Lundbeck enables an omnichannel marketing strategy with a centralized data stack

This is the story how Lundbeck’s digital transformation project delivered an infrastructure that unlocked diverse ways to activate their data


omnichannel marketing campaigns



data sources

centralized in the data warehouse


target top-line impact

for brand, customers, and markets

A vertically integrated pharmaceutical company: from research to commercialization

Founded in 1915 in Copenhagen, Denmark, Lundbeck is a pharmaceutical company specializing in neurological medicine. Employing 6,000 people, they develop and sell treatments for diseases such as depression, Alzheimer and Parkinson’s. Unlike most pharma organizations, they own their entire value chain: from research, to trials, to production and, eventually, commercialization. 

Lundbeck’s value chain ownership gives them a unique opportunity to leverage data. The company’s wide-reaching visibility—from research trials results to sales conversations with medical professionals—can be harnessed as a competitive advantage for the commercialization of their products.

“Since we have a lot of unique systems running, we have a lot of data with different formatting, from different sources,” said Daniel Thoren, Data Engineer at Lundbeck.

The vision: enabling omnichannel marketing campaigns with data

A personal approach to acquisition and upselling

Lundbeck’s marketing team had a vision for how to harvest this data: adopting an omnichannel approach to acquisition and upselling. In this new approach, data from multiple marketing sources—such as marketing analytics and lifecycle campaigns—would be accessible and actionable by the marketing team.

“For example, if a contact doesn’t open our newsletters, marketing could send a personal email or arrange a face-to-face meeting instead,” explained William Møller, Data Engineer at Lundbeck. 

Sales would also benefit from a comprehensive view of their leads and customers.

“By enriching CRM contacts with data points such as website analytics, the team could understand what content interests a given audience and adjust their conversations with medical providers accordingly.”

Marketing’s long-term vision was to use this holistic approach to further enable personalization. Sales reps would know at what time doctors liked to read content, on what topics, and when they are interested in ordering, among other information.

The requirements for data

But, to enable and support marketing’s vision, Lundbeck needed to take their data stack back to the drawing board:

“The omnichannel project quickly became very data-focused,” said William. “It meant that we needed to combine data from many different sources and create a holistic 360 view of our audience—something our old data stack couldn’t really support.”

Bringing Omnichannel Marketing to Life

Better collaboration by moving from on-premise to the cloud

Several teams were trying to crack omnichannel marketing; in the past, Lundbeck had outsourced related projects, but the team was confident that they could accomplish it in-house with the right tools.

“It ignited a desire to liberate our data for digital initiatives from our on-premise systems,” said Lars Schöning, Tech Lead of Lundbeck’s Data Platform Team. The company leverages technology based on the SAP suite, with most of the maintenance outsourced. Although sufficient for corporate purposes, it was not able to support digital omnichannel initiatives.

“We also had other needs that weren’t being met,” added Lars. “The system in place wasn’t accessible to the average developer at the organization.”

A capable but still too-complex infrastructure

Lundbeck’s data team settled on building a data lake on AWS. The new setup gave the team freedom to configure their infrastructure, but problems arose with both accessibility and observability.

“There were more maintainability requirements with AWS than expected. We needed more skilled developers across the board,” said William. “We didn’t have a front-end that was accessible to non-developers. We also didn’t use a friendly language like SQL that would enable the team to work effectively and collaboratively.”

Onboarding dbt Cloud, starting with Snowflake

Sharper data requirements: collaboration, stability, and flexibility

With the learnings from their AWS experience, Lundbeck landed on a third data infrastructure: Snowflake as the data warehouse, with SQL-first dbt Cloud to transform and deploy the data.

“We had a clear vision of what we needed,” said Lars. “A capable solution for all analytical use cases that’s also easy to understand. We also required a resilient and flexible architecture where we’d be able to swap out components later.”

Centralizing marketing data on Snowflake with dbt Cloud and Fivetran

Working alongside the marketing team, the data team got started on their Snowflake instance.

With their new data stack, different marketing sources—such as Google Analytics, Salesforce Marketing Cloud, and Veeva CRM—are ingested into Snowflake via Fivetran. This raw data is transformed in dbt Cloud before being loaded back into the CRM with Hightouch.

The new set-up allowed commercial leadership to access up-to-date marketing data via the team’s BI tool (Qlik Sense). And, it also enabled the marketing omnichannel vision.

The benefits of a centralized marketing data infrastructure

Omnichannel campaigns

Since Lundbeck debuted its new data infrastructure, they’ve launched seven omnichannel marketing campaigns leveraging their improved data access and workflow. 

“This is the first time we’re collecting data from all these sources and united them to get a holistic view,” shared William. “It’s one of the first times we’re actually doing lead generation, with our data stack enriching our CRM with insights sales teams can use to close deals.”

Faster turnaround times for sales representatives

Lundbeck’s various marketing sources are loaded into Snowflake, and after performing data transformations in dbt Cloud, are then passed through Snowpark for advanced analytics and finally are made accessible to business stakeholders to leverage. 

Snowpark is a Python development framework within Snowflake. It meets developers where they are and allows data engineers, data scientists, and data developers to code in a familiar way while executing data pipelines, ML algorithms, and data apps faster and more securely in a single platform inside Snowflake.

“We’re now providing our sales representatives with actionable, current data,” shared Lars.

“We have many use cases for dbt Cloud paired with Snowpark. One of our sales representatives received 300 ML powered suggestions across their accounts based on the data,” said William. “For example, it can suggest sales arrange meetings with doctors who ordered samples on our website.”

This type of integration used to require multiple components, but now Snowflake and Snowpark orchestrated through dbt Cloud, have created new opportunities for Lundbeck.

Centralized data across APAC, EMEA, and LatAm

During the data centralization process, the data team also tackled centralizing international sales data. Lundbeck sells their products across 100 countries, with nearly 50 sales representatives operating in total.

“We combined APAC, EMEA, and LatAm data in one holistic view,” said William. “It’s now possible to do all these new things because we don’t need to create net-new data models for each market or combination of markets.”

“Instead of having 50 different data science teams, we have one data team that has rolled out a solution that works everywhere,” added Lars. “This only became possible with our new data stack and workflow. dbt Cloud has helped us tremendously in harmonizing our global data.”

Increased independence and speed for data stakeholders

Today, all data used commercially by Lundbeck lives in Snowflake. While the data is centralized, data access is decentralized.

“The centralization comes from being able to access all our data in one place, not from the centralization of ownership,” explained Lars, “Different teams have the responsibility of loading and processing this data to meet their needs.”

The workflow, paired with the low barrier to entry for using dbt Cloud, has led to more autonomy for data stakeholders:

“Our data bottleneck problem stemmed from having one central data team review data requests,” said William. “As we mature, we’ve leveraged our tools to give embedded data teams more and more autonomy so we can scale effectively.”