✅ 1.SayPro Define Key Metrics
Establish measurable factors for both expectations and experiences:
- Consumer Expectations: Use surveys, focus groups, online reviews, and pre-sale feedback.
- Real Experiences: Analyze post-purchase surveys, support tickets, return reasons, and NPS (Net Promoter Score).
✅ 2.SayPro Gather Multi-Source Data
Enable SayPro to pull data from:
- Pre-sale touchpoints (ads, product pages, social media)
- Customer service records
- User-generated content (reviews, forums)
- Usage analytics (app logs, feature usage, purchase behavior)
✅ 3.SayPro Use Sentiment & Intent Analysis
Implement AI models in SayPro to:
- Detect expectation keywords (“I hope,” “I expect,” “should be”)
- Identify satisfaction signals post-interaction (“didn’t work,” “loved it”)
✅ 4.SayPro Map Expectation vs. Experience
Use NLP models to compare:
- Promised or assumed value (expectations)
- Reported or actual outcome (experiences)
Example:
- Expectation: “Fast delivery”
- Experience: “It took 5 days to arrive”
Discrepancy: +3 days → Flagged for operational review.
✅ 5.SayPro Dashboard & Alerts
Develop a SayPro dashboard with:
- Real-time discrepancy detection
- Severity scoring (low, moderate, high impact)
- Department-specific alerts (Product, Marketing, Support)
✅ 6.SayPro Root Cause & Trend Analysis
Enable SayPro to:
- Cluster recurring discrepancy types
- Track sources (marketing misalignment, product defects, etc.)
- Recommend corrective actions (training, product changes, messaging tweaks)
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