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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
3 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.
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.
4 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.
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?”
5 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.
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
6 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.
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
7 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.
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
8 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.
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)
9 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.
Product Analytics Platform – Tech Stack
10 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.
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
11 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.
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
12 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.
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
13 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.
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)
14 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.
Cross Sales Opportunities – Descriptive Phase Sample
15 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.
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 (%)
16 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.
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
17 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.
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
18 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.
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
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