Jubilant FoodWorks, the operator of Domino’s Pizza in India, is using machine learning models to determine how and where it increases prices, moving away from uniform price hikes across its restaurant business. During its second-quarter FY27 earnings call on November 6, 2026, CEO and Managing Director Sameer Khetarpal said the company had implemented approximately 140–200 basis points of price increases, calibrated through machine learning across different day parts and sales channels. The strategy is designed to protect profitability without making every customer face the same broad-based price increase.
The pricing approach comes as Jubilant FoodWorks faces higher operating costs, including inflation in dairy, chicken, vegetables, liquefied petroleum gas (LPG) and employee wages. Management said these pressures created an unexpected cost impact of approximately 200 basis points for Domino’s India, of which the company has absorbed around 140 basis points through pricing, delivery efficiencies, discount management and other operational measures. The remaining 60 basis points continue to weigh on the business. The company is also using customer data and personalised discounts to improve its value proposition and encourage more orders.
Key Takeaways
- Jubilant FoodWorks is using machine learning to calibrate Domino’s India price increases.
- CEO Sameer Khetarpal disclosed approximately 140–200 basis points of price increases during the Q2 FY27 earnings call.
- Pricing decisions are adjusted across day parts, such as lunch, dinner and late night, and different ordering channels.
- The company is also using customer data and personalisation models to target discounts.
- Unexpected cost inflation created an estimated 200-basis-point impact on Domino’s India.
- Jubilant has absorbed approximately 140 basis points through pricing and operational measures, leaving 60 basis points unresolved.
- Domino’s India recorded 4.1% like-for-like growth in Q2 FY27, compared with 2.5% in the previous quarter.
- Delivery revenue grew 11.3% year-on-year and represented 75.8% of Domino’s India revenue.
- Jubilant says further price increases remain possible if input costs continue rising.
- The strategy raises questions about pricing transparency and how customer data may influence discounts.
Jubilant FoodWorks Uses Machine Learning to Decide Domino’s Price Increases
The most significant development from Jubilant FoodWorks’ Q2 FY27 earnings call was management’s confirmation that machine learning has become part of its pricing strategy.
Traditionally, restaurant companies have often adjusted menu prices through broad price increases. For example, a company facing higher ingredient costs might increase the prices of several products across its menu.
Jubilant is taking a more targeted approach.
During the earnings call, Khetarpal explained that the company had taken approximately 140–200 basis points of price increases, using machine learning models to calibrate pricing across day parts and channels.
He said the company preferred this approach over blunt, uniform price increases.
The strategy allows Jubilant to examine different sales situations and determine where pricing adjustments may be appropriate.
For example, customer demand during lunch hours may differ from demand late at night. Similarly, the economics of delivery orders can differ from those of dine-in and takeaway transactions.
Machine learning models can help analyse these patterns and support more selective pricing decisions.
What Does Machine-Learning-Based Pricing Mean?
Machine-learning-based pricing involves using historical sales information, customer behaviour, demand patterns and operational data to support pricing decisions.
For a restaurant business such as Domino’s, relevant factors can include:
- Time of day when customers place orders.
- Demand for particular products.
- Ordering channel, including delivery, dine-in and takeaway.
- Historical sales and transaction patterns.
- Promotional performance.
- Customer response to discounts.
- Changes in operating and ingredient costs.
The objective is to understand how pricing adjustments may affect sales volumes, average order values and profitability.
However, Jubilant has not publicly disclosed the precise variables used in its pricing models, the algorithm’s architecture or the exact price differences across individual products and channels.
The company has also not confirmed that every customer receives a different menu price.
It is important to distinguish between channel-based pricing adjustments and personalised discounts. Both can produce different final bills, but they are not necessarily the same practice.
What Are Day Parts and Channels in Domino’s Pricing Strategy?
Two important terms in Jubilant’s explanation are day parts and channels.
Day parts refer to different time periods during which a restaurant operates.
For example, customer behaviour during lunchtime can differ significantly from late-night ordering.
Channels refer to the different ways customers purchase food.
A Domino’s customer may order through the company’s mobile application, use a third-party delivery platform, visit a restaurant for takeaway or eat inside the outlet.
Each channel has different costs, customer behaviour and promotional requirements.
How Pricing Can Differ Across Day Parts and Channels
| Factor | Example | Why it matters |
|---|---|---|
| Lunch | Orders during lunchtime | Customer demand and purchase patterns |
| Dinner | Evening orders | Higher demand in certain locations |
| Late night | Orders after midnight | Different demand and operating conditions |
| Delivery | Food delivered to customers | Rider and delivery-related expenses |
| Takeaway | Customers collect orders | Different operating economics |
| Dine-in | Customers eat at the restaurant | Store experience and seating capacity |
By studying these patterns, Jubilant can identify opportunities to adjust prices or discounts without applying the same increase everywhere.
For instance, a restaurant may have different demand patterns during lunch and late-night periods. The company can use those differences when evaluating pricing decisions.
The precise adjustments made by Domino’s remain undisclosed.
Domino’s Also Uses Personalised Discounts
Pricing is only one part of Jubilant FoodWorks’ broader data-driven strategy.
The company’s shareholder communication also describes the use of personalisation and data models to target discounts.
This means promotional offers can be directed towards specific customer groups or purchasing situations instead of distributing identical discounts to everyone.
For example, a restaurant could use transaction history to identify customers who have not ordered recently and offer them a targeted promotion.
Another customer who regularly orders without discounts may not require the same incentive.
Such strategies can help companies manage promotional spending and improve the effectiveness of discounts.
Why Targeted Discounts Matter
| Traditional discounting | Data-driven discounting |
|---|---|
| Broad promotional offers | Targeted customer offers |
| Similar discounts for many customers | Offers selected using customer and transaction data |
| Less precise measurement | More detailed campaign measurement |
| Potentially higher promotional spending | Opportunity to reduce unnecessary discounts |
| Limited segmentation | Greater customer segmentation |
For Jubilant, targeted discounting is important because promotions can influence both customer demand and restaurant margins.
Giving every customer a discount may increase order volumes, but it can also reduce the amount of revenue earned from each transaction.
Personalised offers can potentially improve that balance.
However, the company has not disclosed which customer data points are used to determine individual discounts or how frequently its models are updated.
Why Is Jubilant FoodWorks Increasing Domino’s Prices?
Rising input costs are a major reason behind the pricing adjustments.
During the Q2 FY27 earnings call, management discussed inflation across several important cost categories.
These included dairy products, chicken, vegetables, LPG and employee wages.
Dairy inflation is particularly important for a pizza business because cheese is a major ingredient.
Higher milk prices can eventually increase cheese procurement costs, although the effect may take time to pass through the supply chain.
The company said it was relatively well covered for cheese supplies during the current financial year, but inflation remained visible across other ingredients.
The West Asia crisis and monsoon-related disruptions were also cited as factors contributing to cost pressure.
Major Cost Pressures Facing Domino’s India
| Cost category | Potential business impact |
|---|---|
| Dairy and cheese | Higher pizza ingredient costs |
| Chicken | Increased costs for chicken-based products |
| Vegetables | Seasonal supply and procurement pressure |
| LPG | Higher cooking and restaurant operating costs |
| Employee wages | Increased store operating expenses |
| Delivery operations | Pressure on cost per delivery |
These pressures can affect profitability even when a restaurant continues to attract customers.
Jubilant therefore needs to balance price increases, procurement efficiencies, promotional spending and operational improvements.
Company Has Absorbed 140 Basis Points of a 200-Basis-Point Cost Shock
One of the most important financial details from the earnings call concerns the cost impact on Domino’s India.
Khetarpal said the business experienced approximately 200 basis points of unexpected costs, particularly because of LPG price increases and wage inflation across states.
The company said it had absorbed around 140 basis points through several measures, leaving approximately 60 basis points still unresolved.
Cost Impact and Mitigation
| Particular | Basis points |
|---|---|
| Unexpected cost impact | 200 bps |
| Impact absorbed through company actions | 140 bps |
| Remaining cost pressure | 60 bps |
100 basis points equal one percentage point.
The 140 basis points absorbed should not be interpreted as coming entirely from price increases. Management described a combination of pricing, lower delivery costs, discount reduction, store operating efficiencies and marketing mix modelling.
The remaining 60 basis points could improve if input costs ease or if the company successfully implements additional operational measures.
Management indicated that further price increases remain an option if required.
Domino’s India Sales Improve in Q2 FY27
The pricing strategy comes as Domino’s India shows signs of improving sales momentum.
According to Jubilant FoodWorks’ Q2 FY27 results, Domino’s India recorded like-for-like growth of 4.1%.
This was an improvement from 2.5% in Q1 FY27 and 0.2% in Q4 FY26.
Like-for-like growth measures sales performance at comparable stores, helping investors understand whether existing outlets are generating more revenue.
The company also reported 9.2% year-on-year growth in Domino’s India revenue and 6.3% growth in order volumes.
Domino’s India Performance
| Metric | Q2 FY27 performance |
|---|---|
| Revenue growth | 9.2% |
| Order growth | 6.3% |
| Like-for-like growth | 4.1% |
| Previous-quarter LFL growth | 2.5% |
| Delivery revenue growth | 11.3% |
| Delivery share of revenue | 75.8% |
The improvement suggests that Domino’s India is generating stronger demand while adjusting prices and managing costs.
However, the company still wants to improve dine-in and takeaway performance.
Management has identified these channels as important opportunities for increasing customer visits and improving store-level economics.
Delivery Remains the Biggest Revenue Channel
Delivery continues to account for the majority of Domino’s India revenue.
Jubilant reported that delivery represented 75.8% of the business during Q2 FY27.
Delivery revenue grew 11.3% year-on-year, supported by higher order volumes and improvements in the company’s digital ordering experience.
The company is also investing in technology to improve delivery operations.
One initiative is smart order batching, which combines nearby orders placed within a short time of each other so that a single rider can deliver multiple orders.
This can help reduce delivery costs and improve operational efficiency.
Jubilant has also improved rider tracking and delivery-time predictions, while upgrading the search functionality in the Domino’s application.
These investments are important because delivery costs can significantly affect the profitability of a restaurant order.
The company also reported approximately 21 million monthly active users across its Domino’s, Popeyes and Hong’s Kitchen applications, with around 6 million monthly transacting users.
Domino’s Is Trying to Revive Dine-In and Takeaway
Although delivery remains the strongest channel, Jubilant is also working to attract customers back to its stores.
The company has introduced a value proposition called My Meal at ₹119, which is available for in-store customers.
It is also refurbishing selected restaurants and improving the front-of-house experience.
Management said infrastructure upgrades had been completed in approximately 20% of the identified dine-in and takeaway-focused stores.
The company is also using customer relationship management initiatives and local marketing to attract customers living near individual outlets.
These measures are intended to increase footfall and improve the economics of dine-in and takeaway operations.
Management believes that even a 1%–2% improvement in like-for-like growth from these channels could meaningfully change the business’s growth trajectory and margin profile.
Does Machine-Learning Pricing Mean Different Customers Pay Different Prices?
The use of machine learning in pricing raises an important question about consumer transparency.
If a company adjusts prices across channels and time periods, customers may encounter different final bills for similar orders.
Personalised discounts can create additional differences.
For example, two customers may receive different promotional offers depending on their purchasing history or eligibility for a campaign.
However, there is an important distinction between:
- Different prices across channels, such as delivery and takeaway.
- Different promotional discounts offered to customer groups.
- Individualised base prices for the same product and transaction conditions.
Jubilant has discussed the first two elements but has not publicly confirmed the third.
The company has also not disclosed the exact customer information used by its models.
This raises broader questions about transparency, data governance and how consumers understand the offers they receive.
As more Indian businesses adopt AI-driven pricing and personalised promotions, clear communication about pricing conditions could become increasingly important.
What This Means for India’s Quick-Service Restaurant Industry
Jubilant FoodWorks’ strategy could offer an example of how artificial intelligence is changing restaurant operations in India.
Quick-service restaurant businesses operate with relatively tight margins and face fluctuations in food, energy, labour and delivery costs.
Uniform price increases can protect margins, but they may also discourage customers who are sensitive to price changes.
Data-driven pricing offers an alternative approach.
Companies can study customer behaviour, demand patterns and operating costs to decide where promotions or price adjustments may be most effective.
Other restaurant operators could explore similar techniques as their digital ordering platforms collect more transaction data.
However, successful implementation requires reliable data, effective algorithms and appropriate customer safeguards.
The technology must also produce measurable improvements in profitability without damaging customer trust.
The Bigger Picture
Jubilant FoodWorks’ use of machine learning for Domino’s pricing shows how AI is moving into everyday business decisions beyond chatbots and content generation. The company is applying data models to a fundamental commercial question: how much should customers pay, and when should a restaurant offer a discount? By adjusting pricing across day parts and channels, Jubilant is attempting to manage inflation while protecting demand and restaurant margins. Its approach reflects a broader shift towards data-led decision-making in India’s quick-service restaurant industry.
The strategy also creates new questions about transparency and consumer rights. Different ordering channels and targeted promotions can lead to different final bills, making it important for businesses to communicate offers and pricing conditions clearly. Jubilant has not disclosed the exact inputs used by its models or confirmed individualised base pricing for customers. Ultimately, the success of machine-learning pricing will depend on whether it improves profitability without weakening customer trust or making the brand appear less affordable.
Frequently Asked Questions
1. How is Jubilant FoodWorks using machine learning?
Jubilant FoodWorks uses machine learning models to calibrate Domino’s India price increases across different times of day and ordering channels. The company also uses data models to target promotional discounts.
2. How much has Domino’s increased prices?
CEO Sameer Khetarpal said the company had taken approximately 140–200 basis points of price increases. This is equivalent to 1.4%–2% in percentage-point terms, but it does not mean every menu item has increased by the same amount.
3. What are day parts in restaurant pricing?
Day parts refer to different operating periods, such as lunch, dinner and late night. Customer demand and order patterns can vary across these periods.
4. Does Domino’s charge different prices to different customers?
Jubilant has confirmed pricing calibration across day parts and channels and the use of targeted discounts. It has not publicly confirmed that the base price of the same product is individually changed for each customer.
5. Why is Domino’s increasing prices?
The company is facing higher costs involving dairy, cheese, chicken, vegetables, LPG and employee wages. These pressures have affected operating margins.
6. How much of the cost inflation has Jubilant absorbed?
Management said Domino’s India faced approximately 200 basis points of unexpected cost pressure and had absorbed around 140 basis points through pricing and operational measures. Approximately 60 basis points remained.
7. How did Domino’s India perform in Q2 FY27?
Domino’s India reported 9.2% revenue growth, 6.3% order growth and 4.1% like-for-like growth during the quarter.
8. Will Domino’s increase prices further?
Management said further price increases remain possible if required to manage inflation and protect profitability.
9. What is the benefit of AI-driven pricing for restaurants?
It can help restaurants understand demand patterns, manage discounts, adjust prices more selectively and improve the balance between customer affordability and operating margins.
Looking Ahead
Jubilant FoodWorks is expected to continue experimenting with machine-learning-based pricing and personalised discounts as it responds to changes in food, energy and labour costs. Management has indicated that further price increases remain possible if inflation persists. Investors will watch whether the remaining 60-basis-point cost pressure can be addressed through improved delivery efficiency, discount management, menu mix and other operational initiatives.
For India’s quick-service restaurant industry, the development highlights how customer data and artificial intelligence could increasingly influence everyday pricing decisions. If Jubilant can improve margins while maintaining order growth and affordability, its approach could become an important example for other restaurant operators. However, the company will also need to maintain transparency around promotional offers and protect customer trust as data-driven pricing becomes more sophisticated.
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