Peak Season Support Readiness Checklist

Why peak season is categorically different

Peak season — primarily BFCM (Black Friday to Cyber Monday) and the following holiday period — isn't just 'more volume.' It's a different support environment. Order volume 3–5x. New customers who don't know your policies. Carrier delays under maximum load. Holiday gifting pressure that makes delivery timing emotionally high stakes. Promotional complexity with sale conditions, exclusions, and coupon stacking.

The stores that handle peak well aren't the ones with the most agents — they're the ones whose AI agents are fully calibrated before volume arrives, whose policies are documented for the new promotions, and whose human team has clear rules for what they handle and what the AI handles. Preparation matters more than headcount.

8 weeks out: AI configuration checklist

Eight weeks before peak, complete these AI configuration tasks. This is the last window for meaningful calibration before the season starts.

• Run an accuracy audit on your current AI agent — sample 50 conversations, grade accuracy by category. Identify the lowest-accuracy category and fix the knowledge source.
• Update your return policy documentation to reflect any holiday-specific changes (many stores extend return windows to late January for holiday gifts).
• Add documentation for your planned holiday promotions before they go live — the agent needs to know the conditions, exclusions, and expiry dates in advance.
• Review carrier performance data from last peak season and update expected shipping timelines. Last year's timelines are not this year's timelines.
• Test the escalation flow end-to-end — trigger an escalation manually and verify that context passes correctly to a human agent.
• Check that your order data connection is pulling live data and not cached data. Under peak load, stale cache is a common failure mode.
• Review your confidence thresholds — if you've been running conservatively, consider whether your calibration data supports loosening them slightly before volume scales up.

4 weeks out: policies and promotions

Four weeks out, your promotional calendar should be finalized. Now is the time to document every promotion in the agent's knowledge base and test each scenario.

1. 1Document every planned promotion with: the discount mechanism, eligible products, exclusions, start and end dates, and what happens if a customer applies a code after expiry.
2. 2Write a holiday shipping calendar and add it to the knowledge base: last-order-by dates for standard, express, and overnight shipping for arrival by December 25. Update this as carrier deadlines are announced.
3. 3Add documentation for gift order handling: can a customer include a gift message? Can they suppress the receipt? Can a different address be specified without order confirmation going to the recipient?
4. 4Add documentation for your extended holiday return policy and how it interacts with final-sale items from promotional events.
5. 5Test 10 holiday-specific question scenarios in your AI agent before they go live: 'Will this arrive by Christmas?' 'Can I apply two promo codes?' 'I bought this as a gift — can the recipient return it?'

2 weeks out: team preparation and escalation rules

Two weeks out, shift focus to your human team — they'll handle the 30% that the AI escalates, and during peak that 30% is significantly larger in absolute number.

• Establish a clear support ops schedule for the peak window — who is covering which hours, what the escalation path is when a primary agent is unavailable, and who has authority to approve exceptions.
• Set exception authority levels — during peak, you'll receive more exception requests. Decide in advance how much discretion agents have (e.g., can approve a late return on orders under $100, need manager approval over $100).
• Prepare canned responses for predictable peak season questions — agents work faster with good templates. Create templates for: carrier delay apology, holiday shipping deadline confirmation, extended return policy explanation, and out-of-stock notification.
• Review and tighten escalation rules for peak — add triggers for high-value orders, gift-related delivery concerns, and any new promotional complexity that the AI isn't trained on yet.
• Brief the team on the AI agent's capabilities — what it can and can't do, what to expect in the handoff summary, and how to flag accuracy issues they see during peak for post-season improvement.

1 week out: final checks

One week out, run final verification on everything. This is the last chance to catch configuration gaps.

During peak: monitoring protocol

During peak, you don't have time to run full QA audits. Use a lightweight daily monitoring protocol instead:

• Every morning: check yesterday's escalation rate vs. the previous week. A significant spike (> 5%) means something new is confusing the agent — find it and fix it.
• Every morning: check CSAT on closed tickets from the previous day. A drop of > 0.3 points should trigger an immediate sample review.
• At midday: check queue length and agent utilization. If the queue is building, identify whether it's a volume spike (expected) or an escalation rate spike (requires investigation).
• Have a designated 'AI agent owner' during peak — one person who can update the knowledge base if a new issue emerges. Don't let knowledge gap fixes wait until after peak; each day of the issue compounds.

Post-peak recovery and learning

The week after peak is when most teams exhale and move on. But the data from peak is the most valuable training data you'll generate all year. Run these post-season activities before it goes stale:

1. 1Pull your peak escalation log and do a full cluster analysis — what were the top 10 escalation reasons? Which ones were avoidable (knowledge gaps) vs. genuinely human-required?
2. 2Run an accuracy audit on 100 peak-period AI conversations — more than your standard 50. Peak generates novel question types that reveal edge cases in your knowledge base.
3. 3Debrief with your human agents — what questions did they handle that surprised them? What information did they wish they had in escalation summaries?
4. 4Update your knowledge base with everything you learned — new carrier delay language, new promotional exception handling, new question types that emerged.
5. 5Set a reminder to start the following year's peak prep in September, not October.