Leveraging Information to Accelerate Client Value

Financial services companies continuously look for innovative ways to obtain more revenue and improve their competitive rank. Leveraging information is now an essential way to achieve these goals and is frequently referred to as “Information Monetization” or “Information Value Release.”

In this paper, we will explore leading edge business capabilities that are emerging in the industry and change the way organizations create value. We will discuss techniques around client profiling and experience, client lifetime value, market evaluation and client segmentation, aligned treatment strategies, client profile enrichment and client offer optimization. These innovative capabilities increase client satisfaction, loyalty and revenue growth, while optimizing servicing costs across client segments. By addressing both revenue uplift and cost optimization, these capabilities can drive increased market share, profitability and market valuation.

Structuring frameworks for organizing information such as a business ontology master approach to integration, enrichment and analytics, forward thinking organizations are delivering timely and relevant insights that are repeatable, trusted and managed with an optimal governance structure. Using a client centric model, we will demonstrate how organizations are leveraging emerging technologies that reduce information complexity and provide value realization to users through business relevant insights.

Client Profiling and Experience

At a high level, leveraging client information to accelerate business value requires a total view of exactly who the client is and all knowable attributes about them. This total client view will both drive the client’s experience and evolve as the result of all of the outcomes from offers made and presented. From all of this information a complete client profile should be generated.

Building a thorough client profiling capability has never been easy for financial services organizations, where organizational silos based on product, channel and technology inhibit a unified client view across their enterprise-wide processes and client interactions. In 2012, the traditional opportunity is compounded by many new sources of information, much of it unstructured, from both internal and external sources.

In order to create a thorough client profile, an information management architecture is required that supports historical and real-time data, as well as both structured and unstructured information. Each piece of client data can provide a more complete picture, enable better decision making and optimize client offers. Transaction data, client details, third party data, client service interactions, emails, texts, clickstreams and social media should all feed the client profile and be available for closer analysis.

The client profiling capability also needs to be linked to a real time analytical engine to provide an optimized client experience. Organizations have strived to incorporate personalization, convenience, simplification and attentiveness as part of their client experience capabilities. Today, the real challenges lay in building capabilities around rapid integration of data, real-time analytics, and user dashboards that provide suggested personalized service offers based on recent interactions and inquiries.

Customer Profiling and Client Experience

All the client experience events involving a client need to be harmonized with the client profile in order to provide a complete picture of both the client’s attributes and history.

In this way, an optimized experience and value can be delivered to clients exactly when, where and how they need it. Service opportunities may arise to respond to a client’s prior negative experience or to reach out proactively to improve on a positive experience.

In order to ensure that the above client experience capabilities meet expectations, KPIs should be identified to track and manage effectiveness over time, with a goal of continual performance improvement and value creation.

There are a growing set of technologies well suited for delivering and servicing the data needs of any client profiling solution, including Customer Relationship Management, Master Data Manage-ment and Entity Identification Management. These technologies would typically be supported by peripheral systems that absorb and distill both structured and unstructured data by means of ETL, traditional data warehouses and data marts, Enterprise Service Buses (ESB), SOA Web Services, as well as Big Data ecosystems to perform analysis utilizing Hadoop, NoSql databases and cloud infrastructure. Also, these capabilities would be coupled with email, chat, voice channel systems and semantic social data, to provide holistic client profiling and personalized client interaction. Optimizing the data management infrastructure helps the firm leverage information as a corporate asset.

Maximizing the value of client profiling and experience can help financial services firms improve client satisfaction, increase sales and enable top-line growth.

Market Evaluation, Client Segmentation and Client Lifetime Value

To create a holistic view of the market and client opportunities that exist for a firm, having perspective is critical. Taking an inside-out and an outside-in view allows a firm to first understand the target and then to direct the resources.

The outside-in view refers to the market. By looking at unique groups defined by needs, interactions and expected reactions, a firm can segment out markets into defined target populations. This analysis also provides an opportunity to determine if a market segment is growing, shrinking, and, generally, a good or bad opportunity to pursue as a firm. Those that are good are markets in which the firm may increase resources and time, while those that are not may be areas to decrease focus.

The inside-out view refers to the client base and the opportunity that the firm has in maintaining, growing and retaining client relationships. A core component of the client segmentation process looks at the lifetime value of a client. A Client Lifetime Value (CLV) approach focuses on scoring clients and prospects by the value they bring to the firm across many different attributes. Lifetime value reflects the net present value of all expected opportunities and costs throughout the client lifecycle. Key variables of CLV calculations are profitability, sustainability, opportunity, servicing costs and marketability.

Clearly, the inside-out view (client) and the outside-in view (market) will crossover each other, the obvious exception being when the firm is looking at new markets. Given this, the two perspectives will need to be harmonized and aligned to ensure that treatment strategies across the client lifecycle consider potential discrepancies between these two perspectives.

For instance, a client with great historical value to the firm in a shrinking market will need to be addressed to ensure the relationship is managed through any possible transition that takes place.  Potential would exist, in these situations, to move a client into a new or growing market segment so as to continue to benefit from a strong client relationship.  There will, of course, also be those situations where the market and the respective CLV will not justify that retention approach and the client will eventually be separated. However, this is not always a bad thing.

Segmentation Drive Treatment Strategies

Analysis of the market and the client base help to determine the client opportunities and distribution, but the question still exists of how to effectively and efficiently treat and service clients. As this is the cost side of the CLV calculation (i.e., not the revenue generating side), it is important to manage costs according to revenues. While a treatment strategy for the top tier segment may focus on providing one-on-one high-touch relationships (high cost), lower tiered segments may have treatment strategies driven by highly automated capabilities for more client directed self-service (lower cost).

Part of managing client relationships is being able to distinguish the good from the bad. Treating all clients with great service sounds appealing but if client revenues do not at least meet and, hopefully, significantly outpace the respective client support costs, that client may not be a client that is good for the firm. This is just one of the benefits of using a CLV driven segmentation and treatment strategy model.

Defining a CLV for clients is not an easy calculation. A varied set of heuristics will need to be used to appropriately develop a CLV calculation and methodology, and determining complex attribute definitions is a prerequisite. A mechanism to generate scorecards, key performance indicators, client profiles and metric predictions will help enable the identification of client opportunities on which the firm should act.

Client Lifetime Value Segmentation and Tiered Treatment Strategies

As new client experience events occur or new information or data points emerge, clients and targets should be re-scored based on the new information and re-aligned to new segments and opportunities as appropriate. In this way, CLV and related segmentation and treatment strategies are a continual learning process that evolves to meet a client’s and a firm’s changing needs.

Client Profile Enrichment

Client profile enrichment based on the total client view is another important trend that extends a client profile to include household members, personal relationships, affinity groups and other affiliations.  The end goal is to enable lead generation and top line growth by linking relationships between parties across different attributes visualized as a relationship map.

In this model, a richer view of households and relationships can be built by combining data from proprietary client profiles and data from the public domain. Semantic analysis may be performed based on relationships and affiliations identified through Facebook, Google, LinkedIn and other party affiliated web sites for universities, employers or other membership organizations. New parties, relationships and affiliations can be defined in a relationship map and confirmed as part of a stewardship function which provides significant client profile enrichment.

Household members with different last names, co-workers, classmates and friends can enrich the existing client profiles significantly and provide new qualified leads. The relationship map can be used to provide financial services companies with new opportunities for extending relationships, developing deeper profiles for related parties and offering personalized services and offerings. For example, the discovery of a married daughter of an existing Tier 1 client may suggest a new treatment strategy based on the more robust client profile.

Client Profile Enrichment


From a technology perspective, building domain ontologies of entities and relationships enriches the client profile. Advanced semantic analysis of social network platforms through natural language processing and data mining platforms, help distill unstructured public domain data into a smaller set of attributes and indicators predefined as relevant to enriching client data. Disparate modules should come together to create a holistic profile which is then open to iterative stewardship.

Client Offer Optimization

Client offer optimization is an area where competition between financial services firms is heating up. This need to provide the client with the “Next Best Action” or “Next Best Offer” is driven by a product personalization engine which identifies the best product for a financial services company to offer a target client at a specific point in time.

Based on the total client view, client models, client clustering, product purchase history and client interaction history, a financial services company needs to have a strong understanding of a specific client as well as other similar clients in the same client segment. Based on these factors, product managers and/or financial advisors can determine the best products to promote at a given time and in what sequence they should be offered.

Real-time analytics, machine learning systems and ongoing predictive statistical analysis are key enablers to enhance scoring and modeling for segmentation and provide predictive purchase behavior. Effective marketing and sales interactions between product managers and advisors can leverage this insight to align client needs with buying behavior. In this way, client offer optimization leads to an improved ability to successfully upsell and cross sell target clients.

Customer Offer Optimization

Success must be continually measured through feedback models to evaluate the effectiveness of marketing and compute a return on investment for specific offers or campaigns. By providing the client with their personal “Next Best Offer” just when they are open to it, the organization improves its return on client spend, continually learns what works, what doesn’t and why, and aligns future offers with emerging client trends.

Strategic insights derived from thorough client profiling, continually refreshed and augmented via real time experience events, empower the firm’s channels to appropriately react and engage clients. Built alongside an adaptive predictive analytics engine, client interactions will be driven by closed loop marketing decisions customized with response indicators and include logical decision points to determine next steps.

Looking Ahead

The business capabilities described above demonstrate how forward thinking financial services companies are currently leveraging information in innovative ways to create value for both clients and the enterprise. Information continues to be a key driver of enabling better decision making. By releasing information value, financial services firms are differentiating themselves from the competition, improving client satisfaction and loyalty and driving growth in the marketplace.

Capabilities such as client profiling, personalized client experiences, client lifetime value, market evaluation, client segmentation, tiered treatment strategies, client profile enrichment and offer optimization can be used individually or in concert to create new value for clients and organizations. Leading edge firms are providing these capabilities, which are enabled by technological advances in information integration, profiling and analytics, as well as virtualization and semantic ontologies.  Through technology advances and well-defined business capabilities, new paradigms have been created for leveraging information to accelerate value for financial services organizations.

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