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.”

KI

volume 2 no. 1 March 2, 2012

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.

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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 offerings based on recent

interactions and inquiries.

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

Client Profiling and Experience

knowledgent insights volume 2 no. 1 3

profiling solution, including Customer Relationship

Management, Master Data Management 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

Evolution of Models / Profiles into Planning and Service

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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 driven 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.

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

Client Lifetime Value Segmentation and Tiered Treatment Strategies

knowledgent insights volume 2 no. 1 5

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.

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.

Client Profile Enrichment

Customer Offer Optimization

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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.

Knowledgent Insights offers business and strategic insights on the latest trends affecting information efficiency and management that drives strategic growth decisions. For more information on the subject matter covered in this issue, contact info@knowledgent.com

About Knowledgent

Knowledgent (www.knowledgent.com) is a leading industry consulting and information management firm. It combines deep industry expertise with advanced information management capabilities to maximize the value of information to empower clients to make informed strategic decisions. Knowledgent leverages large-scale analytics, unstructured data mining, semantic integration and master information management to help clients optimize business operations. Founded in 2009, Knowledgent has offices in Boston, Massachusetts, New York, New York and Warren, New Jersey.

© 2012 Knowledgent Group Inc. All rights reserved.

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