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In 2010, Gap introduced a new logo that was cleaner and more modern, but within days, customers pushed back so hard that the company reverted to the old design. GAP didn’t optimize for what the logo meant to people, such as familiarity, trust, and identity.

Marketing teams still make a version of the same mistake by running surveys with questions they designed, reading analyst reports written by people who've never spoken to their customers, and building content strategies around keywords in a spreadsheet. In these cases, the voice of the customer, raw, unfiltered, and emotionally honest, barely makes it into the room.

Meanwhile, your customers are already writing everything down on G2, Trustpilot, Reddit, Amazon, and Capterra in the form of customer reviews.

They are communicating what they were trying to solve, what frustrated them before they found you, what surprised them, and what almost made them leave, and that's the data focus groups can never get.

In this issue, we’ll walk through an AI workflow for mining that language and turning it into a content strategy that resonates because it's built from the exact words your customers used.

Let's go 🚀

Introducing Claude Design by Anthropic Labs. Anthropic Labs launched Claude Design, which lets you collaborate with Claude to create polished visual work like designs, prototypes, slides, one-pagers, and more. Claude Design is powered by Claude Opus 4.7, and is available in research preview for Claude Pro, Max, Team, and Enterprise subscribers. [Source: Anthropic]

Gemini is now the hardest AI writing tool to detect, but I think that might be a problem for everyone. A recent test found Google Gemini produces writing that's harder to flag as AI-generated than many of its rivals, particularly ChatGPT. Importantly, some AI detection tools failed to spot Gemini-generated writing at all. [Source: Techradar]

Reddit is the #1 most cited domain for B2B/enterprise-related searches, representing 3.36% of all citations across all AI tools. While LLMs provide faster information, Reddit provides the necessary human context to find a business solution. Reddit communities provide essential perspectives from other BDMs that help contextualize and drive decisions. [Source: Reddit Inc]

Why IBM says every brand now needs a GEO playbook. As AI reshapes brand discovery, IBM outlines a 12-part system brands need to stay visible in machine-made decisions. AI tools help people answer questions, compare products, and recommend brands. In many cases, users never even visit a website. [Source: Search Engine Land]

Most people skip this step and end up with a document full of five-star quotes they can use as testimonials.

Before you collect a single review, define four extraction targets:

G2 and Capterra are strong sources for B2B SaaS, where reviewers typically respond to structured questions about the problems the product solved, the alternatives they considered before buying, and what they would change, making the data easier to extract. Search for your product, your main competitors, and products in adjacent categories. 

Trustpilot steers toward service businesses, fintech, e-commerce, and DTC brands. The reviews tend to be more emotionally charged, both positive and negative, which gives you better raw language for empathy-driven content.

Reddit is one of the most valuable and probably the most underused. Search your product category, your competitors by name, and problem-state queries ("how do I stop losing track of X" or "is there anything better than Y"). Reddit gives you pre-purchase conversations, which means you're capturing the exact mindset of someone who hasn't made a decision yet.

Amazon is essential for any physical product category. Sort reviews by "most helpful" to get the ones that have already been validated by other buyers. Read the three-star reviews first; they are the most honest customers who give three stars, liked enough to write but had enough friction to flag it.

Collect 50 to 150 reviews per platform in a spreadsheet or document and label each one with the source, star rating, and product/competitor it refers to. You will need these labels when you're prompting AI.

At this stage of the workflow, you're asking AI to perform pattern recognition at scale to find recurring language, emotional triggers, and content gaps that would take a human analyst days to identify.

Here is a prompt structure that works:

You are a voice-of-customer analyst. I'm going to give you a set of customer reviews for [product/category], and your function is to extract patterns.

Specifically, I want you to identify:

Format your output as a structured report with each section clearly labeled. Under each point, include 2 to 3 direct quotes from reviews as evidence.

Here are the reviews: [paste reviews]

Run this prompt separately for your own product reviews, your top competitor's reviews, and any Reddit threads you've collected. The overlap between what customers say about you and what they say about competitors is where your highest-value content opportunities sit.

Once AI has processed the reviews, you'll have four content assets you can move with immediately.

An SEO content brief built from pain-point language. Take the pain points and map them to search intent. The vocabulary customers use in reviews is often the vocabulary they use in search. An SEO tool like Ahrefs or Semrush can tell you whether those phrases have search volume; the review mining tells you whether they're emotionally true.

A messaging framework built from desired outcomes. The phrases customers use to describe what they want to achieve belong on your homepage, your email subject lines, and your ad copy, using the vocabulary your customers already associate with success.

A content calendar built from objections. Every objection in the review set is a content brief. "I almost didn't buy because I thought setup would take too long" is a YouTube video, a blog post, and an FAQ answer. Map the top five objections to content formats, and you have a month of editorial.

A competitive intelligence report built from competitor reviews. The gaps in your competitor's review set things customers wish the product did, and frustrations that come up repeatedly are positioning opportunities. Build content that directly addresses what their customers are complaining about.

When you prompt AI to write content, whether that's a product page, a landing page, a LinkedIn post, or a blog article, include a "voice of customer" block in your prompt.

"Use the following phrases, which come from actual customer reviews of this category. These are the words our customers already use when describing their problem and desired outcome. Prioritize this vocabulary over generic marketing language."

Then paste in the vocabulary patterns from your extraction report.

This is the difference between content that "sounds customer-centric" and content that is built from actual customer language, and readers feel the difference even when they can't articulate it.

Gymshark built much of its early content strategy around Reddit fitness communities. Rather than leading with brand-first content, the brand aligned its content calendar with the questions their target customers were asking in communities, which explained why their early blog content ranked faster than far larger competitors.

Notion has documented using community forums and review platforms as primary sources for its help center and onboarding content. Instead of guessing where new users get stuck, their team mines community posts to identify the exact moments of friction and addresses them directly in the product experience and support content.

Rising costs. Signal loss. Platform changes. Most paid media fails because it's built for clicks, not revenue.

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Use this prompt with your preferred LLM to turn a competitor's review set into a content strategy brief.

The Scenario:

You are the Head of Content for a B2B SaaS company entering a competitive market. You've collected 80 reviews of your main competitor from G2 and Trustpilot, and you want to use those reviews to build a content strategy that directly targets the gaps in your competitor's customer experience.

The Prompt:

You are a content strategist specializing in voice-of-customer research. I'm going to give you a set of customer reviews for a competitor in my space. Your job is to produce a content strategy brief I can hand to a writer.

Current Situation:

Questions:

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There's a version of content strategy that starts with "what do we want to say?" and a version that starts with "what are our customers already saying?"

The first approach produces content that often feels like marketing, while the second produces content that feels like it was written by someone who genuinely understands the customer’s problems.

The workflow in this issue is systematic: you collect raw language, use AI to find patterns at scale, and build content briefs from what you find. 

Until next time, keep exploring the horizon. 🌅

Alex Lielacher

P.S. If you want your brand to show up in Google AI Mode, ChatGPT, and Perplexity, reach out to my agency, Rise Up Media. That's what we do.