Does your customer data tell a story? When a known customer or prospect appears on a digital channel, or at a physical location, do you have a complete, accurate understanding of the customer’s needs and wants and use that understanding to instantaneously render a decision that matches the customer to the right product or service?
For too many financial services institutions, the answer is no. They have data volume without quality. Or quality without volume. The data is either imperfect or incomplete, and either way the customer experience (CX) suffers. They have a name with slight variations in spelling, and are not sure if the records belong to the same identity. Or they are confident that the records reflect an individual customer, but the profile is either missing a feed from a key data source, or the incoming data is out of date. The result? An inferior customer experience that does not sit well with your customer, increasing the risk of churn, lost cross-sell and upsell opportunities, and weaker customer segmentation.
Data unification and always-on data quality are instrumental in driving superior customer experiences that lead to enhanced customer satisfaction and loyalty, higher engagement, better retention, more cross-sell and upsell opportunities, targeted prospecting, revenue growth, and cost reduction.
Data Unification for the Win
In today’s financial landscape, customers access banking services through mobile apps, through the website, and through traditional retail banks. They interact with chatbots, dial the call center, search for banking products online, and use social media. With each interaction, a customer leaves important signals about their interests and intent, and banks need to pick up on those signals to drive a personalized CX that demonstrates an understanding of the customer.
Data unification helps ensure that financial institutions have a complete understanding of the customer. A customer data platform (CDP) that ingests and unifies data from every source and of every type (structured, unstructured, semi-structured, first-party, third-party, etc.) is invaluable in building such an understanding. It completes the picture, allowing banks to drive a personalized CX that is relevant to the customer’s current situation. With an up-to-date understanding of a customer’s website browsing data, mobile app deposits, transfers, inquiries, balances, retirement planning, etc., the company is able to drive personalized experiences that are perfectly aligned with an individual customer journey.
In highly regulated industries, financial services included, tight protection of customer data is essential, which is why many financial services organizations opt for a CDP that operates in a data-in-place environment, allowing them to maintain a single source of truth for customer data behind the company’s firewall without having to copy and move it.
Always-On Data Quality
Data unification alone is not enough to create a data foundation that organizations can trust to power personalized experiences. The reason, of course, is that when a CDP ingests data from various sources, the data needs to be standardized, enriched, normalized and made ready for business use – ideally at the moment of data ingestion to allow for that real-time, contextual customer understanding.
When comparing CDPs, one thing to be aware of is how the CDP approaches data quality, as there are major differences between, say, a basic match vs. automated data quality with tunable identity resolution that includes householding to provide the most accurate, trustworthy customer profile to drive all business and CX use cases.
Performing always-on data quality processes at the moment of data ingestion is another feature to be aware of. Some CDPs either fail to prioritize data quality, such as limiting identity resolution to a basic, deterministic match, or they rely on third-party vendors for standardization and normalization tasks. The problem in both cases is that the enterprise business user – the marketer or another user – lack visibility into the data quality processes. They do not know where data quality processes have occurred as data is readied for activation, and thus cannot trust the validity or accuracy of the resulting unified profile.
For example, financial institutions have a vested interest in knowing the dynamics of a household; head of household, dependents, life stage, etc. Perhaps a customer has young children, and is interested in saving for university. Or is looking to move to a larger house. Or new empty-nesters are interested in downsizing. A detailed understanding underpins almost every decision for how to engage with someone. Is the person using the mobile app the account owner? What is the significance of a new mailing address, or a name change? Data quality, which includes advanced identity resolution, is the critical step that ensures that the unification of customer data is about more than just volume; it provides organizations with confidence that they are engaging with the intended customer.
Many financial services companies have gaps in their data that limit the effectiveness of a unified customer profile. They are either missing data, data is old, or their MarTech stack comprises different systems that each take a different approach to data quality.
The Redpoint CDP ensures trustworthiness in customer data from ingestion through activation. Redpoint brings together every source of customer data – from across the enterprise. Always-on data quality continuously cleanses, standardizes and normalizes incoming data. And data observability dashboards show users that everything is working as intended to build a consistent, accurate understanding of your customer.
To learn more about how you can power digital-first financial journeys with Redpoint’s secure, privacy-compliant CDP, click here.
