A practical guide for PMMs to choosing the best tools

Discover the essential guide for Product Marketing Managers (PMMs) on choosing the right tools to streamline go-to-market strategies.

All about AI
December 15, 2024
·
5 min
read
Abstract brand form on a purple field

Choosing the right resources is essential for driving efficiency and innovation within Product Marketing teams planning to launch announcements, such as new products or features. For those at the helm of these initiatives, it’s not just about understanding the market; it’s also about equipping the team with tools that foster collaboration, enhance strategic alignment, and simplify the complexities of go-to-market strategies. With countless options available, it can be overwhelming to distinguish what truly adds value from what merely complicates the process.

In this post, I will explore how to find the right tool, focusing on platforms that provide comprehensive solutions for managing GTM strategies, ensuring internal alignment, and facilitating cross-functional workflows.

The trouble with single-purpose tools

The market is overflowing with PMM tools. Still, many tend to fall into two restrictive categories:

  • Content Creation Tools: These platforms focus on elements like copywriting, graphic design, and video editing. They’re fantastic for executing marketing tasks but don’t always help with the broader scope of managing your GTM strategy or tracking campaign impacts.
  • Competitive Intelligence Tools: These are designed to monitor competitors, track market positions, and analyze industry changes. While this information is crucial, it represents just a part of a larger puzzle—it shouldn’t be the only focus. Relying on a jumble of tools for different functions can lead to friction in collaboration, create data silos, and generate blind spots within your overall strategy.

There’s also a third, often overlooked pillar of product marketing and GTM planning and execution that ties everything together: project management. While PMMs are responsible for developing messaging, personas, and product positioning, they are also expected to own the operational side of GTM execution. This includes managing dependencies, driving alignment, tracking progress, and ensuring all stakeholders are on schedule.

Without a clear project management foundation, PMM teams may struggle with cross-functional alignment, launch delays, and confusion around ownership. While content creation tools and intelligence trackers handle tactical inputs for PMMs, a proper GTM tool should act as the central source of execution truth. That’s where tools like ClickUp, Craft.io, Airtable, and Jira come in—providing PMM teams with visibility, alignment, and control over every stage of the GTM process.

What to look for in the right PMM tool

To pinpoint the best tools for your team, consider capabilities that extend beyond tactical tasks and support your strategic initiatives. Here are the key features to prioritize:

  1. GTM Planning & Execution: Look for a platform to plan, execute, and track your GTM strategies in one spot. Features to consider include launch calendars, milestone tracking, and status updates.
  2. Stakeholder Alignment: A centralized system to align your Sales, Product, and Customer Success teams is vital, ensuring everyone can access important messaging, positioning, and product updates.
  3. Insight & Reporting: Metrics that help gauge the success of your GTM initiatives are crucial. They allow you to make data-driven tweaks to your strategies when necessary.
  4. Single Source of Truth: A shared repository for key documents like messaging frameworks, target persona definitions, and launch materials that everyone can access will enhance teamwork and efficiency.

A tool breakdown

Ignition

Wins for an end-to-end AI-driven dedicated GTM launch tracking platform.

Ignition is a comprehensive go-to-market platform designed specifically for Product Marketing Managers. It streamlines the entire GTM lifecycle, allowing teams to plan, execute, and track launches effectively while keeping all stakeholders aligned. With features like templates, calendars, and dashboards, it ensures a clear view of GTM health and progress, from messaging to critical milestones.

Key Differentiators:

  • GTM Playbooks: Pre-built GTM templates make it faster for PMMs to develop and launch go-to-market plans.
  • Centralized Launch Dashboard: Provides real-time visibility into launch readiness, milestones, and blockers, keeping stakeholders in sync.
  • Cross-functional collaboration: Allows product, marketing, and sales alignment around one GTM plan.
  • Executive Visibility: Stakeholders can see live status updates, reducing the need for manual reporting.

Extra Food for Thought

  • AI Automations: Automatically generated battlecards, semi-automated launch plan generation, content generation.
  • Competitive Intelligence: Built-in competitor analysis templates.
  • Revenue Insights: Connect to Salesforce for insights into the value of your product and feature launches.
  • Integrations: Slack, Google Drive, Google Calendar.
  • Best GTM Stage: Full GTM lifecycle.

ClickUp

Wins for deep customization for PMM workflows.

ClickUp is an all-in-one project management and task-tracking platform tailored for PMM teams, enhancing collaboration and GTM execution. With its highly customizable workflows, it allows teams to visualize and manage every launch stage, ensuring seamless cross-functional alignment among Product, Marketing, and Sales.

Key Differentiators:

  • Customizable Workflows: Build and automate workflows that match your team’s GTM stages, from pre-launch to post-launch.
  • All-in-One Dashboard: View tasks, calendars, documents, and chat in a single space.
  • Affordable Entry Pricing: Starts at only $5/user/month.
  • Advanced Automation: Use triggers to automate task assignments, notifications, and reminders.

Closest Competitor: Asana (and monday.com). ClickUp offers deeper customization; Asana is simpler. Choose Asana if you want simplicity; choose ClickUp if you want total customization.

Extra Food for Thought

  • Competitive Intelligence: Use Zapier to pull RSS feeds from competitor updates and Google Alerts.
  • Integrations: Slack, Google Drive, Salesforce, Zapier.
  • Pricing: Starts at $5/user/month.
  • Best GTM Stage: Planning and execution.

Craft.io

Wins for PMMs who want to share a single tool with Product Managers.

Craft.io is a product management platform designed to empower PMMs in cultivating effective collaboration with Product teams. It ensures alignment on GTM strategies while simplifying the management of product releases, with a strong focus on product roadmaps, feature prioritization, and internal alignment.

Key Differentiators:

  • Feature-Level Management: Track development on a feature-level; track feature requests and communicate to collaborating teams.
  • Team Dependency Tracking: Visualize which features are dependent on others—essential for managing launch blockers.
  • Competitive Intelligence at the Feature Level: Attach competitive notes directly to features.
  • Customizable Workflows: Customize workflows to support GTM execution.
  • Direct Integrations: Jira, Slack, and other essential tools.

Closest Competitor: Productboard. Productboard is stronger for customer-facing feedback portals; Craft.io focuses on internal feature development and GTM alignment.

Extra Food for Thought

  • Integrations: Slack, Jira, Salesforce, Azure DevOps, Zapier, Google Sheets.
  • Best GTM Stage: Planning and strategy.

Airtable

Wins for fully customizable GTM tracking.

Airtable combines the functionality of a database and a spreadsheet, making it ideal for managing workflows and tracking go-to-market initiatives. It provides customizable visualizations—grid, kanban, calendar, and gallery views—to handle large sets of structured data.

Key Differentiators:

  • Customizable Data Structures: Define your own data models, from GTM frameworks to asset repositories.
  • Multi-View Dashboards: Instantly switch between views to visualize launch timelines in multiple formats.
  • Robust Automation: Use built-in automation or Zapier for “if this, then that” workflows.
  • Live CI Dashboard: Consolidate RSS feeds, Google Alerts, and news into a single dashboard for competitive intelligence.

Closest Competitor: Smartsheet. Airtable is more flexible and visually dynamic; Smartsheet is stronger for Gantt charts and executive reporting.

Extra Food for Thought

  • Integrations: Slack, Google Sheets, Salesforce.
  • Pricing: Starts at $10/user/month.
  • Best GTM Stage: Planning and data management.

Notion

Wins for a flexible, all-in-one knowledge hub.

Notion serves as a versatile, all-in-one hub for PMM teams, allowing them to seamlessly organize go-to-market plans, messaging frameworks, and essential resources in a collaborative space.

Key Differentiators:

  • All-in-One Knowledge Hub: Centralize GTM messaging, playbooks, and battle cards.
  • Custom Templates: Create and duplicate templates for GTM plans and messaging frameworks.
  • Real-Time Collaboration: Teams can co-edit GTM documents, leaving notes, comments, and action items.
  • API & Data Feeds: Pipe in competitor data or real-time updates via the Notion API.

Closest Competitor: Coda. Notion is simpler and more visual; Coda adds logic, formulas, and automation for dynamic data tracking.

Extra Food for Thought

  • Notion AI: Assists with content generation, idea brainstorming, and document editing.
  • Integrations: Slack, Google Drive, Zapier.
  • Pricing: Starts at $8/user/month.
  • Best GTM Stage: Planning, documentation, and alignment.

Jira + Confluence

Wins for developer-first workflows.

Jira and Confluence work seamlessly together to create a powerful platform for managing GTM launches, especially when product development dependencies are at play. Jira tracks development issues, while Confluence stores messaging, GTM documentation, and strategy playbooks.

Key Differentiators:

  • Dependency Tracking: Track launch blockers and product dependencies directly in Jira.
  • Collaborative Knowledge Hub: Use Confluence as a GTM knowledge base for messaging guides, FAQs, and competitive analysis.
  • Integration with Development Teams: Jira is the native environment for development teams.
  • Advanced Reporting: Track development status tied directly into GTM readiness.

Closest Competitor: Monday.com / Asana. Jira + Confluence is better for Agile development teams; Monday.com is simpler for cross-functional teams.

Extra Food for Thought

  • Integrations: Slack, Google Drive, Zapier.
  • Pricing: $7.75/user/month (Jira) + $5/user/month (Confluence).
  • Best GTM Stage: Launch execution and development collaboration.

When to choose which

Tool

When to Choose It

When NOT to Choose It

ClickUp

Custom workflows, multi-view task tracking, automation

If you want a simple out-of-the-box task tracker

Craft.io

Feature prioritization and aligning product with GTM

If you don’t manage product development

Notion

Building a knowledge hub for GTM playbooks and messaging

If you need automated workflows or task dependencies

Ignition

GTM planning, launch tracking, and status alignment

If you don’t need multiple launches tracked at once

Jira + Confluence

Your dev team already uses Jira and you work with devs

If you don’t need agile sprints, issues, or backlogs

Airtable

Customizable tracking of personas, GTM plans, and CI dashboards

If you don’t have time to build workflows from scratch

Best practices for selecting and implementing the right tool

  1. Start with a Needs Assessment: Take stock of what’s missing in your current GTM process. Are you looking for something to manage plans, enhance team collaboration, or provide solid reporting?
  2. Get Stakeholder Input: Connect with teams across the board—product, sales, and marketing—to ensure everyone has visibility on important GTM milestones.
  3. Check Your Business Stack: Review tools already implemented in your business to evaluate whether you can use any of them, and to evaluate integration needs.
  4. Align Your Budget: Use your budget as a requirement.
  5. Pilot the Tools: Request a trial. Hands-on experience is invaluable.
  6. Focus on Scalability: Look for tools that can grow as your team expands.
  7. Evaluate Integration Options: Ensure the tool integrates with your existing systems like Slack and Salesforce.

Final thoughts

Choosing the right PMM tool is one of the most significant moves you can make for your team’s success. The right choice can streamline your GTM process, improve collaboration, and offer deeper insights into launch outcomes. By focusing on platforms that support planning, alignment, and competitive intelligence, you’ll set yourself up to drive strategy, execute impactful launches, and showcase solid business results.

Choose wisely, and you’ll see a positive ripple effect across your go-to-market strategy.

Mentoring, free forever.

I mentor marketers and technical writers. Breaking in, growing your career, automating with AI and more.

Book a free session
data-augmentation
Data Augmentation
Creating extra training examples by altering existing ones, such as flipping, cropping, rotating or recolouring images, so the model sees more variety.
Text Link
workflow
Workflow
The ordered steps and hand-offs by which work moves from start to finish, who does each one and what triggers the next.
Text Link
verification
Verification
Confirming that a result, label or component is correct by checking it against a reference or a second method.
Text Link
vectorized-execution
Vectorized Execution
A query-engine technique that processes whole columns of values in one CPU instruction instead of row by row, which is what makes engines like DuckDB fast.
Text Link
vector-space
Vector Space
The multi-dimensional space where embeddings live; distance within it stands for how similar two items are.
Text Link
uncertainty-sampling
Uncertainty Sampling
An active-learning strategy that sends the examples the model is least sure about to human reviewers first, because those teach it the most.
Text Link
transformer
Transformer
A neural network architecture built on attention, which weighs every part of the input against every other part; it underpins modern language and vision models.
Text Link
traceability
Traceability
The ability to follow any output back through the data, labels, code and model versions that produced it.
Text Link
tokenization
Tokenization
Splitting text into the units a language model works with, which may be words, parts of words or characters.
Text Link
token-limit
Token Limit
The maximum number of tokens a language model can take in and produce in one request, which caps how much context it can hold.
Text Link
threading
Threading
Running several parts of a program at the same time on separate threads so work overlaps instead of waiting in line.
Text Link
temporal-analysis
Temporal Analysis
Studying how things change over time across frames or records, to detect motion, events, trends or anomalies.
Text Link
sla
SLA
Service Level Agreement: the committed level of service a provider promises, such as uptime, response time or support hours, with consequences if missed.
Text Link
simulation
Simulation
Generating realistic scenarios in software to test or train a system where real data is scarce, risky or expensive to collect.
Text Link
similarity-search-vector-search
Similarity Search (Vector Search)
Finding the items whose embeddings sit closest to a query's embedding, which returns things that look or mean alike rather than share keywords.
Text Link
setup
Setup
The initial configuration of a project: connecting data sources, choosing models and settings, and defining the workflow before work begins.
Text Link
semi-supervised-learning
Semi-Supervised Learning
Training on a small labelled set plus a much larger unlabelled one, using the labelled examples to guide what the model learns from the rest.
Text Link
semantics
Semantics
The meaning behind data rather than its surface form, which lets a system treat "car" and "automobile", or two photos of one scene, as the same thing.
Text Link
segmentation
Segmentation
Dividing an image into regions by labelling every pixel, so the exact shape of each object or area is known.
Text Link
search-attributes
Search Attributes
The fields a search can filter or rank on, such as labels, tags, dates, camera id or model confidence.
Text Link
search-and-retrieval
Search & Retrieval
Finding the items in a collection that best match a query, whether by keyword, metadata or visual and semantic similarity.
Text Link
scene-understanding
Scene Understanding
Interpreting a whole image or video as a scene: what objects are present, where they are and how they relate.
Text Link
saliency-map
Saliency Map
A heat map over an image showing which pixels most influenced a model's prediction, used to explain and debug it.
Text Link
reward-function
Reward Function
In reinforcement learning, the rule that scores each action so the agent learns which behaviour to repeat.
Text Link
review-batch
Review Batch
A set of items grouped for a human reviewer to check in one sitting, often chosen because the model was unsure about them.
Text Link
relational-database
Relational Database
A database that stores data in tables with rows and columns and links them through keys, queried with SQL.
Text Link
regularization
Regularization
Techniques that stop a model memorising its training data, such as penalising large weights or dropping random neurons, so it generalises better.
Text Link
refactoring
Refactoring
Restructuring existing code to make it cleaner and easier to change without altering what it does.
Text Link
recipe
Recipe
A saved, repeatable set of processing steps or settings that can be rerun on new data to get the same kind of result.
Text Link
rag
RAG
Retrieval-Augmented Generation: a language model that first fetches relevant documents and then writes its answer from them, which cuts hallucination.
Text Link
quality-assurance-qa
Quality Assurance (QA)
The checks and processes that make sure a product, dataset or model meets its standard before it ships.
Text Link
predictive-maintenance
Predictive Maintenance
Using sensor data and models to forecast when equipment will fail, so it is serviced just before it does rather than on a fixed schedule or after a breakdown.
Text Link
policy
Policy
In reinforcement learning, the strategy that maps each state to the action the agent should take.
Text Link
pixel
Pixel
The smallest unit of a digital image, a single point with a colour or intensity value; images are grids of them.
Text Link
pgvector
pgvector
A PostgreSQL extension that stores embeddings and runs similarity search inside the database, so vector search needs no separate system.
Text Link
one-shot-learning
One-shot Learning
Learning to recognise a new class from a single example, usually by comparing against a learned notion of similarity.
Text Link
object-storage
Object Storage
Storage that keeps files as objects with metadata in flat buckets rather than folders, built for huge volumes and cheap durability.
Text Link
normalization
Normalization
Rescaling values to a common range or distribution so no single feature dominates and models train more stably.
Text Link
noise
Noise
Random or irrelevant variation in data that hides the real signal, from sensor error, compression, lighting or sloppy labels.
Text Link
mvcc
MVCC
Multi-Version Concurrency Control: a database technique that keeps multiple versions of a row so readers and writers do not block each other.
Text Link
monitoring
Monitoring
Continuously watching a deployed system's health and output quality so drift, errors and slowdowns are caught early.
Text Link
model-versioning
Model Versioning
Tracking each trained model with its data, code and settings, so any version can be reproduced, compared or rolled back.
Text Link
metrics
Metrics
The numbers used to judge a model or system: accuracy, precision, recall, F1, latency and others chosen to match the goal.
Text Link
metric-learning
Metric Learning
Training a model so that similar items end up close together in its embedding space and dissimilar ones far apart.
Text Link
meta-learning
Meta-learning
Training a model to learn how to learn, so it adapts to a new task from very few examples.
Text Link
mask
Mask
A pixel-level map marking exactly which pixels belong to an object or region, the output of segmentation.
Text Link
markov-decision-process-mdp
Markov Decision Process (MDP)
The mathematical frame for reinforcement learning: states, actions, rewards and transition probabilities where the next state depends only on the current one.
Text Link
loss-function
Loss Function
The formula that scores how wrong a model's predictions are during training; training works to make this number as small as possible.
Text Link
latent-feature
Latent Feature
A hidden characteristic a model learns internally that is not directly observed in the data but helps explain it.
Text Link
label-noise
Label Noise
Errors and inconsistencies in labels, from mistakes, ambiguity or disagreeing annotators, which degrade what a model can learn.
Text Link
keypoint-detection
Keypoint Detection
Locating specific points of interest on an object, such as joints on a body or corners on a part, rather than drawing a box around the whole thing.
Text Link
iou
IOU
Intersection over Union: the overlap between a predicted box or mask and the true one, divided by their combined area. 1.0 is a perfect match.
Text Link
inverted-index
Inverted Index
An index that maps each term to the documents containing it, which is how full-text search answers queries quickly.
Text Link
instance-object-instance
Instance (Object instance)
One individual occurrence of an object in an image, as distinct from the class it belongs to.
Text Link
information-extraction
Information Extraction
Pulling structured facts such as names, dates, amounts or relationships out of unstructured text or images.
Text Link
indexing
Indexing
Building a lookup structure over data so searches find matches without scanning everything.
Text Link
image-processing
Image Processing
Operations applied to pixels, such as resizing, filtering, thresholding or colour correction, usually to prepare images for analysis.
Text Link
hyperparameter-selection
Hyperparameter selection
Choosing the settings that control how a model learns, such as learning rate, batch size or network depth, which are set before training rather than learned.
Text Link
grid-search
Grid search
Trying every combination of chosen hyperparameter values in turn and keeping the set that scores best.
Text Link
gradient
Gradient
The direction and size of change in a model's error for each weight, used during training to decide how to adjust that weight.
Text Link
gpu
GPU
Graphics Processing Unit: a chip built for many parallel calculations at once, which makes it the workhorse for training and running neural networks.
Text Link
generative-ai
Generative AI
AI that creates new content such as text, images, code or audio from a prompt, learned from patterns in large training sets.
Text Link
generalization
Generalization
A model's ability to perform well on data it has never seen, rather than only on its training examples.
Text Link
feedback-loop
Feedback Loop
A cycle in which a system's outputs, or human reactions to them, are fed back as training signal so it improves over time.
Text Link
feature-map
Feature Map
The output of one layer in a convolutional network: a grid showing where in the image a particular pattern was detected.
Text Link
feature-engineering
Feature Engineering
Creating or transforming input variables so a model can learn from them more easily, using domain knowledge rather than raw data alone.
Text Link
fairness
Fairness
A model behaving without systematic disadvantage to groups defined by traits such as gender, age or region.
Text Link
exploration-vs-exploitation
Exploration vs. Exploitation
The trade-off in learning systems between trying new actions to discover better options and repeating actions already known to work.
Text Link
evaluation
Evaluation
Measuring how well a model performs against held-out data using agreed metrics, before and after it is deployed.
Text Link
efficiency
Efficiency
Getting the same or better result with less time, compute, money or manual effort.
Text Link
ec2
EC2
Amazon Elastic Compute Cloud: AWS's service for renting virtual servers by the hour, including GPU machines for training.
Text Link
domain-shift
Domain Shift
When the data a model meets in production differs from the data it was trained on, so accuracy drops even though nothing in the model changed.
Text Link
dml
DML
Data Manipulation Language: the SQL commands that read and change rows, such as SELECT, INSERT, UPDATE and DELETE.
Text Link
dino
DINO
A self-supervised vision method from Meta that learns strong image features without labels by teaching a network to match different views of the same image.
Text Link
ddl
DDL
Data Definition Language: the SQL commands that create and change the structure of a database, such as CREATE TABLE and ALTER.
Text Link
database-reliability
Database Reliability
A database's ability to stay available, correct and recoverable under load, failure and change.
Text Link
data-quality
Data Quality
How fit data is for its purpose: accurate, complete, consistent, current and free of duplicates, noise and wrong labels.
Text Link
data-lineage
Data Lineage
The record of where data came from and every transformation it went through, so any result can be traced back to its source.
Text Link
data-leakage
Data Leakage
When information from the test set, or from the future, slips into training, so the model scores well in the lab and fails in production.
Text Link
data-lake
Data Lake
A store that keeps raw data in its original format at any scale, structured or not, for later processing and analysis.
Text Link
data-integrity
Data Integrity
Data that is complete, accurate and unaltered over its whole life, protected from corruption, loss or unauthorised change.
Text Link
data-ingestion
Data Ingestion
Bringing data from its sources into a system where it can be stored, processed and analysed, whether in batches or as a live stream.
Text Link
data-governance
Data Governance
The policies, roles and controls that decide who can access data, how it is kept accurate and how its use is tracked.
Text Link
data-consistency
Data Consistency
Data that stays correct and in agreement across copies, transactions and systems, so every reader sees the same truth.
Text Link
data-balance
Data Balance
How evenly examples are spread across classes or conditions in a dataset; imbalance teaches a model to favour whatever it sees most.
Text Link
cross-validation
Cross-validation
Evaluating a model by splitting the data into several folds, training on some and testing on the rest in turn, to get a more honest accuracy estimate.
Text Link
cosine-similarity
Cosine Similarity
A score between -1 and 1 for how closely two vectors point in the same direction, used to compare embeddings regardless of their length.
Text Link
convolutional-neural-network-cnn
Convolutional Neural Network (CNN)
A neural network built for images: it slides small filters over the picture to detect edges, textures and shapes, then combines them into higher-level features.
Text Link
connected-components
Connected Components
Groups of items linked by similarity so that every member connects to the group through at least one close neighbour, used to find clusters and duplicates.
Text Link
compliance
Compliance
Meeting the laws, standards and internal policies that govern how data is collected, stored, used and audited.
Text Link
collaboration
Collaboration
People and teams working on shared data, models and decisions with a common view of the work, rather than in separate tools and silos.
Text Link
cloud-native
Cloud Native
Software designed from the start to run in the cloud, typically built as containers and services that scale and recover automatically.
Text Link
cloud-computing
Cloud Computing
Renting computing resources such as servers, storage and databases over the internet, scaling up or down on demand instead of owning hardware.
Text Link
cloud-ai
Cloud AI
Running AI training or inference on a provider's infrastructure rather than your own hardware, paying for compute as you use it.
Text Link
clip
CLIP
A model from OpenAI that learns images and text in one shared space, so a sentence can be matched to pictures it describes without task-specific training.
Text Link
caching
Caching
Keeping a copy of frequently used data in fast storage so later requests are served without recomputing or refetching it.
Text Link
buy-vs-build
Buy vs Build
The decision between purchasing an existing product and developing your own, weighing speed, cost, control and long-term maintenance.
Text Link
business-value
Business Value
The measurable benefit a project delivers to the organisation: revenue, cost saved, risk reduced or time recovered.
Text Link
black-box-model
Black-box Model
A model whose internal reasoning cannot be inspected or explained, so you can see what it predicts but not why.
Text Link
big-data
Big Data
Data too large, fast or varied for traditional tools to store and analyse, which calls for distributed storage and processing.
Text Link
Keep reading
Abstract brand form artwork

Your first marketer should answer these five questions first

Three of the five questions every marketing strategy must answer are positioning questions. That is why your first marketing hire should be a product marketer at the core.

first-marketing-hire-product-marketer
Abstract brand form artwork

A funnel framework for running your digital marketing channels

Give every channel one job in the funnel, track the numbers that prove it, and diagnose every missed target. A framework for display, search, email, social and web.

digital-marketing-funnel-framework
Abstract brand form artwork

Display, PPC, SEO and email: what changes when you build each

All four campaigns start with the same audience, message and call to action. Here is what changes when you build display ads, PPC, SEO pages and email.

display-ppc-seo-email-campaign-creation

Tell me what you're building and what's in the way. I read everything.

Great tech doesn't sell itself.
Let's meet. It's free.

Services
All servicesAdvisory & foundational developmentBrand and product marketingCustomer & lifecycle marketingContent & creativeOrganic growth engineOps & internal enablementExpress servicesServices FAQs
By role
Product marketersProduct managersFoundersTechnical communicatorsMarketing leadersPartner marketersLeaders without PMM yetCROs
Tools & Supplies
BlogMentoringProfessional FAQTemplates & guidesTool directory
My Bookmarks
All curated resourcesHubs & review platformsCareer stuffMarketing tips, guides & moreTech stack & AI resourcesCustomer experienceTech writing stuffFree stuffBuilders and innovatorsDesign stuffLearn & growEvents
About
About RachelPortfolioWall of loveLet's meet
cheyfitz logo
© 2026 Rachel Cheyfitz