Are you using ChatGPT for customer research?

You’re probably already using Chat GPT for customer and user research, right?

Which means you’re probably downloading transcripts, entering prompts into ChatGPT, and manually reviewing the results.

This process is time-consuming, dependent on lots of manual intervention, and may miss critical insights due to limits in prompt expertise, not to mention the biggest question, are you confident in the answers you’re getting?

We’ve interviewed over 250 product, product marketing and GTM professionals, and these are their problems / concerns are with the status quo:

Manual effort: There’s too much copy and pasting of manually sourced feedback, which massively limits the scale. Not to mention the attribution of connected metadata like firmographics and commercials.
Prompt expertise: Individual expertise might exist, but that doesn’t scale across the business and lacks consistency and quality control.
Evidence: Traceability and being able to prove the results of generative models with evidence so the trail of breadcrumbs can be followed back to the source.
Industry-Specific Insights: insights that aren’t relevant to specific businesses or industries, producing less tailored and actionable data.
Integration with Workflows: Lack of specialised and purpose-built integrations into product teams' workflows and no alignment with product development processes.
Automated Analysis: No intuitive dashboards and visualisations that help teams quickly grasp the sentiment, trends, and key insights, removing the need for manual data input and interpretation.
Data Privacy and Security: Customer feedback and information is not handled in compliance with industry standards, with models being trained with customer data. E.g. you need the Chat GPT Team version or above to ensure this is not the case.
Collaboration Features: There aren’t any collaboration tools, let alone tools designed specifically for product teams, allowing for streamlined communication and efficient decision-making processes based on customer research insights.
Customer Research Focus: Lacks specialisation and functions to extract, analyse, and apply customer feedback effectively, converting these to actionable recommendations.

Like with any focused solution, it’s all about having a more robust and specific tool for the job that needs to be done. This is particularly the case when scale becomes an issue and there’s an overwhelming amount of customer data to aggregate and analyse.