Creating Customer Personas in the Age of AI

A Webinar

How Indian Consumers Use AI

• 41% of Indian consumers already use AI shopping tools; 40% plan to start
• 60–65% use AI for personalized recommendations and virtual assistants

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“Customers are already using AI. Your personas must reflect AI-shaped behavior.”

• Rapid AI adoption among Indian consumers (shopping, search, smartphones)
• Need to move from fictional profiles to live, data-backed personas
• Traditional personas often age quickly because customer behavior changes faster. AI helps us keep personas current by connecting CRM data, campaign behavior, and interaction data into living audience profiles.

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Why This Topic Now?

What Typical Profiling Does?

What AI Personas Add?

• AI personas go beyond demographics and add motivations, objections, content preferences, and buying readiness. They can also update as new engagement data comes in, so your marketing stays relevant instead of stale.

Profiling Vs AI Personas

Profiling vs AI personas

• Profiling
• Age
• Experience
• Designation
• Company

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• AI personas
• Goals
• Blockers
• Motivation
• Next step

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Old Personas Vs AI personas

How this helps digital marketing

• AI personas make digital marketing smarter because they improve ad targeting, landing-page messaging, content creation, and follow-up journeys. When you know which persona is fee-sensitive, flexibility-first, or leadership-driven, you can tailor the right message and avoid wasting media spend.
• Digital marketing is moving toward personalization at scale. AI personas help brands do that faster, with more consistency across channels and better alignment between media and sales/conversion.

Summary

• Profiling describes leads, but AI personas help you persuade them. That is why AI is becoming central to modern digital marketing.

AI-Driven Persona Workflow

• 4-step framework
• Audit data.
• Identify behavior segments.
• Use AI to draft personas.
• Validate and operationalize.

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Audit Data Sources

• CRM (lead source, campaign, city/tier etc.
• Website/App Analytics (pages visited, time to decision)
• WhatsApp and Call Logs (questions, objections)
• Survey/Interview Data

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Using AI tools

• Upload anonymized data to AI tools or use prompts with summary stats
• Ask AI to group records and narrate personas (goals, behaviors, pain points)

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Validation

• Compare persona claims with current data
• Run short interviews/focus groups with customers from each segment
• Check with frontline teams: sales, counselors, call-center agents

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How India’s Fashion Platform Uses Generative AI and Personalization

Myntra’s AI:

What Myntra shows us about AI and audiences

• AI is now the core pillar of Myntra’s fashion platform, not a side experiment.
• It shows how AI can create business value at scale.
• All of this relies on understanding who the customer is, what they like, and what will reduce friction in their journey.

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The Problem Myntra Solved

• Fashion search is often vague and intent-based.
• Keyword search alone misses user context.
• Shoppers want outfit-level suggestions, not just product matches.

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MyFashionGPT

• Natural-language shopping search.
• Powered by Azure OpenAI Service.
• Helps users search like they talk.

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How it Works?

• User types a natural-language prompt.
• AI interprets intent and context.
• Myntra returns relevant items and look ideas.

Myntra’s wider AI stack

• MyFashionGPT
• Maya
• MyStylist
• Search relevance improvements

Lessons for marketers

• Start with the customer’s intent.
• Replace generic journeys with contextual ones.
• Use AI to match needs to offers faster.

What Myntra shows us about AI and audiences

• AI is now the core pillar of Myntra’s fashion platform, not a side experiment.
• It powers recommendations, vernacular search, My Stylist, Maya chatbot, virtual try‑on, and even 30‑minute M‑Now deliveries.
• All of this relies on understanding who the customer is, what they like, and what will reduce friction in their journey.

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Under the hood: AI = living personas at scale

• Recommendation engines group users by behaviour, preferences, geography, and affluence – this is persona logic at scale, even if they don’t call it that.
• Vernacular search, My Stylist, Maya, and Virtual Try‑On all personalise the journey based on implicit personas: fashion‑forward, value‑seeker, regional‑language user, beauty explorer, etc.
• For Myntra, AI automatically refreshes these segments as people browse, search, try, and buy – personas are not static PDFs; they are updated in real time.

Personas for Weekend MBA

“Today I’m going to show how we can move from raw weekend MBA lead data to useful AI personas. The purpose is not just to describe the audience, but to communicate with them more effectively.

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Profiling of Weekend MBA leads�

• Profiling gives us data
• AI personas give us direction
• Better personas improve marketing and conversion.

“Most teams already have lead records, but data alone does not tell us what to say next. AI personas help turn those records into action by connecting profile details with behavior and intent.”

Profiling of Weekend MBA leads�

• 59% of leads sit in the 2 – 10year band — established professionals past their first promotion, not fresh graduates.
• 13% carry 15+ years — likely over-qualified for a Weekend MBA and a better fit for the Executive Diploma.
• 14% are under 2 years — early-career, most price-sensitive, ask about specializations.
• 45% of the people showed interest when called. There are barriers around affordability and format fit.
• 3% finally joined the course out of 550 leads

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What Weekend MBA profiling tells us�

• Too descriptive, not explanatory.�Age, years of experience, designation, and company are useful fields, but they do n
• Same profile can mean different intent.�Two leads with similar experience can have very different reasons for enquiring, such as growth, credibility, affordability, or weekend fit.ot reveal motivation, urgency, or decision logic
• Objections are not the same as personas.�Notes like “fees high,” “call back later,” “busy,” or “online only” are signals, but profiling alone does not connect those signals into a stable audience pattern
• Weak for message design.�Profiling can tell you the audience size and composition, but not which message, proof point, or CTA will work best for each lead group.

“The data shows that interest alone is not enough. Some leads are blocked by fees, some by time, some by format, and some by simple timing. That is why we need personas — they help us separate surface interest from real decision readiness.”

AI suggested personas �

• Career Upgraders → growth and momentum
• Mid-Career Growth Seekers → leadership and ROI
• Senior Executive Learners → credibility and network
• Budget-Conscious Evaluators → affordability and value
• Flexibility-First Professionals → schedule fit and convenience

“Each persona needs a different proof point and a different call to action. If we match the message to the persona, the campaign feels more relevant, the counselor conversation becomes easier, and the lead is more likely to move forward.”

Career-Upgrader�

Mid-Career Growth Seeker�

Senior Executive Learner�

Budget-Conscious Evaluator�

Flexibility‑First Professional��

Five Segments And Five Messages

Summary of Weekend MBA personas

Plan based on personas

Post-exercise impact�

What Drove The Uplift?�

• Better lead profiling.
• Clearer persona-based communication.
• More relevant proof points and CTAs.
• Faster routing to the right follow-up path.

Better Personas, Better Business Results�

• AI personas are not a theory exercise; they are a conversion system. In our case, they helped lift post-exercise conversion by 35%.
• The real lesson is simple: when we move from raw profiling to persona-driven communication, we don’t just get better insights — we get better business results

Thank You