S&P Capital IQ: Using Predictive Analytics for New Product Development And Product Life Cycle...

Permission to reprint or distribute any content from this presentation requires the prior written approval of S&P Capital IQ®.

Not for distribution to the public. Copyright © 2015 by Standard & Poor’s Financial Services LLC (S&P). All rights reserved.

Thomas Zakrzewski

Head of Architecture and Digital Design – Credit Risk Services Capital IQ

October 6, 2015

Contributors:

Oliver Putz – Managing Director, Product Portfolio Development

Alex Scott - Product Management Analyst

Predictive Analytics In New Product Development And

Product Life Cycle Management

2 Permission to reprint or distribute any content from this presentation requires the prior written approval of S&P Capital IQ. Not for distribution to the public.

Agenda

• Who we are?

• How we (Capital IQ) think about using of predictive analytics for

product innovation?

• Product Analytics

– Analytics evolution cycle

– Understanding a product and product life cycle

– Platform and methodology

– Beneficiaries

• In what ways are we aiming to contribute to the business (case study: cross sales insights)?

• What challenges do we encounter around data, analytics platform and methodology? How do we

intend to tackle those?

• Key takeaways and observations


McGraw Hill Financial

• S&P Capital IQ, a part of McGraw Hill Financial, S&P Capital IQ provides a broad suite of capabilities

designed to help track performance, generate alpha, identify new trading and investment ideas, and perform

risk analysis and mitigation strategies

• Credit Risk Services – Data and Intellectual Property from each respective McGraw-Hill’s division analyzed

and presented in context of various Risks Dimensions

Enterprise

Solutions

Research &

Analytics

• Reference, Ratings1

And Fixed-Income

Data Sets

• Valuations2

• Deep Global And North

America Fundamentals

• CUSIP® Global

Services

• GICS®

• Research3

– Equities

– Funds

– ETFs

• Global Markets

Intelligence3

• Leveraged Commentary

& Data

Sep

ara

tio

n o

f B

usin

esses

• Corporate &

Government Ratings

• Structured Finance

• Quality

• Ratings Operations

Standard & Poor’s

Ratings Services

S&P Capital IQ

• The Platforms

– Capital IQ®

– Global Credit Portal®

• Risk Solutions

• MarketScope® Advisor

Integrated

Desktop

Solutions

S&P Dow Jones

Indices

Benchmark

Solutions

Indices for:

• Equity

• Fixed Income

• Real Estate

• Commodities

• Emerging & Frontier

• Strategy

• Thematic

• Custom

McGraw Hill Financial

Credit Ratings &

Research

1Provided by Standard & Poor’s Ratings Services, which is analytically and editorially independent from any other analytical group at McGraw Hill Financial.2Provided by Standard & Poor’s Securities Evaluations, Inc. (SPSE), a part of S&P Capital IQ and a registered investment adviser with the U.S. Securities and Exchange Commission (SEC).

SPSE’s advisory services include evaluated pricing and model valuation of fixed income securities, certain derivative valuations, and Risk-to-Price® scoring of certain U.S. and European fixed-

income securities. Products and Services provided by SPSE may not be available in all countries or jurisdictions.3S&P Capital IQ’s research reports are prepared in the U.S. by Standard & Poor’s Investment Advisory Services LLC (SPIAS), a part of S&P Capital IQ and a registered investment adviser with the

SEC. Research reports are issued in the U.S. by Standard & Poor’s Financial Services LLC; in the UK by McGraw-Hill Financial Research Europe Limited, which is authorised and regulated by the

Financial Conduct Authority; and by affiliates of SPIAS registered or licensed in certain other jurisdictions. SPIAS also provides non-discretionary investment advice in the U.S.


Predictive Analytics – Holistic Approach To Product Analytics

• What is Predictive Analytics?

– Part of the Business Intelligence, subset of Business Analytics

– Extracting information from data in order to classify data and predict data patterns based on statistical

modeling

– Analysis of historical behavioral data in effort to guide future decisions for given set of conditions

– More effective when threated as a part of the Analytics Evolution Cycle

• Product Analytics Evolution Cycle

Statistics, Math Models and Machine

Learning

Prescribe action, shape Customer behavior and test Products against expected results

Reportingon defined

metrics (Historical and Current data)

Descriptive Analytics

“What happened in the past?”

Predictive Analytics

“What may happen in the

future under given conditions?”

Prescriptive Analytics

“What action should we take knowing the trends?”


Product And Product Analytics

• Understanding the Product in your organization

– What is product, how is developed, how is maintained and used, how is sold and how usage is tracked

– Most products are subscription based, mostly maintained based on feedback from Sales and Product

Management teams, tracking defined by development teams

• Conceptual representation of the product and analytics process

• Product and capturing user behavior

– Product is a collection of features that are pieced together

– Data points are captured for features, product is one of the View Dimensions

– Enable discovery for data points and their relationships – self service capability

• Product Innovation Process

– Strengthen superior features of working product

– Remove inferior features from product

– Support incumbent features

• Realization of Product Evolution Process

– Analyze current data collection captured for Features within a Product

– Predict trends (Features usage in spectrum of Views – Product Dimension, User

Dimension, Business Dimension)

– Suggest changes to existing products and create new products (remove inferior,

strengthen superior, add incumbent)

– Shape Customer behavior, measure success and align to a demand


New Product Development And Product Life Cycle

• Strategic Product Innovation using Predictive Analytics

Idea and research – based on data evidence how current product is being used and by whom new

product can be created by assembling new features compositions and workflows.

Testing and analysis – using data classifications and predictive modeling will validate the idea and

reveal additional insights on features composition for final product.

• Tactical Product Innovation using Predictive Analytics

Introduction and growth – early analysis helps to identify customer groups who likely will respond to

specific marketing materials. Same analysis will provide data insight about Customer behavior to

Sales and Customer Representatives

Maturity: Validate product’s features in order to devise adequate response

Decline: understand the decline patterns and assist formulating next steps which either is product

enhancements, sunset, enhanced growth or divest service – speedup the decision making point

Idea Research Development Testing Analysis Launch

Introduction Growth Maturity Decline


Product Analytics Platform And MethodologyU

se

rs o

f th

e

Pla

tfo

rm

Pro

du

ct

An

aly

tic

s

Pla

tfo

rm

Pro

du

ct/

Fe

atu

res

Product A Product B Product X

Data Collection Tools

Product Analytics and Visualization Tools

Executives

Business

Development

Sales

IT Resources /

Client Support

Marketing

Product

Managers

Content

Creators

End

Customers

…

Other Analytical

Systems, CRM,

Dashboards, etc.

Auxiliary

Systems


Product Analytics Platform And Methodology• Data Collection and Data Blending

– Challenges of data collection and data blending

Data Collection is often substituted by code instrumentation which focuses on performance rather

then customer behavior

– Possible Data Solutions

Utilize current repositories, bridge data gaps with web logs and other application logs

– Envision Outcomes from Data Solutions

Create Metadata repository or repositories for product features and mapped URLs. Collate data into

a single place per product domain(s) (i.e. using flume to consolidate all web logs and applications

logs into a Hadoop storage)


Product Analytics Platform – Tech Stack


Product Analytics – Users And Use CasesU

se

rs o

f th

e

Pla

tfo

rm

Pro

du

ct

An

aly

tic

s

Pla

tfo

rm

Pro

du

ct/

Fe

atu

res

Product A Product B Product X

Data Collection Tools

Product Analytics and Visualization Tools

Executives

Business

Development

Sales

IT Resources /

Client Support

Marketing

Product

Managers

Content

Creators

End

Customers

…

Other Analytical

Systems, CRM,

Dashboards, etc.

Auxiliary

Systems


Product Analytics – Using Sets To identify Use Cases

• Who will benefit from Product Analytics – Users of the Product Analytics Platform

• Use Cases within Analytics Cycle (descriptive, predictive, prescriptive)

– Each Use Case spans three areas: descriptive, predictive and prescriptive

– Each area describes what we measure, how me measure, who will benefit and output to next following

area or next iteration of the product analytics cycle

Domain Dimensions


Product Analytics – Working Toward The End State

• Why are we engaged in Product Analytics?

– Financial and business growth

– Decision making strength

– Know your customer through behavioral data

• Business Goals

– Finance: Increase revenue growth and reduce operating expenses

Innovation of the products, focus on outcome based analysis supported by data, reduce decision

time frame

– Customer: increase retention rate and increase consumption level of products

Realize cross sale opportunities, creating stickiness by presenting the desired content

– Marketing:

Improve marketing efficiency, increase accuracy in targeted marketing, increase reach

– Data: improved analytics, standardization for data tracking and data classification

Creation of Metadata repository for customer behavior data, removing dependencies between data

scientists and developers, ability to review customer behavior cross domains

• Use Cases (examples)

– Market Basket Analysis for cross sale opportunities, IP noncompliance - detecting anomalies in license

usage


Use Case – Cross Sales Opportunities

• Descriptive Phase - inform

– Identify Products and products sales revenue

– Present percentage allocation per product, per revenue

• Predictive Phase - predict

– Find what products were bought by customer across all sales channels and product categories

Use Market Basket Rules model to create viable combination of the products for customers

Use Market Basket Inspect model to analyze rules and determine customers suitable for cross-sale

opportunities or up-sale opportunities

• Prescriptive Phase – make actionable

– Identify customers who most likely will respond to cross-sale activities and engage sales team

representative

– Identify customers who most likely will respond to cross-sale activities and engage Marketing team

representative to supply trial versions and marketing materials to customers who may be interested in

other products

– Identify customers who may respond to up-sale activities if support is 100% (when all customer already

have a given product combination)


Cross Sales Opportunities – Descriptive Phase Sample


Cross Sales Opportunities – Predictive Phase Sample

Alteryx Market Basket Analysis

Alteryx’s market basket analysis uses two tools: rules and inspect.

When paired together these tools create a set of rules based on a

transaction key and item identifier. Each rule contains a cause and

result and is output with three different indicators: support, confidence,

and lift.

Data Inputs

1. Transaction key (Customer)

2. Item identifier (product)

Data Outputs

1. Antecedent

2. Consequent

3. Support (%)

4. Confidence (%)

5. Lift (%)


Cross Sales Opportunities – Predictive Phase Sample

Product Association Analysis

What is it? Why conduct this analysis? How will it be done?

• Product association observes

clients purchasing behavior

for products that are

commonly purchased together

• Analysis output is used to

identify products that should

be packaged together.

Additionally, the analysis

identifies products with a

high confidence of

correlation which creates

opportunities for products

cross-sales or up-sales

• The objective is to generate more

sales and discover cross sale

opportunities based on

observed customer behavior

related to product usage or

product purchase

• Statistical data may reveal

purchasing patterns which a

salesperson globally can make

use of to become sensitive

towards such opportunities

outside of the qualitative

business relationship

• Billing data will be leveraged to

for a 12 month view on what

clients have purchased

• Utilize Alteryx’s Market Basket

analysis tools to identify product

associations

• Analyze sequential purchases to

identify any common trends in

how a customer purchases

• Analyze time between

purchases to understand when

a customer purchases


Cross Sales Opportunities – Prescriptive Phase Sample

Rule 1Rule 2

Rule 3Rule 4

Rule 5Rule 6

Rule 7Rule 8Rule 9

Rule 10 Rule 11

Rule 12Rule 13

Rule 1450%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

0% 2% 4% 6% 8% 10% 12%

Ru

le C

on

fid

en

ce

Rule Support


Key Takeaways And Observations

• Predictive analytics as a powerful innovation tool

– Single tool for data blending and data analytics

– Integrating with visualization tools: Tableau and Qlik

– Expedited delivery of analytics

– Ability to integrate as an analytical application into larger Analytical Frameworks

– Separation of concerns (data scientist and developers)

• Use within Analytics Evolution Cycle

– Don’t operate within a silo of the Predictive Analytics, better results are achieved by showing the

progression and transformation from one cycle to next cycle in the Process

• Overcoming challenges on data reach, organization and output

– Methodology and technology disruptive at times

– Challenges in reaching out to data owners and permissions to access data

• Definition of use cases and overall roadmap shape focus; Project milestones on the way to the end

state

– Challenges in creation of the Use Cases and assigning models or compositions of models

– Challenges in adoption Product Analytics, securing budget and resources

– Choice of milestones and measure of success

Copyright © 2015 by Standard & Poor’s Financial Services LLC (S&P). All rights reserved. No content (including ratings, valuations, credit-related analyses and data, model, software or other application or

output therefrom) or any part thereof (Content) may be modified, reverse engineered, reproduced or distributed in any form by any means, or stored in a database or retrieval system, without the prior written

permission of Standard & Poor’s Financial Services LLC or its affiliates (collectively, S&P). The Content shall not be used for any unlawful or unauthorized purposes. S&P, its affiliates, and any third-party

providers, as well as their directors, officers, shareholders, employees or agents (collectively S&P Parties) do not guarantee the accuracy, completeness, timeliness or availability of the Content. S&P Parties

are not responsible for any errors or omissions, regardless of the cause, for the results obtained from the use of the Content, or for the security or maintenance of any data input by the user. The Content is

provided on an “as is” basis. S&P PARTIES DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY OR

FITNESS FOR A PARTICULAR PURPOSE OR USE, FREEDOM FROM BUGS, SOFTWARE ERRORS OR DEFECTS, THAT THE CONTENT’S FUNCTIONING WILL BE UNINTERRUPTED OR THAT THE

CONTENT WILL OPERATE WITH ANY SOFTWARE OR HARDWARE CONFIGURATION. In no event shall S&P Parties be liable to any party for any direct, indirect, incidental, exemplary, compensatory,

punitive, special or consequential damages, costs, expenses, legal fees, or losses (including, without limitation, lost income or lost profits and opportunity costs) in connection with any use of the Content even if

advised of the possibility of such damages.

Credit-related and other analyses, including ratings, and statements in the Content are statements of opinion as of the date they are expressed and not statements of fact or recommendations to purchase, hold,

or sell any securities or to make any investment decisions. S&P assumes no obligation to update the Content following publication in any form or format. The Content should not be relied on and is not a

substitute for the skill, judgment and experience of the user, its management, employees, advisors and/or clients when making investment and other business decisions. S&P’s opinions and analyses do not

address the suitability of any security. S&P does not act as a fiduciary or an investment advisor except where registered as such. While S&P has obtained information from sources it believes to be reliable, S&P

does not perform an audit and undertakes no duty of due diligence or independent verification of any information it receives.

S&P keeps certain activities of its business units separate from each other in order to preserve the independence and objectivity of their respective activities. As a result, certain business units of S&P may have

information that is not available to other S&P business units. S&P has established policies and procedures to maintain the confidentiality of certain non–public information received in connection with each

analytical process.

S&P may receive compensation for its ratings and certain credit-related analyses, normally from issuers or underwriters of securities or from obligors. S&P reserves the right to disseminate its opinions and

analyses. S&P's public ratings and analyses are made available on its Web sites, www.standardandpoors.com (free of charge), and www.ratingsdirect.com and www.globalcreditportal.com (subscription), and

may be distributed through other means, including via S&P publications and third-party redistributors. Additional information about our ratings fees is available at www.standardandpoors.com/usratingsfees.

STANDARD & POOR’S, S&P and S&P Capital IQ are registered trademarks of Standard & Poor’s Financial Services LLC. CAPITAL IQ is registered trademark of Capital IQ, Inc. All other product or service

names may be the property of their respective owners.

www.spcapitaliq.com

S&P Capital IQ: Using Predictive Analytics for New Product Development And Product Life Cycle...

Data & Analytics

Transcript of S&P Capital IQ: Using Predictive Analytics for New Product Development And Product Life Cycle...