Papa Johns
real-time personalization of loyalty and ordering with generative AI
Papa Johns formalized a multi-year partnership with Google Cloud to personalize loyalty and ordering via BigQuery, Vertex AI, and Gemini, and in 2026 became the first to deploy Google Cloud's omnichannel Food Ordering agent for more than 150 million customers.
Objective
Anticipate the needs of Papa Rewards customers and personalize offers, content, and the ordering journey in real time, to bring loyal members back and drive reordering.
The deployment
In April 2025, Papa Johns expanded its multi-year partnership with Google Cloud and created an innovation team, PJX, to apply BigQuery, Vertex AI, and Gemini to its data. Stated goals: personalize the site and app experience in real time (promo codes and messages based on history, preferences, and location), predict ordering habits to offer deals and shortcuts, and deploy a chatbot and voice ordering. In January 2026, Papa Johns became the first partner to deploy Google Cloud's omnichannel Food Ordering agent, which recognizes Papa Rewards members for a smooth reorder; the national deployment is planned by the end of 2026. The network has more than 6,000 restaurants and serves more than 150 million customers.
Results Proof C
Partnership formalized by official Papa Johns and Google Cloud releases naming the brand, with a first deployment of the ordering agent announced in early 2026. The personalization capabilities are being rolled out and the omnichannel agent is in national rollout by the end of 2026; no quantified performance result is published yet, hence C.
How it works
Documented architectureThe stack in detail
- infra Google BigQuery Data warehouse where Papa Johns centralizes its ordering and Papa Rewards loyalty data.
- plateforme Vertex AI Google Cloud's ML platform used for the personalization models (offers, content, suggestions based on the profile).
- llm Gemini Google's model family used for the setup's generative capabilities (personalization, chatbot, voice ordering).
- outil Food Ordering agent (Google Cloud) Google Cloud's omnichannel ordering agent, of which Papa Johns is the first deployer; it recognizes Papa Rewards members for reordering.
- integrateur Google Cloud Multi-year technology partner, alongside Papa Johns' in-house PJX innovation team.
How it runs, concretely
For ops teams-
1Data unification PJX / data team
Ordering and loyalty data are centralized in BigQuery.
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2Personalization models AI
Vertex AI and Gemini compute offers, content, and suggestions based on the profile.
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3Surface personalization AI / site and app
Site, app, and ordering agent adapt the experience and recognize Papa Rewards members for reordering.
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4Triggered campaigns marketing
Personalized push and email go out based on anticipated occasions and preferences.
The Papa Rewards profile: history, preferences, location. Without member identification and up-to-date ordering data, personalization and proactive reordering fall back to a generic experience.
How your customers perceive this type of use
Sourced studiesLe paradoxe est documente des deux cotes : 71% des consommateurs attendent des interactions personnalisees et 76% sont frustres quand elles manquent (McKinsey, 2021), mais 75% declarent ne pas acheter aupres d'organisations auxquelles ils ne confient pas leurs donnees (Cisco, 2024). La « creepy line » est localisee : messages recus quelques secondes apres une recherche et suivi de localisation sont les pratiques qui mettent le plus mal a l'aise (Periscope by McKinsey, 2019).
Acceptance conditions
- La confiance dans le traitement des donnees precede l'achat : 75% ne achetent pas sans elle (Cisco 2024)
- Un cadre legal protecteur rassure : 59% des consommateurs disent que des lois fortes sur la vie privee les rendent plus a l'aise pour partager des informations dans des applications IA (Cisco 2024)
- La personnalisation elle-meme est attendue quand elle est consentie : environ la moitie des consommateurs (US 55%, UK 52%) disent s'inscrire souvent ou parfois a des services personnalises (Periscope by McKinsey 2019)
Red lines
- Le message declenche quelques secondes apres une recherche ou un achat : deuxieme ou troisieme cause de malaise selon les pays (Periscope by McKinsey 2019)
- Le suivi de localisation percu comme de la surveillance : 40% de malaise en Allemagne et au Royaume-Uni (Periscope by McKinsey 2019)
- Le mesusage des donnees personnelles par l'IA, devenu la premiere inquietude des consommateurs, a 53% et en hausse (Qualtrics 2025)
Sources: McKinsey & Company 2021 · Periscope by McKinsey 2019 · Cisco 2024 · Qualtrics 2025
How to replicate
Inference, not sourcedData prerequisites
- unified ordering and loyalty data
- loyalty program identifying each customer
- contextual signals (location, occasions)
Org prerequisites
- data team or a cloud partner
- active loyalty program
- marketing-consent governance
Possible stack
- data warehouse (BigQuery, Snowflake)
- ML and LLM platform (Vertex AI, Gemini)
- conversational ordering agent
- app and site
The plan, step by step
- Step 1Unify ordering and loyalty data in a cloud warehouse, with each customer identified through the loyalty program.Deliverable: Unified, queryable customer data
- Step 2Build the first personalization models: offers and messages based on history, preferences, and location, tested on a segment.Deliverable: Offer engine in test with a control group
- Step 3Personalize the surfaces (site, app) for identified members and connect triggered campaigns (push, email) to anticipated occasions.Deliverable: Personalized surfaces and campaigns in production
- Step 4Pilot a conversational ordering agent on one channel, with member recognition for reordering.Deliverable: Agent piloted on a limited scope
- Step 5Extend the setup and measure reordering, frequency, and retention against non-exposed cohorts.Deliverable: Quantified retention assessment by cohort
First step: Unify ordering and loyalty data in a warehouse usable by personalization models.
The same pattern, proven in other industries
Boots
machine learning scoring of offer-usage propensity, applied to mail and the app
Meta
AI feed and video recommendation (large-scale models / LLMs)
Michaels
genAI personalization of marketing messaging (copy) at scale, tuned to the brand voice
Sources
- S1 Papa Johns and Google Cloud Team Up to Deliver AI-Powered Pizza Experiences Primary archive pending
- S2 Papa Johns will use Google AI for analytics, marketing Established press archive pending
- S3 Papa Johns and Google Cloud Reimagine the Future of Food Ordering to Better Serve Customers Interested party archive pending
An error, newer info, a source?
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