Driving analytics into action: How to enable business decisions with “big data” and analytics

The financial services industry is looking at “big data” to drive profitable growth. But many companies fail to use analytics effectively. If your firm is a bank beginning a big data initiative, don’t ask, “What data, staff, and software should we install?” Ask, “What decisions do we need help with?” And follow the guidance in this deck.

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Driving analytics into action How to enable business decisions with “big data” and analytics

New York
David Meer Partner +1-212-551-6654 [email protected] Arpan Dasgupta Senior Associate +1-212-551-6034 [email protected]

This report was originally published by Booz & Company in 2012.
Strategy& 2

Executive summary
The financial services industry is looking at “big data” as a way to drive profitable growth. But many companies fail to use analytics effectively. If you are a financial services firm exploring the use of analytics for decision making, you will need to focus first on your purpose. Don’t ask, “What data, staff and software should we install?” Ask, “What decisions do we need help with?” Analytics is used for three types of decisions, each with its own level of sophistication and skill, and each with a greater level of return: (1) direct-to-customer processes, (2) go-to-market processes, and (3) rewriting the profit equation for the entire business system. In this deck, you will see a basic approach to financial services analytics, focusing on empowering the decision makers of the enterprise. It starts with building tools and capabilities, then aiming those tools at pilots and programs in target initiatives, and finally aligning those new practices with your governance and business processes in the enterprise as a whole. Your goal is to develop a multifaceted, robust group of big-data capabilities that are embedded throughout your company.



The financial services industry is looking at “big data” as a way to drive profitable growth
ched n u la has at earlys r e artn d aimed ies in the P l e Acc 0M fun compan 0 a $1 & growth stage ta space a big-d

Over 79% of firms in the banking sect or have implemented b usiness intellig ence/ analytics soluti ons

rpriseConsumer- & ente driven focused big data– ends that apps can identify tr decisions help drive business

American Ex press Busine ss Insights uses sophisticated analytics to pro cess real-time purchasing data

Source: Company websites; American Banker; Analytics Magazine; Aite Group; Strategy& analysis Strategy& 4

But many companies fail to use analytics effectively
Some common problem symptoms
•  It is not clear which decisions need analytics •  There is no agenda-setting process for the development of big data •  The enterprise has big-data capabilities but no consensus about how to use them •  Many decision processes are not driven by data, even with a big-data system in place •  All facts come with a point of view—the data does not resolve differences of opinion •  The IT department owns and manages the entire design process •  People with analytics talent are recruited but not enlisted in a strategic direction •  The enterprise does not retain those people •  The enterprise is still putting in place its success metrics for analytics talent •  Some departments “over-engineer” the processes; others “under-engineer” them



Don’t ask “What data, staff, and software should we install?” Ask “What decisions do we need help with?”
The vast majority of resources and attention are spent here… …but the greatest impact is here

Data Information IT Infrastructure Analytics







You can use analytics to support three types of decisions, each increasing sophistication and organizational change
$ $ $
Analytically driven business systems (Rewrite the profit equation and monetize your big-data capability) Transform the business model Focus: Data and insight embedded systematically across business decisions at all levels Example: Reducing back-office costs and raising the efficiency ratios Drive 50%-100% improvement in long-term shareholder value Degree of Organizational Change



Go-to-market processes (Pricing, sales force management, distribution network design) Redeploy customer-facing resources against analytically defined opportunities Focus: Core revenue and productivity levers—pricing, sales channel roles, and deployment Example: Adjusting pricing and promotional strategies to outpace competitors Drive 5%-15+% improvement in sales productivity while acquiring new customers and wallet share

Direct-to-customer processes (Customer insight, products and services) Apply micro-segmentation and improve frontline execution practices Focus: Customer acquisition and retention; sales of products and services Example: Offering individualized credit/debit card plans to particular segments Drive 5%-7+% sales/revenue lift when done right


Indicates expected financial impact

Sophistication of Skills



Prioritize your activities so that those with broad application and high financial impact are first to benefit from analytics
Business Decision Prioritization
Loan/Credit Card Pricing Performance Measurement Marketing Campaign Monitoring Product Design Product Bundling Customer Retention Customer Acquisition Customer Activation Cross-Sell


Deposit Pricing

This example, from a multinational bank, shows how crossmarket applicability and financial impact became the key criteria, with ease of implementation as a secondary consideration.

Number of Markets

Utilization/Balance Building


Branch Network Design


Distribution Channel Optimization Marketing Campaign Design


Medium Ease of Implementation


High Priority Strategy&

Medium Priority

Low Priority

Financial Impact 8

This means looking for a simpler, easier approach, focused on empowering the decision makers of the enterprise

Build tools and capabilities

Align your governance and business processes

Aim at pilots and programs in target initiatives



1. Build tools and capabilities
•  Develop the required models and analytics tool set •  Secure, mobilize, and empower the right talents •  Provide data and business intelligence with continuous business engagement •  Build the IT infrastructure to enable data provision and ensure quality •  Enable constant cycles of testing and learning



1 Build

Four types of capabilities draw upon big data
Type of Capability Algorithmic capabilities: Tracking activity (often in digital environments) and adjusting your company’s responses in real time •  •  •  •  Used in… Online offers and ad serving Dynamic pricing Call routing Search engine optimization and related techniques

Predictive capabilities: Establishing datadriven guidance for decisions in the midst of uncertainty Descriptive capabilities: Providing a better understanding of business impact and customer response Reporting and MIS capabilities: Monitoring everyday financial and operational performance

•  Sales forecasting •  Pricing •  Picking the next likely product •  Client counts •  Lead lists •  Customer segmentation analysis •  Regular and standardized performance reports •  Business results tracking •  Guiding employee participation and accountability



1 Build

Increase your strategic emphasis on algorithmic and predictive capabilities; invest your time and resources there
Typical FS Company 2011
Algorithmic ~0%% Predictive 10–20% Algorithmic 5–10%

Leading FS Company 2013

Percentage of total firm’s analytic time spent on each type of capability

Descriptive 20–30% Predictive 40–50%

Reporting/MIS 60–70% Descriptive 30–40% Reporting/MIS 10–15%

Source: Interviews; Strategy& analysis Strategy& 12

2. Aim at pilots and programs in target initiatives
•  Identify the profit pools and define value proposition to leverage analytics capabilities in creating


•  Develop and implement specific offers/programs (e.g., cross-sell) as targeted application of

analytics capabilities, including:
–  Field engagement

–  Knowledge dissemination and program refinement –  Frontline communication •  Execute on constant test/learn cycles



2 Aim

Increase your strategic emphasis on algorithmic and predictive capabilities; invest your time and resources there
Lifetime Value


One bank used analytics to develop integrated product offerings for customers, such as those shown here. The lifetime value of a customer with this type of multiple-product loyalty is worth more than the sum of the value of the individual products.


Separate card and credit line

Integrated card and credit line

Lifetime Value



Separate credit line and deposit account

Integrated credit line and deposit account



2 Aim

Identify some places where direct-to-customer support is needed…
Branch Targets vs. Simple Transactions (Proxy for Traffic) This example, from another bank, shows its branches grouped by their current customer traffic (the y-axis) and their targets (the xaxis). Each orange dot is a branch. Two branches (335 and 112) were singled out for analytics to learn why their transaction rates were lower than expected. Branches 507 and 204 were comparison branches with similar traffics but more profitable transactions.

Daily Simple Transactions (Proxy for Traffic)

Branch 204 Target: 934 Txns: 405

Branch 112 Target: 3,909 Txns: 405 Branch 335 Target: 3,116 Txns: 397

Branch 507 Target: 689 Txns: 397

Branch Target



2 Aim

…and aim your analytics to enable improved offerings

In two branches (135 and 141), the analytics yielded new insights about customer segments and the products and services they wanted…

Product Mix Sold …which in turn led to a more successful mix of products offered by the branches. (Details have been omitted to preserve confidentiality.)



3. Align your governance and business processes—to ensure successful targeted application of analytics to drive initiatives
•  Define a governance framework including roles, responsibilities, and decision rights •  Facilitate effective interaction with the businesses and geographies, covering the scope of analytics

support, resource commitment, and data access

•  Establish a joint decision-making forum with the businesses and a project oversight process •  Agree on the performance metrics to measure the results of programs



3 Align

Several organizational design principles have been shown to yield optimal results
Design Principles •  •  Focus the senior executive team on the importance of analytics capabilities Treat fact-based decision making as a best practice Generate information, creating a test/learn culture based on numerous targeted campaigns Design tailored offers to meet customer needs Be committed to building robust analytics capabilities for several years Effectively collect information •  •  •  Increased earnings per share Gains in EBITDA Rising revenue •  Results Fundamental redefinition of how you see your customers



•  • 

•  • 

Increased marketing ROI Successful new product introductions



3 Align

Your goal is to develop a multifaceted, robust group of big-data capabilities, embedded throughout your company
Strategy Identify product offering for customer pools Define investment, success metrics, learning agenda Initiative Development Design program with customer-product-offerchannel criteria Execution Deliver leads, drive communication and training for front line Measurement & Learning


Incorporate learnings into product and program improvement


Insight on product/bundle/ pricing/distribution options, competition


Insight on opportunity size, share of wallet, right to win, economics for the different options

Customer targeting, list creation, test/control cells to maximize ROI List/execution management; necessary branch/direct mail collateral Ensure brand integrity

Measure performance, identify drivers, deliver learnings


Incorporate learnings into delivery protocol, training, collateral design, etc.

Front Line/ Sales Types of analytics required

Inputs on supporting functionality, feasibility, competitor tactics

Execute on leads Collect feedback §  Descriptive/predictive: Effective sales process/protocol/agent §  Descriptive: Root cause, correlative/causative analytics

§  Descriptive: Voice of customer, market research, cluster analyses §  Predictive: Next-best-offer models, LTV uplift model, demand forecast §  Optimization: Investment allocation

§  Predictive: Response, incremental profit models §  Algorithmic: Dynamic offers at channel touch points

Source: Strategy& analysis Strategy& 19

To implement this new system, proceed in phases — first, with a credible pilot project
Phase 1 (Pilot) Phase 2 Phase 3

•  Create basic customer
segmentation scheme

•  Deploy customer value •  Expand use of product

measures for decision making information across lines of business

•  Formulate segment strategy and
allocate resources

•  Create or integrate customer
Build Tools & Capabilities valuation and potential value measures

•  Use internal and external
customer information consistently in value proposition

•  Build prototype data warehouse •  Institute test/learn discipline •  Establish small team of talent

•  Integrate segment and risk data •  Build offer history database •  Build tracking capacity

•  Leverage two-way flow of

information between channel and customers

Aim at Pilots & Programs in Target Initiatives

•  Develop targeting models •  Develop customer value

•  Apply what you learned in the
pilot to wider-scale programs

•  Establish continuously improving
test/learn process

•  Launch pilots

•  Launch second-wave pilots •  Influence and drive relationship
manager (RM) deployment

•  Create context awareness and
targeted value propositions

•  Influence RM calling and
incentive plans

Align Your Governance & Business Processes

•  Engage business and field •  Position pilots •  Track and reward right behaviors

•  Identify and fill data and analytics

•  Focus decision processes on
analytics-based trade-offs place

•  Establish go-forward organization
and governance structure

•  Put required data and analytics in



For instance, you might begin with a revenue-driving pilot, like a targeted home equity line of credit (HELOC) cross-sell
Potential Segmentation Framework (for a HELOC pilot)
% Change in Median Household Income, 2000 to 2011
24 22 20 18 16 14 12 10 8 6 4 2 0 -2 -4 -6 -14 -12 -10 -8 -6 -4 -2 0 2 4 6
Lansing-East Lansing, MI Holland, MI Grand Rapids-Wyoming, MI Kalamazoo-Portage, MI Ann Arbor, MI Atlanta, GA Chicago, IL Cincinnati, OH Raleigh, NC Knoxville, TN Naples-Marco Island, FL Cleveland, OH Augusta, GA-SC Punta Gorda, FL Tampa, FL Rockford, IL Saint Louis, MO-IL Orlando, FL Nashville, TN Columbus, OH Miami, FL Louisville, KY Fort Myers, FL Pittsburgh, PA

Charlotte, NC

No. of Branches 10-40 <10 >40

Dayton, OH

This framework shows a representative sample of metropolitan statistical areas where a bank operated, segmenting branches according to changes in community prosperity levels.

% Change in Median Home Values, 2000 to 2011



Top 10 best practices in managing pilots; they can help you “act your way to a new way of thinking”
10.  Understand each pilot’s objectives from the outset; not all pilots should seek to prove the same points (some may test new cross-sell tactics, while others refine client targeting models, and others find interim methods to increase revenues) Design for Success 9.  Be willing to deviate from current process and procedures to better fulfill the pilot objectives, including adjusting critical compensation schemes 8.  Agree up front on what will define success for the pilot and what information you will need to gather to decide whether to roll out these practices after the pilots are complete 7.  Reflect potential rollout conditions for the pilot; in particular, pilot participants (branches, individual call center agents) should have a mix of skills and experience, and on the whole be average performers 6.  Run the pilots; identify the people responsible for overseeing progress and troubleshooting 5.  Continuously fine-tune the pilots; if pilots are not going as expected, be willing to make midcourse corrections 4.  Show demonstrable, measurable impact (such as increase in sales) for participating individuals in order to motivate the front line and ensure consistent participation 3.  Seek continuous feedback and support from pilot participants: Use pre-pilot discussions to help design pilot initiatives Conduct focus groups during the pilot to fine-tune the process and identify best practices Get participants to champion rollout to their senior management once the pilot is completed 2.  Capture pilot-specific performance data on a regular basis (perhaps weekly), even though some metrics may not be used currently and may require additional resources to compile the data 1.  Secure management focus and frontline commitment to make the pilot a success; senior management involvement is important to regularly review pilot performance and hold periodic check-ins with participants

Prepare Rollout Early

Manage the Pilots Actively

Motivate All Participants



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This report was originally published by Booz & Company in 2012.

© 2012 PwC. All rights reserved. PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Please see www.pwc.com/ structure for further details. Disclaimer: This content is for general information purposes only, and should not be used as a substitute for consultation with professional advisors.