Elevate Your Content and Research with Humanizing LLMs

See how collaborating with LLMs can transform your content by converting customer, expert, and competitor data into actionable insights.

When I think about large language models (LLMs), one major discussion point is their ability to scale content creation. It’s a tool we’re all tempted to lean on heavily. However, balancing efficiency with creativity is key.

With our busy schedules, boosting productivity is essential. Imagine using tools like Claude and ChatGPT not just for speeding up processes, but also for adding a personal touch to your website and making your day-to-day tasks easier, all without sacrificing creativity.

This journey explores how to:

• Analyze customer feedback and questions comprehensively.
• Streamline the gathering of detailed insights from subject matter experts.
• Conduct competitive analysis effectively.

These tasks, often done manually, can be remarkably enhanced with automation, giving you an edge by rooting your approach in customer and market realities instead of working in a vacuum.

By tapping into this information, I can better connect with my audience, avoiding the pitfalls of an echo chamber.

Analyzing Customer Feedback at Scale

One outstanding feature of LLMs is their scalability in processing data, identifying patterns, and uncovering trends—tasks that might otherwise take me or a colleague days or even weeks to complete.

If you’re not part of a global enterprise with a dedicated data team, LLMs are your next best ally to substitute those capabilities. Focusing on customer feedback, for instance, could mean the difference between success and redundancy. The thought of sifting through thousands of NPS surveys doesn’t sound appealing to me, and I doubt it does to you either.

Utilizing raw data uploads into a project knowledge space and having my LLM of choice run its analysis is one way to go. However, I prefer uploading this data into something like BigQuery, using LLMs to write relevant SQL queries for in-depth analysis, ensuring integrity and accuracy.

This approach not only lets me peek behind the analytical curtain, learning SQL by osmosis but also serves as a safeguard against potential inaccuracies or hallucinations often seen with direct LLM data uploads.

The separate handling of data fosters a more reliable, accurate, and actionable insight, preventing the wild goose chases that could arise from misleading automated responses.

Practically speaking, unless overwhelmed by enormous datasets, BigQuery is a free resource (setup might require a credit card, though). And fear not if SQL is new to you; with an LLM, you’re set for success with full query support in place.

Here’s a glimpse into my workflow:

• Generate SQL functions using the LLM.
• Debug and validate data entries.
• Feed LLM with results from SQL queries.
• Create visualizations either with the LLM or via further SQL queries.
• Iterate as necessary.

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Automating Subject Matter Expert Interviews

Frustrations abound when attempting to secure time with subject matter experts, whose schedules often leave them stretched thin.

Why would they want to regurgitate information they’ve already discussed ad nauseam with the manufacturing team? Yet, for marketing purposes, I still need this information to clearly present new features on our platform, offering customers precise details beyond mere specifications.

How to get this coveted expertise? By crafting a customized GPT that can assume the role of interviewer, asking the right questions.

Be advised: customization may vary depending on the launch, product, or service in question. A ChatGPT Plus subscription should suffice for this task.

The guidelines should entail the following:

• Role and tone: Define the interviewer’s persona.
• Context: Clarify learning objectives and rationale.
• Interview structure: Outline initial topics and follow-ups.
• Pacing: Implement a structure of query-response dynamics.
• Closing: Craft a concluding summary or call to action.

Testing it myself, I pretended to be a subject matter expert to refine this tool, always seeking to fit within their limited downtime.

The responses provided can then be further analyzed or converted into draft articles thanks to an LLM.

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Analyzing Competitors for Strategic Insights

While potentially tricky, the strategic examination of competitors can yield profound insights regarding the competitive landscape and personal business gaps.

Here’s a few things I’ve found valuable when dissecting competitor data:

• Aggregating competitors’ reviews helps identify common themes, benefits, and problem areas.
• An analysis of their web copy gives clues into the type of audience they’re targeting and their unique positioning. Combine this with the Wayback Machine to track how messages have evolved over time.
• Job postings can highlight strategic priorities or areas of potential experimentation.
• Social media engagement data can provide insight into customer satisfaction and desire, revealing potential gaps in their customer service.

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Scaling Research Without Losing the Human Thread

Using LLMs alongside extensive datasets allows me to remain grounded in customer realities while being swift in delivering specific, actionable insights through pair programming.

The methods explored within are just starting points. Consider other useful data sources you might already have access to:

• Call transcripts from sales teams.
• Query data from Google Search Console.
• Insights from on-site searches.
• Heatmaps tracking user interactions.

A note of caution—while analytics data is tempting, sticking to qualitative, customer-focused data rather than quantitative metrics leads to richer insights.

Happy exploring!

Inspired by this post on Search Engine Land.