Customer Complaint Trend Analysis with Pareto and Time Series
Analyze complaint data over time to identify trends, seasonal patterns, and the vital few issues causing the majority of customer dissatisfaction.
Transform raw complaint data into actionable insights using Pareto analysis, time series decomposition, and root cause correlation to prioritize improvement efforts.
At a glance
Access
Free prompt
Open to copy without upgrading.
Prompt objective
Transform raw complaint data into actionable insights using Pareto analysis, time series decomposition, and root cause correlation to prioritize improvement efforts.
Real use case
An airline receives 15,000 complaints per quarter across 40 categories. Without trend analysis, they treat all complaints equally instead of focusing on the 3 categories causing 70% of dissatisfaction.
Customize these fields first
Replace the placeholders with your own context before you run the prompt. That usually improves the first output more than adding more instructions later.
Prompt
Perform a complete complaint trend analysis for [COMPANY NAME] using [NUMBER] months of complaint data.\\\\\\\\n\\\\\\\\n**Data Input:**\\\\\\\\n- Complaint records: date, category, subcategory, channel, severity, resolution time, customer segment\\\\\\\\n- Volume: [NUMBER] complaints per month\\\\\\\\n- Categories: [LIST 10-15 CATEGORIES]\\\\\\\\n\\\\\\\\n**Analysis 1 — Pareto (80/20):**\\\\\\\\n- Rank complaint categories by volume\\\\\\\\n- Cumulative percentage chart\\\\\\\\n- Identify the vital few (categories causing 80% of complaints)\\\\\\\\n- Pareto by severity (not just volume)\\\\\\\\n- Pareto by financial impact (compensation cost + churn risk)\\\\\\\\n\\\\\\\\n**Analysis 2 — Time Series Trends:**\\\\\\\\n- Monthly complaint volume trend\\\\\\\\n- Seasonal patterns (holiday spikes, summer dips)\\\\\\\\n- Year-over-year comparison\\\\\\\\n- Moving average (3-month) to smooth noise\\\\\\\\n- Trend direction: improving, stable, or worsening per category\\\\\\\\n\\\\\\\\n**Analysis 3 — Correlation Analysis:**\\\\\\\\n- Complaint volume vs. operational metrics (delivery time, defect rate, etc.)\\\\\\\\n- Lag analysis: do operational issues precede complaint spikes?\\\\\\\\n- Channel shift: are complaints moving from phone to social media?\\\\\\\\n- Geographic patterns: regions with increasing complaints\\\\\\\\n\\\\\\\\n**Analysis 4 — Resolution Effectiveness:**\\\\\\\\n- First-contact resolution rate trend\\\\\\\\n- Average resolution time trend\\\\\\\\n- Reopen rate (complaints that come back)\\\\\\\\n- Customer satisfaction after resolution trend\\\\\\\\n\\\\\\\\n**Output:**\\\\\\\\n- Executive summary: top 3 findings\\\\\\\\n- Priority action list (impact × urgency matrix)\\\\\\\\n- Monthly tracking dashboard template\\\\\\\\n- Early warning indicators for emerging issues\\\\\\\\n\\\\\\\\nTools: [Excel/Python/Power BI]. Include visualization code or spreadsheet formulas.
Open directly in an AI — the text is pre-filled:
How to use this prompt
- 1Replace the key placeholders first: COMPANY NAME, NUMBER, LIST 10-15 CATEGORIES, Excel/Python/Power BI.
- 2Replace any bracketed placeholders like [this] with your own context.
- 3Add extra background information when you want more tailored results.
- 4Combine multiple prompts in one conversation when you need a richer output.
- 5Save your best-performing prompts so they are easy to reuse later.
Next best step
Open the guide first, then branch only if you still need more.
A guide for sales teams that want better prospecting, follow-up, qualification, and pipeline support with AI.
If this prompt is close but not quite right, generate variants next. If the job is recurring, move into the course library after the guide.
Related prompts
View allProfessional Reclame Aqui Responses Using the LEAR Framework
Response templates for the Reclame Aqui platform (Brazil's leading consumer complaint aggregator) using the Listen-Empathize-Apologize-Resolve framework, adapted for the Brazilian consumer protection context.
Best for
Improve brand reputation on Reclame Aqui by responding to complaints with speed, empathy, and resolution focus, targeting the RA1000 seal (platform's highest rating for complaint handling).
Social Media Customer Service Crisis Management Plan
Complete protocol for handling reputation crises that go viral on social media, from initial alert to image recovery.
Best for
Prepare the company to respond quickly and coordinatedly when a complaint goes viral on social media, minimizing reputation damage and turning the crisis into an opportunity.
Complaint Resolution Workflow with SLA and Tiered Compensation
Structured process for resolving complaints with compensation levels proportional to severity, aligned with consumer protection regulations.
Best for
Create a standardized resolution flow that empowers support agents to offer appropriate compensation, reducing resolution time and increasing satisfaction.
Root Cause Analysis of Recurring Customer Complaints Using 5 Whys + Ishikawa Method
Methodology to identify and eliminate root causes of the most frequent customer complaints using quality improvement tools adapted for customer service operations.
Best for
Convert recurring complaints into systemic product, process, and service improvements, reducing ticket volume at the source.
Explore other prompt categories
Move sideways into adjacent libraries when the current category is not the full answer.
Free browsing stays open. Premium prompts unlock the reusable workflow layer.
Use the guides and role paths to validate the job first. Upgrade when you want the full prompt text, editable premium prompts, and the surrounding course paths in one place.
Free access
- Browse guides, role paths, and category pages.
- Preview prompts before you decide to upgrade.
- Find the right starting point without friction.
Membership access
- Unlock premium prompts and the full copy text.
- See more workflow paths and course connections.
- Keep the reusable templates in one place.
