SayPro prompts per batch

Sentiment & Emotion Analysis (20 Prompts)

1. Classify this customer feedback as positive, negative, neutral, or mixed.
2. What is the overall emotional tone of the following feedback?
3. Identify the sentiment trend across this dataset over the last 6 months.
4. Highlight feedback that includes frustration, confusion, or dissatisfaction.
5. From this list, which entries express high satisfaction or gratitude?
6. How does sentiment differ between mobile and web users?
7. Create a sentiment score (1–10) for each entry.
8. Extract emotional keywords used in SayPro service feedback.
9. Compare emotional tone across different SayPro service types.
10. Identify shifts in sentiment before and after a major campaign launch.
11. Detect any sarcasm or passive-aggressive remarks in this feedback.
12. Which entries show evidence of trust or betrayal?
13. Analyze fear-related sentiment related to SayPro security services.
14. Identify confidence-building or trust-affirming feedback.
15. Cluster user emotions using NLP models.
16. Visualize emotional fluctuations across touchpoints.
17. Analyze emotional variance in responses from different age groups.
18. Classify emotion into Joy, Sadness, Anger, Fear, or Surprise.
19. Flag emotionally charged entries needing urgent attention.
20. Generate a sentiment-based heat map from regional feedback.

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🔹 B. Thematic Classification (20 Prompts)

21. Group this feedback into major themes or topics.
22. Tag entries based on the following 12 SayPro themes (e.g. trust, delivery, accessibility).
23. What are the most recurring pain points in the feedback?
24. Identify positive themes that stand out across all comments.
25. Map user feedback to SayPro’s strategic pillars.
26. Detect operational vs. strategic issues in this dataset.
27. What feedback relates to inclusivity and language use?
28. Highlight entries referencing SayPro staff behavior.
29. Separate complaints about cost, value, and pricing.
30. Identify all entries related to SayPro’s app performance.
31. Group feedback related to rural access and infrastructure.
32. Highlight themes of empowerment or self-efficacy.
33. Which comments reference training or education impact?
34. Which entries suggest a need for transparency?
35. Identify suggestions vs. complaints vs. praise.
36. Create a category taxonomy from this sample data.
37. Group feedback into “Service Delivery,” “Support,” and “Communication.”
38. Find feedback themes that cross age groups and demographics.
39. Match each entry to a relevant SayPro business line.
40. Generate a frequency distribution of common topics.

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🔹 C. Brand Perception & Trust (20 Prompts)

41. What is the perceived strength of SayPro’s brand in this dataset?
42. Which entries reflect trust-building behavior by SayPro?
43. Identify entries where trust in SayPro has eroded.
44. How does SayPro’s brand score on transparency and ethics?
45. Find comments describing SayPro as reliable or unreliable.
46. Score feedback on perceived professionalism of SayPro.
47. Analyze how SayPro is viewed by long-term vs. new users.
48. Extract brand values mentioned or implied in the data.
49. Which feedback aligns with SayPro’s mission statement?
50. Identify brand confusion or identity misalignment in user comments.
51. Compare trust perception of SayPro vs. its partners.
52. Find entries showing increased brand loyalty over time.
53. Which words are frequently associated with SayPro’s brand image?
54. Rate each entry for its impact on SayPro’s brand reputation.
55. Group entries by brand strength, risk, or opportunity.
56. Detect perception differences between urban and rural respondents.
57. Flag entries showing advocacy or ambassadorship.
58. Extract user expectations of SayPro’s brand.
59. Identify hidden brand risks based on language cues.
60. Summarize brand perception trends across feedback sources.

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🔹 D. Strategy & KPI Insights (20 Prompts)

61. What are the top strategic issues customers care about most?
62. Identify operational KPIs that can be tracked from this feedback.
63. Generate a weekly KPI dashboard using these entries.
64. Which themes indicate declining or improving service quality?
65. What unmet needs can be inferred from these comments?
66. Suggest 5 actionable strategies based on this dataset.
67. Identify communication gaps affecting service delivery.
68. Translate user complaints into system-level recommendations.
69. What KPIs can be linked to customer satisfaction in this data?
70. Extract time-based trends for performance indicators.
71. Recommend brand positioning strategies from this analysis.
72. Identify signs of service innovation expectations.
73. Extract crisis management lessons from negative feedback.
74. Turn suggestions into policy refinement ideas.
75. List service features that need urgent redesign.
76. Highlight feedback that reflects success against SayPro KPIs.
77. Identify entries that signal readiness for service scale-up.
78. Build a monthly summary of engagement KPIs.
79. Find feedback aligning with national development goals.
80. Prioritize action areas based on frequency and sentiment.

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🔹 E. Multichannel & Demographic Insights (20 Prompts)

81. Compare feedback quality by channel (web, mobile, WhatsApp).
82. Identify age-specific service feedback trends.
83. What do younger users (18–35) say about SayPro courses?
84. How do donor comments differ from program participants?
85. Detect user location (if mentioned) and analyze by province.
86. How does feedback differ in tone by gender (if specified)?
87. What regional linguistic variations appear in responses?
88. Match user tone to communication method (text vs. voice).
89. Cluster entries by engagement type: transactional vs. relationship.
90. Compare urban vs. rural feedback themes.
91. Which services receive the most praise from youth?
92. Find culturally specific language in feedback.
93. Extract tribal or local dialect expressions that reflect tone.
94. Are there any indicators of digital exclusion in the feedback?
95. Flag entries with potential accessibility concerns.
96. Summarize feedback by time of year (seasonal variation).
97. Detect migration-related feedback trends.
98. Compare language sentiment by app UI language.
99. Segment entries based on user familiarity with SayPro.
100. Visualize feedback clusters geographically.