How big data helps Transamerica attract, retain customers

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A goal for just about any organization doing business online is the ability to pull together all the data it has on its customers so it can offer specific products to shoppers in realtime as they move through the “customer journey.”

Transamerica, the insurance and investment giant, tackled that challenge in 2015 with its Enterprise Marketing and Analytics Platform, which allows the company to bring together data from numerous business lines and customer contacts into one holistic view. EMAP is a distributed, scalable and secure platform for storing and accessing data (structured, semi-structured and unstructured) through a Hadoop data lake.

Today, EMAP helps Transamerica understand and improve the customer experience from first outreach to continuing customer-retention efforts.

“We need these fundamental understandings to fuel innovations in product development and service delivery,” said Gerard Rescigno, Transamerica’s executive vice president and chief technology officer.

Forging closer ties with customers

Transamerica, a holding company for U.S.-focused life insurance companies and investment firms, is a subsidiary of Dutch life insurance multinational Aegon. Transamerica provides life and supplemental health insurance, investment and retirement services to 27 million customers.

The size of its customer base and the number of its financial-service products can results in missed opportunities. However, Transamerica uses EMAP to create a comprehensive view of its clients.

“We analyze our customer interactions and behaviors across all our channels,” Rescigno said. “How do they connect with us? What do they look at, want or need? What do they say to us in our voice response systems?”

The company integrates data from across its insurance, retirement and investment lines of business with third-party data. EMAP pulls in data from more than 40 sources, including consumer income and social media data.

All that information is used to identify new patterns, according to a 2016 joint presentation by Transamerica and Cloudera, one of its vendors.

Transamerica then personalize its digital offerings and interactions to suit a given customer or website visitor. “We’re using data to improve our responsiveness and ability to predict customers’ needs,” Rescigno said. For instance, knowing which products to offer customers who are parents of young children versus empty-nesters nearing retirement.

The building blocks

Transamerica decided to tackle its data analytics challenges with a Hadoop-based data lake, which it believed would be more versatile, more powerful and less expensive than a traditional data warehouse solution. Hadoop, part of the Apache open-source project, is a programming framework for processing and storing huge data sets in a distributed computing environment.

The company uses Cloudera’s distributed Enterprise Data Hub for storing structured, semi-structured and unstructured data. Informatica’s Big Data Management (BDM) product handles the vital data management functions, the company said, including data ingestion and integration, data profiling and data quality.

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Transamerica uses different processing engines, such as MapReduce, to parcel out work to various nodes and organize the results. The company also deployed Spark, a fast, in-memory data processing engine that’s particularly efficient with SQL and machine learning.

Using an abstraction layer to build data pipelines provides Transamerica with an architecture that can quickly utilize new processing engines as they become available, the company said. For example, Transamerica started with MapReduce but more often use the faster Apache Spark.

Transamerica relies significantly on machine learning to draw insight from its data. Machine learning automates data analysis through algorithms that iteratively learn to uncover insights they weren’t specifically programmed to find. The company uses H2O, an open source machine learning platform. Using H2O, Transamerica leverages in-memory distributed processing on Hadoop and lets data scientists run large numbers of machine learning models using common programming languages for big data, such as R, Python and Scala.

The insurer also utilizes a set of analytics solutions, notably Alteryx and Datameer, allowing analysts to perform additional computation.

The results are presented using Tableau, which integrates with Hadoop, to provide visualization and dashboarding capabilities within the organization.

The EMAP platform is also designed to maintain the security and appropriate use of the information. Use of personally identifiable information (PII) is also subject to the oversight of Transamerica’s Data Governance Council. The company said that this group is tasked to help ensure that data is secure and is used consistently with customer interests and legal and regulatory obligations.

Thanks to EMAP, Transamerica has fuller access to data that was previously fragmented and siloed, often in legacy systems. Not only is the company able to work with more of its growing data, but it’s able to do so much faster than in the pre-EMAP days.

Big data in practice

Transamerica’s data science and analytics teams now use the EMAP platform to develop, test and deploy predictive models. In the past, the teams were challenged with large data sets that the company couldn’t process efficiently. They are now able to leverage in-memory processing on Hadoop to run models in minutes instead of days. The ability to run queries against the data and obtain answers quickly translates into reduced processing cycles and infrastructure demands.

The company said its effort has made it easier for teams in Transamerica’s technology, business and analytics groups to focus on the customer relationship. The cross-functional collaboration has provided a framework for future joint projects.

The company said it is more agile and better able to explore new ideas for how it interacts with, and serves, its customers.

“EMAP gives us new insights into consumer behavior that we would not have been able to isolate before. We are frequently surprised at what we discover when insights are revealed,” Rescigno said. “They are serendipitous in a way.”

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