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Snowflake Healthcare Irvine, CA

Allergan Aesthetics

Marketing Personalization

Fragmented customer data across brands, apps, and marketing systems prevented Allergan from building a unified direct-to-consumer relationship or personalizing its Allē loyalty journey effectively.

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$1 billion in direct-to-consumer sales
$400 million in new revenue in 2021
10% reduction in JUVEDERM completed a purchase cost per acquisition
3M+ actively engaged Alle loyalty users

Reported impact

Business outcome

Allergan turned Allē into a major direct-to-consumer growth engine, generating more than $1 billion in sales since 2021 while improving campaign speed, acquisition efficiency, and consumer engagement.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Allergan rebuilt its direct-to-consumer engagement stack around Snowflake, Segment, and Twilio Programmable Messaging so consumer interactions from websites, mobile apps, and loyalty experiences could feed one governed customer record. Segment collects and standardizes event data, then loads it into Snowflake to create a single view of each consumer across brands and touchpoints. Product and data-science teams use the Segment-enriched Snowflake data to score audiences and predict relevant offers, products, and content. Those predictions are activated back into applications, websites, and multichannel marketing workflows, while Twilio delivers transactional loyalty messages. Segment reverse ETL and audience-building workflows also remove data silos between analytics, CRM, and downstream engagement tools such as Braze. By keeping collection, storage, modeling, and activation tightly connected, Allergan turned its loyalty program into a faster, more personalized revenue engine.

Data flow

Customer events from Allergan websites and mobile apps are captured in Segment, standardized, and loaded into Snowflake as unified profiles. Data-science scores and audience selections are then pushed into apps, sites, and messaging channels such as Twilio so each consumer receives more relevant offers and content.

Solution architecture

3 components · 3 layers
  1. Ingest
    • Segment Collects, standardizes, and routes customer event data from Allergan digital properties into the central data platform.
  2. Storage
    • Snowflake Stores unified consumer profiles and model outputs as the single source of truth for analytics, audience building, and personalization.
  3. Serving
    • Twilio Programmable Messaging Delivers transactional loyalty and engagement messages to consumers based on platform data and audience logic.

Architecture clues

  • Machine-learning scores are written back into the engagement stack for audience targeting and personalization.
  • Segment reverse ETL automates data activation into downstream engagement applications.
  • Snowflake serves as the centralized golden record for consumer identity and journey analysis.

Evidence from the source

Segment captures and standardizes customer data and automatically loads that data into Allergan's data platform, Snowflake, to create a single view of the customer.
The machine learning team loads custom audiences and data science scores for consumer profiles into the Snowflake data platform.
Twilio Programmable Messaging to send transactional text messages to its loyalty program members
generate over $400 million in new revenue in 2021
reduce our completed purchase cost per acquisition with social media advertising by 10% for JUVEDERM
Transparency Evidence & methodology 95/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Snowflake

    Allergan Aesthetics customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/allergan/
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Aug 10, 2024 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Aug 3, 2026 · Snowflake · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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