How To Build a Product Marketing Knowledge Base To Support AI Marketing
- Chris McLellan
- Oct 3
- 5 min read
Updated: Nov 1
Capturing product research and frameworks in structured tables within a spreadsheet or database is a powerful way to fuel AI-enabled marketing systems while reducing informational drift and document chaos.
At a Glance
Product Marketing is critical for aligning product, marketing, sales, and customer success teams, but it often produces scattered PDFs, slides, and documents that go unused. The result is wasted effort and poor alignment.
This post introduces the concept of 'PMM Knowledge Base', a critical marketing asset that supports AI-enabled marketing systems.
Unlike scattered documents that often duplicate or conflict key information, a spreadsheet-based PMM Knowledge Base is a consistent, collaborative source that is readily usable by Generative AI tools such as ChatGPT Projects, NotebookLM, and Custom GPTs.
Structuring Product Marketing research within a single source of truth enables Generative AI and Agentic AI tools to improve the quality of GTM plans, campaign plans, content schedules, and thought leadership pieces.
In short, a data-centric Product Marketing Knowledge Base transforms fragmented Product Marketing docs into structured tables that teams can collaborate on and AI systems can act on, enabling faster execution, stronger alignment, and better business outcomes.
Too Many Documents, Not Enough Action?
At its core, Product Marketing drives go-to-market strategy for a specific product or product suite.
It acts like a magnet that aligns product, marketing, sales, and customer success teams around a shared objective, ensuring what's s built meets real market needs, resonates with buyers, and drives outcomes like product-market fit, product launches, demand-gen campaigns, pricing, and sales enablement and training.
It’s also one of the most analytical and customer-centric disciplines in the marketing function.
Despite its name, Product Marketing is less about the product itself and more about developing a deep understanding of customers (you know, the frustrating, fallible, and sometimes irrational humans who buy your products) through research frameworks such as personas, value propositions, objection handling, and jobs to be done (JTBD).
Think of Product Marketing like a Rubik’s Cube, where each turn moves you forward, and each side represents a key aspect of the customer experience. The more sides you solve, the clearer the full picture becomes.
But it comes with a cost: Doing it well requires time, coordination, and resources.
As a result, it can represent a "bridge too far" for many marketing teams. They know they should be doing it, but unless there's someone with "Product Marketing" in their job title, it often gets neglected or executed only in bits and pieces, here and there.
This isn’t just anecdotal. Marketers today are dealing with a tsunami of operational data, yet according to a 2024 survey by the Content Marketing Institute, 35% say their content isn’t even data-driven, and nearly 40% admit it isn’t aligned with the customer journey.
That disconnect is exactly the problem I set out to solve by developing a template for a data-centric Product Playbook.
The Product Marketing Knowledge Base
By incorporating a data-centric mindset, marketing teams can compound efficiencies and scale outcomes. This approach is particularly useful for startups and small businesses, where a single marketer (or a very small team) is often responsible for everything from content, branding, advertising, events, customer experience, and more.
A Product Marketing Knowledge Base helps turn deep strategic thinking on Product Marketing into a compact and versatile source of information for multiple use cases, including AI marketing, sales, and customer success.
By working hands-on with clients, I've been iterating on a spreadsheet template for a Product Marketing Knowledge Base to help make these assets simpler and more useful, particularly in an AI marketing context.
I started by employing the ROSES prompt framework to create a Custom GPT on what the tabs and columns in this worksheet would look like.
The result is a multi-tabbed spreadsheet that captures the full-range of Product Marketing information in a compact format.
The result is a unified marketing knowledge base designed for simple management and team collaboration while allowing Generative AI systems to easily understand and act on the insights it contains.
Analysis Without Action = Growth Paralysis
In a perfect world, every marketing team would have the time and resource to connect their dozens (or even hundreds) of SaaS apps to a Data Warehouse or CDP and generate actionable reports and insights.
But that's a situation enjoyed by relatively few marketers; mainly those working in enterprise environments.
One way to bridge the gap is to pair smaller more nimble data assets like Product Knowledge Bases with Generative AI and Agentic AI tools to scale deliverables without escalating budgets or workloads.
There's a long list of deliverables that can be enhanced by adding the Product Marketing Knowledge Base to a wider Marketing Knowledge Base as part of a AI-Enabled Marketing system, or directly to tools like NotebookLM, ChatGPT Projects, and Custom GPTs:
Growth Initiatives
Product Launches
Campaign Plans
Content Calendars
Event Plans
Pricing Plans
Sales Training Decks
Product Guides
Customer Journey Maps
Product Video Scripts
Drafts of Content (posts, ads, scripts, landing pages)
What's Inside the Data-Centric Product Playbook
My Product Playbook template is a Google Sheet that includes tabs for the following research and frameworks:
GTM Checklist
Product 360
Positioning 360
Persona 360s
SWOT
Competitor Intelligence
Market Intelligence (via news, reports)
Customer Insights (via Customer Feedback Loops)
Channel Insights (via app analytics)
Customer Journey Maps
Jobs To Be Done
Objection Handling
Value Propositions
Messaging Framework
Hooks and Storylines
It's like an "informational stock cube" that's packed with insights and ready to be dropped into any marketing recipe to add context, depth, and guardrails.
I've been using it with clients to support their ongoing marketing function and the response has been consistently positive.
Single Source of Product Truth
A spreadsheet-based Product Playbook works best as the single source of truth because it is structured, collaborative, and AI ready. But not every audience wants to work in spreadsheets.
Executives prefer narrative documents, sales teams need training decks, and external partners expect polished PDFs.
By keeping the playbook in a spreadsheet and then using Generative AI to create doc or slide versions, you maintain one consistent master while producing tailored formats for different use cases, without duplication or conflict.
Collaboration For The Win
If you’re using AI tools for Product Marketing and not feeding it structured information from a Product Playbook, in my opinion, you’re leaving productivity gains on the table. That said, a key challenge with any data-centric undertaking is keeping it up-to-date.
Some tables (e.g. Positioning, Personas, Messaging) require only quarterly or annual updating. But others, such as Competitor Analysis and Customer Insights, can benefit from weekly or even daily updates.
One way to manage this is to open up the tables for comments from teams with a vested interest in improving the customer experience, such as BDRs, account managers, customer success reps, customer support reps, and finance.
But however you approach it, structured inputs = better outputs.
Every time.
I’m Chris McLellan
I’m a Certified Chartered Marketer and the Principal at Friends Electric, my personal marketing consultancy that combines foundational marketing, product marketing, and AI marketing approaches to help marketing teams and businesses do more with less and punch above their weight.
Get in touch if you'd like a guided tour of the template and how I use it to accelerate marketing outcomes. 👍
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