In a recent Paris tech event, I had an exchange with data professionals. Our discussion focused on which domain is one of the best for data-driven professionals and find out how to best use the information in today’s big data world.
For my part, from 7+ years experience in Product Management, it’s SaaS Product Management.
I don’t aim to persuade you; this domain just isn’t for everybody, but I’m going to indicate you the importance of knowledge in product management.
Back to basics
To start with, what’s product management?
IBM defines it as ‘a strategic practice that guides the product lifecycle through research, planning, development, product launch, support and optimization to construct products that meet business goals and satisfy customer needs’.
Briefly, constructing a product from scratch and accompanying it through its lifetime so it satisfies a customer need while reaching the corporate’s goals. All monitored by data and KPIs (Key Performance Indicators).
Now, let’s see the definition of a SaaS.
IBM defines it as ‘Software as a service (SaaS) is a cloud-based software delivery model during which providers host applications and make them available to users over the web. SaaS users typically access applications by utilizing an internet browser or an app’.
SaaS is a web-based product that’s accessible, and its models often work under a subscription. To call some famous Saas: Netflix (BtoC), Salesforce, Atlassian, Notion. AI tools and automation tools are also working under the SaaS system. Yes, even ChatGPT, Gemini, n8n and Zapier are using the model.
We are literally surrounded by Saas nowadays!
Now, let’s dig into how product management and data fit with one another.
Why is Saas unique?
We will find 4 levels of analytics: Descriptive, Predictive, Prescriptive and Diagnostic.
1. Descriptive
Most SaaS teams are drowning in data but have no idea find out how to use it. Descriptive evaluation brings clarity through the dashboard and metrics.
Case Study #1: Feature Adoption Crisis
Context: B2B SaaS product, 50k users. Launched a significant feature after 6 months of development. Expected 30% adoption in the primary month was, in point of fact, 8% after 2 months.
- The Problem: The Product team was frustrated: ‘We built what users asked for, why aren’t they using it?’.
- What I did:
- 1. Built a dashboard in Notion tracking: Feature discovery rate (what number of saw it?), Trial rate (what number of clicked?), Adoption rate (what number of used it 3+ times?).
- 2. Segmented by User role (admin vs. end-user), Company size, Acquisition channel.
- The Insight: The feature was hidden 3 levels deep in navigation. Only admins were discovering it, but end-users needed it most. The invention rate was 12% (vs. 80% expected), and the trial rate (amongst discoverers) was 67% (good!). The adoption rate (amongst trialists) reached 89% (excellent). The issue wasn’t the feature; it was the discoverability.
- Impact: Moved feature to most important navigation, added onboarding tooltip. Discovery reached 78% in 2 weeks, and the general adoption increased to 52%.
- Tools used: Mixpanel for tracking, Notion for dashboard and documentation, Figma for design iteration.
- Key learning: Never assume users will find your feature. Instrument your complete journey
2. Diagnostic
When metrics drop, teams panic and make assumptions. Diagnostic analytics uses data to search out the true cause.
Case Study #2: The Mysterious Churn Spike
- Context: SaaS product, $50 MRR (monthly recurrent revenue) average. The monthly churn was historically 5%. It suddenly jumped to 12% in October.
- The Panic: The CEO told me: ‘Competitor launched. We’re losing. Should we cut prices?’.
- What I did:
- 1. Cohort evaluation by signup date.
- 2. Churn reason evaluation (exit surveys).
- 3. Feature usage before churn.
- 4. Support ticket evaluation.
- The Discovery: It wasn’t a contest. It was seasonal. Firms signing up in Sept-Oct (back-to-school rush) had 3x higher churn than in other months. It’s because they were signing up for temporary projects, not everlasting needs. The Usage patterns were the next 80% used <10 times, 60% never invited team, 90% churned at 30 days (trial end).
- The Real Cause: the acquisition campaigns targeted ‘recent projects’ without qualifying long-term need.
- Solution implemented:
- 1. Modified acquisition messaging (long-term value vs. quick wins).
- 2. Added onboarding query: ‘How long is your project?’.
- 3. Different onboarding flow for temporary vs. everlasting users.
- 4. Early engagement scoring to predict churn risk.
- Impact: Seasonal churn still happens, but we now not panic anymore. With a greater qualification during acquisition, the general churn dropped to six.5%.
- Tools used: Amplitude for cohort evaluation, Typeform for exit surveys, n8n to automate data collection, Google Sheets for final evaluation.
- Key learning: Don’t fight symptoms. Use data to search out root causes before acting”.
3. Predictive
Use historical data to predict what’s going to occur. Machine learning may also help.
Case Study #3: Predicting Churn Before It Happens
- Context: SaaS B2B, $100 MRR average, with a Churn rate of 8% monthly, is losing customers abruptly. The exit interviews show: “We stopped using it weeks ago”.
- The Problem: We were reacting to churn as a substitute of stopping it. By the point users cancelled, it was too late to save lots of them.
- What I Built: a Churn Prediction Rating from historical data (the last 30 days) including:
- Login frequency decay (30%).
- Feature usage depth (30%).
- Team collaboration (20%).
- Support tickets spike (15%).
- NPS (Net Promoter Rating) trend (10%): Risk levels: 0–30 green, 31–60 yellow, 61–100 red.
- Implementation:
- 1. Built SQL queries in Metabase.
- 2. Automated every day scoring in n8n.
- 3. Stored in Notion database.
- 4. Triggered alerts to the Customer Success team.
- Example prediction: For an organization XYZ, logins drop, feature usage decreased by greater than 2, and tickets spike. All of that’s causing a 72% risk rating.
- Impact (6 months): Identified at-risk customers 3-4 weeks early, which saved 40% of flagged accounts. The Overall churn dropped from 8% to five.2% Because of a proactive outreach as a substitute of a reactive firefighting.
- Tools used: Mixpanel for behaviour data, SQL for scoring logic, n8n for automation and Notion for Customer Success dashboard.
- Key Learning: “Churn doesn’t occur overnight. Users disengage step by step, and data shows the pattern weeks before they cancel”.
4. Prescriptive
Turning insights into actions. Data shows what happened, why, and what to do next.
Case Study #4: Roadmap Prioritization Nightmare
- Context: We were receiving greater than 50 feature requests for 3 engineers. There have been Conflicting stakeholder opinions (Sales wants enterprise features, Users want UX (User Experience) improvements, the CEO wants AI integration).
- The Chaos: Every stakeholder had ‘data’ to support their priority. For the Sales, it was 5 enterprise deals blocked by missing SSO (single log out), for the Support, it was 200 tickets about slow loading, and for the CEO, all of the Competitors have AI now.
- What I did:
- Step 1: Unified scoring framework (RICE): Reach: What number of users are affected? Impact: How much value per user? (1-3 scale), Confidence: How sure are we? (%) and Effort: Engineering days required.
- Step 2: Added business constraints (MRR impact (estimated), Churn reduction potential, Strategic alignment (AI = priority)).
- Step 3: Built a model in Notion.
- Surprise! The speed optimization scored highest, but everyone was obsessive about AI. The information shows that the Speed affected 10x more users than SSO, 40% of support tickets related to performance and from the User surveys, the speed was the primary grievance. But AI had strategic value (competitive positioning).
- Final Decision: The Roadmap became: for Q1, priority could be the speed (highest RICE, morale boost), for Q2, it could be the SSO (unblocks deals) and can be followed in Q3 by AI for the strategic positioning.
- Impact: Speed shipped in 6 weeks (under estimate!), Churn dropped 4% in 2 months, Enterprise deals closed, the AI launched Q3 on a healthy product.
- Key learning: Data enables trade-off conversations, not only yes/no decisions.
- Tools used: Notion for RICE framework and the roadmap, Amplitude for reach/impact data, Sales CRM for MRR projections and User surveys for confidence scores.
5. Automation & AI: The 2026 layer (how PMs scale)
With recent technologies, product managers can eliminate manual work due to the usage of recent tools.
The world has modified, and product managers should adapt. Automation and IA will show you how to to do less manual work and time-consuming tasks.
Case Study #5: Analyzing 10,000 User Feedbacks
- Context: Growing SaaS from 200 to 2000 users in 6 months. The User feedback is exploding ( 50 support tickets/day; 20 NPS responses/day, 30 feature requests/week, Random feedback in Slack, email, Twitter).
- The Problem: I used to be spending 10 hours/week manually reading and categorizing feedback. I used to be missing patterns and drowning.
- What I built: an n8n Automation workflow:
- 1. Collect feedback from multiple sources, Intercom, Typeform, Linear, Slack.
- 2. Send to Claude API for evaluation (Sentiment; Category, Priority, Extract key themes).
- 3. Store in Notion database with tags.
- 4. Weekly summary dashboard.
Example of an AI evaluation Input:
- AI Output: Sentiment: Negative; Categories: Performance, UX, Priority: Essential, Themes: Speed, Navigation, Export.
- Impact: Evaluation time went from 10h per week to 30minutes per week, the pattern discovery improved (AI spots themes I missed), there have been weekly reports auto-generated, and the trends are visible within the Notion dashboard.
- Insight discovered by AI: After 3 weeks, AI flagged that 40% of ‘slow’ complaints mentioned ‘large datasets’. Humans (me) were categorizing them as ‘performance’ generically. However the AI spotted the pattern: a particular use case with large data. Then, we optimized the scenario specifically, and the complaints dropped quickly by 60%.
- Tools & Setup: n8n, Claude API ($20/month for this volume), Notion API (free). For a complete cost of around ~$20/month, I saved 40 hours per 30 days. The ROI (return on investment) is amazing.
- Key learning: AI doesn’t replace evaluation. It scales your capability to process information and spot patterns.
The fashionable SaaS PM stack
To be efficient, a Product Manager needs to make use of a solid set of tools:
- Analytics tools:
- Mixpanel or Amplitude for the user tracking behaviour.
- Google Analytics for traffic and acquisition.
- Metabase for custom queries and a dashboard.
- Power Bi/Looker/Tableau for dashboard.
- Documentation and roadmap:
- Notion (or Confluence): the only source of Truth.
- Jira for user stories
- Automation tool for feedback collection, alert system, weekly report: N8n, Zapier, Make.
- AI tools: Claude, ChatGPT, Gemini (feedback evaluation, correction, quick research)
- Please note:
- Communication: Slack for team coordination, Loom for asynchronous updates, Lovable or Figma for design and Jira for team coordination.
- Data skills (good to have), having an understanding of knowledge and having the ability to pursue your individual searches without asking an information analyst will prevent time. It’s a superb skill to develop. I like to recommend SQL first, then Python.
Through the use of these tools, your ROI could be multiplied by an undefined number!
How My Background in marketing helps: my unfair advantage
I’ve been in product management for 7 years, but before that, I graduated with a Master’s degree in Marketing. An unexpected advantage, as I used to be already acquainted with how constructing a product has to reply and fill a necessity already existing with lots of the concepts, corresponding to:
- User psychology by utilizing discovery and personas. Tracking metrics just isn’t enough. Understanding WHY a user behaves. Marketing taught me to think like a user. User first, at all times.
- Positioning matters: it may be a reason for your acquisition issue.
- Full funnel considering: my mind doesn’t stop on the delivery of the product. I believe: awareness, discovery, trial, adoption, retention, upgrade.
- Data storytelling: The way to turn data right into a narrative.
The way to start?
From my experience and talking to many PMs, the primary issue I noticed is the lack of know-how of user psychology and business strategy. Having metrics is one thing; understanding them is one other.
This creates a trust deficit.
To not turn out to be a site expert overnight. But enough understanding to speak effectively with the various stakeholders, frame problems from a user perspective, and design solutions that really create value is crucial.
Step one is learning the fundamentals: how products are built, how users make decisions, how businesses measure success, and the way teams collaborate effectively.
The way to do it?
1. Learn Product Management Fundamentals
- Product Strategy: defining a vision, setting goals, and creating roadmaps.
- User Research: gathering insights, conducting interviews, and validating assumptions.
- Analytics & Metrics: selecting and understanding the correct KPIs, organising dashboards, and measuring impact.
- Stakeholder Management: having the ability to communicate with engineering, design, marketing, and leadership while adapting your speech to your interlocutor.
- Tools & Workflows: using Notion for documentation, n8n for automation and a collaboration tool.
2. Construct your PM Tech stack for higher impact
In product management, we wish to construct solutions that drive user value and business results. By taking small but impactful measures:
- Arrange your notion.
- Learn find out how to use AI.
- Learn find out how to use automation.
Do I even have book recommendations?
Yes!
If you should deepen your understanding, listed here are books that shaped my approach:
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If you happen to like frameworks and wish to use them to actual product scenarios, these books are for you.
3. Own your data
As I discussed earlier, having KPIs is nice; understanding them is crucial.
‘What’s one of the best KPI/What KPI are you using?’
Have you ever heard this query before?
It’s a foul query! And in case you replied to it, you might be within the improper.
We’d like to know that there isn’t a best KPI. A KPI working in a particular environment won’t necessarily work in one other situation. To establish a KPI, you first have to determine what it is advisable to understand and watch.
Having Data Analytics basics is de facto good; you’ll have the opportunity to perform your evaluation yourself.
The second advantage is that it’s going to mean you can have deeper conversations with technical teams for heavy data Saas.
4. Understand the Delivery
Even when each roles could look similar, they’re different in nature. A Product Manager builds the product and owns it. He’s answerable for the complete lifecycle.
A Project Manager is in control of the delivery, planning, resources, budget, deadline and scope. In a SaaS, the project is commonly a feature or the product itself.
If you happen to are a Product Manager with Project Management skills, you own the complete cycle.
If you happen to are a Data Driven Product Manager owning the complete cycle, you might be complete.
5. The primary focus is practical and actionable
I’ve been using and constructing automation workflows for some time, and that has saved me a lot time. If you happen to check my templates on n8n, you’ll find a skeleton of what is feasible (with a YouTube video explaining it). You’ll be able to take the template and adapt it to fit your needs. I strongly advise you to adapt these frameworks to your company-specific context. For instance, an automated feedback triage is used when doing a UAT (User Acceptance Testing).
You furthermore may should experiment with different prioritization criteria, test various analytics setups, and construct custom workflows on your team’s needs.
Consider that the target is to develop each your product intuition and your data evaluation skills.
What’s Next?
I hope you’re now convinced in regards to the importance of being a data-driven Product Manager whose skills are valued for his or her impact on users and business.
As someone working every day with cross-functional teams and constructing products, I can confirm there’s a growing need for PMs who can bridge the gap between data and decision-making.
What’s your biggest challenge in becoming a data-driven Product Manager?
Who am I?
I’m Yassin, a Product Manager who expanded into Data Science to bridge the gap between business decisions and technical systems. Learning Python, SQL, and analytics has enabled me to design product insights and automation workflows that connect what teams need with how data behaves. Let’s connect on Linkedin
