BAO Solutions for the Consumer Products Industry

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25 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

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© 2010 IBM Corporation

Smarter Consumer Products

BAO Solutions

for the Consumer Products Industry


Delivering the Future


© 2010 IBM Corporation

Smarter Decisions for Optimized Performance

© 2010 IBM Corporation

Smarter Consumer Products

2

Analysis of the CP industry found key questions our clients are
asking related to BAO transformation

Collaborating with channel partners

How do we?


understand cost through the value chain


track product accurately through the supply chain


optimise inventory and logistics networks


integrate demand signals to optimize operations


run factories for efficiency and effectiveness


trace materials from raw inputs for manufacture to finished
products


manage the environmental impacts of operations and supply
chains


manage supply risks to ensure on
-
shelf availability

Managing business performance

Building life time consumer relationships

How do we?


create meaningful consumer insight


obtain better visibility of marketplace dynamics


leverage shopper insights


build a relationship direct with consumers


use consumer information to innovate


collaborate with channel partners to target specific
consumer segments


collaborate with channel partners to innovate in
differentiated ways relevant to the channel

Managing raw materials to finished goods



How do we?


better understand and prioritize our customers


improve visibility and effectiveness of trade spend


use information to better manage on shelf availability


better understand price elasticity and pricing at a level
meaningful to optimize execution


ensure our products are relevant to the retailer


create new products that balance portfolio expansion with store
SKU rationalization

How do we?


manage commodity prices and currency fluctuations to minimize
financial exposure


manage people resources to optimize operations, and develop
employee morale and careers


give executive visibility to specific functional requirements like
finance


improve business and financial metrics reporting so that the
organization has visibility to a “single version of the truth”


improve reporting of ROI metrics to empower and increase
accountability


Market Drivers

© 2010 IBM Corporation

Smarter Consumer Products

3


Consumer

Insight



Retail

Collaboration




Supply Chain

Management



Manufacturing

Management

Data Integration

Internal Transactional Data

Customer

Consumer

Supply Chain

Enterprise Content Management

Master Data Management and Data Governance

Enterprise


Integrated Information Platform

Clients are facing myriad challenges to become analytically
-
driven

Third Party Data

Client Challenges

© 2010 IBM Corporation

Smarter Consumer Products

4


Consumer

Insight



Retail

Collaboration




Supply Chain

Management



Manufacturing

Management

Data Integration

Internal Transactional Data

Customer

Consumer

Supply Chain

Enterprise Content Management

Master Data Management and Data Governance

Enterprise


Integrated Information Platform

1:1 Consumer

Smart Customer

Collaboration

Smart Supply

Chain


Smart


Factory

Enterprise

Visibility

Delivering the Vision is enabled by six key areas that will deliver
significant incremental value building from core SAP

IBM Solutions

Third Party Data

Integrated Information Infrastructure

© 2010 IBM Corporation

Smarter Consumer Products

5

Reversing the Focus

Individual Projects


Supply Chain







Demand Chain

Project Continuum


Consumer







Systems


Based upon existing services IBM has taken a Supply chain to Demand Chain process focus or has
focused upon individual processes along the continuum that are not interconnected.


By reversing the focus to the End Consumer of our clients products and services we can more
effectively optimize the continuum back through the Supply Chain and Insure we are solving the to
the entire Client’s process continuum.

Disconnected Projects


None


© 2010 IBM Corporation

Smarter Consumer Products

6

Our solutions are underpinned with specific BAO offers


Next Gen Supply Chain


Sales and Operations Planning


Out of Stocks Management (On Shelf Optimization)


Inventory Optimization


SKU Rationalization


Freshness / Unsalablinity Analytics


Product flow optimizer


Supply Chain Visibility Dashboard



Supply Chain Sustainability Management:


Network Optimisation


Financial Performance Visibility


PLI Global Location Strategies


Inventory Optimization



Full Value Traceability:


Supply Chain Risk Management



Smarter Factory:


Production Scheduling



Consumer Lifecycle Visibility


Consumption Based Consumer Segmentation
& Ranking


Shopper Insights


Brand sensitivity and health analysis (CoBRA)


Consumer Message Management


Future Value of Consumer (Consumer Equity
Lifecycle Management
-

CELM)



Consumer Driver Analysis:


Market Basket Analysis


Marketing Mix Model


Price Optimization


Brand sensitivity and health analysis (CoBRA)



Optimised Retail Collaboration


Price Optimisation


Trade Promotion Optimisation


Sales Force Optimisation


SKU Rationalization


Channel / Retailer Dashboard


Integrated Enterprise Dashboard:


Financial Performance (AP Analytics)


Integrated Planning


Enterprise Performance Management


Workforce Optimization



Complex Information Management:


Master Data Management


Data Governance


Data Integration Models


Information Simplification


Data Crunch



IBM Differentiator: Creating a roadmap for our clients to achieve
the transformation using IBM Offers

Collaborating With Channel Partners

Managing Business Performance

Building Life Time Consumer Relationships

Managing Raw Materials To Finished Goods



© 2010 IBM Corporation

Smarter Consumer Products

7

Six Key Products that Drive CP Deliverables

1.

Consumption Based Consumer Segmentation & Ranking

2.

Brand Sensitivity and Health Analysis (CoBRA)

3.

Future Value of Consumer (Consumer Equity Lifecycle

Management
-

CELM)

4.

Out of Stocks Management (On Shelf Optimization)

5.

Freshness / Unsaleability Analytics

6.

Trade Promotion Optimization

© 2010 IBM Corporation

Smarter Consumer Products

Consumption Based

Consumer Segmentation and Ranking

© 2010 IBM Corporation

Smarter Consumer Products

9

CPG Segmentation and Ranking Case Study

Case

A major CPG company with over 200 products


Senior executives were challenged in understanding if their brand management strategy optimized the
profitability of their consumer relationships.



Each brand manager owned the marketing budget and the responsibility of targeting their brands to the
appropriate audience.



Each brand manager was autonomous from the other creating potential for competition within internal
brands.



Although the media buys were made centrally the brand managers owned the ad budgets for their brands.

Problem

Senior management had limited control over the redistribution of budget without causing major issues within
specific brands and had limited statistical justification to move funds from one brand to another.



There was no understanding of the synergies between consumers of any given brand to another.



Consumer profitability was always measured at the brand level, not at the corporate level. Overall

profitability was measured at the corporate level after all charges and knowledge of consumer interaction

was removed from the data (Traditional financial analytics)



$1.785B was being spent on advertizing without any knowledge of overall corporate effectiveness or brand
synergy

Solution

Perform a Consumer Segmentation and Ranking (Baseline) analysis across all brands to determine
if the Return on Marketing Investment (ROMI) is being maximized in the present state and what
would need to be done to improve it.

© 2010 IBM Corporation

Smarter Consumer Products

10

CPG Segmentation and Ranking Case Study

Elit
e

Marginally

Profitable

Break Even

Unprofitable

Income from Operations and Profit
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Income from Operations


Profit

Based on performing the analysis the following potential results were observed;


1.

70% of the overall corporate revenues came from 5% of the total corporate households


There was no brand marketing currently taking place for the corporate brand


The only place where the corporate brand existed was on the corporate web site
and sub branding of certain products (Those products were the most mature)

2.

80% of the profit came from 5% of the households

3.

Only 50% of the media advertising ($1.785Bil) ever touched anyone in the 5%

4.

By adding an additional 1% of the segmented “look alikes” to the 5% (creating 6%), an

additional $400MM would be added to corporate profit.

Results

5%

27%

29%

39%

© 2010 IBM Corporation

Smarter Consumer Products

Consumer Sentiment Analytics

Moving towards CCI

September 2010

© 2010 IBM Corporation

Smarter Consumer Products

12

Value Proposition
-

Consumer Insight Project



Consumer Insights solution:


Analyzes large amounts of cloud
content for discussions of brand,
products and key topics and correlates
with sentiment



Offers sophisticated IBM Cognos
Software reporting and alerting
capabilities combined with unique
search
-
based exploratory user interface



Offers rich “mashup” of Consumer
Insights with Real
-
Time Twitter feeds,
Real
-
Time Video feeds, tag cloud,
Google Trends Search Volume Index, &
more



Allows integration of analysis and
content with on
-
premise applications
and analytics assets



Many IBM patents in data discovery,
manipulation, visualization

Web blogs, social
media, reviews

100000+
discussion
forums

30,000+ news
feeds

2 billion+ blog
postings

© 2010 IBM Corporation

Smarter Consumer Products

13


Consistent:

Customer sentiment is in the eye of the beholder and
subject to interpretation. IBM Cognos Content Analytics ensures that
you minimize the subjectivity and measure all comments.




Consumable & In
-
Context:
Analytics delivered in
-
context with real
-
time
feeds, web 2.0 tag
-
cloud and other rich web
-
centric data. Mashup
delivery maximizes consumability of Content Analytics with a
accessible, information
-
rich, browser environment.



Complete
: Scalable to hundreds of thousands of websites and customer
comments. (e.g. 2 billion blog postings, 100,000+ forums, 30,000+ news
feeds)



Cost effective
: The sheer volume of content produced on the web
prohibits most companies from performing effective analysis. IBM
Cognos Content Analytics helps you review, filter and interpret this
information with minimal time and cost.


THE RESULT?
better insight into consumer opinions and trends, which
gives early insight into shifts in consumer opinion so your organization
can stay ahead of market demand with the right products, portfolio and
optimal marketing mix.


Value Proposition


Key Benefits

© 2010 IBM Corporation

Smarter Consumer Products

14

Over
$12B

in

software investments since
2005


Over
4,000


Dedicated Consultants


Industry Know
-
How
to
Accelerate Time to Value


Largest

Math Department in
Private Industry


Over
$10B in Service
Management
, 13 Cloud
Centers, 8M sqft data centers


IBM


Leading the Business Analytics space..

“IBM, not SAP or Oracle, is now the industry's primo analytics
solution/platform vendor…”

“Since 2006, IBM has deliberately & doggedly constructed an
unparalleled portfolio… it's difficult to see how any
competitors will be able to compete anytime soon…”

© 2010 IBM Corporation

Smarter Consumer Products

15

Proof of Concept Scope Frame


IBM continued

MASH

© 2010 IBM Corporation

Smarter Consumer Products

16

Proof of Concept Scope Frame


IBM continued

MASH

© 2010 IBM Corporation

Smarter Consumer Products

17

Clusters Can Be Overlaid To Consumer Segments To Provide
Reputational Analysis By Segment

Elit
e

Marginally

Profitable

Break Even

Unprofitable

Results

5%

27%

29%

39%

Parm.

Parm.

Taste


Mine


OK


Not

Life


Mine


OK


Not


Demo 1

Style


Mine


OK


Not

Comp.


Mine


OK


Not

Loyal


Yes


Close


Not

Value


Best


Next


Not

Winner


Best


Next


Not

Fit


Yes


Close


Not

Demo 2

Demo 3

Demo 4

Behav. 1

Behav. 2

Behav. 3

Behav. 4

Firm 1

Firm 3

Firm 2

Firm 4

Parm.

Parm.

Relationship Clusters

When attitudinal parameters are matched against segmentation clusters, specific
attributes match behaviors which can be measured and analyzed for social
cluster targeting.

Consumer Profitability

Reputation Clusters

© 2010 IBM Corporation

Smarter Consumer Products

Future Value of Consumer






© 2010 IBM Corporation

Smarter Consumer Products

19

Enterprise Consumer
Experience Data

The first step towards managing the future value of the consumer is a
rich understanding of key consumer groups or segments

Action Clusters have 8
-
13 dimensions


Feature Vectors


based on behavioral
response “voting
records” to aspects of
the client’s value
proposition

Action Clusters are:


Highly
homogeneous



it is
difficult to get into a cluster based
on 13 dimensions, ensuring that the
consumer are very similar to one another


Highly
differentiated



the Action Cluster
process ensures as much “distance” between
clusters as possible


Highly
actionable



because the clusters are
based on the consumer’s response to various
dimensions of the value proposition, they
facilitate highly
-
specific targeting

Supply Chain

Economic

Aggregate unique sets
of data to leverage
direct relationships in
planning and
operations

Social Media

Consumer
Reference

Brand Website
Activity

Preferred
Product
Categories
Preferred
Channel
Participation
in Loyalty
Program
Participation
in Events
Bottle
Registrations
Return /
Exchange
Behavior
Breadth of
Brands/
Categories
Purchased
Length of
Time as
Consumer
Recency
+
Frequency +
Value
Response
to Media
Time until
Repurchase
Preferred
Product
Categories
Preferred
Channel
Participation
in Loyalty
Program
Participation
in Events
Bottle
Registrations
Return /
Exchange
Behavior
Breadth of
Brands/
Categories
Purchased
Length of
Time as
Consumer
Recency
+
Frequency +
Value
Response
to Media
Time until
Repurchase
A

Identify Consumers

Group Consumers

Assign Consumers

B

C

D

E

F

© 2010 IBM Corporation

Smarter Consumer Products

20

Viewing the consumer segments on current and future value
enables strategy development and resource alignment

Shift coverage
to lower cost
channels

Value

Prior Year Value

Future Year Value

Growth Strategy

Engage to capture future
value

Increase Media Spend

Increase Trade Spend

Increase Forecast Accuracy

Resource Implication

Consumer Value Prioritization Framework

Value Strategy

Manage resource
allocation

Allow Higher OSA Risk

Reduce Media Spend

Reduce Trade Spend

Account Priority

Defend

Retain

Grow

Low

Low

High

Retention
Strategy

Engage to increase
future value

New Products

Brand Extensions

High

Add

Maintain or Add

At most
maintain

Add

A

B

C

D

E

F

Defend and Grow
Strategy

Retain and Protect
Market Share

Minimize OSA Risk

Reduce Media Spend

Consumers will migrate between segments. For example, consumers in Segment E can
migrate to higher value Segment A (20%), lower value Segment C (20%), or remain at their
current value profile (60%). Strategies and tactics can help migrate consumers to higher
value segments.

© 2010 IBM Corporation

Smarter Consumer Products

21

Managing consumer segments as a portfolio of consumers enables defining and
aligning treatments and spending to migrate the most consumers to their highest
expected value.

What marketing sequences have had the best and most significant
impact on consumer behavior?

The Challenge

Searchable list of marketing
actions with statistics
(revenue/profit, coverage)
Transition probabilities over
time from one segment to
another
Customer path along the
market segments (path is
highlighted in red and tagged
with individual marketing
actions)
Market dynamics over time
with some statistics (number
of customers in each
segment, transition
probabilities, etc.)
Market dynamics can be
restricted to only a sub
-
set of
marketing actions. Allows for
assessment of action impacts
on value and dynamics.
The Solution

© 2010 IBM Corporation

Smarter Consumer Products

22

CELM generates actionable business intelligence based on a predefined
two step approach.

Step 1
:

Find the targeting policies (sequence of
campaigns) that maximize the consumer value / risk ratio
over a specified time horizon (weeks, months, quarters).

Step 2
:

Determine when to target, and how much to
allocate to each consumer in order to implement the policy
within budget constraints over the specified time horizon.

As a diagnostic tool, CELM allows enterprises to assess the impact of previous marketing
policies on consumer dynamics, across the different loyalty states

Given
:



a time horizon for future planning



a set of consumers with associated value/risk profiles



a set of possible marketing actions



a specified marketing budget


Determine
:



the optimal portfolio of:


--

consumers



--

Actions



which maximizes the value / risk trade
-
off:

--

over the given
Time

horizon

--

within the given
Budget

constraints

© 2010 IBM Corporation

Smarter Consumer Products

Out
-
of
-
Stock Management

(On Shelf Optimization)

© 2010 IBM Corporation

Smarter Consumer Products

24

Advanced flow based analytics yields insights on sales of aged products


In lieu of using costly detailed tracking and auditing
solutions, advanced flow/network analytics enables


Weighted inventory (frequency and age) at each
location


Distribution of store sales by product.

Insights and Action

Information

Situation


Inventory controls are not sufficient to maximize
profitability of the movement of products from
factory to shelf


More effective control mechanisms are necessary
to optimize product availability at the retailer and
through the entire supply chain.



Early indication of insufficient inventory by customer
location


Yield insight into supply chain material flows


Where/when material accumulates


Detect aberrant locations


Customer order levels that do not align with
anticipated demand and their current inventory
levels by date code


Store

Flow In

Flow out

D1, Q1

D2, Q2

D3, Q3

D4, Q4

D5, Q5

0
5
10
15
20
25
30
35
40
<=0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
0
5
10
15
20
25
30
35
40
<=0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
0
5
10
15
20
25
30
35
40
<=0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Shelf

Inventory

Backroom

Inventory

RDC

Inventory

Shelf Availability

Expected % of Product

© 2010 IBM Corporation

Smarter Consumer Products

25

Demand Sensing based upon advanced analytics vastly improves forecast
accuracy

Discrete choice model for retail demand sensing


Estimating market share for each product in each market from syndicated data


Modeling substitution effects among similar products


Based on multinomial
-
logit choice model (McFadden, Nobel Prize 2001)


57% forecast
accuracy when
using only price
history

68% forecast
accuracy when
using price and
brand information

83% forecast
accuracy

when using
price, brand and
product information

© 2010 IBM Corporation

Smarter Consumer Products

26

On
-
Shelf Availability (OSA) Analysis reduces risk of Out
-
of
-
Stock
Events


Analyze stock
-
out risk profile for each retail
store


Extend span of control across multiple
echelons for comprehensive improvements
across the supply chain


Align replenishment an stocking plans with
forecasted demand

Situation


Pattern of frontloading for
promotional events, often resulting
in large imbalances between
shipment and consumption volumes


Integrate data sourced from both internal
and external sources:


Manufacturer onHand, Sales, Forecast,
Orders.


RDC / VMI Orders, OnHand, Sales,
Receipts


Store Sales, OnHand, Receipts, Orders

Information

Insights and Action


Improved supply chain results enabled by
integrated information:


Supply chain end
-
to
-
end shelf back to
supplier views


New insights, enabling intelligent
collaboration with customer and peer
business units


Improved planning and promotion
execution to minimize working capital

© 2010 IBM Corporation

Smarter Consumer Products

27

DC Analysis Report enables CP manufacturers flexible selection across
multiple filters

The navigator on top
right has links for 6
store detail reports

DC Analysis Report is
displayed as default. Click on
any of the report links in the
navigator to show other reports

Customer Filter:

Select Customer and
optionally select Customer
Ship To (displayed by
clicking the >>> button)

Click on Finish button to
run the report

Product Filter:

Select Brand and optionally
Category and Segment
(displayed by clicking the >>>
buttons). Additional sub filters
also available

Time Filter:

Select Year and optionally
Quarter, Month, Week and
Day (displayed by clicking
the >>> buttons)

Select Group/Pivot


Customer
, Brand, Division, Type Group,
Manufacturing Plant


© 2010 IBM Corporation

Smarter Consumer Products

28

VMI Dashboard (default home page for most CP manufacturer users)


The page is divided in 4 views aggregated across
FY 2010


Summary by Retailer


Summary by Brand


Summary by Division


Charts by Retailer / Brand / Division


Each view is sorted in descending order of the
service level indicating top ‘n’ issues requiring
attention.


To see more rows, click on the ‘Page down’ link
below each table.


Enables
changing
default sort

Selection for
bar chart
display of
selected view
and KPI


Right click on the first 2 columns of each table
allows for


Drill Down: Drill down one level in the
hierarchy


Drill Up: Drill up one level in the hierarchy


Go To: Jump to another report (across
business functions) based on current context
(i.e. selected customer, brand or division)

© 2010 IBM Corporation

Smarter Consumer Products

Freshness/Unsaleables Analysis

© 2010 IBM Corporation

Smarter Consumer Products

30

Freshness/Unsaleables Analysis improves visibility of freshness


Determine optimized stocking positions for perishable goods for each retail store


Maximize freshness


Utilizes inputs from flow analytics and retail demand sensing


Enables action plans for diagnosing and managing freshness

For example, DC5 has
higher age score than
other DCs

1.
Validate with DC

2.
Diagnose ordering
and inventory
management
issues

© 2010 IBM Corporation

Smarter Consumer Products

31

Store Current Report enables CP manufacturers quick selection into
Current Year, Qtr, Period, Week and Day views

Customer Filter:

Select Customer and
optionally select Banner
and Store (displayed by
clicking the >>> button)

Click on Finish button to
run the report

Select Group/Pivot


Customer
, Brand, Division, Type Group,
Manufacturing Plant


Time Filter:

Select current time period
(Year, Quarter, Period,
Week, Day)

Product Filter:

Select Brand and optionally
Category and Segment
(displayed by clicking the >>>
buttons). Additional sub filters
also available

© 2010 IBM Corporation

Smarter Consumer Products

Trade Promotion Optimization

© 2010 IBM Corporation

Smarter Consumer Products

33

Trade Promotion Optimization

Top 10 reasons why CPG company’s data is a mess and the impact to TPM

1.
Incomplete, inaccurate and outdated customer & product data

2.
No Common View of The Customer

3.
Limited visibility into Customer Structure & Route To Market

4.
No Common Analytics / Reporting

5.
No reconciliation of 3
rd

party data (IRI, ACNielsen, POS, etc.)

6.
No Common Process for Data Governance

7.
L
limited Cross
-
application Integration

8.
Limited Cross
-
Functional Collaboration

9.
Inability to relate and manage customer and product data

10.
Multiple, segregated points of entry for master data

Unsuccessful Trade promotions is costing millions of dollars to CP Companies….


Less than 30% of Trade Promotion programs are profitable.


The average return per dollar spent in promotions was about $0.65.


Only 30% of CPG firms measure their promotional results

Trade Promotion is typically the second largest expense on the income statement averaging about 10
-
20% of gross
sales and 65
-
75% of marketing spend. In 2009, total trade spend is estimated between $80 and $100 billion, up 33% in
the past 5 years. Companies are continuing to spend more and more, yet a recent industry study estimates that more
than 80% of consumer products promotions are not measured. You can not improve what you do not measure.

© 2010 IBM Corporation

Smarter Consumer Products

34

Why should consumer products companies implement the industry
-
recognized Trade Promotion Optimization (TPO) preferred practices?



Effective trade promotion management is not focused on decreasing the funds spent on
promotional activities, but instead increasing the efficiency, profitability, and visibility of these
investments. Trade promotion is not just a business process improvement or software
system


it is a closed
-
loop, cross
-
functional strategy. Like most business processes, TPO
is not a destination but a journey. It requires continual improvement and collaboration across
the firm. Quantifiable optimization of promoted events needs to become part of every
discussion for senior management.


The seven major areas of the trade promotion management lifecycle for consumer products
manufacturer are:


Customer business planning




Budget allocation




Promotion development and modeling




Retail execution and monitoring




Settlements




Management reporting and post event analysis




Category optimization and insight


© 2010 IBM Corporation

Smarter Consumer Products

35

According to AMR Research trade promotion data lives in multiple
systems

DATA CLASSES




Customer



Customer Hierarchy



Address



Product



Product Hierarchy



Pricing




SYSTEMS



CRM


ERP


TPM


POS


3
rd

Party


DW

© 2010 IBM Corporation

Smarter Consumer Products

36

The problem is caused by having partial information across
multiple sources

Financial
System



Siloed



Complex



Non
-
Integrated



Inaccurate Data



Manual Maintenance



Non
-
Standardization

Business

Intelligence

ERP

CRM

Marketing &

Category Mgt

Field

Execution

Demand
Control

Trade

Promotions

Demand

Planning

External Data
Providers



Limited Views



Inaccurate Data



Unreliable



Incomplete Hierarchy



Redundancy

Business Processes

Data Layer



Incomplete Data



Data Latency



Non
-
Integrated



Inaccurate Data

POS

Spreadsheet &

Other offline tools

Multiple
TPM

Systems

© 2010 IBM Corporation

Smarter Consumer Products

37

Complex Distribution Channels create disjointed views of customer
activity from multiple sources

CP Company

Manufacturing

Plants

Distributor

Distribution

Center

Distribution

Center

Wholesaler

Retailer

Distribution

Store

Store

Store

Store

Consumer

Household

Distributor

Center

Customer Data

Product Data

Demand

Planning

ERP

Brand

Performance

POS

Field

Execution

Legacy

Trade

Impact

TPM

Demand

Control

TPM

3
rd

Party Data

IRI, ACNeilsen, Spectra

Products

Products

Distribution

Retail

Web site

© 2010 IBM Corporation

Smarter Consumer Products

38


Increased success rate of trade promotions & predictive analytics



Superior account management and service leadership


Opportunity cost due to improved new product introduction
processes and accurate data


Data Optimization Effort


Master Data Management (MDM)


Enable capability to manage parent customer and product
hierarchies.


Provide secured access to accurate data based on the role or position
for data governance.


Enable advanced business intelligence and analytics systems


Disparate data from multiple sources (ERP, Siebel TPM, Legacy) resulted in
inconsistent, inaccurate and incomplete customer and product information.


Lack of a strategic approach to enterprise customer and product information
management to quickly respond to current market conditions.



Lack of an enterprise analytics and data management approach supporting
financials, supply chain and operations, sales and trade management leading.




Inability to plan and execute the way customers execute & go
-
to
-
market.
Syndicated data (IRI, AC Nielsen), is an important source of retail POS and
Promotional Performance data and is currently managed by Marketing and
housed externally to the business.



Inability to define multiple relationships/hierarchies within accounts and
products, and retain history.



Lack of data standardization & distributed data governance can hinder
productivity performance.




M&A / Brand Acquisition activity is costly and resource intensive.



Due to change in market conditions and intense competition a need for better
target customers with relevant promotions became a priority. Better managing
and controlling promotional results via better data management and improved
processes.

Global Consumer Packaged Goods Company

Client & Product Data Optimization and Road to Predictive Analytics

Benefits

Solution

Root

Cause

Business

Problem

Situation

End Users

Business Use

Customer

Data Integration

between applications


Account
Managers &
Marketing


Streamlined
Customer &
Product Creation
Process

$

Products


Trade
Fund

Analytics

Trade

Promotions

Accounts

Customer /

Product /

Account

Management


Customers

Employees


Global

Corporations

Regional

Distributors

Retailers

$

Customer
Support


© 2010 IBM Corporation

Smarter Consumer Products

39

Product

Development


Manufacturing

Planning &

Procurement

Sales &

Marketing

Distribution



Increased success rate of
trade promotions & analytics


Superior account
management and service
leadership


Improved delivery
performance and forecast


Productivity Gain and
reduction from data
availability, accuracy,
enrichment from 3
rd

party
sources




Data Optimization



Reduce Shipment Errors


Improved Billing Performance


Increased revenue due to
improved delivery






Reduced Operation Costs


Reduced Raw Material Spend
and Scrap


Reduce Regulatory Exposure
& Avoid Penalties






Opportunity cost
due to improved
new product
introduction
processes and
accurate data






Improved decision making through use of
predictive analytics


Pre
-

and post
-
promotion analysis to
understand the drivers of lift




Information

Technology



$$

$$$$

$$

$$$

$$

$

ROI Impact of Trade Promotion Optimization

© 2010 IBM Corporation

Smarter Consumer Products

40

All roads lead to DSR…

End
users
access

Distribution

POS

Data

Data

Data

Results

Insights


Insights

Aggregate Data,

KPIs, Analytics,

Alerts


Shopper Insights
Analysis


Customer Choice
Modeling


Pricing

Data

Demand

Signal Repository (DSR)

Retailer

CP Company


Promotion Management


Portfolio Optimization


SKU Rationalization


Our goal is to build (or optimize) Demand Signal Repositories (DSR) for our CP clients and leverage
the DSR to launch additional BI and Advanced Analytics Solutions initiatives.

© 2010 IBM Corporation

Smarter Consumer Products

41

Contact


Steve Horne


shorne@us.ibm.com