Quick Answer
For search, voice, and "just tell me what to do".
Different offers solve different jobs, attract different buyers, and trigger different objections-so each product needs its own persona model.
Key Takeaways:
- Personas are offer-specific.
- Proof needs differ by product (case studies vs demos vs guarantees).
- Messaging should match stage-of-awareness per offer.
- AI makes it fast to build and maintain multiple personas.
Playbook
List each offer and its job-to-be-done in one sentence.
Collect customer language per offer (keep datasets separate).
Ask AI to extract goals, fears, objections, and triggers per offer.
Write landing page sections and FAQs that match each persona.
Update quarterly or after major product changes.
Common Pitfalls
- Mixing data from multiple offers.
- Copying one landing page structure for every product.
- Ignoring proof alignment (wrong proof for the wrong buyer).
Metrics to Track
Conversion rate per offer
Sales cycle length
Refund rate
Upsell rate
FAQ
How many personas per product?
Usually 1–2. Start with the dominant buyer type and add a second only if you see clearly different objections and proof needs.
What if I only have a few customers?
Start with qualitative inputs (calls, emails), build a first persona, and update as data grows. The first version is a hypothesis.
Can I reuse messaging across products?
You can reuse frameworks, but the hook, proof, and CTA should still match the specific job and buyer psychology of each product.
Related Reading
Next: browse the hub or explore AI Operations.