Most teams treat customer memory like one giant log. That's like a doctor prescribing without checking your vitals, history, or past case notes. You might get lucky. But usually, you get it wrong.
Shreya, a CX executive, picks up a frustrated customer. Her screen isn't a transcript dump. It's a doctor's chart with three kinds of memory:
- What it is: Last 3 messages, tone, and what's burning right now.
- Why it matters: Without this, you're that doctor who keeps saying: "Sorry, what symptom again?"
- Technical reality: This sits in cache (think Redis).
- What it is: Purchases, churn risk, lifetime value… and crucially, which marketing notifications they've actually engaged with, and through which channel.
- Why it matters: That detail is comfort ammo during a glitch — the difference between calming a patient or losing them.
- Technical reality: This lives in a KV/NoSQL store.
- What it is: The billing glitch from last quarter. The escalation where they almost churned. The moment you won them back.
- Why it matters: A timeline of events and emotions, not just "data points."
- Technical reality: This fits a time-series DB (Timescale, Influx).
Put together, these three memories create continuity. Continuity builds trust. Trust reduces churn — and even a 1% drop means ~$1M saved on a $100M book.
But why stop at service? The same memory rails that help Shreya rescue a customer in distress can also power hyper-personalised marketing when things are going well. If you already know which channel works, what messages resonate, and what moments matter — you're not running campaigns, you're running conversations. ROI compounds.
This project demonstrates what happens when you get customer memory right. It's a Customer Intelligence Platform that showcases:
- Unified Customer View: All three memory types in one interface
- AI-Powered Recommendations: Contextual actions based on complete customer history
- Real-time Decision Support: Tools that help CX teams act, not just react
- 📊 Interactive Dashboard: See how integrated customer memory looks in practice (
input/sample_customer_platform.html) - 🎬 Animated Demonstrations: Watch AI recommendations in action (
output/sample_customer_platform.gif) - 🛠️ Sample Generation Toolkit: Reference screenshot and GIF generators for documentation (
src/generate_screenshot.py,src/generate_gif.py)
The future isn't AI that "chats." It's AI that remembers — in the right way, in the right database.
The real question is: are you treating memory as an afterthought… or as your competitive moat?
Because if your CX system forgot your last interaction, would you trust it?
Why should your customers?
Generate screenshots and animations of the sample customer intelligence platform (input/sample_customer_platform.html):
# Install dependencies
pip install -r requirements.txt
# Install browser for automation
playwright install chromium# Create dashboard screenshot
python3.10 src/generate_screenshot.py
# Create animated demo with AI recommendations
python3.10 src/generate_gif.pyoutput/sample_customer_platform.png- High-resolution dashboard screenshotoutput/sample_customer_platform.gif- Animated demonstration of features
├── input/ # Source dashboard files
├── src/ # Automation toolkit
│ ├── generate_screenshot.py
│ ├── generate_gif.py
│ └── config.py
├── output/ # Generated content files
│ └── temp/ # Intermediate files (preserved for debugging)
└── README.md # This file
Modify src/config.py to customize:
- Viewport dimensions for different screen sizes
- Animation timing and frame rates
- Quality settings for output files
- Cropping parameters for content detection
For developers interested in the sample generator toolkit, advanced configuration, and extensibility options, see the Technical Documentation.
- Python 3.10+
- Playwright (browser automation)
- Pillow (image processing)