Allergan Relaunches Its Alle Loyalty Program and Generates $1 Billion in Direct-to-Consumer Sales

With Snowflake and Segment, Allergan has built an intelligent data platform to deliver the right message directly to consumers and unlock more than a billion dollars in sales.  



Reduction in JUVÉDERM “completed a purchase” cost per acquisition


Actively engaged Allē loyalty users

A healthcare worker looking at data on a tablet
Allergan logo
Retail and Healthcare
Irvine, CA

A new path to growth

Allergan Aesthetics is a global pharmaceutical company that manufactures BOTOX® Cosmetic, along with a collection of other beauty products like JUVÉDERM® and LATISSE®, used by over 4 million people every year. Historically, Allergan had a B2B2C business model, selling its products to healthcare providers who then resold them to end consumers. As a result, Allergan invested the vast majority of its marketing focus in the doctors reselling its products and much less energy on end consumers. Facing more competition, Allergan knew it needed to build better direct relationships with customers to continue growing.

Story Highlights
  • Extensive network of connectors, drivers, programming languages and utilities:  Connecting Snowflake with Segment helped Allergan build complete profiles of their customers.

  • Data science and ML:  Snowflake and Segment enabled Allergan to use machine learning models to reduce the cost per acquisition by 41%.

  • Democratizing data and removing silos: Snowflake and Segment reverse ETL removes data silos by automating data across downstream applications.

Fragmented, disconnected legacy architecture meant poor customer experiences

Before Snowflake and Segment, Allergan’s legacy architecture had significant gaps. Customer data was disconnected across its portfolio of brands and digital products, and the team was unable to tie users together across digital experiences. The legacy architecture was unable to track “events,” or actions taken by customers, which were critical to the team’s ability to deliver more direct, personalized experiences.  

Allergan’s data processes were operationally burdensome and hard to scale. Data was not available in a self-service model. A data analyst would query a database, generate a report, import it into Excel and then email it across the organization. Additionally, ingesting data involved streaming limited customer information from its website and mobile apps to a Microsoft SQL server. Then, the data engineering team piped the data to a legacy CRM system, which the marketing team used to send generic customer emails and messages.  

Mehrdad Hosnieh Farahani, Lead Principal Data Scientist at Allergan, explains these challenges: “There wasn’t any cohesion throughout our different brands and digital entities from a marketing perspective, so there was a lot of lost opportunity. Because there wasn’t a centralized golden record of a consumer, there wasn’t an understanding of a consumer’s journey from marketing channels, to registration and treatment. There were key pieces missing in our data architecture.”

Fragmented data led to a generic, one-size-fits-all legacy loyalty program app, Brilliant Distinctions. Half of the users in the program were added by their doctors and didn’t know they were signed up. As the app had only 2.6 out of 5 stars in the App Store, the team knew they needed to improve the user experience and engage with customers in more personal ways.

Building an intelligent data platform for personalized customer engagement

Facing more competition, Allergan knew it needed to leverage data more efficiently to build better direct relationships with patients to continue to grow. However, in order to do that, the team needed to rebuild its technology stack and create a centralized source of customer data. Tory Brady, CTO of Allergan Aesthetics, and his team stepped up to build an intelligent customer engagement strategy to effectively nurture direct customer relationships.  

The team selected:

  • Segment to collect, unify and connect its customer data and enable accurate user profiles.
  • Snowflake as a single source of truth for its data to power machine learning models and inform audience building.
  • Twilio Programmable Messaging to send transactional text messages to its loyalty program members in order to create seamless customer experiences.

Twilio Programmable Messaging to send transactional text messages to its loyalty program members in order to create seamless customer experiences.


Intelligent Customer Engagement

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. Using the Segment-enriched data in Snowflake, Allergan’s product engineering team built out machine learning models to predict relevant offers, products and content for each customer. They pipe these predictions into their applications and websites to deliver personalized content and increase engagement.  

Vishwanath Tanneeru, Lead Data Architect at Allergan, explains how Segment and Snowflake together have benefited the company’s data science and marketing initiatives: “The machine learning team loads custom audiences and data science scores for consumer profiles into the Snowflake data platform. Now, marketing, personalization and CRM teams can use Segment’s audience builder to create target audience groups and reach them through multiple marketing channels supported by Segment. This end to-end automation has been quick and easy to accomplish with Segment and Snowflake and added instant value to our business.”

“With Segment collecting this data and seamlessly ingesting it into Snowflake, our analysts and BI teams are able to generate dashboards and insights quickly. This has reduced our time to market and sped up our decision-making.”

Vishwanath Tanneeru
Lead Data Architect, Allergan Aesthetics

Over $1 billion in sales driven since 2021 by direct-to-consumer

Allergan’s new tech stack has enabled it to engage directly with customers in more meaningful, timely and impactful ways, ultimately building stronger customer relationships and transforming Allergan into a direct-to-consumer business. By focusing on the customer and enabling personalized customer communications throughout Allē, Allergan was able to generate over $400 million in new revenue in 2021. Brady sees Snowflake and Segment as a future-proof system that can nimbly adapt to unforeseen changes, such as integrations stemming from mergers and acquisitions.

10% reduction in JUVÉDERM® “completed a purchase” cost per acquisition

Allergan Aesthetics leverages data science and machine learning with Snowflake and Segment to optimize its customer acquisition cost. “Snowflake and Segment enabled us to use machine learning models to quickly iterate on campaigns we have running,” Farahani says. “As a result, we were able to reduce our completed purchase cost per acquisition with social media advertising by 10% for JUVÉDERM®. The integration helped us deliver campaigns quickly, and the machine learning insights improved the campaign outcomes.”

Making Allergan’s tools more user-friendly

Segment, Snowflake and Twilio have made Allergan’s marketing technology stack more flexible and customer-centric. Marketing can move faster and easily test new tools, and Product can build out new, user-focused features and functionalities quickly. Allergan is exploring new integrations between Segment and Snowflake. Segment’s new reverse ETL tool helps Allergan further remove data silos across their platform. With reverse ETL, Allergan is able to automate data quality assurance between their data platform and applications like Braze and improve data consistency. Christine Li, Allergan’s Head of Marketing Technology, views Snowflake and Segment as a core part of their infrastructure and plans on continuing to invest in them moving forward.

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