One strong month does not establish a café’s maintainable sales. It may capture school holidays, a festival, favourable weather, a temporary closure nearby, tourism, office attendance or an unusually large catering order. A buyer should rebuild demand over several years and compare genuinely similar dates before treating any month as representative.

The practical question is not whether the peak happened. It is whether the conditions that produced it will recur, whether weaker periods have been fully captured and whether the buyer’s operating plan can withstand them. Start with daily transaction evidence, attach dated local-demand drivers, and model a base case from repeatable periods rather than the seller’s best result.

Obtain enough history to see a cycle

Request at least 24 months of daily point-of-sale exports and seek 36 months where reliable records exist. A single year can still mislead if it contains abnormal closures, major construction, a one-off event or disruption at a competitor. Obtain the same period for merchant settlements, bank deposits, delivery platforms, catering invoices, rosters, opening hours, menu prices, refunds, discounts and temporary closure logs.

For each day, preserve the underlying detail rather than accepting a monthly total:

  • net sales, transactions and, if recorded consistently, dine-in covers;
  • sales and transactions by breakfast, lunch and afternoon daypart;
  • dine-in, takeaway, delivery and catering sales;
  • average transaction value, discounts, refunds and voids;
  • actual opening and closing times, not merely advertised hours;
  • staffing hours and material changes to menu prices or capacity; and
  • notes for holidays, local events, weather, roadworks, outages and closures.

The Australian Government’s hospitality guidance says demand can depend on location, local population, tourism and travel patterns, time of year, weather and economic conditions (business.gov.au). Those are useful investigation categories, but they are not ready-made explanations. Each claimed driver should be matched to a date and tested against the café’s records.

Compare like with like

Do not compare December with February and call the difference growth. Build a calendar that aligns weekdays, public holidays, school and university terms, local events and the timing of Easter. Then compare each month with the same month in earlier years and each weekday with equivalent weekdays.

Use weekly and daily medians alongside averages. A large catering order or three exceptional Saturdays can lift a monthly average without improving the typical trading day. Separate price effects from volume: higher sales after a menu increase may coexist with fewer transactions. Separate changes in trading hours too; a month with five Sundays open is not directly comparable with one in which the café closed every Sunday.

Dayparts matter. A site near offices may be strong on weekday mornings but quiet during holiday periods. A coastal café may rely on weekends and visitors. A site near a university may lose trade between teaching periods. Test these ideas using POS timestamps, rosters and channel reports rather than relying on the seller’s description of the neighbourhood.

Create a demand calendar beside the sales dataset. Possible inputs include official school and university calendars, council event schedules, transport or roadwork notices, office-building occupancy information, nearby venue calendars and dated records of competitor openings or closures. Confirm whether a recurring event is expected to continue after settlement; historical benefit does not create a contractual right to future foot traffic.

For a tourism-exposed location, use official regional information as context. Tourism Research Australia’s domestic regional mobility data reports monthly visitor movement and compares a region with the same month in the previous year. TRA also cautions that its mobility methodology changed in June 2025. A buyer should therefore note series changes and use regional data to challenge or support a hypothesis—not to prove one café’s revenue.

For resident demand, the ABS QuickStats tool provides demographic summaries for selected areas and comparisons with wider geographies. It can help frame questions about the local catchment, but Census data is periodic and does not reveal the café’s current customer count. Test recent apartment completions, office occupancy and precinct changes separately.

Treat weather as data, not an anecdote

Weather can move trade in different directions. Rain may reduce outdoor seating and passing foot traffic while increasing delivery; heat may alter menu mix; a fine weekend may help a coastal site but draw customers away from an indoor shopping precinct. Do not apply one universal weather assumption.

Tag daily sales with observations from a suitably nearby Bureau of Meteorology station. The Bureau’s Climate Data Online provides statistics, recent observations and historical climate data in formats including tables and CSV files. The Bureau notes that stations collect different measures and that availability can change over time, so document the chosen station, distance, fields and missing dates.

Compare similar weekdays under similar conditions across several periods. Ask whether indoor and outdoor capacity, delivery availability or operating hours changed at the same time. Correlation is not causation, but a dated comparison is more useful than “rain always hurts us”.

Rebuild a maintainable demand case

Construct three views rather than annualising the strongest month:

  1. Observed history: actual monthly, weekly and daypart results, reconciled to source records.
  2. Normalised base case: like-for-like periods after separately identifying documented closures, one-off orders and non-recurring events. Normalising is a judgement for specialist review, not permission to remove every weak month.
  3. Downside case: repeatable sales with conservative assumptions for the café’s vulnerable demand drivers, while retaining fixed costs and realistic staffing needs.

Suppose, as a hypothetical example, a café records $96,000 of net sales in one summer month and $62,000 in the median of the other eleven months. The strong month coincides with a regional event and four large catering invoices. A buyer should not multiply $96,000 by twelve. Instead, isolate the event-period, catering and ordinary café sales; check comparable summer months; confirm whether the event and contracts continue; and test whether the business can meet rent, wages and other commitments near the lower run rate. The figures are illustrative only, not an industry benchmark or valuation method.

Use decision gates before relying on the forecast

Question More supportive evidence Reason to pause
Is the peak recurring? Several comparable periods show a similar pattern Only one strong month or seller-prepared summary is available
Is local demand identifiable? Dated events, calendars and channel data align with POS results Explanations shift or cannot be tied to dates
Are comparisons like for like? Weekdays, hours, prices, closures and channels are controlled Longer hours or price rises are presented as demand growth
Can the buyer repeat the result? Key events, access, staffing and channels are reasonably expected to continue Trade depends on a temporary competitor closure or one customer
Does the downside remain workable? Conservative sales support realistic labour and occupancy costs The model requires peak-month sales to meet ordinary commitments

Ask an accountant to test the reconstruction and normalisation assumptions. A lawyer can review contractual rights, event or catering arrangements and transaction conditions. Local authorities, landlords and relevant specialists may need to confirm access, trading or precinct issues. External datasets inform the investigation; they do not replace transaction-level evidence or a valuation.

Sources

Disclaimer

This article provides general information only. It does not constitute legal, tax, accounting, financial, credit, valuation or other professional advice. Circumstances differ. Obtain advice from appropriately qualified advisers before acting or entering a transaction.