Grow Demand
What shift in customer demand opens the window for this business now?
Demand Shift
NZ household bargain-hunting behaviour normalised across all income brackets after the 2022-2024 cost-of-living shock. Discount-retail share of national household spend has expanded materially, with the discount tier gaining share from mid-tier brands. The shift has been continuous since 2023 and is not reverting at current household-income trajectories.
The audience is expanding even as data infrastructure stays still. Every weekly catalogue reaches a larger, more discerning bargain-hunter who increasingly cannot tell if a Crackerjack store has the deal before they drive across town.
Sources confirming demand shift
- Stats NZ Retail Trade quarterly — discount/variety sub-category growth outpacing total retail since 2023
- NZX-disclosed The Warehouse Group multi-quarter margin commentary citing discount-tier pressure
- NZ Herald and Stuff cost-of-living retail coverage 2024-2025
Buyer Segments
Three primary consumer segments serve Crackerjack today:
The Weekly Stock-Up
- ~50% of basket revenue [INFERRED]
- Household shopper buying grocery + cleaning + household basics on a weekly cycle
- Cares about Was-Now-Save tags and verified in-stock deals
- Will drive 10-15 minutes for a confirmed deal
The Treasure-Hunt Discovery
- ~30% of basket revenue [INFERRED]
- Discretionary shopper — comes for one item, leaves with five
- Cares about unpredictable inventory and discovery
- Visit cadence: weekly or fortnightly
The Catalogue Reader
- ~20% of basket revenue [INFERRED]
- Opt-in email-club or printed-catalogue audience
- Plans a trip around a saleId — highest revenue per trip
- Lowest visit frequency; deepest catalogue engagement
Buying Signal Inventory
Five named signals that should drive every catalogue saleId decision but currently do not — because attribution data is absent:
The 5 buying signals — minutes saved per cycle when surfaced
- saleId payback velocity — how many days did the last comparable saleId take to recover its production cost in attributed in-store revenue?
- Category mix in last 5 catalogues — is this saleId over-indexing on one category, leaving others under-served?
- Store-level deal density — does the saleId's product mix match the stock position in each of the 15 stores? (right product, right store)
- FX-cost trajectory on the SKUs being promoted — promoting a SKU whose landed cost is rising fast destroys margin
- Catalogue-channel response rate — email-club open + click vs printed-distribution scan rate per saleId
Of these 5 signals, 0 are currently surfaced in a decision tool. All exist as raw data; none is attributed. Trip avoided when the saleId is the wrong fit; deal confirmed when it is right.
Capture Strategy
Three channels active today + one capture asset + a verb-led CTA:
Channels
- Channel 1 — Weekly digital catalogue (saleId-driven viewer): high-cadence, repeatable. Audience evidence: all 3 segments engage.
- Channel 2 — Email-club newsletter: opt-in audience already captured. Audience evidence: Catalogue Reader + Treasure-Hunt Discovery.
- Channel 3 — Physical store treasure-hunt experience: once they walk in, basket-build is automatic. Audience evidence: cross-segment.
Capture asset: the saleId ROI dashboard (Stage 1 UC2). After every catalogue cycle, the Marketing-Buying review meeting becomes evidence-based. Not customer-facing — the internal instrument that lets Crackerjack pick better saleIds next time.
CTA (internal, verb-led): Marketing + Buying jointly approve the next saleId selection within 48 hours of post-cycle review, using attributed revenue data from the previous cycle. From Month 2 onwards: every catalogue decision uses evidence, not intuition. Customer compounds: better deals, more relevant SKUs, trip confirmed not gambled. Subscribe behaviour follows.