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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.

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Turning big data into big revenue

  • 1. Unlock data possibilities Turning Big Data into Big Revenue Oliver Halter Principal, Management Consulting
  • 2. #1 Why this is important
  • 3. 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™
  • 4. 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™
  • 5. 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™
  • 6. 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™
  • 7. 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 
  • 8. #2 How can your organization adapt and execute?
  • 9. 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
  • 10. 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
  • 11. 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
  • 12. 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
  • 13. 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
  • 14. 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
  • 15. 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
  • 17. #3 Putting Big Data to work: Case Studies
  • 18. 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
  • 19. 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
  • 20. 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
  • 21. 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
  • 22. 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
  • 23. 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
  • 24. 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.