8 Analytics Trends that CMOs should watch out for in 2014

8 Analytics Trends that CMOs should watch out for in 2014 eMetrics Summit San Francisco, CA March 17, 2014 John Lucker, Principal Global Advanced Analytics & Modeling Market Leader Deloitte Consulting LLP Email: [email protected] Twitter: @JohnLucker

Copyright © 2014 Deloitte Development LLC. All rights reserved. 1 Analytics Trends 2014

About Today’s Speaker – John Lucker

John Lucker Principal Deloitte Consulting (860) 725-3022 [email protected]

•  Deloitte’s Global Advanced Analytics and Modeling Market Leader and a leader of the Deloitte Analytics Institute

•  Provides clients with end-to-end strategy, business, operational, and technical consulting services in the areas of advanced business analytics, predictive modeling, data mining, scoring and rules engines, and numerous other advanced analytics business solution approaches. His clients are in many industries including insurance, banking and financial services, retail, consumer products, telecomm, healthcare, life sciences, media, hospitality and others.

•  Author of more than 50 articles and papers on a variety of advanced analytics, predictive modeling and analytic business and technology topics

•  Speaker at numerous global conferences on a variety of analytics and business solutions topics and published in a number of journals and professional publications.

•  Co-inventor of US Patent 8,036,919 for a “Licensed Professional Scoring System and Method” and US Patent 8,145,507 for a “Commercial Insurance Scoring System and Method” and US Patent 8,200,511 for a “Method & System for Determining the Importance of Individual Variables in a Statistical Model” and three pending patents

•  Education: University of Rochester – B.A.; University of Rochester – Simon School – M.B.A.


About Today’s Topic – 8 Analytics Trends for CMOs

•  Few areas of business today are changing faster than where and how analytics are being used. Turn your head for a second and—boom—you’re falling to the back of the pack.

•  While CMOs often set the bar when it comes to the why and how of analytics, big data, visualization, predictive modeling and new technologies have created tons of data-driven opportunities for CMOs to take advantage of. Which ones are most important?

•  We will discuss 8 analytics trends that CMOs should be paying attention to in 2014 that can deliver tangible, timely value for their business.


8 Key Trends in Business Analytics for CMOs

1.  Know your customer

2.  The talent crunch

3.  Do you have access to the right data?

4.  The rise of the Chief Analytics Officer?

5.  Do you have the appropriate data tools?

6.  Analytics drives industry

7.  Pricing for profitability

8.  The privacy paradox

Know your customer


Know Your Customer

1.  Sell more to customers you have got

2.  Losing certain customers might be OK

3.  Do not waste time on unlikely customers

4.  Price your products right

5.  Understand where you can improve


Cognitive Bias example – Credit card story

Fact: I haven’t obtained a new credit card in 23 years. Question: Over the past 30 months, I have received 140+ credit card offers from just two banks? Typical Response: We are very successful doing things as we have evolved them. We have made significant investments in customer segmentation. You are an exception and this doesn’t happen to most people.


So what could these companies have known about me?

• They could derive / purchase data about my propensities • They could better understand what I do with them – avoid

annoyance or look to expand effectively • They could evaluate if I have ever done business with them

and analyze if this is the best way to start • They could evaluate the extent to which I do business with

them and determine if they are “nickel and diming” me • They could clean their data to avoid duplicates, offers to

minors, etc. • They could look for ways to cross-sell me by bundling offers

in some meaningful way • They could understand my affinities – do I appreciate such

offers? Do I think it is a waste of paper? • They could determine if I’m even worth the effort and

expense at all


Cognitive Bias Examples from my Consulting Experience

Examples •  Comment from a Financial Services executive in a pricing meeting: “I recommend

we increase interest rates (APR) on our accessory motor lending products by 30 basis points this year because this summer will be very hot and the demand for motor “toys” will increase”.

•  How does the executive know what the weather will be like in a few months? Can she be sure that demand for motor “toys” will go up?

•  During a price optimization exercise a food retailer decided not to price different flavors of a product differently despite empirical evidence that some flavors were significantly more sensitive to price. They (wrongly) discussed that other food products were not priced that way. By pricing all flavors the same the company was unable to obtain a gross margin benefit of more than $4 million in the first year.

•  Comment from a Retail executive: “we don’t care as much about the needs of men in our catalog and stores” because 80% of our customers are women.

•  But what if sales are saturated for women and there could be a latent opportunity to sell more to men? If the infrastructure is there to sell to men (since 20% of sales is to men) then why not see if there is a way to increase male traffic to stores and catalog?


Redefining Success

Talent Crunch?


The talent crunch

•  Conventional wisdom says companies are facing a large supply gap of data analytics talent at all levels.

•  Entry-level positions are challenging to fill, and there’s also a major drought at the most senior levels, though few such leaders are needed overall.

•  Professionals who can deliver and communicate data-backed insights that create business value—not just number crunchers—are especially hard to find.

•  Is that conventional wisdom right, or are other forces at play? Some of the current talent crunch is a function of resource hoarding, recruiting talent beyond what is actually needed.

Two sides to the story


What makes a great Data Scientist? •  Creativity – someone who looks at problems, issues and challenges and

naturally navigates to places typically not thought of.

•  Curiosity – someone who becomes a student of the organization and looks for ways to sense, detect and identify issues and problems needing attention. Someone who has a natural affinity for out-of-the-box and non-obvious issues.

•  Results and Team Orientation – someone who recognizes that success comes from getting things done, not merely doing things – whether individually or with others. This requires determination, intellectual rigor, patience and leadership.

•  Fact-Oriented with a Gut – someone who understands the power of fact orientation and the dangers of subjectivity and over reliance on gut.

•  Technical Diversity – someone who understands that having experience in many tools and methods is essential. Also the person is part programmer, data manager, statistician, business analyst, strategist and other skills.

•  Communicator – someone who can express complex concepts to the layman

Do you have access to the right data?


Emerging Data Platforms – Two Sides to the Story

•  There’s no question that big data platforms have become a critical capability for organizations of virtually every shape and size.

•  Easy-to-use software, from conventional enterprise data warehouse (EDW) to the new Hadoop platform, make complex data accessible and understandable for almost any business user.

•  Some say that conventional enterprise data warehouses are now a vanishing breed, giving way to new big data platforms such as Hadoop clusters. These new platforms cost a fraction per terabyte to store and process data compared to what traditional warehouses do.

•  Hadoop is cheaper than the data appliances of the past decade. What’s more - Hadoop platforms don’t just store data—they can also perform substantial processing tasks and even some analytics.


Do you have access to the underlying data?

•  Some people think of Big Data as anything that doesn’t fit in an Excel spreadsheet!

•  In some simple contexts, Big Data is used to refer to any form of advanced statistical analytics or predictive modeling using diverse internal/external data sources

•  In many contexts, Big Data is shorthand for the granular and numerous data sources used for analytics projects - big, small, old, new, structured, unstructured, etc.

•  In more esoteric data contexts, some examples include geospatial, social/sentiment, audio/video, mobile information, telematics, telephonic, internet searching, web logs, etc.

Big Data refers to internal and external data that is multi-structured, generated from diverse sources in real-time and in

large volumes making it beyond the ability of traditional technology to capture, manage and process within a tolerable

amount of elapsed time


The Importance of the Big Data Trend & How to Capitalize On It

16

Source: British Retail Consortium (BRC) Analysis

While Big Data has driven IT spending, hype should not drive the attention away from data quality, relevance, redundancy, and most importantly, insights

Data Quality Data cleaning might take more effort than data analysis. The magnitude of bad data will get amplified with the increase in type of sources, and volume of data – but keep the 80:20 Rule in mind

Interpretability Substantial amount of data is unstructured and hence liable to different interpretation by different people and machines

Business decisions stand the risk of being based on biased interpretations

Relevance The amount of data stored will have contextual relevance with individuals analyzing the data.

‘One Man’s Signal is another Man’s Noise’

Privacy Issues As organizations capture more volumes of data from various sources, they are more susceptible to disturbing privacy concerns

Especially as more and more consumer data is being used, organizations will have to be sensitive about the data they use

Redundancy There are chances, organizations are capturing same data from multiple sources, multiple times

e.g., Tweets, updates

Organizations should be wary they are not investing in capturing one data point from multiple sources

Novelty Most of the time, a lot of data captured from Big Data sources is already captured in existing data available with the enterprise

Big Data investments should focus on finding new insights

Dilutes Value Focus With Big Data hype, a lot of attention is going into collection, storage, and access of Big Data

This has diverted attention from analysis and ultimate use of data

Knowing what organizations want to do with the data might also be an important question to consider

Avoid “Hoarding”

Complex Big Data is still confusing to many professionals

After the hype subsides, efforts into making it less complex, and user friendly should ensue


Big Data, Big Noise – But Perhaps Not Enough Focus on Strategy & Value Understanding Big Data as completely as possible and clearly identifying its links to various value levers is critical before designing any solutions - Value, Strategy, Decisions, Analytics, Execution, Assets

Info

rmat

ion

Dem

and

Info

rmat

ion

Supp

ly

Value-driven Strategies Value Chain Execution

Information Assets

Data Warehouse

Insights, Analytics & Measurement

Value Drivers

Improvement Levers

Data Sources

Decisions

Governance & Management


Big Data Potential Roadmap A Big Data Roadmap begins with the decision makers and their strategic “crunchy” questions and then proceeds to the data sources and technologies that are required to address the needs.

Identify Opportunities

Assess Current Capabilities

Identify and Define Use Cases

Implement Pilots and Prototypes

Adopt strategic ones in Production

§  Identify strategic priorities and ask crunchy questions

§  Assess §  Data and application landscape including archives §  Analytics and BI capabilities including skills §  Assess new technology adoptions §  IT strategy, priorities, policies, budget and investments §  Current projects §  Current data, analytics and BI problems

§  Based on the assessments and business priorities identify and prioritize big data use cases

§  Identify tools, technologies and processes to implement pilots. Define and compute metrics on incremental value added §  Prioritize and

implement successful, high value initiatives in production

1

2 3

4 5

The rise of the Chief Analytics Officer


The rise of the Chief Analytics (and Big Data) Officer

•  A few years ago, there were no Chief Analytics Officers, no Chief Data Officers, no Chief Science Officers, and no heads of Big Data. Today, there are many—in the tens, if not the hundreds. That in itself is evidence of a trend.

•  Can C-level analytics and big data positions help organizations do more with their resources? We don’t have a lot of data on this yet, but the answer seems likely to be yes.

•  The more important question, though is do you have the right big data leadership in your organization? And do they live by the credo – “don’t confuse effort with results”?

•  Another C-level role may be overkill? – Well, these roles may well become pervasive in the long run, but we should have someone to lead the use of them today and for the foreseeable future. Organizational structures always should be flexible, and when we see an important new capability arriving on the scene, we should create a role to manage it.



The rise of the Chief Analytics (and Big Data) Officer

•  Too much time spent on data management versus analytics and value?

•  Need to focus on End-To-End Execution of the analytics story

End-to-End Business

Value

1. Business Strategy

2. Big Data Analytics

3. Biz & Ops Implement & Integration 4.

Technology Integration

5. Org & Change

Management

6. Performance Management

Do you have the appropriate data tools?


Do you have the appropriate tools?

•  The rise of big data has many implications – it’s not just about how much data you have – but also having the right data tools to manage it.

•  From machine learning to discovery and visual exploration to pattern and relationship identification, today’s big data tools have come along way. These tools can easily affirm the adage that a picture is worth a thousand words. Or, in this case, numbers.

•  Big data platforms are rising in prominence as these help in converting huge amount of data into sense-making insights to drive business decisions.

•  Here a few tools you should be likely considering to manage your data in order to drive insights for your business :

Data Discovery

Machine Learning


Data Visualization


What’s Important About Data Visualization Capabilities?

•  Presents results of sophisticated analysis on large and complex data sets in understandable & actionable formats

•  Makes insights accessible to a much broader audience based on user experience, appetite, aptitude, ineptitude

•  Helps allocate the scarcest resource a decision maker has: attention

•  Increases communications impact with key stakeholders (C-Suite, Boards, media, analysts, customers, etc.)

•  Know Your Audience - is not for everyone – should allow easy interaction with data for those who want to dive deeper

•  Capability requires appropriate information architecture, governance, security, software, staffing, training


Visualization Possibilities are Limited Only by Imagination

Streamgraph Force-directed graphs Tree Maps Sunburst

Word Tag Cloud Bubble Chart Many Eye Bubble Chart Time Series Analysis

Geospatial Parallel chord Calendar View Heat Maps


Some Visualization Examples: Network Relationship Maps

Each doctor is a blue circle whose size is proportional to the amount of drugs

prescribed.

Doctors are linked if they share an organizational

affiliation or have common patients.

The thickness of the

edge is proportional to the number of shared

patients.

The three red dots are individuals thought to be key influencers. This network graph shows that others may be equally or more influential.


Some Visualization Examples: Heat Maps


Some Visualization Examples: Infographics

http://www.youtube.com/watch?v=g5rGm6veAhg&feature=player_embedded&safety_mode=true&persist_safety_mode=1#!


Machine learning finds a big data niche

•  Machine learning isn’t a new idea. It’s been around in theoretical form since the 1960s and in academic use since the ’70s and ’80s. Broadly speaking, machine learning is the ability of computers to learn from data.

•  In analytics, it typically means the semi-automated development of predictive and prescriptive models that get better over time. The software learns how to better fit the data, separating meaningful signals from meaningless noise.

•  Machine learning methods and results tend to be “black boxes” that don’t sit well with business people.

•  Machine learning can improve analytics performance, but it’s a double-edged sword. Humans have to maintain control as they’re ultimately accountable for the outcomes – people specify inputs and interpret outputs, people adjust model parameters to fit business purposes, people interpret for executive decision makers.

•  Modeling factory concepts is gaining steady adoption. .



Comparing Techniques and Diminishing Returns ¡  Example: predicting

health claims for a sample of older insureds with specific co-morbidities.

¡  Iterative process used to build Regression, GLM models.

•  Easily interpretable models

•  Regression & GLM models custom built by hand

¡  Machine learning models fit on same data using all available variables

•  Took little effort to run, but are “black boxes”

¡  Algorithmic complexity should not be a substitute for ease of understanding/ adoption

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

cumulative % population

cum

ulat

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% e

xpen

ditu

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Predicting Medical ClaimsOut-of-Sample Comparison of Multiple Techniques

Log-linear RegressionGamma RegressionRandom ForestSupport Vector MachineMARS

Analytics drives industry


Media & Entertainment Industry

But there is still room for improvement

The entertainment industry has begun to

explore shifting from human judgment

and experience to using analytics to

determine which movies, television

programs, plays, and books customers

want to experience. Relativity Media, a

film producer and studio, reportedly

employs an analytics algorithm to decide

which movies to make.

The track record in the entertainment

industry of predicting consumer interest

has been poor. Many Hollywood films fail

to make money, television programs are

quickly canceled, and, among the

hundreds of thousands of books

published each year, few sell more than

100 copies.

Analytics drives entertainment


How do I get started?

As retailers have pushed into new sales

channels, it has become more difficult to

track and manage the customer

experience. But today’s analytics tools

are able to bridge the gap, drawing in

data from siloed data sets—plus external

data on demographics, psychographics,

share of wallet, and more—to create a

more complete understanding of the

customer experience through their eyes.

Commit Your people should be committed to data quality—and to acting on the insights that come from analytics. Identify patterns Your organization already knows some of the patterns that bring new customers in—it’s just as important to know when they’re on the way out. Plan for intervention You don’t have to have analytics insights in hand before developing concrete plans for retaining or winning back at-risk customers.

Customer Analytics for Retail

Retail Industry


“Crunchy Question” Approach to Realizing Analytic Value

Crunchy questions are practical, detailed inquiries into tough business issues — roll-up-your-sleeves questions. These are designed to lay the groundwork for action.

Characteristics of Crunchy Questions §  Specific §  Relate to a particular business process and aligned with

strategic goals §  Focus on optimizing or innovating, not merely informing §  Consider change relative to other indicators or processes §  Leverage and integrate internal and external inputs §  More forward or inward looking than backward looking §  More about differentiation than just comparison §  Consider various scenarios §  Actionable, i.e. more about “do it” than “prove it” §  Require advanced tools and techniques to answer


The “Crunchy Question” Approach to Realizing Analytic Value

Retail

§  Who is our customer? (Are you sure?)

§  Are our campaigns reaching the right customers with the right message at the right time?

§  Is our brand relevant to the customers we want to attract?

§  What is the ROI on marketing investments? Which add value and which do not?

Media & Entertainment

§  How can we improve supply chain efficiency without exposing ourselves to excessive risk?

§  Who are the next thousand customers we’re at risk of losing—and why?

§  How many customers will we lose if we increase prices?

§  Where exactly are we leaking margin?

§  Where should we set up our next distribution center?

Pricing for profitability


Pricing analytics can unlock smart value creation

•  Analytics can lead the way on pricing and customer profitability.

•  Facing growing complexity and a multi-channel business environment, companies need to be able to answer fundamental business questions:

Who is my most profitable customer?

What is my most profitable product or region?

•  Pricing analytics can help improve margins and deliver the insights required to take pricing strategies to the next level.

•  Pricing analytics can also help executives more clearly understand both the internal and external factors affecting profitability at a granular level.


Example pricing roadmap to optimize performance

•  Highlights which cost-to-serve elements can be reduced in order to keep a larger portion of the list price

•  Visually allows sales reps to determine what elements they can adjust in negotiations with a customer

List Price

$6.00

Order Size Discount Competitive

Discount

$5.78

Invoice Price

Payment Terms

Discount Annual Volume Bonus Off-Invoice

Promotions $4.47

Pocket Price

Co-op Advertising Freight

$0.10 $0.12

$0.30

$0.37

$0.35

$0.20 $0.09

25.5% off list!

The Privacy Paradox


Putting big data and privacy under the microscope

There’s a paradox at the heart of the big data opportunity

– the more powerful it is, the more dangerous it has the

potential to be

Clearly, companies are walking a tightrope when it comes to

balancing the need to deliver a better experience with the

need to do right by consumers on the issue of privacy.


Here’s the debate

Some customers want us to

know them better.

They are delighted when they

feel that a company really

understands their needs.

Some think it’s just creepy!

For them, it may feel like

they’re under surveillance.

And nobody wants that!


A few examples illustrating conundrums

•  Fitness bands – privacy, EULAs, what they know about users, what users want to know, monetization

•  The value equation can’t survive if it’s lopsided – both parties need to derive value

•  Regulations and low face value life insurance underwriting


Three ways to walk the tightrope

#1: Privacy policies, end-user-licensing agreements (EULAs), opt-in actions

#2: Just because you can, doesn’t mean you should #3: Be transparent


Some Great Reading on Big Data

Email Me At: [email protected] and I will send these to you


Some Great Reading on Privacy and Big Data Ethics

Email Me At: [email protected] and I will send these to you

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