Lake Pajamas — Customer Service Playbook

Purpose and strategic objectives

This document outlines a professional, KPI-driven approach to customer service for Lake Pajamas — a premium sleepwear brand — designed to protect brand equity, reduce return costs, and increase lifetime value. The objective is to convert service interactions into revenue opportunities while maintaining an average Customer Satisfaction (CSAT) score above 90% and minimizing operational friction for returns and exchanges.

Operational goals should be explicit and time-bound: respond to all inbound digital inquiries within 24 hours (target: 95% of tickets), resolve 70–85% of issues on first contact, and process returns or exchanges within 48–72 hours of receipt at the warehouse. These targets allow the team to measure performance and justify staffing or tooling investments.

Customer experience standards and KPIs

Define a small set of measurable KPIs that guide daily work and quarterly reviews. Recommended primary KPIs: CSAT (target ≥ 90%), First Contact Resolution (FCR, target 70–85%), Average Handle Time (AHT, email 8–20 minutes equivalent; chat 6–12 minutes; phone 6–10 minutes), and Net Promoter Score (NPS, target ≥ 35 for premium apparel). Secondary KPIs include ticket backlog (target ≤ 48 hours) and return processing time (target ≤ 5 business days from receipt to refund).

Use data cadence: daily dashboards for ticket volumes and SLA breaches, weekly trend reports for FCR and CSAT, and a quarterly strategic review tied to marketing campaigns and seasonal peaks. Track revenue-impact metrics as well — for example, average order value (AOV) uplift after an assisted-sell interaction and conversion rate on post-service promotional offers (aim for 5–15% conversion on targeted offers).

Channels, SLAs, and staffing model

Operate an omnichannel model but prioritize channels by cost-to-serve and customer expectation. Typical set: email, web chat, phone, Instagram/DMs, and a self-service knowledge base. Target SLAs: live chat initial response < 2 minutes during staffed hours, phone answer within 90 seconds (or < 3 rings), email initial response within 24 hours, social DM within 4–8 hours. Maintain extended coverage during peak seasons (holiday sales, new launches) with hours like Mon–Sun 8:00–20:00 local time for chat and social.

Staffing guidelines: one full-time equivalent (FTE) can realistically handle 75–120 tickets per day depending on complexity and channel mix; adjust for peak multipliers (2–3x ticket volume during major promotions). Use part-time or seasonal agents for November–January when return volumes rise by 60–120% depending on campaign scale. Cross-train warehouse staff to assist with basic ticket triage during extreme peaks.

Returns, exchanges, and fulfillment details

Design clear, fair policies that reduce friction while protecting margin. A standard premium-brand policy looks like: free returns within 30 days of delivery, items must be unworn with tags, exchanges processed immediately upon availability, refunds issued within 5–7 business days after warehouse inspection. For higher-cost items (for example, sets priced $95–$250), require signature confirmation on return shipments over $200 to reduce loss.

Operationally, set processing SLAs at the warehouse: intake and inspect returns within 48 hours of arrival, update the order system with disposition code (resell, refurbish, destroy), and trigger refund or exchange once disposition is confirmed. Track return reason codes by percentage (fit issues, quality complaints, buyer’s remorse) and aim to reduce fit-related returns by 15–25% annually via improved size guidance and fit photos.

Escalation patterns, training, and quality assurance

Create a two-tier escalation path: Tier 1 handles standard inquiries (order status, size advice, returns), Tier 2 handles quality complaints, chargebacks, and legal/PR risks. Escalation thresholds should be numeric and time-based — for example, any unresolved claim older than 72 hours or any potential repurchase loss > $150 escalates automatically. Include a documented exception approval matrix with monetary limits for goodwill gestures.

Invest in continuous training and QA: weekly 60-minute coaching sessions, monthly role-plays for difficult scenarios, and a quarterly QA program reviewing a statistically significant sample of tickets (e.g., 5% of weekly tickets with a minimum of 50 interactions). Use a scorecard with weighted criteria: empathy (30%), resolution accuracy (30%), policy adherence (20%), and upsell/cross-sell opportunities handled (20%).

Practical tools, templates, and proactive communications

Equip agents with an integrated helpdesk (ticketing + chat + social) and a centralized CRM that shows order history, returns history, previous tickets, and lifetime spend. Implement canned responses for frequent questions but require agents to personalize the first sentence. Maintain a public knowledge base and size guide that includes exact garment measurements in centimeters and inches, and photographic fit examples for sizes XS–XXL to reduce pre-sale uncertainty.

• High-value templates and actions: (1) Pre-shipment delay notice — template with ETA and 10% promo code for future purchase; (2) Refund-completed message — include tracking, timeline (5–7 business days), and next-step for exchanges; (3) Quality-claim workflow — request photos + batch ID, escalate to QA within 24 hours, authorize return label if validated. Each template should include expected SLA and agent sign-off language.
• Proactive monitoring: run monthly social listening reports, measure sentiment change after major product drops, and maintain a post-purchase NPS survey sent 7 days after delivery with a target 15% response rate.

Measuring ROI and continuous improvement

Bind customer service metrics to financial outcomes: track cost per ticket, cost per resolved return, revenue recovered via assisted sell, and CLTV changes following service improvements. Benchmark cost per ticket in apparel e-commerce (industry ranges typically $3–$12 depending on automation and channel mix) and use that to evaluate investments in AI chatbots or additional agents.

Finally, schedule quarterly retrospectives to align customer service with product design and marketing. If fit issues are the top return reason (>30%), prioritize product development changes for the next season. Use hard numbers when presenting to leadership: estimated savings per percentage point reduction in returns, projected revenue from improved FCR, and customer retention gains tied to CSAT improvements above the 90% target.