Allergan Aesthetics, an AbbVie company, develops, manufactures, and markets a portfolio of aesthetics brands and products. Its aesthetics portfolio includes facial injectables, body contouring, plastics, and skin care.
Allergan Data Labs (ADL) is a group within Allergan with a mission to grow the company’s medical aesthetics business with actionable intelligence. According to Tory Brady, Associate Vice President, Product, Engineering & Data, “Allergan Data Labs was originally created to be a center of excellence for data science in support of the aesthetics business. We’ve now grown into much more, establishing performance marketing and product engineering functions as well.”
ADL engages and delights consumers through personalized and frictionless digital experiences. In this post, we’ll detail how ADL used Snowflake and Segment to delight their customers. Snowflake and Segment partnered in 2018 to enable rapid analysis of customer data at scale.
Seamless integration between Snowflake and Segment
Vishwanath Tanneeru, Lead Data Architect, Allergan Aesthetics, is part of the team that integrated Snowflake and Segment.
“It was a pleasant surprise that Segment and Snowflake integrated really well and required very little effort on our end,” said Tanneeru. “The events that are collected across Segment are automatically archived into Snowflake, making it easy for us to access that data and leverage it to drive website traffic.”
“The benefits of using Snowflake and Segment together aren’t possible if each platform was used independently,” he said.
Companies the size of Allergan Aesthetics have a myriad of tools and systems that generate and store data. Synchronizing data across these systems can be a complex task. Allergan Aesthetics sought a data infrastructure that was plug-and-play.
According to Brady, “Snowflake and Segment gave us a simple data integration, where Snowflake is a downstream recipient of the events we’re tracking in Segment. It literally was a toggle button we turned on while putting our credentials in for Snowflake.” 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.
Move to self-service reporting reduces time-to-market
Allergan Aesthetics is a data-driven organization. Its 1,500+ employee sales organization leverages data to make decisions and interact with customers. Before Snowflake, however, data was not available in a self-service model. A data analyst would query a database, generate a report, import it into Excel, then email it across the organization.
« Snowflake allowed us to aggregate data easily from all of our sources,” said Tanneeru. “We took that data and started building a data mart on top of it. That data mart drives a number of dashboards we’ve built that cater to different groups. At the same time, Snowflake has given our analysts access to the data. They can generate a report that will help our leadership make decisions quickly.”
Since moving to Snowflake, Excel reports are no longer sent around via email.
Segment enables Tanneeru to track how many people are visiting Allergan Aesthetics’ web properties, what they’re doing and how they’re interacting. “With Segment collecting this data and seamlessly ingesting it into Snowflake, our analysts and BI teams are able to generate dashboards and insights quickly,” he explained. “This has reduced our time-to-market and sped up our decision making.”
Leveraging data science and machine learning to improve CAC
Allergan Aesthetics leverages data science and machine learning with Snowflake and Segment to optimize its customer acquisition cost (CAC).
A machine learning team at ADL generates audience profiles and loads them into Snowflake. From Snowflake, ADL uses Segment’s SQL Traits to create audience groups, then uses channels (supported by Segment) to target those audiences. “SQL Traits allows us to enrich our customer profiles with any data in our data warehouse, whether it’s transaction data or outputs from our data science team’s data models,” Mehrdad Farahani, Principal Data Scientist at Allergan Aesthetics.
According to Farahani, “Snowflake and Segment enabled us to use machine learning models to quickly iterate on campaigns we have running. As a result, we were able to reduce our completed purchase cost per acquisition with social media advertising by 41%. The integration helped us deliver campaigns quickly and the machine learning insights improved the campaign outcomes.”
Allē Flash Rewards with personalized offers
Allergan Aesthetics is prototyping a Flash Rewards channel that delivers personalized offers to customers. The company is partnering with select providers to facilitate these offers at their practices. “We’re building a feature so that when you enter a provider’s office, you scan a QR code,” said Brady. “Our system makes an API call to Segment, which is aware of audience attributes. We can then deliver a personalized offer to the customer who’s standing at the front desk.”
These audience attributes could include data on which web properties a user has visited and which Allergan products they searched for. “At the point in time when they scan that QR code, we generate a personalized offer with the assistance of a medical professional to shepherd that journey further to completion,” said Brady.
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’s business into a direct-to-consumer one. By focusing on the customer and enabling personalized customer communications throughout Allē, Allergan was able to generate $400M+ in new revenue last year.
- 400M+ in direct-to-consumer sales driven in 2021 YTD
- 4.9 out of 5 Allē App Store rating
- 3M+ Allē loyalty users
- 41% reduction in “completed a purchase” CPA
Secure data sharing and the Snowflake Data Marketplace
Going forward, Allergan Aesthetics is evaluating adding Snowflake secure data sharing and the Snowflake Data Marketplace. The Data Marketplace enables the company to query external data sources and seamlessly join them with its own data.
“I can see the benefit of sharing data without the overhead of doing additional ETL to bring that data insight, data duplication, and access,” said Tanneeru. “Secure data sharing is a major breakthrough that I haven’t seen previously. This feature becomes even more important with our partners in Europe and Asia. It’s a very neat feature for us to have within Snowflake.”