# SkillShelf — Full Documentation

> Certified, open-source AI workflows for ecommerce teams.
> Maintained by Cartful Solutions, Inc. — https://cartful.com

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## Skills

## Learn

### AI Tools for Ecommerce Teams

- URL: https://skillshelf.ai/learn/ai-tools-for-ecommerce-teams/
- Description: What Claude, ChatGPT, and similar tools actually are, why ecommerce teams are using them, and what you can do with one right now—no setup required.
- Last updated: 2026-02-24

You've probably heard that your competitors are using AI. You may have tried it once and gotten something mediocre. Or you've been meaning to look into it but weren't sure where to start.

This article covers what AI tools actually are, why they're particularly useful for ecommerce work, and what you can do with one today.

## What we mean by "AI tools"

When people say "AI tools" in this context, they mostly mean large language model (LLM) assistants—conversational tools you interact with by typing. The most widely used ones are:

- **Claude** (made by Anthropic)
- **ChatGPT** (made by OpenAI)
- **Gemini** (made by Google)

All three work similarly: you describe what you need in plain language, and the tool generates text in response. No code. No special training. You just write to it like you're explaining something to a smart colleague.

This is different from older AI tools that required training data, machine learning pipelines, or dedicated IT resources. These tools are ready to use the moment you open them.

## Why ecommerce teams in particular

Ecommerce operations involve enormous amounts of repetitive, language-heavy work:

- Writing and rewriting product descriptions
- Adapting copy for different channels (marketplace, email, social)
- Pulling insights from customer reviews and surveys
- Drafting email campaigns and subject lines
- Standardizing inconsistent supplier-provided data
- Documenting processes and SOPs

Most of this work is high-volume and time-consuming, but the individual tasks follow recognizable patterns. That's exactly where AI tools shine. They're much better at "write me 40 variations of this description in this tone" than at strategic decisions that require judgment about your specific business.

## What you can do right now

You don't need any setup to get value from an AI tool. Open Claude or ChatGPT and try any of the following:

**Rewrite something.** Paste in a product description that's not working and ask it to make it clearer, more persuasive, or match a specific tone. Compare the output to what you had.

**Summarize reviews.** Copy 20 customer reviews from any product and ask: *"Summarize the main things customers like and dislike about this product."* You'll have a VOC summary in under a minute.

**Draft an email.** Give it a brief: product name, sale percentage, audience segment, and ask it to write a campaign email. It won't be perfect, but it'll give you a draft faster than starting from scratch.

**Clean up data.** Paste messy, inconsistent product attribute data and ask it to standardize the formatting. It handles this remarkably well.

The results won't always be perfect. Iteration is part of the process—but even a rough first draft that you edit is faster than writing from scratch.

## Where SkillShelf comes in

AI tools are powerful by default, but they work even better when given specific, carefully crafted instructions. That's what skills are: pre-written instruction sets built for particular ecommerce jobs.

Instead of figuring out how to prompt an AI tool to write product descriptions, you can install a skill that already knows how to do that job—including asking you the right questions, handling edge cases, and producing output in a useful format.

Browse the [skill catalog](/), or if you want to understand more about what AI is actually good (and bad) at before diving in, read the next article.

---

### What AI Is Good and Bad at

- URL: https://skillshelf.ai/learn/what-ai-is-good-and-bad-at/
- Description: A plain-language breakdown of where AI tools reliably help ecommerce teams, where they fall short, and how to calibrate your expectations.
- Last updated: 2026-02-24

AI tools can feel like magic when they work and frustrating when they don't. The difference usually comes down to whether you're asking them to do something they're actually good at.

Here's a practical breakdown for ecommerce teams.

## What AI does well

### Writing, rewriting, and reformatting

This is AI's strongest area. Give it text and tell it what to do with it—make it shorter, change the tone, adapt it for a different channel, expand a bullet into a paragraph—and it executes reliably. Volume doesn't matter much: whether you need one rewrite or a hundred, the effort is the same on your end.

### Summarizing large amounts of text

Customer reviews, survey responses, support tickets, competitor descriptions—AI reads and summarizes faster than any human. Ask it to pull out the top five complaints from 200 reviews, and it will do a credible job in seconds.

### Generating variations

Subject line A/B tests, product title variants, headline options—AI can produce a dozen variations from one brief. Most won't be perfect, but having options to evaluate is faster than writing each one from scratch.

### Extracting structured information from unstructured text

Ask it to pull size, color, material, and care instructions from a pile of inconsistent supplier descriptions and return them in a table. This is tedious work for humans and easy work for AI.

### Following complex, detailed instructions

Well-written prompts produce reliably structured output. If you tell it exactly what format you want, what to include, what to avoid, and what your audience cares about, it will follow those instructions consistently.

## What AI does badly

### Knowing what just happened

AI tools have a training cutoff—a point in time after which they don't know anything. Claude's knowledge doesn't include last week's industry news, your latest inventory, or your current pricing. Always verify anything time-sensitive.

### Making judgment calls about your brand

AI doesn't know that your brand voice is "direct but warm, never salesy" unless you tell it. It doesn't know that you avoid the word "premium" because your CEO hates it. Without that context, it defaults to generic. The more context you provide, the better the output.

### Arithmetic and financial calculations

Language models are not spreadsheets. They can reason about numbers conversationally, but for actual calculations—margin analysis, pricing tables, forecasting—use a spreadsheet and feed the results to AI for narrative interpretation. Don't rely on it to multiply or percentage-calculate accurately.

### Real-time or proprietary data

AI doesn't have access to your catalog, your analytics, your customer list, or your supplier portal unless you paste that data into the conversation. It can work with data you provide—it just can't fetch it.

### Consistent factual accuracy

AI can confidently state things that aren't true. This is called hallucination. It's more likely to happen with specific facts, statistics, or anything where there isn't a lot of training data. Always review outputs before they go anywhere a customer might see them.

## The right mental model

Think of an AI tool as a very capable text worker who:
- Can write, edit, summarize, and format anything
- Works fast and doesn't get tired
- Needs clear instructions—vague requests produce vague results
- Doesn't know anything about your business unless you tell it
- Can be wrong, and won't always flag when it is

That framing makes it easier to know when to reach for AI (any high-volume, language-heavy task with clear criteria) and when not to (strategic decisions, anything requiring proprietary data you haven't provided, numerical calculations).

## What this means for ecommerce

The highest-value uses for ecommerce teams stay squarely in AI's strengths:

- Product content creation and reformatting (writing, rewriting, adapting)
- Customer feedback analysis (summarizing, extracting themes)
- Email and campaign drafting (variations, personalization copy)
- Data cleanup and standardization (normalizing inconsistent attributes)
- Process documentation (turning rough notes into SOPs)

The lowest-value uses are the ones that rely on AI's weaknesses: up-to-date competitor pricing, accurate margin calculations, or anything that requires accessing your actual store data without providing it first.

Ready to get better results? Read [Getting Better Results from AI](/learn/getting-better-results-from-ai/) next.

---

### Getting Better Results from AI

- URL: https://skillshelf.ai/learn/getting-better-results-from-ai/
- Description: Why your AI outputs might be underwhelming, and the practical techniques that reliably produce better work—without advanced prompting knowledge.
- Last updated: 2026-02-24

If you've used Claude or ChatGPT and found the results mediocre, you're not alone. The gap between underwhelming and genuinely useful output usually comes down to how you write your requests—not the tool itself.

You don't need to learn "prompt engineering" as a discipline. A handful of practical habits will get you most of the way there.

## Be specific about what you want

Vague requests produce vague results. This is the single biggest lever.

**Weak:** *"Write a product description for this jacket."*

**Stronger:** *"Write a 75-word product description for this jacket. The audience is outdoor enthusiasts who prioritize function over fashion. Focus on the waterproofing and packability. Avoid lifestyle language like 'adventure-ready.' End with a specific use case."*

The second version tells the AI what to write, how long to make it, who it's for, what to emphasize, what to avoid, and how to end. That's not a trick—it's just being clear about what you need.

## Give it context about your brand and customers

AI doesn't know anything about your business unless you tell it. Include relevant context at the start of any conversation where brand consistency matters:

- What your brand voice is like (and what it's not)
- Who your customer is
- What channel this is for
- Any specific terminology to use or avoid

You can create a short "brand context" block that you paste at the start of sessions:

> *We're a mid-market outdoor apparel brand. Our voice is direct, practical, and confident—never flowery or aspirational. Our customer is 35-55, buys on function, and doesn't respond to "adventure" or lifestyle messaging. We avoid superlatives. We prefer specifics: "fits in a jacket pocket" over "ultra-packable."*

The [Document Brand Voice skill](/skills/document-brand-voice/) can generate this block from your existing content, which you can then reuse across sessions.

## Show it an example of what "good" looks like

Examples are more effective than descriptions. If you have existing content you're happy with, include it:

> *"Here's a product description we like: [paste example]. Write three more descriptions in the same style for these products: [paste specs]."*

The AI calibrates to your example rather than its defaults. This works especially well when your brand has a distinctive voice that's hard to describe but easy to recognize.

## Treat it as a conversation, not a one-shot query

Most people write one request, get a result, and either use it or don't. That's not how it works best.

Treat it like a back-and-forth:

1. Ask for a first draft
2. Tell it what to keep, what to change, and what's missing
3. Ask for a revised version
4. Repeat as needed

*"Good start—make it shorter, cut the second sentence, and lead with the price point instead of the product name."*

You don't need to rewrite the entire prompt each time. Incremental refinement is faster.

## Tell it what format you want the output in

If you need specific output for downstream use, specify the format:

- *"Return the results as a markdown table with columns for Product Name, Meta Title, and Meta Description."*
- *"Give me the output as a numbered list."*
- *"Format this as a JSON object with keys: title, description, tags."*

Consistent output format makes it easy to paste results directly into a spreadsheet or system without reformatting.

## Common mistakes ecommerce teams make

**Asking it to make things "better" without defining better.** Better for who? In what way? Specify what you mean.

**Not providing the source data.** If you want a product description, paste the spec sheet or product attributes. Don't make it invent details.

**Accepting the first output.** The first draft is a starting point. One round of feedback usually produces significantly better results.

**Using it for tasks outside its strengths.** Asking AI to calculate accurate margin percentages or tell you what your competitors are charging right now will lead to disappointment. Stick to language tasks.

**Treating every conversation as independent.** Within a session, the AI remembers the conversation. Use that—give context once at the start, then build on it through the session rather than repeating yourself in every message.

## A practical workflow

For most ecommerce content tasks, this structure works well:

1. **Set context:** Brand voice, audience, channel (one paragraph)
2. **Provide the source material:** Product spec, data, or brief
3. **Make a specific request:** Include format, length, and any constraints
4. **Review and refine:** Give targeted feedback, ask for a revision
5. **Check before using:** Verify any specific claims, especially specs and features

Once you're comfortable with this approach, AI skills make it even easier—they handle the prompting structure for you. Read [Installing and Using AI Skills](/learn/installing-and-using-ai-skills/) to see how.

---

### Installing and Using AI Skills

- URL: https://skillshelf.ai/learn/installing-and-using-ai-skills/
- Description: What an AI skill is, how it differs from writing your own prompts, how to install one in Claude, and what to expect when you use it.
- Last updated: 2026-02-24

If you've been writing your own prompts to get things done with AI, skills are the next step. They do the prompt-writing for you—and usually do it better than a prompt you'd write yourself in five minutes.

## What a skill is

A skill is a pre-written set of instructions that you load into an AI tool to specialize it for a particular job.

When you install a skill, you're giving the AI a detailed briefing: here's what we're doing, here's how you should approach it, here are the questions you should ask, here's the format the output should take. That briefing was written and tested by someone who has done that specific task many times.

A good skill isn't just a long prompt—it defines a complete interaction flow. It knows when to ask you for information, what to do with that information, how to handle edge cases, and what the finished output should look like.

## How skills differ from prompts you write

When you write a prompt yourself, you're starting fresh each time. You have to remember to include context about your brand, specify the format, note what to avoid, and describe what good output looks like. This takes effort, and results vary.

A skill handles all of that. You provide your specific inputs—the product you're describing, the content samples for your brand voice—and the skill handles the rest of the interaction.

The other difference is testing. Skills on SkillShelf are reviewed by engineers who verify that the output quality holds up across different inputs. You're getting something that's been evaluated, not something someone wrote once and hoped worked.

## How to install a skill in Claude

Claude supports a feature called **Project Instructions** (sometimes called System Prompt or Custom Instructions, depending on the version you're using). Installing a skill means pasting the skill's instructions into that field.

**Step-by-step:**

1. Open the skill page on SkillShelf and copy the skill content (the full text of the SKILL.md file)
2. In Claude, create a new Project or open an existing one
3. Open the Project Instructions field (usually via the project settings or a pencil icon)
4. Paste the skill content into the instructions field
5. Save, then start a new conversation in that project

The skill is now active. When you start a conversation, Claude will behave according to the skill's instructions rather than its defaults.

You can create one project per skill, or combine a few related skills into a single project depending on how you work.

## What to expect when you use a skill

A well-designed skill will guide you through the process—you don't need to figure out what to provide or how to structure your request. The skill's introduction usually tells you what it needs from you.

For example, the [Document Brand Voice skill](/skills/document-brand-voice/) opens by asking you to paste 5–10 samples of your existing content. It then analyzes them, offers you three different interpretations of your brand voice to confirm which one fits, and generates a complete structured brand voice guide. You provide the inputs; the skill handles the rest.

Some things to keep in mind:

**It's still a conversation.** Skills streamline the interaction but don't eliminate it. You'll still need to review output, confirm choices, and provide feedback when something isn't right.

**Your inputs affect output quality.** The better the material you provide (richer product specs, more representative content samples), the better the output will be. Garbage in, garbage out still applies.

**You can modify the output.** Nothing the skill produces is final. Treat it as a high-quality draft that you edit, not a finished artifact.

**Skills are context-specific.** A skill built for writing Amazon listings will produce different results from a general product description. Use the skill designed for your specific use case.

## A note on other AI tools

Skills on SkillShelf use the open SKILL.md format. The instructions work with Claude, but the underlying approach is compatible with ChatGPT and other tools as well—you can paste the instruction content into a custom GPT or ChatGPT system prompt and get similar results.

## Start with a beginner skill

If this is your first time using a skill, start with a **Beginner**-level skill—these are designed for immediate use with minimal setup. The [Document Brand Voice skill](/skills/document-brand-voice/) is a good starting point because its output (a brand voice guide) is useful on its own and also feeds into other content skills.

Browse the [skill catalog](/) by category to find what fits your most pressing job.

---

### Writing Product Content with AI

- URL: https://skillshelf.ai/learn/writing-product-content-with-ai/
- Description: How to use AI to produce product descriptions, titles, and meta copy that actually sounds like you—and how to handle the brand voice problem.
- Last updated: 2026-02-24

Product content is one of the highest-volume, highest-repetition writing jobs in ecommerce. You need descriptions for hundreds or thousands of products, often across multiple channels, each with slightly different requirements. AI handles this kind of work well—if you set it up correctly.

## The brand voice problem (and how to solve it)

The most common complaint about AI-written product content is that it sounds generic. "Premium," "top-notch," "exceptional quality"—these phrases appear everywhere because that's what AI defaults to when it doesn't know better.

The fix is giving the AI your brand voice before you ask it to write anything. This isn't complicated, but it does require a little work upfront.

**Option 1: Write a voice brief.** Describe your tone in 2–3 sentences. Include what your brand sounds like and what it doesn't. Even a rough brief is better than nothing.

> *"Direct and functional, never aspirational. We describe products by what they do, not how they make you feel. Avoid adjectives like premium, luxury, or exceptional. Be specific: fabric weight over 'soft,' exact dimensions over 'compact.'"*

**Option 2: Use the Document Brand Voice skill.** Paste 5–10 samples of content you're happy with—existing product descriptions, email copy, anything that represents your voice—and let the skill extract a structured voice guide from them. This usually produces a more complete and reusable document than writing one yourself.

Once you have a voice guide, paste it at the start of every content session. The AI will calibrate to it rather than defaulting to generic.

## A worked example: product description from spec

Here's how a session might go for a new product description.

**You provide:**

> *[Paste your voice guide]*
>
> *Write a 100-word product page description for this jacket:*
> *- Shell: 3-layer GORE-TEX, 20,000mm waterproof rating*
> *- Weight: 312g (size M)*
> *- Packable: stuffs into its own chest pocket*
> *- Zipper pockets: 2 exterior, 1 interior*
> *- Fit: athletic, slightly shorter hem*
> *- Colors: slate, black, moss*
> *- Price: $248*

**AI returns a draft.** You review it.

**You respond:**

> *"Good structure. The second sentence is too listy—rewrite it as flowing copy. Move the weight earlier, it's a key selling point for our customer. Cut 'designed for' in the first line."*

**AI revises.** Usually the second draft is close to usable.

This whole exchange takes a few minutes. Writing from scratch or hunting for the right words takes much longer.

## Adapting for different channels

The same product often needs different copy depending on where it appears:

| Channel | Length | Emphasis |
|---|---|---|
| Product page | 100–150 words | Benefits + specs |
| Marketplace (Amazon) | 5 bullets + description | Features + keywords |
| Email/promotional | 30–50 words | Single hook |
| Meta description | 150–160 characters | Click-worthy summary |

Rather than writing four separate briefs, write one good product description and ask AI to adapt it:

> *"Using this description as the source, write: (1) five Amazon-style feature bullets, (2) a 40-word promotional email teaser, (3) a meta description under 155 characters."*

One input, four outputs. Review and adjust each one.

## SEO titles and meta descriptions at scale

For catalog-level SEO content, AI can generate meta titles and descriptions for dozens of products at once if you provide the product data in a structured format.

Paste a table of product names, categories, and key features, and ask:

> *"For each product in this list, write an SEO meta title (under 60 characters) and meta description (under 155 characters). Focus on the primary search intent for each category. Format the output as a table with columns: Product, Meta Title, Meta Description."*

This works reliably for straightforward catalog items. Products with more nuanced positioning—where knowing how they compete matters—benefit from individual attention.

## What to review before publishing

AI product content needs a human check before it goes live:

- **Verify all specs.** AI can't know your actual product specs—it only knows what you told it. Make sure measurements, materials, and features are accurate.
- **Check compliance claims.** Anything that makes a specific performance or safety claim needs verification.
- **Read it aloud.** If it sounds stiff or unnatural, revise it. AI can still produce awkward constructions even with good guidance.
- **Confirm brand alignment.** Even with a voice guide, the AI will occasionally slip into patterns that don't fit. Catch them before publishing.

## Relevant skills

The [Product Content category](/category/product-content/) has skills built specifically for these jobs—product descriptions, Amazon listings, SEO meta copy, and more. Each skill includes the interaction flow and output format. If you're doing this work regularly, a skill will save you setup time on every session.

---

### Using AI for Email and Lifecycle Campaigns

- URL: https://skillshelf.ai/learn/using-ai-for-email-campaigns/
- Description: How ecommerce teams use AI to write subject lines, draft campaigns, generate A/B variants, and move faster without sacrificing quality.
- Last updated: 2026-02-24

Email is high-volume, deadline-driven, and heavily dependent on writing quality. That's a good match for AI. Teams that use it well don't just produce drafts faster—they get more options to test, catch more edge cases before send, and free up time for strategy.

Here's how to put it to work on actual email jobs.

## Subject lines and preview text

Subject line writing is one of the easiest AI tasks to get right. AI is good at generating variations, and subject lines are short enough to review quickly.

Give it the context it needs:

> *"Write 10 subject line options for a 20% off sale email to our winter collection. Audience: women 30–50 who buy performance-oriented outdoor clothing. Our tone is direct, not hype-y. No emojis. Avoid 'Don't miss' and 'Last chance.' Pair each subject line with a preview text option (under 90 characters)."*

Then scan the list for the 3–4 worth testing. Usually you'll keep parts of a few and combine them rather than using one as-is.

For high-stakes sends, ask for subject lines across different angles—urgency, curiosity, benefit-led, specificity—so you're not A/B testing variations of the same approach.

## Brief to draft: a campaign workflow

For standard campaign emails, a simple brief-to-draft workflow gets you to a usable first draft in a few minutes.

**Step 1: Build a brief**

Answer these questions (even briefly):
- What's the goal of this email? (click to product page, redeem offer, read content)
- What's the offer or hook? (sale, new arrival, content, re-engagement)
- Who is the audience? (all subscribers, purchase-history segment, lapsing customers)
- What action should the reader take?
- Any restrictions? (tone, what to avoid, length limit)

**Step 2: Write the request**

> *"Write a promotional email for our spring collection launch. Goal: drive traffic to the new collection page. Audience: existing customers who've purchased from us in the last 12 months. Voice: [paste voice guide or brief description]. CTA: 'Shop the collection.' Length: short—two short paragraphs + CTA. No discount mentioned. New arrivals angle."*

**Step 3: Review and refine**

Read it as your customer would. Does the first sentence earn continued reading? Is the CTA clear? Does it sound like your brand? Give targeted feedback and iterate.

## Generating A/B variants

AI makes it easy to generate substantive variants—not just word swaps, but different structural approaches.

> *"Here's our control email: [paste email]. Write two alternative versions: one that leads with the product benefit instead of the brand story, and one that's 30% shorter with the same key message."*

These give you real variants to test rather than trivial differences. You can also ask for variants with different CTAs, different opening hooks, or personalized versus generic versions.

## Lifecycle and triggered emails

For lifecycle flows—welcome series, abandoned cart, post-purchase, re-engagement—AI can draft the full sequence from a brief.

> *"Write a 3-email abandoned cart sequence. The brand sells premium kitchen equipment. Voice: knowledgeable and warm, like a chef talking to a home cook. Email 1 (1 hour after abandon): soft reminder, no discount. Email 2 (24 hours): feature the product benefit more specifically. Email 3 (72 hours): add a small incentive (10% off). Each email should be short—under 100 words of body copy."*

Review the sequence as a whole: does the progression make sense? Is the tone consistent? Does the urgency build appropriately?

## Segmentation copy

Personalized email copy—where you vary messaging by segment—is tedious to write but easy for AI.

> *"We're sending a product launch email to three segments: (1) customers who bought the previous version of this product, (2) customers who browsed it but didn't buy, (3) new customers who've never bought in this category. Write a version of this email for each segment: [paste base email]. Keep the structure the same but vary the hook and first paragraph to match each audience's relationship with the product."*

Three tailored versions in the time it takes to write one.

## What to check before sending

AI-drafted email copy needs review before it goes to your list:

- **Check the CTA** links to the right place (AI can't set your links—you have to)
- **Verify any numbers** mentioned: prices, discounts, dates, stock claims
- **Read for tone** — AI can drift toward generic warmth that doesn't match your brand
- **Preview text matters** — make sure it works as a second subject line, not a preview of your logo alt text
- **Test the unsubscribe** and compliance elements (AI won't include these—you add them)

## The bigger opportunity

The teams getting the most value here aren't just using AI to write faster—they're using it to do work they weren't doing before. More A/B tests. More segmented copy. More lifecycle touchpoints. AI lowers the marginal cost of more email, which means more opportunities to learn what works.

See the [Email & Lifecycle category](/category/email-and-lifecycle/) for skills built around specific email jobs.

---

### Analyzing Customer Feedback with AI

- URL: https://skillshelf.ai/learn/analyzing-customer-feedback-with-ai/
- Description: How to use AI to turn raw reviews, survey responses, and support tickets into structured insight—without spending days in a spreadsheet.
- Last updated: 2026-02-24

Customer feedback is everywhere in ecommerce: product reviews, post-purchase surveys, NPS comments, support tickets, return reasons, social mentions. Most teams collect it; few have time to analyze it systematically. AI makes systematic analysis fast enough to actually do.

## What AI can do with feedback

AI is particularly good at:

- **Theme extraction** — identifying the recurring topics across hundreds of reviews
- **Sentiment analysis** — distinguishing positive, negative, and neutral mentions
- **Attribute-level breakdown** — what do customers say about fit? about packaging? about delivery?
- **Quote surfacing** — pulling specific language customers use that's worth capturing
- **Cross-product comparison** — how does feedback on Product A differ from Product B?

These are tasks that would take hours manually. With AI, they take minutes—and produce structured output you can share, not just personal notes.

## Starting simple: review summary

The easiest entry point is asking AI to summarize a batch of reviews.

Copy reviews from your product page, Shopify admin, Amazon Seller Central, or wherever you manage them. Paste them into a conversation and ask:

> *"Here are 50 customer reviews for our fleece jacket. Summarize: (1) the top 5 things customers praise, (2) the top 5 complaints or concerns, (3) any patterns you notice about who's buying it or how they're using it."*

You'll get a useful summary in under a minute. This is good for quarterly product reviews, merchandising decisions, or quickly getting up to speed on a product you haven't been tracking closely.

## Going deeper: attribute-level analysis

For more granular insight, give AI a framework to organize around.

> *"Analyze these reviews and break down sentiment for each of the following attributes: fit/sizing, fabric/feel, durability, color accuracy, packaging, and delivery experience. For each attribute, note whether feedback is predominantly positive, mixed, or negative, and pull 2–3 representative quotes."*

This produces a structured output that's immediately useful for product teams, buyers, or anyone responsible for that product's performance.

## Survey and NPS analysis

Open-ended survey responses and NPS comments are harder to analyze than reviews because they don't follow a predictable structure. AI handles this well.

Export your survey responses to a spreadsheet, copy the open-text column, and paste it:

> *"Here are open-text responses from our post-purchase survey. Most respondents rated their experience 7 or higher. For those who rated 6 or lower (flagged with [LOW]), extract: (1) the primary reason for dissatisfaction, (2) any specific product or service named, (3) any suggestions they offered. For all responses, identify the top 5 themes in what customers say they valued most."*

You can do this segmentation and analysis manually, but it takes hours. With AI it takes minutes.

## Comparing feedback across products

When you have feedback for multiple products, AI can identify what's consistent (a brand-level issue) versus what's product-specific.

> *"Here are reviews for three products in our boot category: [Product A reviews], [Product B reviews], [Product C reviews]. Identify: (1) feedback themes that appear across all three products, (2) feedback themes unique to each product, (3) any patterns in how customers describe sizing across the three."*

This kind of analysis is valuable for buying decisions, product development feedback, and category-level content updates.

## Extracting the voice of the customer

One of the highest-value uses of feedback analysis is pulling the exact language customers use to describe your products and their experience. This language is gold for copy.

> *"From these reviews, extract the specific phrases and words customers use to describe how this jacket feels to wear. I'm looking for their natural language, not a summary—the actual words they choose."*

You'll end up with phrases like "not too puffy," "moves with me," "doesn't feel like a sleeping bag"—language that's more credible and specific than what a copywriter invents. Feed this into your product content sessions.

## Building a reusable VOC document

If you analyze feedback consistently across products, you can build a living VOC (voice of customer) document that synthesizes what you've learned. AI can help structure and maintain this.

> *"Here's what we've learned from customer feedback over the last quarter across our jacket category: [paste summaries]. Write a structured VOC summary that covers: (1) what customers most value in this category, (2) the most common pain points, (3) the language they use to describe what they want, (4) any unmet needs that came up more than once."*

This document becomes useful input for product briefs, marketing planning, and content strategy.

## Limitations to keep in mind

**AI can miss nuance.** Sarcasm, cultural references, and context-dependent meaning can be misread. Review the AI's conclusions against a sample of the raw data.

**Skew in your data affects the analysis.** If most reviewers are highly satisfied customers (which is common—unhappy customers return products, not reviews), your analysis will reflect that skew. Acknowledge it when sharing findings.

**AI can't access your review platforms directly.** You'll need to export or copy the data. For high-volume analysis, a CSV export from Shopify, Yotpo, or your review platform is more manageable than copy-pasting.

## Relevant skills

The [Customer Research & Voice of Customer category](/category/customer-research-and-voice-of-customer/) has skills built for systematic feedback analysis. If you're doing this work regularly, a skill will give you a consistent, structured approach rather than reinventing the prompt each time.

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