Most MSPs already have valuable sales and marketing language sitting in public reviews.
Customers tell you what they noticed, what changed after hiring you, what frustrated them before, and what they value enough to mention without being prompted. That is far more useful than asking AI to invent pain points for an imaginary buyer.
You can collect those reviews in a spreadsheet, give them to a free AI tool, and use the results to improve website copy, sales collateral, case study planning, and objection handling. The process is simple, but one rule matters: AI can organize what customers actually said. It cannot turn a vague compliment into a claim a customer never made.
Start with public reviews you already have
Use public reviews or testimonials you have permission to use. Google Business Profile is a good starting point for many MSPs. You can also include website testimonials and published case studies in the same file.
Open a spreadsheet, such as Google Sheets, and create these columns:
- Customer or reviewer name, if publicly shown
- Review date
- Star rating, if applicable
- Exact review text
- Source URL
- Service mentioned
- Notes for anything that requires later verification
Do not rewrite reviews during collection. Copy the language exactly as written. If a review says, “They answer the phone,” do not change it to “always-on rapid-response support.” Those are different claims.
You also do not need hundreds of reviews to get started. Begin with what you have. The goal is to make customer feedback searchable before deciding what your marketing should emphasize.
Use AI to identify patterns and themes
Once your reviews are in a spreadsheet, copy a manageable batch into ChatGPT Free. Do not paste private ticket notes, customer records, contracts, or other proprietary information into a public AI tool. Public reviews work well because customers have already chosen to share that information publicly.
Give the model a focused task. For example:
Analyze only the customer reviews below. Do not add facts or infer results that are not stated. Create a table with the exact customer phrase, problem or concern mentioned, outcome mentioned, service or capability mentioned, and a suggested marketing theme. If a review does not support one of those fields, leave it blank. Keep every quoted phrase exactly as written and identify which review it came from.
Then paste the reviews below the prompt.
That instruction is important. If a review says an MSP is “easy to reach,” the model should not turn that into “guaranteed five-minute response times.” You are gathering evidence, not creating stronger claims.
Repeat the process with additional batches if needed, then add the results back into your spreadsheet.
Build a proof library for sales and marketing
Now sort the results by recurring themes so sales teams can reference them during prospecting.
You may find customers repeatedly mention responsiveness, communication during outages, onboarding support, security guidance, Microsoft 365 expertise, or the ability to explain technical issues clearly.
Avoid deciding in advance what you want customers to value. Let the reviews reveal the patterns.
Create a second spreadsheet tab with these columns:
- Theme
- Supporting review excerpts
- Source links
- Number of reviews supporting the theme
- Where the proof could be used
- Whether customer approval is needed before using the claim outside the original review
Now your sales team has evidence to answer questions such as, “What are you actually better at?” Marketing teams gain authentic customer language for service pages, and writers have a stronger starting point for case study interviews.
Turn customer insights into stronger messaging
The AI output is not your finished marketing copy. Use it to identify what deserves more attention.
If several customers praise response times, compare those comments with your website messaging. You may discover that a key differentiator is buried or overlooked.
If reviews consistently mention clear communication, highlight that strength on service pages, proposals, or sales decks. If multiple clients describe the same challenge before hiring you, their language can help you write stronger problem statements.
Reviews can also expose weaknesses in your marketing.
For example, your homepage may emphasize “strategic technology leadership,” yet customers never mention strategy, leadership, or planning. That does not automatically mean your positioning is wrong. It does mean you should investigate the gap rather than asking AI to validate your existing assumptions.
You can also use your review library when planning case studies. Look for customers who describe both a specific problem and a measurable improvement. That creates a much stronger interview starting point than a generic questionnaire.
Make AI show its work
Never publish an AI-generated summary of customer feedback without checking the original reviews.
Every insight should link back to a source URL and the exact wording in your spreadsheet. If the model groups five reviews under “fast response,” verify that all five support that conclusion. If it suggests a statistic such as “most customers mention responsiveness,” count the reviews yourself before using the claim.
This is why the spreadsheet matters. Instead of dumping reviews into a chatbot and asking what customers like about your business, you create a source trail that makes verification easy.
The AI handles the sorting. You decide what the evidence means and where it belongs in your sales process.
If you need help organizing a broader strategy around those messages, this free MSP marketing guide is a helpful place to start.
If you want support building a sales and marketing process around what buyers actually care about, schedule a call with Carrie Richardson.
Photo: Sorapop Udomsri/ Shutterstock
This post originally appeared on Smarter MSP.