Peak-Season Support Readiness Checklist

☐ Pick the peak window and dates: [event name], [start date] to [end date]

☐ Pull volume from the same window last year (or the last comparable spike) as your baseline

☐ Estimate this year's multiplier vs. baseline and write it down: [e.g. 2x]

☐ Break the forecast down by channel: website, WhatsApp, Telegram, Instagram, email

☐ Note expected peak hours and time zones so coverage matches demand

☐ Set targets for the window: first-response time [target], resolution time [target], CSAT [target]

☐ Set your AI-handled rate target: aim for the AI agent to fully resolve [target %] of conversations

☐ List the top 10 questions you expect during the peak: [shipping cutoffs], [returns], [discount codes], [stock], ...

☐ Write or update an answer for each, including this peak's specific details

☐ Add the peak's key facts: [order-by date for delivery], [sale dates], [promo terms], [extended return window]

☐ Remove or unpublish anything out of date so the agent can't surface stale info

☐ Add answers for edge cases that spike at peak: [delayed orders], [out of stock], [failed payments]

☐ Test the agent with 10 real peak questions and fix any wrong or vague answers

☐ Confirm answers are consistent across website, WhatsApp, Telegram, Instagram, and email, and keep WhatsApp and Instagram replies shorter than your website and email versions so they read cleanly on mobile

☐ Set a proactive message on high-intent pages: [e.g. "Order by [date] for delivery before [holiday]"]

☐ Add a proactive nudge on the cart or checkout page to catch hesitation

☐ Build macros for the top repeat questions so agents reply in one click: [where's my order], [returns], [promo code]

☐ Pre-write a holding reply for when wait times climb: ["Thanks for your patience, we're seeing high volume and will reply within [time]"]

☐ Set an out-of-hours message that tells people the AI agent can help now and a human follows up by [time]

☐ Add an auto-tag or routing rule for urgent topics: [payment failed], [missing order], [damaged item]

☐ Set the CRM to capture every new conversation as a lead or contact, tagged with source and channel, so the record is complete the moment a chat starts

☐ Build the coverage schedule to match forecast peak hours, not just normal business hours

☐ Name a point person for each shift: [name], [channel], [hours]

☐ Brief any temporary or cross-team helpers on the top peak questions and where to find answers

☐ Confirm everyone can access the shared inbox and CRM, and test logins before the peak starts

☐ Set the threshold for when the AI agent hands off to a human: [e.g. refund over $X, angry tone, repeated question]

☐ Agree on a backup plan if volume beats the forecast: [who gets pulled in], [what gets paused]

☐ List the most likely incidents: [site outage], [payment failure], [stockout], [shipping delay], [promo bug]

☐ For each, write the holding message customers should get and who approves it

☐ Name the escalation owner and how to reach them fast: [name], [phone or channel]

☐ Decide what the AI agent says during a known incident so answers stay on-message: ["We're aware of [issue] and working on it; expect an update by [time]"]

☐ Set a simple severity scale: [low: monitor], [medium: notify lead], [high: all hands]

☐ Confirm how you'll reach affected customers if needed: [email], [WhatsApp], [status update]

☐ During the peak, check daily: response time, resolution time, AI-handled rate, and CSAT against targets

☐ Watch for questions the AI agent couldn't answer and add them to the knowledge base same day

☐ Flag any topic spiking unexpectedly and decide whether it needs a new macro or proactive message

☐ After the peak, compare actuals to forecast and targets and note the gaps

☐ List the top 5 things that worked and the top 5 to fix before the next spike

☐ Export new leads and contacts captured during the peak and hand them to sales or follow-up

☐ Save this checklist with your notes so next peak starts ahead