At month-end, a growing restaurant group may see two comforting lines in its MIS: total sales are up and another outlet has opened. Neither line answers the harder question. Are the older restaurants doing more business, or did the new unit simply add sales to the top?
Same-store sales growth, also called SSSG, like-for-like or LFL growth, is the cleanest first answer. It removes new outlets from the comparison. For an operator, the useful version does not stop at one percentage; it sits beside transactions, average bill and contribution margin.
The cohort is the metric
The formula is simple:
Same-store sales growth = (current comparable sales ÷ prior comparable sales − 1) × 100
The difficult part is deciding which outlets are comparable. Write that rule before looking at the result. A group might include restaurants that were open before the start of the previous financial year, or locations with at least 12 or 13 full months of trading. It may exclude a restaurant closed for a major renovation, relocated to a different catchment or split when a delivery area was redrawn.
| Number | The question it answers | What it leaves out |
|---|---|---|
| Total sales growth | Did the whole group sell more? | How much came from new outlets |
| Same-store sales growth | Did the comparable outlet base sell more? | Traffic, price, mix and margin |
| Transaction growth | Did comparable outlets serve more paid orders? | How much each order was worth |
| Average net bill | Did revenue per transaction change? | Whether the order was profitable |
| Contribution margin | Did each sale leave more after variable costs? | Rent, central overhead and capital cost |
An outlet should appear on both sides of the fraction or neither. If the current window contains a restaurant that did not exist in the prior window, the calculation is no longer like-for-like.
Current Indian filings show the difference
The Q4 FY26 disclosures from Indian restaurant groups make the distinction visible. Jubilant FoodWorks reported that Domino’s India revenue grew 5.0% year on year in the quarter, while mature-store LFL growth was 0.2% and 59 stores were added. Its presentation defines LFL as year-on-year revenue growth for non-split mature restaurants opened before the previous financial year; the disclosed cohort contained 1,638 stores (Jubilant FoodWorks Q4/FY26 presentation).
Devyani International’s exchange filing reports a different current example. KFC India Q4 revenue grew 14.6%, SSSG was 4.9%, and the brand ended the quarter with 783 stores. The wider Devyani group added 217 net stores during FY26 (Devyani International Q4/FY26 presentation).
These are not scores in a league table. The brands, formats and cohort definitions differ, and Devyani’s 217 additions cover its group rather than KFC India alone. The point is narrower: revenue growth, store additions and same-store growth are separate facts. A July 2026 synthesis of company filings found that the listed chains in its comparison all expanded their networks, while their Q4 comparable-store figures were far less uniform (The Daily Datum).
A new outlet can grow the group. Only a comparable cohort tells you whether the old outlets grew with it.
Split sales growth before celebrating it
Restaurant sales are the product of two numbers:
Net sales = paid transactions × average net bill
That makes the first explanation pass straightforward. If same-store sales are positive, check whether comparable outlets handled more paid transactions, earned more per transaction, or both. Keep complimentary orders, cancelled orders, refunds, aggregator-funded discounts and restaurant-funded discounts on a consistent basis.
| Driver | What to inspect | A misleading conclusion to avoid |
|---|---|---|
| Transactions | Dine-in bills, delivery orders and takeaway orders | “Demand improved” when only prices rose |
| Average net bill | Menu price, discount, channel and product mix | “Customers traded up” when a discount ended |
| Trading availability | Comparable open days and hours | “The outlet weakened” when it lost trading days |
| Catchment overlap | Sales and orders before and after a nearby opening | “The new outlet created all-new demand” |
A positive comp can therefore coexist with fewer transactions if menu prices or mix lift the average bill. A negative comp can coexist with steady customer demand if an outlet lost operating days. The percentage flags movement; the operating rows explain it.
What same-store sales cannot tell you
Whether price or volume did the work
Input inflation may force a menu-price change. That can lift the average bill while order count stays flat or falls. For Chennai ingredient costs, a dated wholesale price-list check can help explain whether purchase rates moved; it is not a substitute for the restaurant’s own invoices or transaction data.
Whether the extra sale improved margin
A promotion can raise orders and sales while leaving less contribution per order. Delivery mix can change packaging and commission cost. A higher-ticket menu item can carry a higher ingredient cost. Read same-store sales beside food cost, discount funding and channel contribution.
The related guide to food-cost percentage shows why purchases alone are not cost of goods sold and why an inventory count belongs in the calculation.
Whether a new outlet took sales from an old one
Suppose a group divides one delivery catchment between two kitchens. The new unit records sales from day one, while the older comparable outlet may lose orders from streets it no longer serves. That is not automatically a failed opening or a weak old store. Review the two catchments together, along with delivery time, capacity and contribution.
Whether the cohort has become easier
Closed outlets usually disappear from future comparable sets. Renovated or relocated stores may be excluded temporarily. Neither treatment is inherently wrong, but both change the base. Keep a small bridge showing which outlets entered or left the cohort and why.
Put five numbers on the monthly review
The review sheet can stay short. For the comparable outlet cohort, keep these five rows:
| Review row | Use |
|---|---|
| Same-store net sales growth | The headline movement |
| Paid transaction growth | The traffic or order-count direction |
| Average net bill growth | Revenue per paid order after restaurant-funded discounts and refunds |
| Contribution per transaction | Net sales less food, packaging, aggregator and other order-linked costs |
| Sales per labour hour | A basic view of whether the roster moved with demand |
One row without the others invites the wrong fix. Falling transactions with a higher bill may call for an occasion, value or service review. Higher transactions with falling contribution may point to discount or channel mix. Positive sales with weaker sales per labour hour may mean the growth is buying overtime rather than productivity.
If food cost is the unclear row, Mikro’s food cost calculator runs the arithmetic in the browser; the figures entered do not leave the device. Use actual stock and sales data, not a target percentage reverse-engineered to look tidy.
Build the comparison once, then repeat it
A monthly comparable-outlet routine
Record the minimum trading age and the treatment of closures, relocations, remodels and split delivery areas. Keep the rule with the report.
Compare the same dates and trading days. Note festival shifts, temporary closure and unusual lost hours rather than burying them in the percentage.
Use net sales on a consistent discount, refund and tax basis. Reconcile POS and aggregator orders before calculating the average bill.
Put contribution per transaction and sales per labour hour beside SSSG. These two rows stop volume bought through discounting or overtime from looking healthier than it is.
Write one supported reason for the change: transactions, price, mix, availability, cannibalisation or cost. Then choose the operating response.