This document discusses analytics in cross-selling in the retail banking sector. It outlines different approaches to cross-selling that leverage analytics, such as predictive analytics based on customer data models, rules-based approaches, and value-based approaches. It also discusses challenges in using analytics for cross-selling like lack of expertise, need for clean data, and operational difficulties. Emerging trends in analytics that could improve cross-selling are discussed, such as demand for packaged analytic applications and interest in real-time and advanced analytics.

1. Analytics in Financial Services
Business Analytics
Tap into the true value
of analytics
Organize, analyze, and apply data
to compete decisively

2. Content
Preface
From the Editors’ Desk
Analytics for a New Decade
01. Post-Crisis Analytics: Six Imperatives 05
02. Structuring the Unstructured Data: The Convergence of 13
Structured and Unstructured Analytics
Revitalize Risk Management
03. Fusing Economic Forecasts with Credit Risk Analysis 21
04. Unstructured Data Analytics for Enterprise Resilience 29
05. Why Real-Time Risk Decisions Require Transaction Analytics 37
Optimize to Drive Profits
06. Ten Questions to Ask of Your Optimization Solution 47
07. Practical Challenges of Portfolio Optimization 55
Understand Your Customer
08. Analytics in Cross Selling – A Retail Banking Perspective 61
09. Analytics as a Solution for Attrition 69
10. Customer Spend Analysis: Unlocking the True Value of a Transaction 77
0
11. A Dynamic 360 Dashboard: A Solution for Comprehensive 85
Customer Understanding
Fight Fraud More Effectively
12. Developing a Smarter Solution for Card Fraud Protection 93
13. Using Adaptive Analytics to Combat New Fraud Schemes 103
14. To Fight Fraud, Connecting Decisions is a Must 109
Improve Model Performance
15. Productizing Analytic Innovation: The Quest for Quality, 117
Standardization and Technology Governance
Leverage Analytics Across Lines of Business
16. Analytics in Retail Banking: Why and How? 125
17. Business Analytics in the Wealth Management Space 135

3. Analytics in
Financial
Services
08
Yamini Aparna Kona Balwant C. Surti
Analytics in Cross Selling – Senior Consultant, Industry Principal and
Infosys Technologies Head-Solutions Architecture
A Retail Banking Perspective Limited and Design Group,
Finacle Solutions Consulting
Practice,
Infosys Technologies
Limited
The case for cross-selling to the existing customers of a bank is an easy one—the difficult
part is executing it. Today, there are several different techniques for cross-selling effectively.
The common thread that runs across them is data and analytics. Predictive analytics based
on various models have created offers that are just right, just in time. Data mining and
analytics have helped in discovering trends and populating models that are the backbone
of predictive analytics. Value analytics is another approach to cross-selling that is available.
The call center, the branch, the web—every distribution/ service channel—all leverage
analytics in some way to cater to the entire gamut of customer needs—not just what the
customer seeks. This article analyzes the different ways in which cross-selling works
with analytics, its intrinsic challenges, and the emerging trends in the analytics field.
clients becomes increasingly difficult and
Why Cross-Selling is Imperative
expensive in a highly commoditized industry,
selling more products to existing customers
The experience of many financial institutions makes great business sense for a bank. It is an
shows that the cost of selling an additional excellent way to increase revenues and indirectly
product to a current customer is one-fifth improve customer retention, because customers
the cost of selling the same product to a
with more products tend to be more loyal.
new customer. This explains why cross-
Customer attrition rates are inversely proportional
selling, i.e., selling a bundle of products and
to the number of products held—the more products
services to the client (usually an existing one),
you sell to the customer, the lesser is the chance of
is being increasingly considered the cornerstone
the customer leaving you. As a result, moving
of the retail financial industry.
from a silo-product mentality to a consultative
As other sources of organic growth (for example, selling approach has resulted in a proliferation of
loan demand) have slowed, and adding new cross-sell initiatives in the banking segment.

4. effective in the hands of a skilled advisor
Approaches to Cross-Selling who can extract portfolio-related
information from a client. This approach
Cross-selling is selling additional products also has the advantage of revaluing the
to existing customers or prospects. It may portfolio at periodic intervals and
happen along with the initial sale or after coming up with other opportunities for
the initial sale is made. Often, the customer cross-selling.
may not explicitly mention specific needs
4. Predictive Analytics-based Approach:
or be aware that the bank offers products
This refers to a set of approaches where a
that meet their needs—cross-selling taps into
model (or a set of models) characterizes
this unmet potential using a variety of
customer buying behavior for financial
techniques:
products. Past customer data is used to
1. Person-based Approach: This is based build, refine and modify predictive
on either the skill of the Customer models. These models are used to predict
Service Representative (CSR) or through future customer buying—information
a structured question-based approach. In used to generate customer offers.
either case, the emphasis here is to elicit
In many circumstances, current or recent
the need through customer interaction.
transactions are used as trigger points in
Often, the skill of the CSR is the deciding
the system, and very often, the current
factor of success, and little or no use of
customer interaction is used as the means
analytics is made.
to deliver the offer. Trigger-based models
2. Rules-based Approach: The system can range from simple to sophisticated.
defines a set of rules and uses the Advanced versions can analyze a current
information collected from the customer online transaction and couple it with past
to arrive at a cross-selling offer. Some data to present relevant offers. Offline
analysis of the customer data is made. For offers are also often analyzed to come up
example, while processing a loan with the best channel for delivery of the
application, enough information is offer (for example, by mail, through a
available to decide whether the prospect call, etc.) and some offers may be made
qualifies for a credit card as well. using a combination of channels used in
an orchestrated manner to get the
3. Value-based Approach: This follows a
customer hooked (for example, a teaser
portfolio approach to the customer's
mail, with a click to a website or a phone
assets and liabilities with the bank. Here,
number to call or meet a particular
a customer is given a scenario with one
branch officer). The success or failure of
product that he or she has asked for.
an offer is also an input to the model to
Then, based on other information
improve future success rate.
obtained from the customer, alternate
scenarios are offered. Certain value 5. Social Networking-based Approaches:
metrics (for example, net assets, These are not yet prevalent in retail
installments per month, average rate of banking, but here again, a person's social
interest paid, etc.) under multiple networks, likes, dislikes, preferences,
scenarios with additional products are recommendations from network friends,
presented to the customer— highlighting and products used by others in the
benefits and opportunities for growth. network, can be analyzed using
Value-based approaches are often more sophisticated models to arrive at probable
cross-selling opportunities. One relevant
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5. Increased role of data and analytics in cross-selling Figure 1
Predictive
Value Social networks
Rules
Person
non-financial example is Amazon's 1. Data Mining can uncover potential
product recommendation engine that is customers who can be targets for cross-selling,
based on users who make similar and lead to generation of off-line offers.
purchases. (Refer Figure – 1 for “Increased
2. CRM Systems for sales, marketing and
Role of Analytics in Cross-Selling”.)
servicing, can use online analytics to
Barring the first approach, where the number make cross-selling offers.
crunching is done mostly in a person's brain,
every other approach calls for heavy use of 3. Predictive Analytics can be used to
analytics—the analysis of data, as well as the make both online and offline offers by
creation of models, rules engines, and offer predicting most likely choices of the
databases. customer based on past data.
Analytics in cross-selling Figure 2
Other technology
used in cross-selling
includes event Reporting
processing, rules
engines and more.
Text Business
Analytics Intelligence
Cross-
selling
Predictive Data
Analytics Mining
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6. Role of Analytics in purpose of cross-selling. Though they
Cross-selling may not be part of a suite of products,
point solutions are easy to integrate with
existing point-of-sale/ service solutions.
The role of analytics in cross-selling is
Often, these solutions are an easy way of
described in Figure 3.
bringing cross-selling to an existing
environment with minimal changes to
Cross-Selling Solutions existing systems. Most of them rely on
specific technologies and some rely on a
combination of technologies. Examples
1. Home-grown or Assembled Solutions: include Finacle Customer Analytics,
Amongst internal initiatives to use Customer XPs, and TIBCO's Cross-
predictive analytics, the most common Selling Solutions.
application is often cross-selling. In-
4. Channel-specific Solutions: Some
house data warehouses provide the data,
solutions are designed around specific
and business intelligence tools, predictive
channels—a call center, for example. These
analytics tools, rules engines and coding
solutions can monitor call center volumes,
provide cross-selling solutions. and trigger extensive cross-selling with
2. CRM Solutions: CRM solutions from incoming calls if the call volume is low.
leading vendors—such as SAP, Oracle, When call volumes are high, opportunities
etc.—come with cross-selling modules, for follow-up are generated. Similarly,
which can be configured and used along outbound call prioritization can be done,
with the sales and marketing modules of based not only on probable success rates,
the solution. CRM analytics are used to but also based on higher probability of
provide the data and power the cross- cross-selling.
selling engine, with the operational CRM
providing the delivery. Some core Challenges in Leveraging
banking solution suites that offer a CRM
Analytics
solution also offer cross-selling solutions
Analytics certainly present a summative view
through their customer analytics module
of customer transactional and behavioral
(for example, Finacle Analyz).
patterns. However, the following challenges
3. Point Solutions: These are specific are slowing down the adoption of analytics by
solutions that are made for the primary financial institutions:
Role of analytics in cross-selling Figure 3
Role Illustrative Examples of Analytics Used
1. Actual process of cross-selling Predictive Analytics, Portfolio Analysis
2. Analyzing past data to uncover trends Data Mining, Reporting, Business
and changes in customer preferences Intelligence
3. Measuring effectiveness of cross-selling Reporting, Web-analytics, Channel Analytics
64

7. n Expertise: A combination of
Lack of and software. This adds to the cost of
domain knowledge and data analysis implementing analytics models, which
ability, a pre-requisite for effective are already considered on the pricey
implementation of analytics, continues side—especially by small and medium
to be elusive. A banking end-user, banking enterprises. In addition, lengthy,
though an expert in his domain, interactive database queries and complex
often faces a challenge to interpret analytics scoring processes can congest
and analyze the myriad statistics networks and adversely affect database
thrown up by the analytics platform. performance.
A data analyst can compile the statistics
· Need for Real-time and Advanced
quickly, but is dependent on the business
Analytics: End users are no longer
user's domain expertise to organize
content with analyzing historical data
and analyze the data and communicate
and understanding past sales patterns.
it in the form the end-user needs it, to
Financial organizations now want real-
facilitate an actionable decision.
time data streaming and analysis that
The whole process may involve several
facilitates on-the-spot business decisions.
iterations, resulting in a significant
User demands are fast moving from
lag time between data collection and
“what happened” scenarios to “what
action and frustration on both sides.
may/ will happen” to be prepared with a
Predictive analytics, especially, are
ready action plan. Analytics models are
considered a niche realm, requiring
expected to answer what will be the
extensive training for effective
possible outcomes out of action A vs.
implementation.
action B. This requires high performance
n for Clean Data: Statistical
· Need analytics models that are capable of real-
models are only as good as the data time data analysis. There is growing
fed into them. The majority of statistical interest among banks in advanced
models not only demand accurate data analytics—though implementation has
with the least possible approximations, yet to pick up. (Refer Figure - 4 for
but also require that data be scrubbed “Industry Level Advanced Analytics
and neatly formatted in a particular Adoption Trends”.)
way to ensure quick and meaningful/
actionable recommendations. However, Emerging Trends in the
a significant portion of the customer Analytics Field
data, maintained by banks happens to
be inconsistent and siloed, making it Over the past couple of years, business
difficult to meet the formatting standards intelligence—of which analytics are a
of analytics models. part—has been catching the attention of
financial services industry decision-
n
· Operational Difficulties: The process
makers, who are realizing the need to
of deploying sophisticated analytics
transform the increased amount of
models usually involves accessing available disparate customer transaction
data from and/ or transferring data pattern data into actionable information.
among numerous machines and Keeping with the growing interest, the
operating platforms—requiring seamless following important trends are observed in
interoperability of various applications the analytics field:
65

8. Industry-level advanced analytics adoption trends Figure 4
“What are your firm’s plans to adopt the following business intelligence technologies?”
Expanding/ Implementing/ Planning to Planning to Interested Not Don’t
upgrading implemented implement in implement in but no interested know
implementation the next 12 a year or more plans
months
Reporting tools 31% 31% 12% 9% 10% 5% 2%
Data visualization, dashboards 17% 22% 18% 13% 19% 9% 3%
Specialized database engines 18% 15% 9% 8% 21% 22% 7%
Business performance solutions 16% 11% 10% 11% 27% 16% 8%
Decision support solutions 15% 11% 10% 10% 28% 20% 7%
Data quality Management 15% 10% 11% 10% 28% 18% 8%
Advanced analytics 9% 11% 10% 10% 29% 22% 9%
Complex event processing 8% 5% 6% 6% 28% 34% 13%
Text analytics 9% 3% 7% 6% 28% 33% 13%
1%
In-process analytics 3% 29% 41% 19%
2% 4%
Base: 853 North American and European software decision-makers responsible for
packaged applications (percentages may not total 100 because of rounding)
Source: "The State Of Business Intelligence Software And Emerging Trends: 2010." Forrester Research. May 10, 2010
n Analytics Applications are
Packaged business intelligence vendors are
in Demand – Business users, especially expected to find great traction. Many
financial institutions, are increasingly small to medium-sized banks are leaning
demanding packaged analytic towards SaaS models that allow the user
applications that are specifically to use the application through
designed for online marketing/ cross- affordable monthly subscriptions
selling, fraud detection, online credit without heavy IT or manpower
analysis, online trading/ investment investments. Small and medium-sized
advisory, and others. To date, many banks will leverage SaaS to architect
organizations have attempted in-house analytics applications that meet with
customization of analytics applications their specific requirements.
to meet such specific ends. Such
n
Open Source Solutions Gain Traction
re-architecture may no longer be
– Open source analytics solutions are fast
necessary with the emergence of
eating into the market share of on-
sophisticated event-driven/ complex
premise solution providers. Apart
event-processing products and predictive
from low cost, convenience is also a
analytics platforms that can support
contributing factor—open source
these capabilities.
solutions can be deployed alongside on-
n as a Service (SaaS) Finds
Software premise solutions. Open source is
Demand with Smaller Banks – SaaS providing an opportunity for recession-
66

9. hit organizations to experiment with a features that will support simulation
mix-and-match model and acquire using historical data, which helps
components of analytics solutions from experimentation before starting the
various providers at a fraction of the actual analysis.
price. Just as one might assemble spare nInitiatives will Catch Up with
Green
parts in the backyard, businesses are Analytics Vendors –Initial green efforts
toying with the concept of reaching out in the analytics/ business intelligence
to best-of-breed open source vendors for field have come from hardware vendors,
various phases of the analytics resulting in reduced energy consumption.
process—from charting to data Software vendors are expected to enter the
crunching, statistic modeling, predicting, market with offerings that will enable
and reporting. The soaring sales of companies to monitor their emissions
vendors—such as Pentaho and and sustainability exercises.
JasperSoft—bear testimony to the
growing popularity of open source in the
Conclusion
analytics field.
n
Mash-ups Make an Entry – Over the Analytics have a key role to play in
next couple of years, many analytics helping the banks to increase revenue
applications are expected to be deployed by discovering and fulfilling genuine
through coarse-grained application customer needs. The pressure to increase
mash-ups, which provide a cost-effective sales is even more urgent now than ever
means to embed analytics into business before and the use of online analytics and
process—without involving major
predictive analytics can make the job of
re-architecture work.
cross-selling a non-invasive, seamless part
n
Improving Analytics Literacy – of every customer interaction. Predictive
Vendors are realizing that providing analytics provide the much-needed,
applications with rich graphical data-based support to cross-selling, which
representations and complex will convert the task of “selling more” into
dashboards is not enough to satisfy an act of “fulfilling a customer need” by
business users, unless the users have preemption. By ensuring that the cross-sell
a means of deciphering the output. That is aimed at optimizing value to the
is why we will begin to see vendors customer, banks can gain additional
churning out flexible and user- business as well as customer loyalty and
friendly models with built-in training stickiness.
67