How Householding Data Grows Financial Services Revenues and AUM

Highlights

  • Financial services firms can use household data to deliver improved customer experiences, grow customer relationships and increase customer share or AUM.
  • Customer household data is especially effective for financial planning. Extended data provides financial advisors a more comprehensive customer profile and considers goals beyond the client to include the client's family or center of influence.
  • Aggregating related party data and rolling up financial or other accounts to the client, household, and extended household creates a more comprehensive customer view. This information can be used to better manage financial products, accounts and holdings, and deliver more relevant and personalized financial product recommendations.
Johnny Grow Revenue Growth Consulting

Households are groups of related accounts that may include individuals, businesses or even assets such as trusts and estates.

They are usually based on family units or preexisting relationships, such as a married couple and their children. Once household members are logically connected, financial advisors can select data such as savings, checking, brokerage, loans, credit cards or AUM to be aggregated by household so the bank or advisor can better understand customer financial goals and decision making.

Financial Services Household

See how banks, wealth management firms and insurance companies can use household data to grow financial services customer relationships, revenues and AUM.

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Customer Householding Benefits

Understanding the customer as part of their family unit or in context of who is most important to them improves customer comprehension, financial planning and the delivery of rewarding customer experiences. It allows bankers, financial advisors and wealth managers to suggest more relevant financial products, achieve more upsells and cross-sales, and increase AUM.

Relationship managers can apply customer household data to deliver more relevant, personalized and contextual communications, engagement and offers. For example, customer household data can surface combined risks that benefit from risk management and mitigation services.

Other financial services benefits from customer household data include:

  • New client acquisitions from the extended household members.
  • Generational wealth transfer services from a client to heirs.
  • Accelerated new customer onboarding, KYC or financial product sales. For example, new debt products such as credit cards or loans can extract data from related accounts to speed the underwriting process.
  • Improved marketing effectiveness and conversions. Household data is the ultimate customer segmentation for banks and financial services firms. It enables marketers to create offers using customer insights from more than one customer dimension. And when only one offer is sent to each household the marketing campaign does not double count conversion responses. More importantly, only sending relevant offers to each household, and not redundant offers to each member in the household, shows the bank or financial service firm takes the time to understand their clients.
  • Also, for marketers than enrich customer data with third party data, many times that third-party consumer data is only available by household.

Clients also benefit from seeing how their investment, retirement, debt and other accounts roll up into a single or consolidated view that can be centrally and easily managed.

CRM Software for Household Data Process Automation

Technology is needed to manage customer household data at scale. The Customer Relationship Management (CRM) system is the customer system of record and the best place to configure and automate household data.

The top CRM systems for financial services firms have basic methods to track household data. But to be candid, they don't do much with the data. Data is only valuable if it creates user, customer or business outcomes. Here are some tips to design and configure household data with the aim of using that data to increase customer acquisitions, customer share and customer retention.

  • Start with a household data model that allows multiple household types, head of household designation and permits cascading levels of nesting relationships. Household types may include parent-child and arbitrary relationships, such as the nuclear family, the extended family unit and business designations. It should also allow assets (i.e., estates or trusts) and professional network affiliations such as accountants and lawyers. Configuring household data by type permits business process automation for different types of transactions.
  • Automate reciprocal roles to define the bi-directional relationships among individuals. So, when you identify a client's children, those children contact records automatically designate the client as their parent. Some CRM systems such as Microsoft Dynamics and Salesforce do this automatically while others require creating a business rule or workflow.
  • Allow granular selection of what financial products or customer activities should roll up for consolidated viewing at household levels. Also, recognize that some household members may have only partial ownership interests. Not all financial products need to roll up. In fact, there may be client preference, privacy or regulatory reasons for some accounts not to roll up. Aggregating account data is useful in identifying financial planning opportunities. However, most CRM software struggle with this requirement so custom workflows may be required.
360 Customer View with Household Data
  • Household data aggregation should also roll up certain key performance indicators (KPIs). For example, we typically roll up metrics such as customer lifetime value, AUM and household value indices (HVI) (a calculation based on customer value indices (CVI)). This aggregated data can then be used for more precise customer segmentation, financial planning and marketing campaigns.
  • You will need to determine how to automatically designate household relationships. With most CRM systems, matching occurs based on a combination of surname, address and Tax ID (TIN or SSN). It's a start but you will need to create additional workflows to surface household relationships and approve matches. Creating fuzzy queries will surface otherwise hidden associations. You will also need the capability to split and merge members in a household. Some CRM systems have this functionality, and some don't.
  • Associating related people is a task made complex by multiple surnames, addresses, Tax IDs and customer relationships that may span multiple bank locations. One way to simplify the task is to invite customers to complete the process themselves. They may be incented by increased financial services that come with increased AUM or just by the ability to see all their information in one place.
  • Household data may also impact other segments. For example, many banks and financial service firms provide different levels service based upon household AUM. Some firms may grant enhanced money fund rates or discounted fees based on combined household business. Others define perks such as financial services concierge programs.
  • CRM software can use artificial intelligence (AI) algorithms to deliver highly relevant next best financial product recommendations based on the relationships among customer accounts. CRM AI can also be applied to the household value index to show what customers purchase, and what they could or should purchase, based on their likely purchase propensity by product category.
  • It's important that the CRM application be able to produce views and reports at any level of the household hierarchy.