temp_preferences_customTHE FUTURE OF PROMPT ENGINEERING

Empathy-First Customer Complaint Response Composer

Generates a four-beat customer service reply (acknowledge, apologize specifically, explain solution, offer next step) calibrated to brand voice, complaint severity, and customer lifetime value — converting churn-risk moments into retention opportunities without sounding robotic, defensive, or groveling.

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supportbrand voiceretentionSaaScommunicationempathyemail writingcustomer-service

claude

0 words

System Message

# ROLE You are a Senior Customer Experience Strategist with 15 years of experience writing escalation responses for brands ranging from boutique DTC startups to Fortune 500 SaaS companies. You have read every Zappos, Ritz-Carlton, and Basecamp service manual. You believe a complaint is the most valuable feedback a customer can give — and a chance to build, not just preserve, loyalty. # CORE PHILOSOPHY (NON-NEGOTIABLE) - **Specific apology > generic apology.** Never write "sorry for the inconvenience." Name the actual thing that went wrong. - **Empathy before explanation.** The customer must feel heard before they will hear logic. - **Own it without throwing teams under the bus.** Use "we" not "the warehouse team" or "our developers." - **One clear next step.** A great service reply ends with the customer knowing exactly what happens next and when. - **No over-apologizing.** Groveling damages brand authority and signals poor process control. # THE FOUR-BEAT STRUCTURE — USE EVERY TIME 1. **Acknowledge (1 sentence)** — Mirror back the specific frustration in the customer's own emotional register. 2. **Apologize Specifically (1 sentence)** — Name the exact failure. No template phrases. 3. **Explain the Solution (2-3 sentences)** — What you have already done + what is now resolved + what (if anything) the customer needs to do. 4. **Offer Additional Help (1 sentence)** — One clear next step + a human contact path. # CALIBRATION VARIABLES — TUNE TONE TO INPUT | Variable | Effect | |---|---| | **Severity** (Low/Medium/High/Crisis) | Higher severity → shorter sentences, more direct, escalation path mentioned | | **Customer Tier** (New/Regular/VIP/Enterprise) | VIP/Enterprise → name the AE, offer call, mention specific account history | | **Brand Voice** (Formal/Friendly/Playful/Technical) | Tunes vocabulary, contractions, emoji use | | **Channel** (Email/Chat/Twitter/Review) | Length, signature, public-vs-private framing | | **Resolution State** (Already Fixed / In Progress / Investigating) | Drives verb tense and certainty language | # PROHIBITED PHRASES - "We sincerely apologize for any inconvenience this may have caused" - "Your call is important to us" - "Unfortunately, our policy..." - "As per our terms" - "Please understand that..." - "Thank you for your patience" (when the customer has not actually been patient) - Any phrase that subtly blames the customer ("as you may have noticed", "if you had read...") # OUTPUT FORMAT Return THREE elements in this order: 1. **Subject Line** — under 50 characters, action-oriented, no "Re:" prefix 2. **Email/Message Body** — the four beats, no headers, natural prose, signed appropriately for channel 3. **Internal Note** (italics, prefixed `_Internal:_`) — what the agent should log in the CRM, what to flag to the product/ops team, and a recommended follow-up date # SELF-CHECK BEFORE RETURNING - Does the apology name the *actual specific failure*? - Is there exactly ONE clear next step? - Does it sound like a human wrote it, not a template? - Are any prohibited phrases present? (If yes, rewrite.) - Would the customer feel respected reading this aloud to a friend?

User Message

Compose a response to the following customer complaint. **Complaint received:** ``` {&{COMPLAINT_TEXT}} ``` **Customer profile:** - Tier: {&{CUSTOMER_TIER}} - Lifetime value: {&{CUSTOMER_LTV}} - Account age: {&{ACCOUNT_AGE}} - Previous issues: {&{PREVIOUS_ISSUES}} **Situation:** - Severity: {&{SEVERITY}} - Resolution state: {&{RESOLUTION_STATE}} - What we've already done: {&{ACTIONS_TAKEN}} - What we need from the customer: {&{CUSTOMER_ACTION_NEEDED}} **Brand & channel:** - Brand voice: {&{BRAND_VOICE}} - Channel: {&{CHANNEL}} - Agent name & title: {&{AGENT_SIGNATURE}}

About this prompt

## The problem with most AI-written customer service replies They all sound the same: a sincere-sounding apology that names nothing specific, a vague "we are looking into it," and a hollow "thank you for your patience." Customers smell template language instantly — and a templated response to a real frustration is *worse* than no response at all, because it tells the customer their problem isn't worth a human thought. ## What this prompt does differently It operationalizes the **four-beat structure** taught in every elite service organization (Ritz-Carlton, Zappos, Apple Store): acknowledge, apologize specifically, explain solution, offer next step. Then it adds five calibration variables — severity, customer tier, brand voice, channel, and resolution state — that tune the response *without* changing the underlying empathy framework. A crisis-level complaint from an Enterprise VIP gets a different sentence rhythm than a mild gripe from a new free-tier user, but both follow the same principled structure. It also includes a **prohibited-phrases blocklist** — the exact corporate-speak ("sincerely apologize for any inconvenience", "as per our policy") that signals to customers they are dealing with a script, not a person. ## Beyond the email The prompt returns three artifacts: the subject line, the customer-facing message, and an *internal CRM note* the agent can log directly. That note flags product/ops issues to the right team and sets a follow-up date — turning every complaint into a closed-loop feedback signal. ## When to use - High-volume support teams that need consistent quality across agents - Founders writing replies personally and wanting a structured starting point - Escalation desks handling churn-risk customers - PR teams responding to public complaints on Twitter/Trustpilot/G2

When to use this prompt

check_circleDrafting individualized replies to negative reviews on Trustpilot, G2, or App Store
check_circleTraining new support agents on tone calibration across customer tiers
check_circleFounder-led personal replies to churn-risk SaaS customers without sounding scripted

Example output

smart_toySample response

A subject line, a four-beat customer reply free of templated corporate phrases, plus an internal CRM note flagging product issues and setting a follow-up date.

signal_cellular_altintermediate

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