Retail support volume rises about 42% over the holidays, one rent-payments fintech sees 4x its normal chat volume every rent week, and the IRS answered 21% of the 48.1 million calls it received during the 2026 filing season. Seasonal hires cannot close a gap that size, because they arrive weeks before they are useful and leave weeks after the peak. What works is a capacity plan: AI absorbs the predictable tail of the surge, your team is held back for the cases that need judgement, and the setup is rehearsed against last year's tickets before the first peak day.
Key takeaways
Size the spike from public benchmarks: retail support volume rises about 42% in the holiday season (Zendesk), Cyber Monday reached $14.25 billion online with $16 million spent every minute at peak (Adobe), the IRS had received 142.2 million returns by April 24, 2026, and 23.1 million people selected marketplace coverage in one enrolment window (CMS).
Seasonal hiring is shrinking and slow: NRF expected 265,000 to 365,000 holiday hires in 2025 against 442,000 in 2024, and BLS data shows the January layoff follows the December buildup every year.
Decision rule: hand a contact reason to AI for the peak only if it is high-volume during the spike, resolvable from a documented procedure plus system data, and reversible if wrong. Everything else gets an escalation path, not an automation attempt.
Rehearse, do not hope: run the agent against last peak's real tickets, red-team the season-specific failure modes, and set a numeric readiness gate for go-live.
How big do seasonal support spikes actually get?
Bigger than most staffing plans assume, and concentrated in a handful of days and contact reasons. These are the public figures worth putting in front of a finance partner.
Peak | What the published data shows |
|---|---|
Retail holiday season | Support ticket volume for retailers rises about 42% (Zendesk). NRF forecast 2025 November and December sales of $1.01 to $1.02 trillion, the first time past $1 trillion (NRF). Deloitte projected e-commerce growth of 7% to 9% for the 2025 season (Deloitte). |
Cyber Monday | $14.25 billion online in one day, with $16 million spent every minute between 8 pm and 10 pm (Adobe). The full season was $257.8 billion over 61 days (Adobe), so Cyber Monday ran at more than three times an average day. Order tickets follow with a lag of one to three days. |
Tax season | 142.2 million individual returns received by April 24, 2026, and 534 million IRS.gov visits, up 59.8% on the prior year (IRS). The IRS received 48.1 million calls, assistors answered 9.9 million (21%), and average hold time was 14 minutes; on the balance-due line the average wait was 45 minutes and 31% of calls were answered (National Taxpayer Advocate). |
Open enrolment | 23.1 million consumers selected or were auto-enrolled in marketplace coverage during the 2026 Open Enrollment Period (CMS). Each selection can generate an ID card, eligibility or premium question within weeks. |
Rent week | One rent-payments fintech reports 4x its normal chat volume during rent week versus the rest of the month, twelve times a year: the most predictable peak on this list. |
Two things matter for planning. First, the multiple is not uniform: Cyber Monday is a one-day 3x event, tax season a twelve-week plateau with a cliff at the deadline, rent week a monthly 4x. Second, the reasons that break are the predictable ones. In the IRS data, two of the three highest-volume lines (balance due and identity verification) were the worst served. Routine, high-repetition contacts collapsed the queue anyway. That is the shape of every seasonal failure: the boring tickets crowd out the ones that need a person.
Why does seasonal hiring fail to close the gap?
Because the hire arrives late, ramps during the peak, and leaves with your institutional knowledge in January. The public labour data shows all three.
Hiring lags the signal. NRF expected retailers to hire between 265,000 and 365,000 seasonal workers in 2025, down from 442,000 in 2024, and noted retailers were waiting to add staff until demand strengthened. The requisition then opens after volume starts, and a November hire is still learning your returns policy on Cyber Monday.
Ramp overlaps the peak. BLS classifies customer service representatives as needing short-term on-the-job training, with a 2025 median wage of $44,770 a year or $21.53 an hour across 2.67 million jobs (BLS Occupational Outlook Handbook). The generic skills are quick. Your product, policies, systems and compliance rules are not, and a new agent is least accurate in exactly the weeks you hired them for. If your onboarding takes six weeks, any hire made after volume starts rising becomes productive after the peak. Ten seasonal agents for eight weeks at the BLS median wage is roughly $69,000 in wages alone (computed at 40 hours a week), before recruiting, training, supervision and the quality cost of their first weeks.
The capacity unwinds in January. BLS reports that retail employment in seasonal-hiring industries rose by 494,000 from October to December 2023 and was followed by layoffs in January and February; in the three years before the pandemic, retailers added an average of 591,000 seasonal jobs and then laid off an average of 84,000 more employees than they had hired (BLS, The Economics Daily). Every year the knowledge of how your peak behaves leaves with the people who lived it.
Which tickets should AI absorb during a peak, and which should it not?
AI should absorb the predictable tail: contact reasons that balloon during the spike, follow a documented procedure, and can be resolved from data your systems already hold. Apply three tests to each reason in your forecast.
Volume test. Does this reason grow faster than the total during the peak? Order status and returns initiation do in retail; refund status and document re-sends in tax season; ID cards and plan-change receipts in open enrolment; payment confirmation and failed-payment retries in rent week.
Procedure test. Can you write the resolution as steps a new agent would follow, with the systems they would check? If the answer depends on discretion (a goodwill credit, a hardship arrangement, whether a claim is covered), it fails this test.
Reversibility test. If the agent gets it wrong, can the outcome be undone cheaply? Re-sending a document is reversible. Closing an account is not. Set dollar and action limits accordingly and route anything above them to a person.
A reason that passes all three is a candidate for end-to-end resolution: the agent looks up the record, takes the action, and confirms back to the customer without a human touching the ticket. A reason that fails any test still gets AI help, but as triage and context-gathering ahead of an escalation, not a resolution attempt. This distinction is the whole plan. A bot that answers FAQs and hands the rest to a queue does not remove the peak; it defers it into the queue you were trying to protect. The difference between resolution and deflection is the difference between capacity and a delay. The separate list of issues that should never be automated applies with more force when volume is high and everyone is tired.
Five steps to a peak capacity plan
Forecast the mix, not the total. Pull the last two peaks by day and by contact reason, then overlay growth, new products and planned promotions. Treat a 42% lift as a floor for retail and plan the busiest day at three times an average one. A mix forecast tells you how many people you need once the tail is automated.
Set the human floor. Estimate escalations on the worst realistic day (as a planning heuristic, 15% to 30% of AI-handled conversations in a first season) and staff the judgement queue for that number, not for total volume. This is the headcount you protect from overtime.
Assign the tail to AI, top reasons first. Build the five to ten reasons that pass all three tests before touching the long tail. Where steps must happen in a fixed order (verify identity, check status, then act), build deterministic workflows. Where the conversation needs flexibility, describe the policy in plain language. Most peaks need both in the same ticket.
Write the escalation triggers as rules. Panic language, hardship, a complaint or regulator, a repeat contact within 48 hours, a request above a dollar threshold, and any failed system call should each hand off automatically, transcript and gathered data attached.
Set a readiness gate and a rollback. Decide the resolution rate and error rate each workflow must hit in simulation before go-live, who signs it off, and how any single workflow gets switched off in under a minute if live QA shows a problem. Write these down in September, not on the peak day.
What should you rehearse before the peak?
Four things: the workflows against real history, the season-specific ways they fail, the escalation path end to end, and the outage you hope will not happen.
Simulate against last peak's tickets. Replay real transcripts from the same period last year through the agent and score each workflow separately: resolution rate per reason, failure cases with transcripts attached, and the reasons where the agent guessed. Tune and rerun until each workflow clears its gate. This step turns a launch from a hope into a number.
Red-team the seasonal failure modes. Peaks bring their own abuse patterns: promo stacking and refund fraud in retail, identity claims against pending refunds in tax season, coverage-date disputes in enrolment, and distressed customers everywhere. Write adversarial scenarios for each and confirm the agent declines, escalates or holds the line. A practical red-team method is to have your most experienced agents try to break the workflows they know best.
Rehearse the handoff. Trigger each escalation rule deliberately and follow the ticket into the human queue. Check the routing, that context arrived, and that the customer was not asked to repeat themselves. Do it on every channel, because a voice handoff and a chat handoff fail differently.
Drill the bad day. Pick the worst plausible incident for your peak (a payment processor outage in rent week, a courier delay on the last shipping day, a refund backlog in April) and pre-build the holding workflow: what the agent says, what it cannot promise, and when it stops and escalates. Set guardrails before the drill: required disclosures, refund caps, no account closures, inbound message checks for injection attempts, and outbound checks so nothing leaves without passing policy.
What this looks like in practice: a worked example
Take a consumer tax-filing fintech heading into April. Last year its top four peak reasons were refund status, re-sending a filed return, failed document uploads and deadline questions, covered by contractors who started in February. This year the team builds those four reasons in Lorikeet as workflows, with identity check and status lookup as deterministic steps and the customer explanation in plain language, so one conversation moves between the two without a handoff. Before launch they run the agent over last April's transcripts through pre-launch simulations and a red-team pass on refund-identity claims, then set guardrails that keep the agent from promising a refund date it cannot verify. Anything mentioning an audit, hardship or a complaint escalates with context attached, and Coach reviews 100% of conversations rather than a sample, so a workflow drifting on day three is caught on day three through automated QA. The same workflows run across chat, email and voice, because the deadline-week caller is often the person who emailed yesterday.
Published results from Lorikeet customers running this pattern: Summ cut tax-time resolution time by 97%, taking chat and email first responses from about 30 minutes with human agents to under a minute. Flex handles 4x its normal chat volume during rent week and cut median conversation time to resolution by half. Eucalyptus, whose spikes follow news events rather than a calendar, has built, tested and launched a complex multi-step workflow within 45 minutes of an announcement, and lifted CSAT by 10 points once triage put the hardest cases in front of its best agents. The economics follow the curve rather than the forecast: Lorikeet charges per resolution, about $0.80 for a chat or email resolution, unresolved tickets are not billed, so capacity reserved for a spike that never came costs nothing. That model, plus SOC 2, PII redaction and data residency options, is why it is common in financial services teams whose peaks are set by regulators and tax calendars. To run this test on your own history, bring last peak's tickets to a demo and simulate them before committing.
One honest limitation. Last year's tickets do not contain this year's new product, policy change or outage, so the first peak always surfaces reasons the simulation did not cover; budget people to catch them. Lorikeet also cannot fix an upstream problem: if refunds are genuinely delayed, the agent can explain and set expectations, but it cannot make the money move faster. Measure on your own tickets rather than on any platform-wide rate.
What still needs a human
The judgement queue, staffed properly, because AI has cleared the noise that used to hide it. Keep people on hardship and vulnerability conversations, on anything the customer asks to take to a person, and on disputes, where the agent gathers facts and never forms an opinion on whether a claim is covered. Keep clinicians or licensed staff on medical and regulated advice. Keep an experienced agent on any ticket the workflow has no path for, because a novel failure during a peak is where a confident wrong answer does the most damage. And keep someone reading QA output every day of the peak; the first peak on AI is a learning season, and the tuning is human work.
Seasonal spikes are predictable in timing and unpredictable in size, the worst combination for a plan built on headcount committed in advance. Move the predictable tail to an agent tested against your own history, keep your people for the cases that need them, and the peak stops being the week your best agents burn out and becomes the week your plan gets measured.
