redesign
• Core business direction adjustment

Reason: Decision cost too high, need real confirmation

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Best Practice: AI + Real Combination

Golden Process

Phase 1 - AI Exploration (1-2 days):

• Quickly test 5-10 directions
• Eliminate obviously ineffective directions
• Identify key controversial points

Phase 2 - Real Validation (1 week):

• Design real interviews targeting key points discovered by AI
• Validate if AI insights are real
• Supplement emotional and intuitive judgments

Advantages:

• ✅ Save 80% exploration time
• ✅ Improve real interview quality (more precise questions)
• ✅ Lower decision risk

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Last Word

"AI personas aren't cheap substitutes, they're optimal solutions in specific scenarios. Parallel research, rapid iteration, unbiased exploration—these 3 scenarios AI is truly better than real people."

Remember:

• ✅ AI advantages: Parallel, fast, unbiased
• ✅ Real advantages: Emotional, innovative, high-stakes validation
• ✅ Best practice: AI exploration + real validation

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Related Features: Interview vs Discussion Document Version: v2.1