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3 Steps to Targeted Engagement Perfection

Data Append, selection and modeling

Everyone — who hasn’t been living under a rock for the past few years — knows Amazon. With nearly $72B in digital annual revenue, Amazon has claimed the number one spot for ecommerce sales in the retail industry. Household brand names Wal-Mart and Apple take second and third place, respectively. However, the combined digital annual sales of these two retail giants only matches one-third of Amazon’s digital annual revenue (eMarketer, 2015).

To achieve this enormous digital annual revenue gap from its competitors, Amazon must be doing something right! So what principles can wealth management marketers take from Amazon and apply to create competitive advantage?

Amazon does many things well, but one competitive differentiator is the ability to cross-sell or suggestively sell to customers. Through relevant engagement, Amazon makes suggestions for books, music, or movies customers will like — which is all driven by an individual’s past purchases, personal information, and behaviors.  

Like Amazon, institutional wealth management businesses can use targeted engagement and education programs to cross-sell and retain participants. These programs are conducted through addressable tactics and present messaging to specific individuals regardless of channel: direct mail, email, addressable display (both owned and open web), social, landing pages, newsletters, and personalized website experiences (pre/post log in). These programs create a strong association between the participant and the brand, vastly increasing the odds that you will retain their assets when they change jobs or retire; and it opens the door for a broader relationship outside the plan.

Executing on these programs is not easy; however, it is certainly not impossible. It requires targeted education and communication to the right participants at the right time. Here is a three-step guide for using data to drive targeted engagement and education programs: 

Step 1: Append your participant data

Enhancing your first-party participant data with third-party data is critical. This should include lifestyle, behavioral, and detailed wealth and financial data outside the plan to create a rich, actionable picture of participants by life stage and wealth level. This information will inform relevant education campaigns and online experiences while allowing you to route higher-asset participants to call centers that employ more sophisticated representatives.

Step 2: Develop life-stage and wealth-based segments

Segment clustering is critical in building the foundation needed to connect people to the right information and resources at your firm. Here are a few examples of how life-stage and wealth-based data can inform education programs:

  • Connecting higher net-worth participants, mass affluent participants, and individuals with specific planning needs with advisors of relevant expertise and levels of knowledge.  
  • Identify targets that align with your value proposition. For example, individuals that have a strong do-it-yourself mindset are good targets for self-directed brokerage, but not wealth management services, as they avoid fees and take their own advice. If you don’t have a good product in this area, then focus on participants with needs that align with the products you have to offer. 

Step 3: Assess Participant Lifetime Value Potential

Finally, the ability to segment prospects and customers into future value-based groups allows you to align marketing spend to return. Understanding specific tangible behaviors that are discrete, measurable, and build lifetime value (e.g., engagement with web tools, call center history, product cross-sell) is the first step in building the foundation to an efficient program. Developing a suite of models that predict propensity to respond to an offer or product is integral when developing an engine that allows you to rotate messaging while presenting the participant with the next-best message for them.

Lifetime value (LTV) can seem like an overwhelming task to accomplish. However, it can be approached with increasing degrees of sophistication. Base LTV can be evaluated with some simple knowledge of revenue and expenses at the customer-product level with just a few months of historical data. However, with most wealth management firms, it is a long developing relationship with lengthy consideration and decision-making time frames. Therefore, the more historical data you have at your disposal, the better the results.

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