In partnership with
Read enough complaints and you start to see the same story repeat.
Customers expected the product to save time, but it created more work. They expected the service to feel simple, but needed help at every step. They expected the price to feel justified, but could not see a clear result.
That gap is useful. It shows you what customers care about, where your message may be setting the wrong expectation, and which parts of your offer need to be explained more clearly.
The customer’s wording matters, too. A phrase that appears again and again can be more useful than a polished internal description. It reflects how people actually describe the problem, which makes it a strong starting point for sharper positioning and more relevant marketing.
Try this prompt:
Review these customer complaints:
[Paste complaints here]
Find the repeated frustrations and group similar comments together.
For each group, explain:
- What customers expected
- What they experienced instead
- The exact words they use to describe the problem
- What this suggests we should emphasize in our positioning
Then write:
1. Three clear positioning angles based on the strongest patterns
2. Five customer phrases we could use in our marketing
3. Three claims or promises we should stop making because the complaints do not support them
Base your analysis on repeated patterns, not isolated comments. Keep the language simple and specific.
Your best positioning may already be hiding in the complaints you’re collecting.
Happy prompting!
Is Your Training Data Actually Model-Ready?
If you're fine-tuning a speech model, you've probably hit this wall: DNSMOS gives you a score, but it doesn't tell you whether the data behind that score is actually right for your model.
Treat it as a pass/fail gate and you'll end up training on audio that looks clean on paper but drags down real-world performance—while good source data gets tossed for no reason.
Voices' CTO DJ Jalali (with the team's senior audio and voice data engineers) just published a free white paper that breaks down the four-step calibration framework they use internally to set model-specific quality thresholds instead of trusting the raw DNSMOS number. It also covers where DNSMOS breaks down and how Voices validates audio for custom datasets at scale.
The Future of AI in Marketing. Your Shortcut to Smarter, Faster Marketing.
This guide distills 10 AI strategies from industry leaders that are transforming marketing.
Learn how HubSpot's engineering team achieved 15-20% productivity gains with AI
Learn how AI-driven emails achieved 94% higher conversion rates
Discover 7 ways to enhance your marketing strategy with AI.