Starbucks
personalized recommendations and offers through reinforcement learning on loyalty data
Starbucks pushes personalized recommendations to 16 million active Rewards members through its Deep Brew reinforcement learning engine hosted on Microsoft Azure, cited by the CEO on the Q1 2024 earnings call.
Key points
- Personalized recommendations and offers when the Starbucks Rewards app opens.
- Deep Brew reinforcement learning engine hosted on Microsoft Azure.
- 16 million members targeted, out of a US base of 34.3 million active members.
- Evidence A, mixed-signals status (Deep Brew cited less in earnings since 2024).
Objective
Increase visit frequency and spend of Starbucks Rewards members by pushing the right offer or product each time the app opens, to anchor regular customers and bring back less frequent ones.
The deployment
Deep Brew is Starbucks's in-house AI platform, launched in 2019. Its personalization component runs on a reinforcement learning engine hosted in Microsoft Azure. When a Rewards member opens the app, the system proposes products and offers computed from their order history, the store, the time, the weather, and the preferences of customers with similar profiles. The same engine is used to design offers targeted by member cohorts. Starbucks reports 16 million active members receiving these recommendations in the app, out of a US loyalty base of 34.3 million active members in the first quarter of fiscal 2024.
Results Proof A
Deep Brew is named by CEO Laxman Narasimhan on the Q1 FY2024 earnings call as a lever for offer personalization, and the reinforcement learning architecture is documented and quantified (16 million members) by the Microsoft customer story. Concordant sources: primary earnings + platform.
How it works
Documented architectureThe stack in detail
- plateforme Deep Brew Starbucks's in-house AI platform (2019), whose personalization component computes offers and recommendations per member
- infra Microsoft Azure cloud that hosts the personalization engine, documented by the Microsoft customer story
- outil Moteur d'apprentissage par renforcement ranks offers and products per member from order history, store, time, weather, and similar profiles
- infra Application mobile + Starbucks Rewards delivery surface and identification key for the customer (34.3 million active US members in Q1 FY2024)
How it runs, concretely
For ops teams-
1Signal collection data team
Order history, store, time, weather, and preferences of similar members feed into the profile.
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2Scoring of offers and products AI
The reinforcement learning engine ranks, for each member, the recommendations most likely to trigger an order.
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3Display in the app Application
The personalized recommendations and offers appear when the app opens.
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4Response measurement data team
Order, offer redemption, and frequency are measured and fed back to the engine.
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5Retraining data and marketing team
The engine adjusts its recommendations with the new signals; marketing defines the cohorts to incentivize.
The member's first-party transactional history and their reaction to offers. Without a loyalty identifier linking orders to a person, the engine loses its material and falls back to generic per-store recommendations.
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
- first-party transactional history
- loyalty program identifying each customer
- contextual signals (time, store, weather)
Org prerequisites
- internal data science team
- active loyalty program with a member base
- offer measurement loop
Possible stack
- recommendation engine (reinforcement learning or collaborative filtering)
- cloud (Azure, GCP or AWS)
- CDP
- mobile app
The plan, step by step
- Step 1Unify the member profile: consolidate the transactional history of loyalty members into a usable profileDeliverable: Single customer profile with orders, store, and context
- Step 2Build v1 of the engine: collaborative filtering or simple offer scoring on the chosen cloudDeliverable: Scoring model evaluated offline on history
- Step 3Expose recommendations when the app opens for a member segment, A/B against generic offersDeliverable: Personalization live and measured on a segment
- Step 4Move to reinforcement learning: connect the response to offers (order, redemption) as a reward signalDeliverable: Continuous retraining loop in production
- Step 5Equip marketing: control over the cohorts to incentivize and the design of targeted offersDeliverable: Cohort-based targeting operational, routine measurement of frequency and basket
First step: Unify the order history of loyalty members into a profile usable by a recommendation engine.
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 Starbucks (SBUX) Q1 2024 Earnings Call Transcript Established press archive pending
- S2 Starbucks turns to technology to brew up a more personal connection with its customers Interested party archive pending
- S3 Starbucks Uses AI-Powered Personalized Rewards to Boost Frequency and Spend Secondary archive pending
An error, newer info, a source?
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