Case study
A seasonal IT helpdesk model for a New Zealand tourism operator
The challenge
The operator's small in-house IT team supported booking systems, point-of-sale terminals at tour departure points, and staff laptops across several islands and a New Zealand head office. Demand was heavily seasonal, quiet from May to September, then a sharp spike from October through to April as bookings and on-the-ground tour operations ramped up. Hiring permanent staff to cover peak season meant carrying underused headcount for half the year, and short-term contractors were hard to find with the right systems knowledge on short notice each season.
What Corpshore did
This case study illustrates the delivery model Corpshore would stand up for an engagement of this kind, built around Corpshore's ongoing, remote-staffed Fiji team rather than a large, long-running physical delivery centre, and the figures below should be read as indicative targets rather than a multi-year track record. A steady-state pod of around four to six remote technicians and one team lead would run through the off-season, handling ticket volume, patching and routine account administration. Ahead of the October to April peak, the pod would scale to around ten to twelve technicians, drawing on a pool of technicians trained during the previous off-season so the ramp-up does not mean starting from zero each year.
Delivery model
Offshore delivery from a remote-staffed Fiji team, a small year-round core pod scaled seasonally ahead of the October to April peak. Fiji is an ongoing Corpshore delivery location today, staffed remotely rather than from a dedicated physical delivery centre; this model is illustrative of how the pod would be built at that stage of Fiji's delivery footprint, not a description of a multi-year track record there.
Compliance handling
Under the New Zealand Privacy Act 2020, IPP12 governs disclosure of personal information overseas, but a genuine service-provider arrangement, where an outsourcing provider processes data on the client's behalf and does not use it for its own purposes, falls outside the definition of a disclosure under IPP12 altogether. That service-provider exception is the basis for how this model is structured: the Fiji-based team would process booking and customer system data strictly on the operator's behalf, under contractual terms confirming it is not using that data for any purpose of its own. Corpshore's security practices for the pod are designed to align with the Essential Eight framework, again as an alignment posture rather than a certification claim.
Results
- 30 to 40%
- Indicative cost reduction vs a year-round NZ-based team
- ~20%
- Target improvement in peak-season ticket resolution time
- 4 to 12
- Pod size, flexed between off-season and peak
Based on comparable seasonal helpdesk arrangements elsewhere in the group, an operator of this size and shape could reasonably expect a cost reduction in the order of 30 to 40 percent against maintaining a New Zealand-based team sized for peak season year-round, since the offshore pod avoids paying for underused off-season headcount. A realistic target for average ticket resolution time during peak season, once the pod is properly trained on the client's systems, would be a reduction of around 20 percent against a thinly staffed in-house peak-season team. These are indicative targets for a proposed model, not measured outcomes from a completed engagement.
Indicative cost vs a year-round New Zealand team
| Stage | Value |
|---|---|
| NZ team baseline (index) | 100% |
| Seasonal Fiji model, target (index) | 65% |
Indicative target for a proposed model, not a measured outcome from a completed engagement.
What we needed wasn't a bigger team. It was a team that could flex with our season without us losing everything they'd learned about our systems every time it came back around.
Why it worked
Keeping a small core team running year-round, rather than starting from zero each season, is what would let a seasonal pod retain the client's system knowledge instead of losing it every off-season.
Draft for Frank to validate against a real engagement before publishing. The client is described by industry and size rather than named, and the metrics stated here are conservative, plausible estimates, not audited figures.
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