Data has always underpinned the financial services industry—from real-time pricing data in making trade decisions to company fundamentals in portfolio construction and demographic data to inform insurance underwriting. Market events are captured by market data that are then translated by financial services professionals into actionable insights and business decisions.
However, as banks and insurance providers look to differentiate themselves, many are looking to enrich their understanding of their customers and their needs, and are increasingly developing strategies to harness deeper insights about their customers. They are seeing other industries, largely led by media and retail, adopt technology and innovate quickly through an enhanced view of customer behaviors, lifestyle choices, and interactions. Organizations are capturing customer data points in every minute detail, and then analyzing and interrogating that data to inform business, product, and marketing decisions. Enabled by the explosion of data through online interactions, retailers are, for example, accommodating their customers with greater personalization, mobility options, and the ability to access products and services in one seamless integrated shopping experience with online interfaces. The retail banking and insurance sectors are now facing the challenge of operating in a way that can meet similar customer expectations for its own digital customer experiences. In fact, according to a 2021 report by MX, “Nearly 70% of consumers say they’d like their banking experience to be similar to the experiences they have with Netflix, Amazon, and other tech companies when it comes to offering personalized recommendations.”
For many financial services organizations, this is critical for business resilience and long-term growth needs. Since the financial crisis of 2008, public perception and trust of the financial services industry have been consistently poor. In an age where social and cultural attitudes have also shifted dramatically, the balance of power is now pivoted to the customer. With a sense of self-determination in how they spend or invest their money, organizations are faced with the challenge of evolving their business operations to reflect new customer dynamics and preferences for self-service models of engagement. Institutions are faced with new ways of enhancing their services, providing better, faster, and more personalized services, but many financial services organizations are finding themselves failing to meet the changing demands of their customers, often ending in poor customer experiences.
This evolving consumer expectation, coupled with the systemic challenges facing the overall industry since the financial crisis—including ongoing regulatory pressures, emerging competitive financial technology (FinTech) threats, and rising costs—means that banks have to reassess how they build and retain long-term value with their customers. Simply put, a financial services organization’s ability to nurture a customer increases the customer’s lifetime value—from when they first open a checking or savings account at a local branch, start a life insurance policy, buy their first home, or trade in digital assets to when they seek financial advice to help them manage their investment portfolios.
But for banks and insurance providers to have a more efficient approach to customer targeting, acquisition, engagement, and retention, they must first mobilize their data, invest in their technology stacks, and pivot organizationally to build a more holistic approach to understanding who their customers are.
Tied Down by Legacy Weight
For many banks and insurance providers, years of organic and inorganic investment, building, and acquisitions have created siloed and duplicative technology architectures, different and competing content stores and data models, and differing levels of governance, authentication, and data access controls.
This means that traditional financial services organizations face:
- Data access challenges and an inability to bring together business-critical data from across the organization and from third parties
- Data velocity challenges because of an elongated data pipeline as teams focus on identifying and ingesting relevant third-party data with varying degrees of readiness
- Data versioning challenges because of data copying and multiple versions of the truth that result in inconsistencies and increased error risks
- Data entitlement challenges that limit who has access to what data and for what purpose, ultimately creating cost and compliance issues
And as we continue to see industry consolidation, with regional banks merging (SunTrust and BB&T to form Truist) or banks acquiring trading platforms (Morgan Stanley and E-Trade), these data and technology challenges will likely persist.
For a large bank or insurance provider, this means an inconsistent or incomplete view of the customer and their needs because of the resource-intensive or costly data replication required to aggregate customer data from across the business. This also results in delayed customer insights and analytics, including Next Best Action and product suitability. For financial advisers, branch managers, and insurance brokers, these challenges have revenue implications, especially if they’re unable to accurately sell the right products to the right customers.
And more critically, being in a heavily regulated industry, financial services organizations face potential regulatory, reputational, and financial risks if they do not have strong data governance and security controls when accessing and sharing customer data and other personally identifiable information (PII).
Move Customer 360 Processes to the Data Cloud
To solve some of these challenges, financial services organizations have been turning to Snowflake and the Financial Services Data Cloud to increase efficiency, reduce complexity, create a central customer data platform, and establish real-time, event-driven, and cloud-based customer data processes. All of this is underpinned by data collaboration, enrichment, and security capabilities.
As a single data platform, the Financial Services Data Cloud enables organizations to thrive in a data-intensive, highly regulated, and competitive environment. This means breaking down data and technology silos across different lines of business, vendors, and partners to enable different workflows, from marketing and customer segmentation to predictive analytics and Next Best Action. Snowflake removes ETL, allowing data to be near-instantly accessible and distributed.
This sharing and collaboration is backed by robust cross-cloud governance controls and policies that follow the data—not the cloud. Policies are consistently enforced, simplifying governance at scale, reducing risk, and unlocking value from even sensitive or regulated data.
As we noted earlier, data underpins the financial services industry. Snowflake makes it incredibly easy to access data and collaborate with data. Snowflake Data Marketplace houses over 1,000 data sets from industry-leading data providers, such as FactSet, S&P Global, Experian, ZoomInfo, and Foursquare.
This means that:
- Marketing and customer insights teams can leverage ADP’s U.S. Workforce Demographic and Income data or Knoema’s U.S. Bureau of Labor and Employment database to perform customer segmentation and wealth analyses
- Sales teams can perform buyer propensity modeling, sales territory planning, and analyses of total addressable markets by bringing together customer data with ZoomInfo data sets
- Relationship managers and insurance providers can incorporate Next Best Action or product suitability insights by bringing together customer CRM data with consumer persona models that determine financial capacity, investment styles and behaviors, and other characteristics using Neustar and Equifax’s Asset-Based Customer Segmentation data
- Financial advisers and private bankers can build preference-based financial plans or portfolios based on investor attitudes toward risks and expected returns. This includes incorporation of ESG considerations using FactSet/Truvalue Labs’ ESG Scores DataFeed
Snowflake Data Marketplace allows data access with minimal to no ETL and redefines a user’s try, find, and buy experience. And more importantly, allows a fast and seamless way to enrich internal first-party data that creates new and differentiated customer insights.
Mobilizing sensitive data across different teams, lines of business, and with partners and vendors is challenging. Data privacy laws and compliance considerations limit what can be shared, impacting how financial services organizations are able to generate insight, collaborate, or manage risk.
When it comes to accessing and querying against customer data, data security is a core component of the Financial Services Data Cloud. Apart from features such as dynamic data masking and end-to-end encryption for data in transit and at rest, Snowflake also offers data clean room capabilities.
While a new concept in financial services, data clean rooms have been widely used by other industries such as technology, media, and advertising. It is a capability that allows organizations to aggregate customer data internally across businesses and teams, and externally from third parties. Data clean rooms enable data sharing, double-blind joins, and restricted queries that result in different organizations sharing and matching customer data without having to expose any underlying data.
This means that organizations are able to mobilize and leverage proprietary and third-party data in a secure way to power customer insights. See how the advertising arm of a large media company leveraged Snowflake’s data clean rooms to build scalable targeting and customer analytics across their portfolio of brands.
Financial services organizations can:
- Collaborate across multiple lines of business internally, or with financial services partners such as other banks, payment processors, or data providers, or even organizations in other industries such as retail, technology, or advertising
- Share and match customer data from outside your organization
- Validate query request and incorporate machine learning (ML) to enable predictive customer analytics
- Build partnership strategies, marketing initiatives, cross-sell and up-sell opportunities, and Next Best Action insight
Once financial services organizations are able to address their data collaboration, data enrichment, and data security challenges, they are then able to build out the processes and steps required for a customer 360 workload.
Generally, we have seen different organizations focus on the following six steps:
- Mapping, matching, and modeling—Identify and understand your customer and all of the related data. This requires organizations to define data taxonomies, key data subject areas, and logical data models.
- Aggregation—Consolidate customer data into a single customer profile with single-source data lineage. This includes all types of data, such as account, transaction, and reference data, into a centralized master data repository.
- Conflation—Establish the domain owner that governs the definition and rules for the data consumed or provided by a business process.
- Data Insights—Derive insights that automate and optimize processes. This may include leveraging AI and ML.
- Data Share and Enrichment—Enrich your customer data from other data domains across the organization, from third-party data providers, and from partners.
- Action—Break down organizational silos by creating a data-driven culture where collaboration is encouraged.
With the Financial Services Data Cloud, Snowflake partners with financial services organizations to streamline these steps at the enterprise level. With each of Snowflake’s core workloads—including Data Engineering, Data Warehouse, Data Lake, Data Science, Data Application, and Data Sharing—our customers are able to meet their customer 360 requirements.
At Western Union, Snowflake helped business leaders gain a more comprehensive view of their customers by consolidating more than 30 data stores and eliminating resource contention.
With more than 550,000 agent locations globally, Western Union helps over 150 million people and businesses send and receive money. And as consumers moved online, Western Union grew its digital money transfer services, which became its fastest-growing line of business. This translated to large volumes of customer data and transaction data being fed into a legacy data architecture that included multiple on-premises data warehouses.
When we spoke with Deepak Murthy, Western Union’s Data Engineering Support Ops Leader, he noted that “large amounts of data were copied up to five times due to different ingestion processes, which created dissimilarities in the data and questions about mismatched data sets.” As a result, there were significant challenges to provisioning users, ensuring 24/7 uptime, performing maintenance, and developing customer 360 insights.
Snowflake partnered with Western Union to modernize its technology stack with a multi-cluster shared data architecture that scaled instantly to handle Western Union’s data, users, and workloads without resource contention. This resulted in more than 50% lower data warehouse costs. Snowflake’s integration with other third-party applications and workflow tools also allowed business leaders to build near real-time reporting on transaction volume, customer insights, and other analytics that directly influenced sales and marketing initiatives.
The Most Important Step
We had previously outlined six general steps that financial services organizations need to consider when building out an enterprise approach to customer 360. However, there was one step, and perhaps the most important step, missing: to have a long-term vision.
Faced with organizational and cost restraints, many institutions today have a modest and somewhat limited scope to their customer data. At the end of the day, aggregating, consolidating, and centralizing customer data is simply table stakes. Without the ability to assess long-term business needs or capture future data requirements, financial services organizations may miss out on the data opportunities of the future.
When Western Union partnered with Snowflake, it was significantly expanding and investing in its digital money transfer services. The mobile application and online portal brought in a significant increase in customer and transaction data volume compared with its traditional brick-and-mortar business channels. With the Snowflake Data Cloud, the company was able to more quickly account for, mobilize, and translate that customer data into actionable insights.
Data truly does underpin the financial services industry. However, most of the time it is the long-term business strategy and vision that ultimately translates the data that an organization has into a true differentiator and, in this instance, a highly personalized customer experience.