Financial Institutions Collect All Kinds of Data
When it comes to collecting client data, Al Slamecka, the financial services industry lead for Cisco, puts it simply: “All types of customer-related data are relevant. Financial services institutions use any and all customer-related data for any number of customer-centric purposes. This may include real-time decision-making, customer segmentation, personalized services, risk analysis and fraud detection.”
Or as L’Hostis and Warner put it, institutions must strive “to identify and deliver the right experience to the right customer in real time based on everything you know about the customer.”
To get there, firms should think broadly about their data sources. Social media platforms, mobile applications, in-branch activities, ATMs, contact centers and payment networks are all good places to mine data, in addition to customer data platforms. Information may take the form of transaction messages, texts or voice calls.
Beyond data access, enterprises also need the right IT infrastructure in place to set the stage for success in customer personalization, such as a master data warehouse or data lake, which unifies customer data across various platforms for analytics purposes.
How Financial Institutions Can Build Better Customer Profiles
Equipped with customer data and operational frameworks, firms have the foundation they need to build better customer profiles. According to Slamecka, three categories of technologies can help achieve this goal:
- Analytics tools. “Financial institutions have several tools available to them to help build accurate customer profiles,” he says. “These range from general business analytics tools such as Tableau and Microsoft Power BI to more specialized analytics tools for compliance, fraud, anti-money laundering and know-your-customer functions.”
- Cloud data solutions. There is growing interest in data cloud solutions that leverage the elasticity of cloud computing and storage to offer dynamically scalable and high-performance Data Warehouses as a Service. These solutions enable firms to conduct analytics without the constraints of on-premises resources to build better profiles, faster.
- Artificial intelligence and machine learning. “Perhaps most importantly,” says Slamecka, “financial firms are turning to AI and machine learning for numerous customer-centric analytics purposes — from building, testing and refining data models that improve personalization and real-time decision-making, to embedding advanced natural language processing and customer experience management capabilities across all touchpoints.”
Bringing these technologies to bear on a customer personalization program is a three-phase process, according to Forrester. First, financial services firms must align their technology with an overall personalization strategy by getting organizational buy-in and establishing a vision for success. They should then prioritize the personalization objectives that will deliver the best outcomes for both the customer and the institution, ideally starting with a pilot project that can be scaled over time. Finally, institutions should coordinate technology investments by building internal talent focused on personalization.
“The success of a customer-led personalization strategy depends on a well-coordinated, cross-functional technology investment roadmap championed by both business and technology executives,” L’Hostis and Warner say.
Challenges to Building Customer Profiles for Financial Institutions
Even the best personalization efforts come with potential hurdles.
“Security is the biggest challenge that financial service institutions face because they’re dealing with critical, sensitive personal data about customers’ finances,” Slamecka says. “To protect this data, many institutions are using a hybrid cloud model that secures the connection and encrypts the data moving between a financial institution’s own data center and the cloud.”
Finally, Slamecka notes that companies must deliver an exceptional application experience. In practice, this requires complete visibility across both hybrid and multicloud environments. To achieve this goal, Slamecka points to solutions such as Cisco Full-Stack Observability, which centralizes and correlates application performance across all IT domains to help boost performance and isolate potential issues. Cradlepoint, Radware and Alteon also provide the kind of application performance monitoring solutions that institutions may find useful in their personalization efforts.
The bottom line: Personalized customer profiles are paramount for financial institutions. Achieving this goal requires a combination of in-depth analytics, AI and the cloud to meet — and exceed — consumer expectations.