Archived: Merkle Case Study | Advertising & Marketing | AWS

Through data and digital transformations, Merkle helps companies across healthcare, financial services, manufacturing, and other industries to create personalized omnichannel marketing that transforms their customers’ experiences. It offers services for creative, analytics, technology, consulting, and data management. Merkle manages more than 150 marketing databases and 3.7 billion customer records and has delivered 150,000 campaigns.

The onset of far-reaching data privacy regulations, such as the General Data Protection Regulation in the European Union and the California Consumer Privacy Act, led to a seismic shift in digital marketing. Major internet browsers have removed third-party cookies, which were once the standard source of audience data and identifiers that companies would use to inform their advertising and marketing campaigns. Now, marketers can only use consented and anonymized customer data, not personally identifiable information. That data must be collected and used in compliance with regulations. “Marketing teams have to migrate to solutions that enable them to do that successfully,” says Sunil Rao, senior vice president of analytics at Merkle. “They need innovation in consented ways of reaching customers and of measuring the impact of marketing in a secure and privacy-compliant manner.”

An AWS customer since 2016, Merkle decided to build Merkury on AWS. In 2020, Merkle became an AWS Select Consulting Partner, signed an AWS strategic collaborative agreement, and accelerated the development of a simple-to-use solution for its customers. “AWS brings the technology, and Merkle brings our marketing expertise so that we can create something of value to our end customers,” says Ankur Jain, senior vice president and global cloud practice lead at Merkle.

Companies can use Merkury to own, build, and activate marketing campaigns based on customer 360s—detailed profiles of their customers—without cookies. The solution instead uses an organization’s first-party customer relationship management data and interactions such as website visits, logins, outbound email campaigns, and addressable media reach. The customer 360 practice informs cross-channel targeting, personalization, measurement, and more.

When a global entertainment company reopened its theme parks worldwide, for example, it wanted to elevate guests’ experiences through targeted marketing and personalized offerings. The company used Merkury to build a 360-degree view of high-value guests, who are four to five times more valuable than the average guest, by capturing data such as where they stayed, which rides they went on, and whether they had seasonal or day passes. “We used AWS to bring those interactions together to build a customer 360, then used advanced analytics to personalize the experience on the website, in the park—anywhere that particular guest is interacting with the brand,” explains Jain.

Another feature of Merkury is data clean rooms, which enable marketers to analyze and join shared first-party data with partners in a privacy-safe environment and to control how much of the underlying raw data is exposed to other parties. “Clean rooms remove a layer of overhead and restrictions when we put these analytical environments together,” says Jon Regan, vice president of technology and data management at Merkle. “They simplify the approach to compliance and security because there’s no personally identifiable information in there.”

On AWS, Merkle can avoid infrastructure maintenance and yearlong development times and can focus on delivering quality to customers. “Everything is prepackaged within the fully managed services of AWS,” says Jain. “The pain of setting up the infrastructure, installing software, and managing the environment on a daily basis is taken away. Our innovation cycle is shortened.” Customers of Merkle and of AWS can seamlessly purchase Merkury from AWS Marketplace, a digital catalog that makes it simple to find, test, buy, and deploy software that runs on AWS. “Developing this offering for every customer in a unique way would be very costly,” says Jain. “For our customers on AWS, we can have them up and running with our solution within weeks, if not days, whereas it would take months if we did it from scratch.”

The data for customer 360s resides in Amazon Redshift, a fast cloud data warehouse that makes it simple to gain new insights from data. “We use Amazon Redshift on a frequent basis to bring the data to a usable state,” says Jain. “We run structured query language queries and point business analytics against it.” Housing the raw data from customers’ marketing systems is Amazon Simple Storage Service (Amazon S3), an object storage service that offers industry-leading scalability, data availability, security, and performance. To transform that data for processing, Merkle uses AWS Glue, a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning, and application development.

As real-time engagement becomes more important, Merkle uses AWS Lambda—a serverless compute service that lets companies run code without provisioning or managing servers—for near-real-time data transformations. And Merkle enables near-real-time streaming using Amazon Kinesis, which makes it simple to collect, process, and analyze near-real-time streaming data so that companies can get timely insights and react quickly to new information. Merkle also uses artificial intelligence and machine learning services from AWS. For example, it identifies objects within creative advertisements using Amazon Rekognition, which makes it simple to add image and video analysis to applications using highly scalable deep-learning technology.