# Problem Statement Examples

## Example 1: Slack (Early Problem Statement)

**Problem Framing Narrative:**

**I am:** A software developer on a distributed team
- Working across multiple time zones with teammates I rarely see in person
- Collaborating on complex technical projects requiring frequent communication
- Drowning in email threads that are hard to follow and unsearchable

**Trying to:**
- Communicate in real-time with my team without losing context or important decisions

**But:**
- Email is too slow and threads get buried
- Skype/IM is ephemeral—conversations disappear and can't be searched later
- Important decisions get lost, forcing me to re-ask questions or hunt through old messages

**Because:**
- No tool combines real-time chat with persistent, searchable history

**Which makes me feel:**
- Frustrated, inefficient, and disconnected from my team

**Context & Constraints:**
- Distributed teams across time zones
- Need for asynchronous + synchronous communication
- Must integrate with dev tools (GitHub, Jira, CI/CD)

**Final Problem Statement:**
"Distributed software teams need a way to communicate in real-time with persistent, searchable history because email is too slow and IM is ephemeral, which causes lost context and repeated questions."

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## Example 2: Bad Problem Statement (Solution in Disguise)

**Problem Framing Narrative:**

**I am:** A product manager

**Trying to:**
- Use AI-powered analytics

**But:**
- We don't have AI features

**Because:**
- Our competitors do

**Which makes me feel:**
- Behind the times

**Final Problem Statement:**
"We need AI-powered analytics to compete."

**Why this fails:**
- "Trying to" is a solution, not an outcome
- "But" is a missing feature, not a barrier
- "Because" is competitor-driven, not user-driven
- "Makes me feel" is about the PM, not the user

**How to fix it:**
Reframe from the user's perspective:

**I am:** A product manager analyzing user behavior across multiple features

**Trying to:**
- Identify which user segments are at risk of churning within the next 30 days

**But:**
- Current analytics require manual SQL queries and hours of data wrangling
- By the time I identify at-risk users, many have already churned

**Because:**
- Our analytics tools don't surface predictive insights automatically

**Which makes me feel:**
- Reactive instead of proactive, and like I'm failing to retain customers

**Final Problem Statement:**
"Product managers need a way to proactively identify at-risk users before they churn because current analytics are manual and slow, which causes missed retention opportunities."
