Example work product · Spend diagnostic

Two-year spend diagnostic
Multi-location boutique fitness operator

Three studios in Vancouver, BC. We took two fiscal years of raw general-ledger exports and turned them into a cleansed spend cube, a normalized supplier master, and a prioritized savings pipeline — in the client's first two weeks.

24 moof general-ledger history reviewed
55%of operating expense addressable
3,478transaction lines cleansed
539 → 509raw payees normalized to vendors
3.0–6.1%of opex identified as savings pipeline

1 · Separate the addressable from the untouchable

Every figure ties exactly to the client's GL subtotals. Labour, tax, financing, and amortization are ring-fenced so savings targets are honest. All values indexed: FY24 total operating expense = 100.

Operating expense (indexed, FY24 = 100)FY24FY25Change
Addressable spend50.959.6+8.7
Non-addressable (labour, tax, financing, amortization)49.149.4+0.3
Total operating expense100.0109.0+9.0

Addressable spend grew 17% year-over-year while revenue-driven costs stayed flat — the growth was concentrated in payment processing, retail merchandise, and travel. That pattern set the priorities for the savings review.

2 · The savings pipeline: seven levers

Each lever is applied to its FY25 spend base, with conservative and stretch estimates expressed as a share of total operating expense. Presented to the client as preliminary and validated with finance before any commitment.

#LeverFY25 base
(% of addressable)		Conservative
(% of opex)		Stretch
(% of opex)		Range
1Card-program tail-spend controls & policy11%0.61%1.21%
2Occupancy — CAM reconciliation & lease audit33%0.58%1.36%
3Advertising & media buying review6%0.45%0.91%
4Cleaning services competitive re-bid (3 sites)5%0.40%0.73%
5Payment processing & platform fee renegotiation14%0.38%0.76%
6Retail COGS supplier consolidation6%0.30%0.63%
7Software & telecom consolidation4%0.30%0.55%
Total identified3.0%6.1%

What the data work looked like

  • Two years of raw GL exports (5,700+ rows each) cleansed to a 3,478-line expense transaction record.
  • 539 raw payee strings — wires, e-transfers, card descriptors — decoded via memo and account context and normalized to ~509 real vendors.
  • Every category total reconciled back to the GL, with data-quality caveats stated explicitly (one fiscal year under-captured ~1.3% in two nested accounts — we said so, and used the authoritative GL figures).
  • A short "confirm these" list of bank-feed labels sent back to the owner to put real vendor names to.

What the client received

  • A five-tab spend pack: executive summary, category view, supplier master, savings opportunities, and full drill-down transaction data.
  • Addressable vs. non-addressable split so targets were credible to the owner and her accountant.
  • A prioritized, sized savings pipeline — not a generic benchmark percentage.
  • A next-step plan: which levers to validate first and what execution ProcureVenn would run.

Drawn from a real ProcureVenn client engagement. Client identity and vendor names have been anonymized, and all financial figures normalized to index points and percentages to protect client confidentiality; the method, structure, and proportions of the analysis are as delivered.