Unlock data possibilities Turning Big
Data into Big Revenue Oliver Halter Principal, Management Consulting

#1 Why this is important

PwC's Global Data & Analytics
Survey: Big Decisions™ • Big decisions have a big impact on future profitability; however, more big decisions are made opportunistically than deliberately >$1bn • Highly data‐driven companies are three times more likely to report significant improvement in decision making, but only 1 in 3 executives say their organization is highly data‐driven. • The majority of executives rely more on experience and advice than data to make business‐defining choices. • Many executives are skeptical or frustrated by the practical application of data and analytics for big decisions, especially in emerging markets. 62% 3X Data Quality Usefulness 1,135 senior executives interviewed from across the world representing a total of 18 industries where majority (74%) of companies reported annual revenues last year of at least $1bn Source: PwC’s Global Data & Analytics Survey: Big Decisions™

Making strategic decisions Company leaders often rely on gut instinct to guide them—what we think of as the ‘art’ of strategic decision making. But what about the ‘science’ side of the equation: data and analytics?
85% of CEOs told us that data and analytics creates value for their organizations. The question becomes— where and how are they realizing that value? While 94% of respondents said that senior management believe they are prepared to make their next big decision… … just 38% relied on data and analytics to do so. The majority of respondents (59%) in our survey pegged their next big decision at a value of $100 million or more And 16% said its impact to the business was in the $1 billion to $5 billion range. How you approach these pivotal decisions matters 85% 94% 38% 59% 16% Source: PwC’s Global Data & Analytics Survey: Big Decisions™

Big impact on future profitability
Big decisions have a big impact on future profitability; nevertheless, more big decisions are made 'in the moment' (either reactively or opportunistically) than deliberately. 4% 9% 15% 18% 25% 30% Mandatory Reactive Experimental Deliberate Delayed Opportunistic Motivators of Big DecisionsImpact on Profitability < $10m $10m to $100m $100m to $1bn $1bn to $5bn >$5bn NA 1 in 3 33% Source: PwC’s Global Data & Analytics Survey: Big Decisions™

Big improvement on decision making
Highly data‐driven companies are three times more likely to report significant improvement in decision making, but only 1 in 3 executives say their organization is highly data‐driven. Significant Improvement?How Data Driven? 0% 10% 20% 30% 40% Highly data-driven Somewhat data-driven Partly data-driven Somewhat data‐driven Highly data‐ driven Partly data‐ driven Other 43% 14% 15% 3X 1 in 3 Source: PwC’s Global Data & Analytics Survey: Big Decisions™

Organizations that delay starting the
Big Data journey risk being leapfrogged by more data- savvy competitors 58% PwC’s Digital IQ Survey 2014 respondents who indicated transitioning from data to insight is a major challenge

#2 How can your organization
adapt and execute?

Many organizations face challenges in
adapting to the recent trends in the Big Data landscape Information explosion due to Digitization, Internet of Things and external data have increased the number of data sources, volumes and complexity available for analytics to achieve competitive advantage Proliferation of commoditized technologies to enable speed and sophistication of high volume data processing and analytics have contributed to a complex technology landscape Enterprises have to balance near‐term and long‐term goals while enabling data and analytics capabilities in an agile manner, to realize iterative business value before committing to long‐term investments Data monetization strategies are increasingly adopted among competitors across many industries to develop innovative products/services and generate new revenue streams

Big Data into Big Revenue – Journey Building Blocks DISCOVERYDISCOVERY INSIGHTSINSIGHTS ACTIONSACTIONS
OUTCOMESOUTCOMES Discover value in your internal and external data Apply analytic techniques on internal and external data for tailored, value creating insights Make decisions; deliver quick wins; build operational capabilities to enhance products and services “Observations to Information” “Information to Insights” “Insights to Actions” “Actions to Outcomes” OPERATING MODELOPERATING MODEL Test and learn; link insights and actions to financial and operational metrics; enhance shareholder value SHAREHOLDER VALUE CREATION How can companies adapt and execute? The ‘DIAO’ mindset

Discovery: Observations to Information D1. Idea IntakeD1. Idea Intake
• Develop a process to intake and build a pipeline of ideas on improving business decisions with data and analytics, both from internal organization resources and external partners D2. Idea QualificationD2. Idea Qualification • Qualify ideas based on potential business value (financial, operational, risk or quality metrics) D3. Identify Data AssetsD3. Identify Data Assets • Identify internal/external data sets required to unlock the value out of the idea; e.g., data sets may cover a broad spectrum of domains, namely customers, products, services, sensors, demographics, social media D4. Platform, Tools and Infra.D4. Platform, Tools and Infra. • Develop ‘data lake’ architecture; make technology decisions and operationalize infrastructure to capture and store data assets from internal and external sources DD II AA OO

Insights: Information to Insights I1. Analytics TechniquesI1. Analytics Techniques
• Categorize the type of analytics techniques (forecasting, clustering, regression, time series, machine learning, etc.) required for the ideas and map analytics tools to purpose I2. Analytics ArchitectureI2. Analytics Architecture • Develop the ‘right fit’ architecture with tools to enable a rapid prototyping environment. Consider scalable in‐memory analytics and visualization tools as core components I3. Ideation SandboxesI3. Ideation Sandboxes • Develop a holistic ideation sandbox strategy and tool environment to empower practitioners in their data discovery process. Consider cloud models and tools available as an enabler I4. Process AgilityI4. Process Agility • Develop efficient processes in the discovery lifecycle which promotes agility and eliminates administrative bottlenecks; e.g., a self‐service sandbox provisioning model DD II AA OO

Actions: Insights to Actions A1. Decision ModelA1. Decision Model
• Define decision models and rights that categorize and specify the decisions that get made, insights, options, subsequent actions and potential for automation A2. AutomationA2. Automation • Integrate and automate decisions made from models with company’s existing business processes, operations and technology in real‐ time; e.g., Are your sales processes ready to handle the predicted cross‐sell / up‐sell scenarios?A3. Embed resultsA3. Embed results • Embedding decision results into new products and services design could be a game changer and avenue for many organizations to add shareholder value DD II AA OO

Outcomes: Actions to Outcomes O1. Impact LinkageO1. Impact Linkage
• Establish tighter link and integration between insights generated, actions taken and impact to financial, operational and risk metrics O2. Monitor and ObserveO2. Monitor and Observe • Monitor any deviation from the expected outcome of predicted business impact, filter external factors (e.g., inflation, dynamic market trends) to measure effectiveness of management decisions O3. Test and LearnO3. Test and Learn • Foster ‘test and learn’ culture where people can implement change in decisions and actions in a limited form, observe the results, and change the model to reflect reality O4. Data MonetizationO4. Data Monetization • Explore monetization strategies with the insights gained as an additional revenue source for the organization; e.g., licensing fee for aggregated data sets as an event indicator DD II AA OO

Four Primary Types of Operating Models • Team typically reports to the CIO and provides data delivery, reporting and business
intelligence services • Investment focused on Infrastructure and Tools • Primary focus on acquiring, storing, managing and reporting the information as opposed to developing deep analytic modeling skills • Less focus on innovation and usage of 3rd party data Information EnablerInformation Enabler • Team reports to functional leaders (e.g., Marketing, Sales, etc.) that build targeted data marts and analytic models to improve functional performance • Relies on the services provided in the “information enabler model” as well as their own specialists to enable data capabilities • Heavy focus on 3rd party data and exploring new analytic techniques and tools FunctionalFunctional Cross FunctionalCross Functional • The group reports to business unit or P&L owners (e.g., chief digital officer, VP of online/mobile) and creates value by embedding data and analytics‐driven offerings into new or existing products and services • Focus is on the impact to revenue, profit and shareholder value growth • Investments are made in innovation and 3rd party data, as well as deep analytic models Business Unit / P&L Owner Business Unit / P&L Owner • Team reports to a cross‐ functional business role (e.g., CFO, COO) to deliver cross‐ functional analysis to support strategic, financial and operational decisions that span multiple functions • Investments are made in innovation, 3rd party data sets and tools, as well as proprietary analytic models • Skills include data scientists and deep quantitative experience The Data and Analytics Operating Model Determines Your Speed to New Value Operating with a DIAO mindset requires rethinking the data and analytics operating model

Key takeaways Big decisions have a big impact on future profitability. Organizations which delay embedding data and analytics in their decision making culture will be left far behind their competition.
Adopt the DIAO mindset. Start small, validate existing decisions, select the necessary infrastructure, drive new decisions, understand the ROI, invest and scale. A robust operating model is critical. Adopt an operating model which fits the culture of your organization and foster a collaborative and agile ‘test and learn’ culture to enable innovation. For your organization to win … Unleash analytics and empower talent to drive insights to action across your business.

#3 Putting Big Data to
work: Case Studies

Make space for profits! Consumer
product goods company • Inventory stock out average of 13% vs. 8% industry average • Difficulty accurately predicting demand across a distribution network of over 1000 area sales managers • Supply chain challenges: backroom inventory at 24% of volume – and rising • Sought a demand driven inventory and shelf optimization system that provided accurate demand forecasts for use by sales managers on a daily basis • Design and execution of a pilot initiative — Time series analysis models predict demand at a store SKU level — Forecasting variables include effects of price, promotions, seasonality, product sales velocity, day of the week , delivery constraints and others • Develop business case, design, develop, roll out and implement solution • Measure performance and results • Out of stock conditions reduced on average to 6% • Improved cash flows due to reduced back room inventory • Projected $30m EBITDA contribution a year. Business Issues Action Results

Complete forecast creation for 3 wks Make space
for profits! Big Data, analytics and decisions 1. DATA 1 Classification of products based on average volume sales Complete Sales Data High Volume Items Low Volume Items 2 Classification of high volume items based on formats and volume of sales 2. ANALYTICS 3 Low Volume Item Forecast Forecasting for low volume items based n the sales of last 8 weeks 4 Input sales data in respective time series for every combination + Complete Price Information (past 2 years) 5 Forecast calculation for every sales‐item combination based on best time series model 6 High Volume Item Forecast 7 Correct the sales time series based on discount data to get base demand 3. DECISIONS +Daily Sales Information for past 12 weeks 8 Splitting the weekly forecast 9 Handheld Area Sales Manager Updated Forecast Make overrides if necessary NEXT DAY DELIVERY

New revenue from where streets
have no names B2B specialty pharmaceutical sector • Flat revenues over three years • Recent 16% reduction of sales force • Inefficient sales force optimization, workloads rewards and compensation • Poor employee morale • Big Data pilot using advance analytics • Development of a customer value assessment framework • Identification of high value customer segments • New targeting strategy • Redesign of sales territories • Reprioritization of sales resources and deployment • Development of a business case for 2012 revenue impact • 5%‐7% revenue lift • More efficient sales force (16% leaner) • Improved insight into high potential accounts Business Issues Action Results

New revenue from where streets
have no names Customer segmentation and sales targeting 1. DATA 2. ANALYTICS 3. DECISIONS Master Data Data integration… Patient Data • Office location • Visit frequency • Services used Consumer Data • Demographic • Insurance • Lifestyle Sales Data • Sales agent location by market / territory • Product revenue by agent / market / territory Customer segmentation… Who to target? Value based segmentation techniques determine • High potential customers • Best potential customers When to target and where? An independent RFM process was run to segment priority customers by: • Average spend per prescription refill • Average time between prescription orders • Transactions by zip code Redesign sales territories and sales force deployment…. Define Principles 1 Define Constraints 2 Perform Optimization 3 Calculate Metrics 4 Target Markets & Customers 5 Define workload, potential and performance based principles to act as territory balancing criteria Build constraints to meet specifications(e.g. balanced workload) and maintain geographic continuity Use statistical tools and algorithms to meet design objectives and constraints Calculate and forecast key metrics of new territories Generate customer level targeting lists. Develop a visual representation of targeted and omitted customers on potential map

Consumer insights journey Global retailer
company • Goal was to enhance how they spend $400m in customer based marketing across multiple channels annually to get the largest return on our investment (higher sales, margins) • Biggest foundational challenges identified was the number of Customer Data silos, quality of data and analytics around the enterprise causing customer disappoints and hurting sales (e.g. thanked 20,000 customers for purchases they never made, misplaced loyalty points in other customer accounts) • Company was spending $4‐5m annually in marketing messages and campaign activities with improvement opportunities • Funded an enterprise wide initiative for Customer Data to — Integrate the customer data across multiple channels – stores, online, mobile under one analytics repository — better understand the transactions and interactions of all its customers across all of its channels by the usage of analytics (Customer Identification, Segmentation, Clustering) — Use the insights generated using analytics to better target customer based on their preferences. Integrated the results into 1‐1 marketing and personalization initiatives like the online recommendation engine • Increased gross margin (GM) per customer by capturing 10% more margin for 5% of identified customer across each of our value tiers • Improved efficiency in the TV/Digital marketing spend, duplicate mail savings and identified cost take outs of ~5m in annual budgets • Increased offer conversion rate by 10% on a quarterly basis • Projecting hard benefits in the range of 50 – 55m this year in Net Operating Profits as a cumulative effect of the customer data program Business Issues Action Results

Consumer insights journey Big Data,
analytics and decisions 1. DATA 2. ANALYTICS 3. DECISIONS 1 Single view of customer transactions and interactions for products and services across all channels Stores Online Mobile Single View of Customer 2 Created multiple rich segments of customers integrated across channels based on a set of key drivers through segmentations and clustering techniques to enable personalized targeting of offers and promotions Customer Engagement Customer Value Customer Behavior Demographics Best Customers Important Opportunistic Uncommitted Price Sensitive Quality before Price Product based promotions New Customer Most Loyal Retained /Reactivated Prefer online shopping Buy online, pickup store Filtered a sample of most loyal members who mattered and shopped online Decision /Personalization Engine Passed the insights to the personalization/ decision engine feeding the online and mobile portals Mobile Web Shopping Portal 3 Presented relevant offers, recommendations. Increased conversion rate, profits and customer delight

Thank you © 2015 PricewaterhouseCoopers
LLP. All rights reserved. PwC refers to the United States member firm, and may sometimes refer to the PwC network. Each member firm is a separate legal entity. Please see www.pwc.com/structure for further details.