Persona Development Tools

A new open-source Python library called TinyTroupe is here to redefine how we simulate human behavior using LLMs, advancing the field of AI agents. TinyTroupe allows you to create TinyPersons – simulated agents with unique personalities, goals, and interests – capable of interacting within custom TinyWorld environments. Unlike other LLM-based simulation approaches that focus on gaming, this library targets business scenarios, creating and interacting with AI-powered personas to test products, ads, and ideas before spending real money. Think running a focus group with AI-powered physicians, lawyers, or knowledge workers. The library enables diverse applications, from evaluating digital campaigns with simulated audiences and running AI-powered focus groups at scale to generating realistic test inputs for software, collecting requirements from specific personas, and creating domain-specific training datasets. This work could accelerate research in autonomous AI agents by providing a controlled environment to study agent-to-agent and human-to-agent interactions, such as in customer support and sales. Code and examples https://lnkd.in/g9TqYiVZ P.S. I've just open-sourced Voice Lab, a framework to evaluate LLM-powered agents across different models, prompts, and personas https://lnkd.in/gAaZ-tkA

Persona work is exhausting. You’ve got hundreds of sales calls recorded in Gong and somewhere in there are the insights your exec team needs to make critical business decisions. You spend the week digging through call recordings, listening to call snippets, copy-pasting customer quotes into Docs, trying to get revops to give you data from the CRM on personas tagged to historical opportunities, tabbing through spreadsheets, and creating a slide deck with everything you’ve learned. And in that time 40 new sales calls with that target persona have taken place. Those same insights need to be refreshed for Monday's board meeting.  Most "AI-powered marketing platforms" don’t solve these kinds of problems. Certainly not the “AI CMO.” They'll surface generic insights you can get from a web search. The real gold is still only found via spending days manually digging through six tools. The raw intelligence only YOUR company has. This is where WRITER is different. Last month, our product marketing team asked WRITER to build an interactive persona application for them. It listened to 350 Gong calls, cross-referenced with opportunity data in our CRM, filled in gaps where opportunities were sponsored by the persona but NOT tagged on the opportunity, generated the insights, and built a dashboard (WRITER voice, formatted) in a SINGLE flow. 2 weeks of work → 35 minutes of WRITER working autonomously. And when 40 new calls come in tomorrow? That dashboard is automatically updated, at a link the whole marketing team can access. Your personas stay CURRENT, not static. WRITER works end-to-end across your systems – Snowflake, HubSpot, Google Docs, Asana, Slack, Gong, and more – actually executing instead of just assisting. Which means you get to spend the bulk of your time on strategy, creativity, and driving the business forward. We don’t need AI CMOs. We need to help people go from 20% strategy / 80% execution to 80% strategy / 20% execution.

Many Product teams think they enable Sales. They don’t. Why? They educate reps about “us.” Product one-pagers Feature-by-feature competitor grids Vague positioning (“We’re the most flexible”) Generic benefits (“We help you grow revenue”) None of that helps sellers because buyers don't care about your product.  They care about themselves and their problems. One way to help enterprise reps connect with buyers is to train them to empathize deeply with buyer personas. It starts with a great Persona Guide that contains things like: A day-in-the-life Core responsibilities How they are measured Top problems  Questions to engage them Stories that resonate Once you have a solid Persona Guide, you can turn it into multi-media training tools in a few minutes using tools like Google’s Notebook LM. I did this recently for a client. Here's exactly how I do it: 1️⃣ Create a structured Persona Guide template in Google Docs Example: https://lnkd.in/eK9JGhaY 2️⃣ Use Claude to draft the Guide by feeding it the template, company docs, competitor sites, industry research, primary studies, case studies, and more 3️⃣ Iterate and refine to remove anything generic or unsupported by evidence 4️⃣ Finalize and upload to Google Notebook LM 5️⃣ In minutes, Notebook LM turned it into: A podcast episode (16 mins) - perfect for listening in the car or while exercising https://lnkd.in/epvhPEPc A short training video (7 mins) - brings the persona guide alive https://lnkd.in/e54D3Dr4 A slide deck - for people who like pictures AND reading https://lnkd.in/etWEHx8P A quiz - to test your knowledge https://lnkd.in/eAd9Dddp A set of flashcards - to practice  https://lnkd.in/e_fuRYYP An infographic - to distill it down simply https://lnkd.in/ePQ9STdE Now reps don’t just read about the product. They empathize with the persona. They hear it and see it. They test themselves on it. They internalize it. That’s how you build resonance. When reps truly understand their buyers' world... They ask better questions. They tell relevant stories. They earn credibility faster. They close more deals. Product knowledge is table stakes. Buyer empathy is what wins. And this is just the first step. You can use Persona Guides to create Pain-based Points-of-View for prospecting Discovery questions that resonate Business case templates that speak the persona’s language And much more 👇 What have you done to help sellers empathize with buyers and their problems?

🔬 Meet TinyTroupe: Microsoft's experimental multi-agent Python library that makes AI-powered persona simulation easy and quick. Imagine having a virtual team available 24/7, complete with customizable personalities, interests, and real-world behaviors. [Repo in the comments] What sets it apart technically: 1. Advanced Persona Engine - Creates "TinyPersons" with persistent memory and consistent behaviors - Fully customizable attributes: age, occupation, skills, preferences - Each agent maintains unique views and biases, unlike traditional AI assistants - Enables realistic multi-agent interactions in controlled environments 2. Business-Focused Use Cases - Ad campaign testing before spend - Software usability evaluation with synthetic users - Training data generation for AI models - Product feedback from simulated industry experts - Virtual focus groups for brainstorming The game-changer? Unlike frameworks like AutoGen, TinyTroupe specializes in simulating human behavior rather than task automation. Each agent brings their own background, pain points, and decision-making patterns to the table. Real-world impact: One simulated focus group provided 47 unique product insights in a single session, with machine-readable output for automated analysis. 🤔 Question for the architects: How would you approach validating the authenticity of simulated personas against real-world behavior patterns? #ArtificialIntelligence #ProductDevelopment #Innovation #Microsoft #OpenSource #UserResearch

Breaking into new personas in 2025? Here's how to leverage AI to build persona-based messaging. ⛔️ Mistake: Don't wing it with new personas. Don't set up your reps for failure. ✅ Step 1: Gather great data Persona creation is garbage in / garbage out. Feed AI with solid info: - Transcripts of sales calls - Competitor content - Key influencers to follow - Transcripts of customer calls ✅ Step 2: Feed into AI I like ChatGPT. But this can work with the others. Leverage this prompt: Take the attached [sales call transcripts, case studies, etc] and turn this into an Outbound Squad Messaging Matrix. The messaging should be written using the customer’s voice. This messaging matrix should be formatted into a table with these four columns: 1) Priorities Format this into a statement like this: [headline]. [outcome] + [avoid problem]. - Headline: What is top of mind for your prospect’s peers? Imagine you have a dozen of your prospects gathered in a room. All working at similar companies in the same role. What is top of mind for that entire group right now? What trends are they worried about or focused on? What do they want your help with? - Outcome: What outcomes do they want? What are the specific outcomes, metrics, or KPIs they want to improve? - Avoid problem: What problem do they want to avoid? What problem are they hoping to address or solve? Here's an example: Skill gaps & staffing. Find and attract the right talent to accomplish our IT business goals—while avoiding unnecessary costs and project delays. 2) Current solutions Now think about how the prospect is getting the job done. People: Are they hiring, reducing headcount, etc? Process: Are they implementing a specific process? Technology: Are they using technology? A competitor? 3) Problems Problems are what get in the way of priorities. This is what your prospect hopes their current solution will help with. This sounds like: “Manually processing payroll is labor intensive and frustrating for me.” But get to the impact on the business. This sounds like: “Our team is manually processing payroll across multiple systems. We need to hire extra employees just to handle the manual work, and we can’t hire as quickly as we need to. We won’t hit our hiring targets this year.” Help me define the problem in the customer's voice. 4) Aspirations This is your prospect’s desired future state. These should be similar to the outcomes your solution provides to your customers. ~~~ This is for: [company name] who sells [solution] to [persona]. Example clients of theirs are [insert examples] ✅ Step 3: Validate findings with real buyers NEVER rely on AI alone. - Take this to similar personas at your org - Take it to board members - Hire industry-expert consultants - Validate with customers ~~~ Leverage this approach to quickly build persona-based messaging to help your outbound/selling efforts. Was this helpful? Tag someone on your team who could benefit from this.

Telling an LLM to "act as an expert" is lazy and doesn't work. I tested 47 persona configurations across Claude, GPT-5, and Gemini. The results were mathematically significant: • Generic Personas ("Act as a marketer"): 60% Quality. • Specific Personas ("Act as a B2B SaaS VP"): 94% Quality. Here is the problem: When you type "Act as a marketer," the AI averages every marketer on the internet. It blends Seth Godin with a college intern. You get the average. To get "Consultant-Level" output, you need to constrain the model. I developed a 5-Element Framework that forces the AI to niche down. 1. Role + Seniority Don't say "Developer." Say "Senior Backend Engineer with 8 years in distributed systems." Seniority dictates decision-making. 2. Industry Context A "Product Manager" in crypto thinks differently than a "Product Manager" in healthcare. Define the domain. 3. Methodologies Give it a brain. "You use the Jobs-To-Be-Done framework and prioritize shipping over perfection." 4. Constraints (Crucial) Real experts work with limits. "You have a $50k budget and a 6-week timeline." Without constraints, the AI hallucinates success. 5. Output Format Don't ask for "an analysis." Ask for "a 2-page executive brief with 3 strategic options and a risk assessment." I packaged this framework + my top 23 pre-built personas into a cheat sheet. It's called "The Advanced Persona Architect." Want the PDF? Like and comment "PERSONA" below. I'll send it over.

⚡️ Using LLM-Generated Personas to Evaluate Landing Pages via Semantic Similarity Rating 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Marketers and product teams have relied on focus groups, surveys, and A/B testing to understand buyer intent for years. They're expensive, slow, and difficult to scale. 𝗧𝗵𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵: A recent paper from PyMC Labs introduces Semantic Similarity Rating (SSR) — validated against 57 consumer surveys and 9,300+ human responses, the methodology shows LLM-generated personas can replicate human purchase intent with roughly 90% reliability. After digging into it, I wanted to see what happens when you apply SSR to real-world marketing and SEO challenges. All of this led to building an R&D prototype with LOCOMOTIVE Agency that evaluates how landing pages perform across synthetic buyer personas while also analyzing brand visibility in Google AI Overviews. 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗱𝗼𝗲𝘀: 𝗔𝗜 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Identifies brands appearing in AI Overviews for a product category and scores their visibility and recommendation strength (salience) 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗣𝗲𝗿𝘀𝗼𝗻𝗮 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻: Generates buyer personas (b2b or b2c) and has each one evaluate landing page content against their purchase criteria 𝗗𝘂𝗮𝗹 𝗦𝗰𝗼𝗿𝗶𝗻𝗴: Compares implicit sentiment (SSR) with explicit free-text responses (FLR) to surface gaps between what a page says and how it's actually perceived by users 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗚𝗮𝗽𝘀: Surfaces recurring messaging strengths/weaknesses of landing pages across personas and competitors 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗼𝗿𝘁: Complete datasets available for deeper analysis and team workflows I'm especially interested in seeing how this methodology performs across different industries and product categories. If you're working in product marketing, SEO, competitive intelligence, or product development, I'd love to hear your thoughts. 📖 Learn more about the research: https://lnkd.in/g87FVveS 🛠️ Try the tool: https://lnkd.in/gNFiD3pR Special thanks to Matthew Kay and others for testing the tool and providing thoughtful feedback during development. NOTE: This is an R&D prototype, not a replacement for real customer research. The goal is faster, scalable, directional feedback to help teams prioritize what to test. The tool is not perfect, and there are limitations due to web scraping. If you have any questions regarding how to best utilize the tool for your own research, send me a DM.

13 years in B2B marketing. And I’ve fought the good fight. Sales has always been my internal customer, and I’ve spent countless hours trying to build buyer personas that actually help close deals. But let’s be real—most of the time, these personas feel off. Why? Because we’ve been stuck using outdated methods. The buyer personas we build are usually based on slides, interviews with our happiest customers, or a few assumptions about what our buyers care about. And then they sit in a folder, instantly outdated the moment they’re created. The truth is people change their minds. A lot. What worked last quarter doesn’t always work now, and we’ve got to keep up. That’s why I’m excited about Live Personas from Replicate Labs. They’ve created something I wish I had years ago—AI-powered buyer personas built from real-time data and enriched with the knowledge we have in-house. No more guessing. Now, you can actually interact with your persona, ask questions, and get answers based on what’s happening right now. Imagine doing roleplays with a prospect that’s built from real-time LinkedIn content and insights. That’s a game-changer. This is how we stay ahead, stay relevant, and win. If you’re tired of outdated personas and want to see how Live Personas can make a difference, check out this short video. Replicate Labs has just raised the bar for sales and marketing alignment. Again.

𝗔𝗿𝗲 𝘆𝗼𝘂 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘁𝗼 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗱𝗲𝗳𝗶𝗻𝗲 𝘄𝗵𝗼 𝘆𝗼𝘂𝗿 𝘁𝗮𝗿𝗴𝗲𝘁 𝗮𝘂𝗱𝗶𝗲𝗻𝗰𝗲 𝗶𝘀? AI chatbots can help you create customer personas, an essential part of customer discovery. When I work with clients to help them define their target audience, I use AI tools like Microsoft Pilot to generate detailed customer personas. This helps entrepreneurs understand who they serve and how to meet their needs. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁: 1️⃣ Type this prompt into an AI-powered chatbot like Claude, ChatGPT, or Pilot: "𝘠𝘰𝘶 𝘢𝘳𝘦 𝘢 𝘮𝘢𝘳𝘬𝘦𝘵 𝘳𝘦𝘴𝘦𝘢𝘳𝘤𝘩𝘦𝘳. 𝘐 𝘸𝘪𝘭𝘭 𝘱𝘳𝘰𝘷𝘪𝘥𝘦 𝘺𝘰𝘶 𝘸𝘪𝘵𝘩 𝘢 𝘱𝘳𝘰𝘥𝘶𝘤𝘵, 𝘴𝘦𝘳𝘷𝘪𝘤𝘦, 𝘰𝘳 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘪𝘥𝘦𝘢 𝘢𝘯𝘥 𝘢 𝘱𝘳𝘰𝘣𝘭𝘦𝘮 𝘐 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘴𝘰𝘭𝘷𝘦 𝘧𝘰𝘳 𝘮𝘺 𝘵𝘢𝘳𝘨𝘦𝘵 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳. 𝘗𝘭𝘦𝘢𝘴𝘦 𝘥𝘰 𝘵𝘩𝘦 𝘧𝘰𝘭𝘭𝘰𝘸𝘪𝘯𝘨: 𝘗𝘳𝘰𝘷𝘪𝘥𝘦 2 𝘵𝘰 5 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘴, 𝘵𝘩𝘦𝘪𝘳 𝘯𝘦𝘦𝘥𝘴, 𝘴𝘵𝘰𝘳𝘪𝘦𝘴, 𝘦𝘮𝘰𝘵𝘪𝘰𝘯𝘴, 𝘢𝘯𝘥 𝘮𝘰𝘵𝘪𝘷𝘢𝘵𝘪𝘰𝘯𝘴 𝘢𝘯𝘥 𝘸𝘩𝘦𝘳𝘦 𝘐 𝘤𝘢𝘯 𝘧𝘪𝘯𝘥 𝘵𝘩𝘦𝘮. 𝘊𝘳𝘦𝘢𝘵𝘦 𝘢 𝘵𝘢𝘣𝘭𝘦. 𝘈𝘴𝘬 𝘧𝘰𝘳 𝘧𝘦𝘦𝘥𝘣𝘢𝘤𝘬 𝘰𝘯 𝘵𝘩𝘦 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘴, 𝘱𝘳𝘰𝘮𝘱𝘵𝘪𝘯𝘨 𝘪𝘧 𝘵𝘩𝘦𝘳𝘦 𝘪𝘴 𝘢𝘯𝘺𝘵𝘩𝘪𝘯𝘨 𝘵𝘰 𝘢𝘥𝘥, 𝘳𝘦𝘮𝘰𝘷𝘦, 𝘰𝘳 𝘦𝘥𝘪𝘵." 2️⃣ The AI chatbot will then prompt you with: "𝘗𝘭𝘦𝘢𝘴𝘦 𝘱𝘳𝘰𝘷𝘪𝘥𝘦 𝘮𝘦 𝘸𝘪𝘵𝘩 𝘢 𝘱𝘳𝘰𝘥𝘶𝘤𝘵, 𝘴𝘦𝘳𝘷𝘪𝘤𝘦, 𝘰𝘳 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘪𝘥𝘦𝘢 𝘺𝘰𝘶'𝘳𝘦 𝘸𝘰𝘳𝘬𝘪𝘯𝘨 𝘰𝘯, 𝘢𝘭𝘰𝘯𝘨 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘱𝘳𝘰𝘣𝘭𝘦𝘮 𝘺𝘰𝘶 𝘢𝘳𝘦 𝘴𝘰𝘭𝘷𝘪𝘯𝘨 𝘧𝘰𝘳 𝘺𝘰𝘶𝘳 𝘵𝘢𝘳𝘨𝘦𝘵 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳.." 3️⃣ Answer this question for the AI chatbot. 4️⃣ After you answer the question, the AI chatbot will return with a list of customer personas you can use to better understand your audience. When I used this prompt for my holistic business advising services, here are two persona names with needs the AI chatbot generated: 1️⃣ Resilient Rachel: ↔️ Lost job ↔️ Needs help starting her business ↔️ Needs guidance managing stress 2️⃣ Growth-seeker Gary: ↔️ Solopreneur consultant ↔️ Needs help transitioning from solopreneur to business owner ↔️ Needs strategies for scaling These personas can guide your marketing strategies, workshop content, and customer engagement efforts. If you want to refine your customer personas, AI can be a helpful tool. Are you ready to give AI a try in refining your customer personas? Share the name of a sample customer persona you've created.