Jubilant FoodWorks, which operates Domino's India, has implemented machine learning models to fine-tune price increases by 140-200 basis points. Instead of blunt hikes, they're now personalizing prices by time of day, order channel, and even customer segment. This shifts the battle against rising cheese costs directly onto individual customers' willingness to pay.
How We Got Here
Domino's previously used A/B testing for price changes in August 2025, even rolling back a Big Big Pizza hike after customer backlash. By May 2026, management claimed 14-16 weeks of "strong experimentation" led to "store specific and discount specific variations" before the current ML models.
The Numbers
- CEO Sameer Khetarpal stated 140-200 basis points of calibrated price increases during the Q2 FY27 earnings call on November 6.
- Pricing now varies by "day parts" (time slots like lunch/dinner) and "channels" (delivery/dine-in), with similar ML models applied to discount targeting.
- MediaNama founder Nikhil Pahwa warned in a 2017 editorial that such data-driven pricing reduces consumer surplus and maximizes corporate profit.
- Jubilant rolled back a "Big Big Pizza" price increase in August 2025 after customer reaction, signaling early trials with data-led pricing.
- CFO Suman Hegde mentioned "store specific and discount specific variation" in May 2026, indicating earlier moves towards granular pricing before the full ML deployment.
What Happens Next
🇮🇳 Why This Matters for India
For product managers and founders building consumer tech in Mumbai and Delhi, this demonstrates how mature online QSRs leverage data to optimize revenue from individual users, not just segments.
The Take
This is a clear win for Jubilant's top line in the short term, but it risks alienating their most loyal and data-rich customers who discover they're paying more. Expect consumer backlash to escalate as dynamic pricing becomes more obvious, forcing more transparency from QSRs within 12 months.
Source:
MediaNama ↗