turning big data into big revenue

Unlock data possibilities

Turning Big Data into Big Revenue

Oliver HalterPrincipal, 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 decisionsCompany 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 tomake 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%


Big impact on future profitabilityBig 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%


Big improvement on decision makingHighly 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


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 OwnerBusiness 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 decisions1. 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

3Low 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

6High 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

9Handheld

Area Sales Manager

UpdatedForecast

Make overridesif necessary

NEXT DAY DELIVERY

New revenue from where streets have no namesB2B 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


New revenue from where streets have no namesCustomer segmentation and sales targeting1. 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 customersWhen 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….

DefinePrinciples

1Define Constraints

2PerformOptimization

3Calculate Metrics

4Target 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 journeyGlobal 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


Consumer insights journeyBig Data, analytics and decisions1. 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.

turning big data into big revenue

Data & Analytics

data possibilities

big data journey risk

data savvy competitors

big impact

big improvement

x data quality usefulness

bn source

senior executives