Swiggy vs Zomato analytics for restaurants

Running Swiggy and Zomato without comparing their real economics is common and costly. This guide builds the framework Indian restaurants need to measure return per platform, by city and outlet, and to spend promotion budgets where they earn the most.

Why does the Swiggy against Zomato question matter?

Almost every restaurant that takes delivery orders lists on both. The unspoken assumption is that being on both all the time beats being selective. That deserves a check. Each platform charges commission on every order. Each has promotion programmes that make you fund discounts. Each needs attention for ratings, menu updates and customer queries. Being on both has a real cost that differs by outlet, city and category.

A better question is not “Swiggy or Zomato?” but “how do the economics of each platform compare for each of our outlets, and how should that shape our promotion spend?” Only data answers that, and most chains do not yet have the setup to answer it rigorously.

How do commission structures differ?

Commission rates are negotiated one by one based on volume, category and market, and they are not published. Charges for premium placement, packaging surcharges and payment processing also vary and are often left out of headline rates.

The difference matters because a few percentage points on a large monthly order value is a sizeable cost against typical delivery margins. Operators who negotiate without data, not knowing volume and average order value per platform, do worse in negotiations.

Here is a worked example with assumed figures. On a ₹500 order with a 20 percent commission, the restaurant receives ₹400. After food cost, packaging and allocated overhead, the net margin on that order may be thin. Even a small commission change shifts the picture noticeably.

How does average order value differ?

Average order value (AOV) often differs between the platforms. One platform’s users may skew toward higher incomes and premium restaurants, and the other’s toward broader and more price-sensitive demand, with a subscription that attracts frequent, lower-value orders. For lower-AOV menus, such as biryani, South Indian tiffin or budget Chinese, one platform may bring more orders at lower value, while the other brings fewer, larger ones.

Neither pattern is universal. Measure actual AOV per platform per outlet instead of assuming the market pattern applies to your category and location. A premium North Indian restaurant and a cloud kitchen biryani brand may see opposite results.

Why is platform strength so local?

Which platform leads varies a lot by city. One may be stronger in some metros, the other in different ones, and a few cities are competitive. In tier-2 cities, one platform can be well ahead on active users and delivery network density.

A chain in many cities cannot use one platform strategy everywhere. Spend promotions, set negotiation priority and put menu effort city by city based on real platform data, not national assumptions.

How do you build a city-level platform comparison?

• Pull about three months of order data from both platforms for each outlet: order count, gross order value, commission, promotion spend you funded and net payout.
• Calculate net revenue per order per platform: gross order value minus commission minus restaurant-funded discount.
• Compare order volume trends over time. Is one platform growing faster at a specific outlet?
• Check rejection and cancellation rates per platform. High rejection on one platform at an outlet hurts that outlet’s visibility there disproportionately.
• Overlay rating trends per platform. Ratings move differently on each, and a falling rating is often worth fixing before you spend on promotions there.

How do ratings affect visibility and revenue?

Both platforms use ratings as a major input to ranking, which decides where you appear in a customer’s feed. A restaurant with a better rating on one platform than the other gets different organic visibility on each, whatever it spends on promotion. This compounds. Lower ratings reduce visibility, which cuts orders, and if the cause is operational, the remaining orders can come from less satisfied customers and bring more bad reviews.

Find out which platform’s rating is under pressure and why. Mining review text and classifying negative reviews (delivery time, food quality, packaging, wrong items, cold food) shows whether the fault lies with the restaurant or the delivery partner, which need different fixes.

How do subscription programmes change the economics?

Subscription programmes change the economics of each order. When a subscriber orders, the platform absorbs the delivery fee. But the restaurant-side economics are complex and often misunderstood.

On the dine-in side, a subscription may give members discounts at registered restaurants, partly funded by the restaurant through a revenue-sharing arrangement. Operators who have not modelled the true cost for their covers may find the programme brings volume without profit. For delivery, subscribers may order more often and with a different price sensitivity. Track AOV and margin for subscription and other orders separately for each platform.

How should analytics guide promotion spend?

Both platforms sell featured placement, discount-funded campaigns and banners in the app. They ask the restaurant to fund discounts in return for higher placement. Without analytics, restaurants follow the platform sales team’s advice.

With analytics, you calculate the effective cost per extra order from promotion on each platform at each outlet, and invest where that cost is lowest compared with the order’s margin. That means measuring order volumes before and during the promotion, allowing for season and day of week. Chains that do this properly often find that one platform consistently gives a lower cost per extra order at certain outlets, and shifting budget toward it raises revenue and margin without raising total spend.

What about customer overlap?

A customer who orders on Swiggy one week and Zomato the next is one customer generating revenue through two platforms. Overlap matters for exclusive promotions, which could alienate customers who prefer the other platform, and for judging the extra value of each listing.

Direct measurement is hard because neither platform shares customer identity. You can only use proxies such as ordering time patterns, area-level addresses and AOV distributions to estimate overlap.

How does Restrologic help?

Our restaurant analytics joins Swiggy and Zomato data with your POS data for a side-by-side view by outlet and chain. The dashboards track net revenue per order after commission and promotion cost, AOV trends, rating trajectories and promotion return for each platform. For multi-city chains, we add city-level platform breakdowns. Restrologic POS has direct Swiggy and Zomato integration with ratings and complaints. To get your own comparison, contact us.