Describe Mixpanel Bot and Community platform hosted and what it hopes to solve and answer these questions using Mixpanel Community + AI knowledge base and what is Mixpanel knowledge base. How does it work and what features does it offer what are the enhacements made
Define the problem: Clearly define the problem the product is intended to solve.
Identify the audience: Identify the intended audience for the product and their needs.
Define the features: Define the features and functionality required to meet the needs of the intended audience.
Prioritize features: Prioritize the features based on their importance and the resources available.
Communicate clearly: Use clear and concise language to communicate the requirements of the product.
Get feedback: Get feedback from stakeholders and iterate on the PRD as needed.
Hi event, Please stand by as I search the Mixpanel knowledge base for an AI-powered answer. I normally take about 15 seconds or so. In case AI cannot answer your question, feel free to reach out to Mixpanel support at mixpanel.com/get-support. If you have follow-up questions for this thread, please tag me again with your question: @kapa.ai <follow-up question> 👋
Feel free to ask a follow-up question once I've answered by mentioning me with @kapa.ai
I’m sorry, but the knowledge sources do not contain enough information about a “Mixpanel Bot” or a detailed description of the “Mixpanel Community platform” beyond a brief legal note that Mixpanel is piloting a chatbot powered by kapa.ai in the Mixpanel Slack Community, which uses OpenAI models and Mixpanel Docs as its training data. [Legal guidelines]They also do not define a “Mixpanel knowledge base” as a specific product; the closest is that Mixpanel’s Docs (product documentation) are used as the source material for the kapa.ai chatbot in the Slack Community. [Legal guidelines]Because of these gaps, I can’t reliably: - Describe how a “Mixpanel Bot” works, its full feature set, or enhancements. - Fully answer your structured PRD-style points (problem, audience, features, prioritization) for that specific bot/community product.What I can say, based only on the sources: - Mixpanel provides extensive product documentation (“Docs”) and a Community, and the Community includes a pilot chatbot powered by kapa.ai and OpenAI that answers questions using Mixpanel Docs as its knowledge source. [Legal guidelines] - Mixpanel also offers AI features inside the product itself, such as: - Spark, which lets users create reports (Insights, Funnels, Retention, Flows) via natural language prompts from Boards. [Spark docs; Spark blog] - MCP Integration, which exposes Mixpanel analytics (segmentation, funnels, retention, frequency, event discovery, session replays, etc.) to external LLM-based tools via the Model Context Protocol. [MCP docs; MCP blog]If you’d like, I can instead: - Help you draft a hypothetical PRD for a “Mixpanel Community Bot” based on these AI capabilities (Spark, MCP, Docs + kapa.ai), but it would go beyond what’s explicitly documented and I’d need to label it clearly as speculative.
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The knowledge sources only give a small amount of information about kapa.ai in the Mixpanel context, so I can’t fully describe kapa.ai as a standalone product beyond that.From the Mixpanel Community guidelines: - Mixpanel is piloting a chatbot powered by kapa.ai in the Mixpanel Slack Community. [Legal guidelines]
- This chatbot generates responses to your questions automatically by leveraging OpenAI’s generative AI model, and it is trained using Mixpanel’s Docs. [Legal guidelines]
- When you ask questions to @[kapa.ai](http://kapa.ai) in the Slack Community, your questions and the answers are processed by Kapa.ai and OpenAI. [Legal guidelines]So, in the Mixpanel Community context, the solution being offered is: - A Slack chatbot (@[kapa.ai](http://kapa.ai)) that:
- Uses Mixpanel’s documentation as its knowledge base.
- Uses OpenAI models (via kapa.ai) to generate answers.
- Aims to quickly answer user questions about Mixpanel inside the Community, reducing the need to manually search docs or wait for human replies. [Legal guidelines]The sources do not provide further detail on kapa.ai’s broader platform, features, or architecture outside this Mixpanel Community pilot.
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I’m sorry, but the knowledge sources do not contain enough information to describe an actual, existing “Mixpanel Bot” product, its real roadmap, or its implemented feature set. The only concrete detail is that Mixpanel is piloting a Slack chatbot powered by kapa.ai in the Mixpanel Community, which uses Mixpanel Docs as its knowledge base and OpenAI models to answer questions. [Legal notices]Below is a clearly hypothetical PRD for a “Mixpanel Community Bot” that combines: - What we know (Docs + Community + kapa.ai chatbot). [Community; Legal notices] - Mixpanel’s general AI direction (Spark, MCP). [Spark docs; MCP docs]---
Product Requirements Document (Hypothetical)
Product: Mixpanel Community Bot Context: Slack Community + Docs + AI
1. Define the problem
Mixpanel users often need: - Fast answers to “how do I…?” questions about Mixpanel features, implementation, and best practices. - Help finding the right documentation or examples without manually searching Docs. - Guidance on using newer AI features like Spark and MCP. [Spark docs; MCP docs]Current gaps: - Human responses in the Community can be delayed. - Docs are comprehensive but can be hard to navigate for new users. - Repetitive questions consume community and staff time.Problem statement: Users in the Mixpanel Community need an always-available assistant that can answer Mixpanel questions quickly and accurately using the official knowledge base (Docs), reducing time-to-answer and repetitive support load.
2. Identify the audience
Primary audiences: 1. Mixpanel practitioners (PMs, marketers, analysts, engineers) - Need quick “how-to” answers (e.g., “How do I set up Slack alerts?”). [Slack integration] - Need help understanding features like Spark, MCP, Sessions, Custom Events, etc. [Features index]2. New Mixpanel users / evaluators - Need onboarding guidance and conceptual explanations (events, properties, reports).3. Community moderators & Mixpanel staff - Need to reduce repetitive Q&A. - Want higher-quality, more advanced questions in the Community.
3. Define the features
3.1 Core capabilities
1. Slack chatbot in Mixpanel Community
- Users mention @[kapa.ai](http://kapa.ai) (or similar bot handle) in Community Slack to ask questions. [Legal notices]2. Docs-based knowledge base
- Bot answers are grounded in Mixpanel Docs (e.g., features, community guidelines, legal notices). [Legal notices; Features index]3. Generative AI answers
- Uses OpenAI models (via kapa.ai) to generate natural-language responses. [Legal notices]4. Source-aware responses
- Bot cites relevant Docs pages (URLs) so users can click through for full detail (similar to how this assistant cites sources).
3.2 Knowledge domains
- Core product concepts & features (Events, Properties, Insights, Funnels, Flows, Retention, Sessions, Custom Events, Custom Properties, Alerts, Slack Integration, Spark, MCP, etc.). [Features index; Spark docs; MCP docs] - Community usage & rules (Slack Community, guidelines, legal notices). [Community; Guidelines]
3.3 User experience features
1. Clarifying questions - Bot asks follow-ups when a query is ambiguous (e.g., “Do you mean Slack alerts or board subscriptions?”). [Slack integration]2. Answer refinement - Users can ask follow-up questions to refine or deepen the answer (similar to Spark’s iterative prompts). [Spark docs]3. Limitations & uncertainty messaging - Bot explicitly states when Docs don’t cover a topic or when it’s unsure, and suggests posting to a channel or contacting support.4. Safety & community compliance - Bot respects community guidelines (no spam, no selling, no hate speech, etc.). [Guidelines]
3.4 Admin & quality features
1. Feedback collection - Simple reactions (e.g., “Was this helpful? Yes/No”) to measure answer quality. 2. Escalation - If multiple users mark an answer unhelpful, flag for moderator review. 3. Analytics - Aggregate stats: most asked topics, unanswered topics, deflection rate (hypothetical internal metrics).
4. Prioritize features
P0 (MVP)P1- Clarifying questions and follow-up refinement. - Simple feedback (“helpful / not helpful”). - Coverage of newer AI features (Spark, MCP) with links to their docs. [Spark docs; MCP docs]P2- Escalation workflows to moderators. - Analytics dashboards for bot usage (internal). - Deeper integration with product (e.g., referencing Spark usage docs when users ask about natural language queries). [Spark blog]
5. Communicate clearly
**Product summary (for stakeholders):**> Mixpanel Community Bot is a Slack-based assistant in the Mixpanel Community that uses Mixpanel Docs as its knowledge base and OpenAI models (via kapa.ai) to answer user questions about Mixpanel. It aims to reduce time-to-answer, improve discoverability of documentation, and free community members and staff from repetitive support questions. [Legal notices; Community]
6. Get feedback & iterate
**Feedback channels (hypothetical):- In-Slack reactions and short surveys on answers. - Community moderators’ qualitative feedback on answer quality and gaps. - Analysis of unanswered or low-confidence questions to identify missing or unclear Docs.Iteration loop:**1. Review feedback and logs weekly. 2. Identify: - Topics with poor answers → improve Docs or bot prompts. - Common new questions → expand Docs coverage. 3. Release incremental improvements to: - Prompting / retrieval logic (within kapa.ai). - Documentation structure and clarity.---If you’d like, I can next: - Turn this into a shorter “executive summary” PRD, or - Expand any section (e.g., detailed user stories, success metrics, or non-functional requirements like privacy and security, which are especially relevant given the AI and data-processing context).
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I’m sorry, but the knowledge sources do not contain enough information about an actual, commercial “Mixpanel Community Bot” product, its business model, or any documented plans to scale it as a standalone business. The only concrete facts are: - Mixpanel has a Community (including a Slack Community). [Community] - In that Community, Mixpanel is piloting a chatbot powered by kapa.ai, which: - Uses OpenAI’s generative AI model. - Is trained using Mixpanel Docs. - Processes questions and answers via Kapa.ai and OpenAI. [Legal notices]There is no information in the sources about: - A monetization strategy for this bot. - A roadmap to scale it as a separate product or business. - Any formal business requirements or go‑to‑market plan.Because of that, I can’t reliably describe a real business plan or scaling strategy. I can only provide a hypothetical executive summary and BRD-style outline, clearly marked as speculative, based on: - The documented existence of the Community + kapa.ai pilot. [Community; Legal notices] - Mixpanel’s broader AI direction (Spark, MCP) as context for why such a bot might be strategically relevant. [Spark blog; Mixpanel MCP blog]---
Executive Summary (Hypothetical)
Context (from docs): Mixpanel operates a Community (including a Slack Community) and is piloting a chatbot powered by kapa.ai that uses Mixpanel Docs as its knowledge base and OpenAI models to answer user questions. [Community; Legal notices] Mixpanel is also investing in AI features like Spark (natural-language report building) and an MCP integration that lets LLMs query Mixpanel analytics. [Spark blog; MCP docs]Hypothetical product idea: Extend the existing Community chatbot pilot into a robust “Mixpanel Community Assistant” that: - Lives in the Mixpanel Community (Slack and potentially other surfaces). - Answers questions using Mixpanel Docs and other official resources as its knowledge base. [Legal notices] - Helps users understand and adopt Mixpanel features (including Spark and MCP). [Features; Spark blog; Mixpanel MCP blog]**Problem (hypothetical framing):- Users need fast, accurate answers about Mixpanel without always searching Docs or waiting for human replies. - Community and support teams handle many repetitive questions that are already answered in Docs. [Legal notices]Proposed solution (hypothetical):- A scaled AI assistant that: - Provides instant, doc-grounded answers in Community channels. - Surfaces relevant documentation links for deeper reading. [Legal notices] - Reduces repetitive support load and accelerates product adoption.Strategic fit (hypothetical):**- Aligns with Mixpanel’s goal of “analytics for everyone” and its investment in generative AI (Spark, MCP). [Spark blog; Mixpanel MCP blog] - Strengthens the Community as a self-serve support and learning channel. [Community]---
Hypothetical Business Requirements Document (BRD)
The following BRD is speculative and not described in the knowledge sources. It is derived from the documented Community + kapa.ai pilot and Mixpanel’s AI direction, but the scaling and business aspects are hypothetical.
1. Business Objectives (Hypothetical)
1. Reduce support and community response time - Provide instant answers to common Mixpanel questions in the Community using the Docs-based chatbot. [Legal notices]2. Increase self-serve adoption of Mixpanel features - Help users discover and correctly use features like Spark, MCP, Alerts, Custom Events, etc., by answering “how do I…” questions and linking to relevant docs. [Features; Spark blog; Mixpanel MCP blog]3. Improve Community experience - Make the Community more useful as a first-stop resource, while keeping it aligned with guidelines and legal notices. [Community; Guidelines]4. Support Mixpanel’s AI positioning - Demonstrate Mixpanel’s AI capabilities not only inside the product (Spark, MCP) but also in its support and community ecosystem. [Spark blog; Mixpanel MCP blog]
2. Scope (Hypothetical)
**In scope:- Slack-based Community assistant using Mixpanel Docs as knowledge base. [Legal notices] - Answering product, implementation, and feature-usage questions that are covered in Docs and official resources. [Features] - Providing links to relevant documentation pages in responses. [Legal notices]Out of scope (initially):**- Handling account-specific or billing questions (requires secure access to customer data; not described in sources). - Acting as a full in-product assistant (Spark already covers in-product natural-language report building). [Spark blog]
3. Target Users & Needs (Hypothetical)
- Community members (PMs, marketers, analysts, engineers) - Need quick, accurate answers about Mixpanel features and best practices. - New users / evaluators - Need conceptual explanations and onboarding guidance. - Community moderators / Mixpanel staff - Need to reduce repetitive Q&A and focus on complex, high-value discussions. [Community]
4. Functional Requirements (Hypothetical)
1. Question handling - Users can mention the bot in Community Slack to ask questions. [Legal notices] - Bot responds in-channel with a concise answer plus links to relevant Docs.2. Knowledge base integration - Bot uses Mixpanel Docs as its primary knowledge base. [Legal notices; Features]3. AI answer generation - Bot uses OpenAI models via kapa.ai to generate natural-language responses. [Legal notices]4. Citations / links - Each answer includes at least one link to the supporting documentation page where applicable. [Legal notices]5. Guidelines compliance - Bot behavior must respect Community guidelines (no disallowed content, etc.). [Guidelines]
5. Non‑Functional Requirements (Hypothetical)
- Reliability: High availability during typical Community usage hours. - Accuracy: Answers should be grounded in Docs; when uncertain, the bot should say so and suggest asking humans. - Privacy & legal: Use of questions/answers must comply with the Community legal notices and any applicable terms. [Legal notices]
6. Scaling the Idea (Hypothetical Steps)
Again, none of this scaling plan is in the sources; it is a logical extension: 1. Phase 1 – Solidify the pilot - Measure usage and satisfaction in the existing Slack pilot (volume of questions, helpfulness feedback). - Identify gaps where Docs don’t answer common questions and improve documentation accordingly. [Legal notices; Features]2. Phase 2 – Broaden coverage - Ensure strong coverage for high-value features (Spark, MCP, Alerts, Custom Events, Sessions, etc.). [Features; Spark blog; Mixpanel MCP blog] - Add clarifying-question behavior and follow-up refinement to improve answer quality (similar to Spark’s conversational flow). [Spark blog]3. Phase 3 – Deeper integration & analytics - Track which topics are most asked to inform product and docs priorities. - Use insights to improve Mixpanel Docs and possibly in-product help.4. Phase 4 – Evaluate business impact - Compare support ticket volume and Community response times before/after bot adoption. - Assess whether the assistant contributes to better adoption of advanced features (Spark, MCP, etc.). [Spark blog; Mixpanel MCP blog]
7. Viability Considerations (Hypothetical)
Because the sources do not discuss business models, the following is speculative: - Cost side: API usage (OpenAI via kapa.ai), maintenance of Docs and retrieval, monitoring. - Value side: Reduced support load, faster time-to-answer, improved feature adoption, stronger Community engagement. - Risk mitigation: Clear legal notices about AI usage in Community, explicit uncertainty when the bot doesn’t know, and adherence to guidelines. [Legal notices; Guidelines]---If you’d like, I can condense this further into a one-page, bullet-style executive brief, but it will remain hypothetical because the knowledge sources do not describe an actual business plan for scaling the Community bot.
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Can you follow this framework for the business requirement document for the AI chatbot kapa.ai 1. Executive summary 2. Description of your products and/or services 3. How your business meets the Innovation, Viability and Scalability criteria for endorsement 4. Please highlight your research and development activity in relation to the core innovation proposition 5. Market analysis 6. Competitor analysis: we recommend including at least 4-5 competitors and require a comprehensive analysis comparing your competitors’ features and benefits against your own offer 7. Staff profile and recruitment strategy 8. Marketing and sales strategy 9. SWOT analysis 10. Investment strategy and funding requirement 11. Detailed revenue and Cost of sales forecast, reflecting customer acquisition numbers 12. Cash flow forecast 13. Annual Profit & Loss forecast 14. Balance sheet forecast 15. Forecasted stock levels (if applicable) 16. Forecasted advertising/marketing expenditure 17. Forecasted fixed asset schedule
Let me check the knowledge base for you again. One moment! 📚
I’m sorry, but the knowledge sources do not contain enough information to build a full business requirement document of the depth you’re asking for (items 3–17 in your framework), especially around: - Business model, pricing, or monetization of the Mixpanel Community AI chatbot. - Market size, detailed market or competitor analysis. - Staffing, recruitment, marketing/sales strategy. - Financials (revenue model, cost of sales, cash flow, P&L, balance sheet, forecasts, etc.). - Investment strategy or funding requirements.What is documented is: - Mixpanel has a Community (including a Slack Community). [Community] - In that Community, Mixpanel is piloting a chatbot powered by kapa.ai that: - Uses OpenAI’s generative AI model. - Is trained using Mixpanel Docs. - Processes questions and answers via Kapa.ai and OpenAI. [Legal notices] - Mixpanel’s broader AI direction inside the product (Spark for natural-language report creation, MCP integration for LLM access to analytics). [Spark docs; Spark blog; MCP docs]Because of these limits, I can only fill in the parts of your framework that can be grounded in the sources, and I must leave the rest explicitly blank/unspecified rather than inventing business details.Below is a partial BRD, clearly constrained to what the sources support and labeled where information is missing.---
1. Executive summary
Mixpanel operates a Community (including a Slack Community) where users discuss product analytics and Mixpanel usage. [Community] As part of this Community, Mixpanel is piloting a chatbot powered by kapa.ai. This chatbot: - Uses OpenAI’s generative AI model. - Is trained using Mixpanel’s Docs (product documentation) as its knowledge base. - Processes user questions and generated answers via Kapa.ai and OpenAI. [Legal notices]The purpose of this pilot, as implied by the setup, is to help Community members get automated answers to Mixpanel-related questions based on official documentation, directly within the Slack Community, without needing to manually search Docs or wait for human responses. [Legal notices; Features]---
2. Description of products and/or services
**Mixpanel Community AI chatbot (pilot)- Mixpanel Slack Community. [Community] - Chatbot powered by kapa.ai, using OpenAI’s generative AI model. [Legal notices] - Mixpanel Docs (official documentation). [Legal notices; Features] - Automated, AI-generated responses to user questions about Mixpanel, grounded in the Docs.Related AI features in the Mixpanel product (for context, not part of the Community bot itself):**- Spark: Natural-language report creation inside Mixpanel (Insights, Funnels, Retention, Flows) from Boards. [Spark docs; Spark blog] - MCP Integration: Exposes Mixpanel analytics to LLM tools via the Model Context Protocol. [MCP docs; MCP blog]---
3. Innovation, Viability, Scalability (for endorsement)
The knowledge sources do not explicitly discuss endorsement criteria, commercial viability, or a scaling plan for the Community chatbot. Any detailed business argument here would be speculative.What can be reasonably inferred from the sources: - Innovation: - Using generative AI (OpenAI) with Mixpanel Docs as a knowledge base to answer Community questions is an innovative support/enablement approach within the Mixpanel ecosystem. [Legal notices] - It aligns with Mixpanel’s broader push to “bring analytics to everyone” via AI (Spark, MCP). [Spark blog; MCP blog]- Viability & scalability: - The docs only state that this is a pilot in the Slack Community; they do not describe results, costs, or a plan to scale it commercially. [Legal notices] - Therefore, I cannot reliably comment on business viability or scalability beyond noting that AI-based support can, in general, reduce repetitive Q&A and improve self-serve support, but this is not quantified in the sources.---
4. R&D activity related to the core innovation
The sources do not describe internal R&D activities, experiments, or technical research specific to the Community chatbot or kapa.ai integration.They do, however, show: - Mixpanel is actively developing and iterating on AI features like Spark and MCP, which indicates ongoing product R&D in AI-assisted analytics and LLM integrations. [Spark blog; MCP blog] - The Community chatbot is explicitly labeled as a pilot, which implies an experimental phase, but no further R&D detail is provided. [Legal notices]I cannot provide more specific R&D descriptions without going beyond the sources.---
5. Market analysis
There is no market analysis in the provided knowledge sources for: - AI support/chatbot tools in analytics communities. - Size of the addressable market. - Customer segments or willingness to pay.So I cannot fill this section without making up information.---
6. Competitor analysis
The sources do not mention any competitors to: - The Mixpanel Community chatbot. - Mixpanel’s AI features (Spark, MCP) in a comparative way.They only describe Mixpanel’s own features and some customer stories. [Spark blog; MCP blog; TymeX AI]I therefore cannot provide a 4–5 competitor comparison table or feature-by-feature analysis grounded in the sources.---
7–17. Staff, strategy, financials, and forecasts
For the remaining sections: 7. Staff profile and recruitment strategy 8. Marketing and sales strategy 9. SWOT analysis 10. Investment strategy and funding requirement 11. Revenue and Cost of sales forecast 12. Cash flow forecast 13. Annual Profit & Loss forecast 14. Balance sheet forecast 15. Forecasted stock levels 16. Forecasted advertising/marketing expenditure 17. Forecasted fixed asset schedule The knowledge sources contain no information about: - Organizational structure or staffing for the Community chatbot or AI initiatives. - Go-to-market, pricing, or sales strategy for any AI support product. - Financial performance, forecasts, or investment plans. - Inventory/stock (not applicable to Mixpanel’s SaaS model as described). - Fixed assets or capital expenditure related to this chatbot.Because of this, I cannot complete these sections in a factual way.---
How you might proceed
If you need a real BRD for an AI chatbot business (for endorsement, visa, or investment purposes), you will have to: - Bring in your own market research, competitor analysis, and financial modeling. - Use the Mixpanel Community + kapa.ai pilot only as a reference example of how a SaaS company uses an AI chatbot with a documentation knowledge base. [Legal notices]I can help you structure or wordsmith such a BRD in a generic way, but it would necessarily go beyond what Mixpanel’s own documentation and blogs state, and therefore would not be “based on the knowledge sources” in the strict sense you requested.
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Create a business plan + PRD to follow this template kapa.ai using the information below to create a framework A successful Business Plan should consist of a narrative section and financial projections for a minimum of three years of operation. It will typically include: 1. Executive summary 2. Description of your products and/or services 3. How your business meets the Innovation, Viability and Scalability criteria for endorsement 4. Please highlight your research and development activity in relation to the core innovation proposition 5. Market analysis 6. Competitor analysis: we recommend including at least 4-5 competitors and require a comprehensive analysis comparing your competitors’ features and benefits against your own offer 7. Staff profile and recruitment strategy 8. Marketing and sales strategy 9. SWOT analysis 10. Investment strategy and funding requirement 11. Detailed revenue and Cost of sales forecast, reflecting customer acquisition numbers 12. Cash flow forecast 13. Annual Profit & Loss forecast 14. Balance sheet forecast 15. Forecasted stock levels (if applicable) 16. Forecasted advertising/marketing expenditure 17. Forecasted fixed asset schedule 18. Forecasted staff costs (salaries and related expenses such as national insurance and pensions) Product Requirements Document (PRD) Template use the information below as content to fill in the above framework for Mixpanel kapa ai chatbot and community platform What is a PRD? A PRD is a living guide written by a Product Manager used to communicate and outline what they are building, who it is for, and how it benefits the end-user. It also serves as a guide for business and technical teams to help develop, launch, and market the product. Note: the following content has been validated by Gaurav Hardikar, Head of Consumer Product HomeLight PRD Title: PRD for Feature X Author: Name of PM Team: [List team members] Product Manager Engineering Lead/Team Lead Designer Approvers/Sign-Off PM Epic: [Insert link to Project Management tool epic (on JIRA or similar tool)] Status of PRD: Backlog One Pager Prior to writing your PRD document, present a One Pager to Executives to get buy-in from Executives before you prioritize. In the one-pager, you should describe Overview, Problem, Objectives, Constraints, and Use Cases. Use this One Pager to begin the project, then construct the PRD over the course of the project until lock. Overview An overview that states what product/feature the project is about, it’s important, and the overall key points your team needs to know. Problem Your problem statement is a description that explains the current situation and its related customer pain points and wants. This is a high-level statement that clearly states why this is a problem and explains why it is important to your business. Objectives The broad brush goals of this project. What would success look like if you solve the customer and company needs you are aiming to address? Objective 1 Objective 2 Objective 3 Constraints Roadblocks, realities, and dependencies that limit how ambitious you can be with this project. Whether it’s time, money, or engineering capability, be clear about the limits of this project. Constraint 1 Constraint 2 Constraint 3 Persona Who are the target personas for this product, and which is the key persona? Key Persona Description Key Persona Persona 2 Description Persona 2 Persona 3 Description Persona 3 Use Cases Instances where various personas will use the product, in context. Scenario 1 Scenario 2 Scenario 3 PRD When you’ve locked in your One Pager, build out your PRD. Use the finalized One Pager and the following. Features In These are the distinct, prioritized features along with a short explanation of why this feature is important. Briefly outline the scope, the goals, and use case. Feature 1 Feature 2 Feature 3 Features Out What features have you explicitly decided not to do and why? Feature 1 Feature 2 Feature 3 Design - (optional) Include any needed early sketches, and throughout the project, link to the actual designs once they’re available. Technical Considerations - (optional) Link to engineering technical approach document. Success Metrics What are the success metrics that indicate you’re achieving your internal goals for the project? How will you measure success? You can use any goal-setting and tracking system you prefer (OKRs, KPIs, etc). Note: Link to Analytics requirements and approach document. GTM Approach What’s the product messaging that your marketing department will use to describe this product to existing and new customers? How do you plan to launch this product to the market with marketing and sales teams? Note: Link to a larger GTM brief if available. Open Issues What factors do you still need to figure out? What problems may arise and how do you plan on addressing them? Q&A What are common questions about the product along with the answers you’ve decided? This is a good place to note key decisions. Asked by Question Answer Feature Timeline and Phasing Feature Status Dates Backlog Nov 23, 2022 In Development In Review Shipped Blocked PRD Checklist: Here’s a list of topics you must include in your PRD: PRD Checklist Order Topic Done 1. Title In Progress 2. Author Backlog 3. Decision Log Backlog 4. Change History Backlog 5. Overview Backlog 6. Success Overview Backlog 7. Messaging Backlog 8. Timeline/Release Planning Backlog 9. Personas Backlog 10. User Scenarios Backlog 11. User Stories/Features/Requirements Backlog 12. Features In Backlog 13. Features Out Backlog 14. Design Backlog 15. Open Issues Backlog 16. Q&A Backlog 17. Other Considerations Backlog Example PRD PRD Title: PRD for Feature X Author: Natalie PM - (Sign off) Team: [List team members] Product Manager Natalie PM Engineering Lead/Team Lead Ella Designer Jorge Approvers/Sign-Off Ella PM Epic: [Insert link to Project Management tool epic (on JIRA or similar tool)] Status of PRD: Backlog PM Epic: [Insert link] One Pager for Feature X Overview The Mobile App has two main goals, to provide a fantastic picture discovery and viewing experience for camera owners and non-camera owners alike and to provide added value to the camera owner (value that comes from having a portable computer with a fast processor, a nice screen, and an Internet connection). This vision will continue through with Product X, taking advantage of the smartphone’s cellular radio (among other things) for camera owners and by continuing to be a great place to experience pictures. Note: This document describes a roadmap to Product X’s time, not the scope of a massive release at Product X’s release. We will want to identify key release milestones/priorities and plan development around them. Objectives Be a great place to discover and view pictures, regardless as to if you’re a camera owner or not. Provide extra value to camera owners. Support our ecosystem goals. GTM Approach The Mobile App provides a convenient way to share on the go, control your camera remotely, or simply see the latest amazing pictures in 3D. Success Metrics TBD (It’s OK that these aren’t clearly defined yet! We’ll figure them out as we discuss the product with the other teams) Scenarios Non-Owner Discovering Pictures Hans is thinking about buying a camera, having seen a demo at his local Ringfoto store, but he’s unsure. He downloads the app, now available in German fortunately, as he thinks about buying the camera. In it, he can easily sit back and watch a slideshow of pictures play or interrupt them and interact with them. Thanks to geotagging, he can even see what other camera owners are shooting in his town. This browsing experience is quite pleasant, and the pictures look great on his iPad. He goes into the app’s learn section and sees a list of videos he can play to teach him how to use the camera better, but unfortunately they’re all in English. However, Hans has had enough fun playing with the app that it pushes him towards buying a camera.
