Ulta Beauty
AI personalization of offers and product recommendations on loyalty data
More than 95% of Ulta Beauty's revenue comes from its roughly 44 million Ulta Beauty Rewards members, whose first-party data feeds machine-learning personalization of offers, recommendations, and AI-augmented beauty advice.
Objective
Retain a member base that drives most of the revenue by pushing each member the recommendations and offers matching their purchases, and reactivating customers who drop off before they leave.
The deployment
The Ulta Beauty Rewards program has about 44 million active members, from whom more than 95% of revenue comes. Ulta unifies these members' first-party data (purchase history, browsing, preferences, phone number as cross-channel identifier) into profiles used by machine learning to personalize product recommendations, emails, and offers. The retailer retargets members who have become inactive through paid advertising. On the more recent AI layer, Ulta integrated loyalty identifiers into its Skin Advisor interface to propose skincare routines based on purchase history and real-time stock, and is testing a virtual beauty advisor. Kelly Mahoney sums up the setup's raw material when she calls data the company's crown jewel.
Results Proof C
Three consistent press sources (Glossy, PYMNTS, CX Dive) cite Ulta by name with figures coherent among themselves and with the brand's financial results (more than 95% of sales from members, a loyalty base of 44 to 47 million depending on the period). Convergence of established sources.
How it works
Documented architectureThe stack in detail
- infra Profils clients unifies Ulta (in-house) Members' first-party Rewards data (purchase history, browsing, preferences) unified with the phone number as cross-channel identifier.
- llm Machine learning de recommandation et de ciblage (in-house) Scoring and personalization of product recommendations, emails, and offers; exact models not disclosed.
- outil Skin Advisor Skincare advice interface tied to the loyalty identifier: routines proposed from purchase history and real-time stock.
- plateforme Plateformes publicitaires de retargeting Paid reactivation of members who have become inactive; platforms not publicly named.
How it runs, concretely
For ops teams-
1Unifying member data Data team
Purchase history, browsing, preferences, and contact details are centralized into a single profile.
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2Scoring and recommendation AI
Machine learning ranks for each member the products and offers to feature.
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3Personalized delivery Marketing and platform
Emails, in-app offers, and product recommendations are pushed to the member, updated near real time.
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4Reactivating inactives Marketing
Members who drop off are retargeted through paid advertising before they slip into loss.
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5AI-augmented advice AI
Skin Advisor ties in the loyalty identifier to propose routines based on history and real-time stock.
Purchase and browsing history tied to the loyalty profile, with the phone number as cross-channel key. Without that linkage, the member becomes anonymous again and the recommendation loses its basis.
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
- members' first-party data (purchase, browsing)
- a reliable cross-channel identifier
- a loyalty program with high penetration
Org prerequisites
- an equipped growth and CRM team
- unified customer profiles
- paid retargeting capability on the loyalty base
Possible stack
- a CDP or customer warehouse
- a recommendation engine
- an email and in-app orchestration platform
- a conversational AI component for advice
The plan, step by step
- Step 1Unify members' purchases, browsing, and preferences into a single profile, with a stable cross-channel identifier (the phone at Ulta).Deliverable: A usable unified member profile.
- Step 2Put scoring and recommendation in place, personalize emails and in-app offers with a control group.Deliverable: First personalized campaigns measured against control.
- Step 3Build reactivation audiences for inactive members and connect them to paid retargeting.Deliverable: A reactivation loop in production with a cost per reactivated member.
- Step 4Add the advice component (routines based on history and stock) and read the overall retention impact.Deliverable: An advice assistant in pilot and an annual retention review.
First step: Unify members' purchase and browsing data into a usable profile, with a stable cross-channel identifier.
The same pattern, proven in other industries
Meta
AI feed and video recommendation (large-scale models / LLMs)
Netflix
content recommendation engine
Nike
personalized exclusive offers and access based on member engagement and history
Sources
- S1 Ulta Beauty Strategies: Inside Ulta's 44-million-member loyalty program Secondary archive pending
- S2 Ulta Beauty's AI Strategy Drives a 95% Customer Repurchase Rate Secondary archive pending
- S3 Ulta sees AI as a personalization tool - and its loyalty program as the fuel Secondary archive pending
An error, newer info, a source?
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