SlideShare a Scribd company logo
1 of 2
Download to read offline
Information Technology
                                                                          Consumer Packaged Goods



               Kneebone Cross-Marketing Performance Technology




Kneebone 3.0 is a Software Platform built exclusively for marketing that integrates and automates data collection and delivery from all
marketing data sources. Kneebone is purpose-built for marketing performance management. Kneebone offers a suite of Software as a
Service (SaaS) Applications designed to provide marketers with the capability to analyze, visualize, collaborate and plan resulting in
better decision making.


The Client: This client is a multi-national consumer packaged         Challenges: To integrate all relevant marketing information in a
goods company with many Brands crossing multiple lines of             seamless way to give a single view of sales and marketing
business. Kneebone was used to track and model all brands across      initiatives.
two lines of business. A major need was to also track and model       Success: For the first time, the client could track and plan
competitor brands. The project was restricted to a single national    marketing campaigns against a live unified view of sales data.
division of the company.



                      Marketing Data                                                            Sales Data
All Media Events and Spend:         All Coupons:                      AC Nielsen: Weekly SKU-             Walmart: Regional and
 TV/Radio Spots                     Coupon Details                  Level sales data collected by       Metropolitan SKU-Level
 Print Inserts with vendor          Coupon Redemptions              Region, Channel, and Brand          weekly sales data. To be
 Outdoor                                                             Descriptors for Client and          combined with AC Nielsen
 Online and Social Media           Flyer Coop:                       Competitor products.                sales data for a complete
 Direct Mail                        Flyer Details                                                       view.
                                     PDF Flyer Images                This is the main dataset for
All data is date stamped with                                         the client and will be fully        Shipments: All SKU-Level
distributions and spend.            All data is date stamped with     base lined for both client and      date-stamped shipments from
                                    distributions and spend.          competitor sales tracking.          Client to Stores.



                                                              Conditions
Indices: All Index conditions       Weather: All weather data is      Consumer Feedback: All              Online: All online conditions
are date-stamped and can be         date-stamped and aligned          conditions are date-stamped         are date-stamped and have
overlaid with all other data.       with Client regional              and have a tree descriptor and      key web site page descriptors
                                    definitions.                      type hierarchy.                     and referrers (Google etc,)
   Stock price                      Average Temperature                                                 defined.
   Consumer Price Index             Total Rain Fall                    Consumer feedback
                                     Total Snow Fall                    Call to consumer TFN               Web Site Activity
                                                                         Consumer complaints                Web Site Referrals
Media Data: The client provided permission for their Media               Walmart: The client passes on their Walmart data to Kneebone
vendor to send standardized order Insert reports to Kneebone.            for inclusion in the platform. The data is in Excel cross-tab format
Kneebone is experienced with all of the standardized media               and adheres to the Walmart calendar year definitions. Kneebone
reporting systems including DDS, AdTrak, and Fumius.                     has custom ETL processes to clean and normalize the data into the
                                                                         client’s fiscal year definitions and use the same terminology as all
The reports come in Excel format and include all print and               other sales and marketing data in the platform (AC Nielsen etc.).
broadcast spot and ad level details with insert dates, distributions,
and costs.                                                               Coupon Data: A third party vendor manages coupon program
                                                                         for the client. Kneebone receives detailed PDF reports on coupon
Kneebone has developed automated ETL processes for cleaning              details and redemptions. Kneebone has custom ETL process to rip
and normalizing media data. This is a very important function since      data from these reports, clean, and normalize it into the common
media data is typically freely entered and contains many variations      client platform.
and misspellings for the same entity.
                                                                         Flyer Coop Data: The client uses online service that collects
Unlike consultative or statistical modeling approaches to                details on all flyers on a national basis for all brands and all
marketing performance management, Kneebone uses granular                 competitive brands. The data includes all aspects of the flyer date
“per event” marketing data. We don’t just capture aggregated             and duration, the region and channel, the product, price point, and
spending against media classifications; we capture each and every        special offers.
event to the individual TV spot or print ad. Hence, as an added
benefit, the client can now use Kneebone as a record of their            The data also includes scanned images of individual flyer pages.
media purchasing without having to go through the vendor for             Kneebone custom ETL processes to download data and assemble
every report.                                                            concatenated Flyers. The flyer images are visible within Kneebone
                                                                         and can be downloaded in their assembled state. The flyer data is
AC Nielsen: The client provided a license to access their Inf*Act        repurposed to become useful within a broader context.
OLAP national sales data for all brands. Kneebone pulls the data
from a multi-processor server based installation and ports the data      Shipments: The client provides all SKU-Level date-stamped
into our custom AC Nielsen ETL process where it is cleaned,              shipments to Stores. These shipments are modeled for historical
normalized and combined with all other data resident in                  behaviour and base lines for them can be used in predictive supply
Kneebone.                                                                modeling.

Now the client can see instantly data that used to take hours to         Conditions: All conditional data is date-stamped and provided in
compile from desktop reporting applications. Moreover, they can          a classification hierarchy enabling it be overlaid with all other data.
see it in conjunction with other sales data (Walmart) and all            Conditions of note include:
marketing events and conditions. And the AC Nielsen data is now
fully base lined with learned history that can be used in forward                     Stock price & Consumer Price Index
looking analytics. This repurposing of existing data is a huge                        Average daily temperature
benefit of the Kneebone platform.
                                                                                      Total daily rain & snow Fall
                                                                                      Consumer feedback calls to call center

                                                                         Online: Kneebone has automated ETL tools to extract data from a
                                                                         variety of Online Analytic system including Google Analytics,
                                                                         Proximity and raw web logs. All online data is date-stamped and
                                                                         has key web site, page, and referrers (Google etc,) defined.

                                                                                 Web site daily site and page activity
                                                                                 Web site daily referrals

                                                                         When a URL is used in a marketing event (print, Online, etc.),
                                                                         activity on the URL can be attached to the event as performance
                                                                         and full base line technology on on tap.




                                                         322 King St. W., Suite 400         Tel: 416 599-4001
                                                         Toronto, ON                        Fax: 416 637-9300
                                                         Canada M5V 1J2                     www.kneebone.com

More Related Content

Viewers also liked

Advanced Operating Models Research Insights: Marketing
Advanced Operating Models  Research Insights: MarketingAdvanced Operating Models  Research Insights: Marketing
Advanced Operating Models Research Insights: MarketingGenpact Ltd
 
Accenture: Commercial analytics insights CPG Companies 27-7-12
Accenture: Commercial analytics insights CPG Companies 27-7-12 Accenture: Commercial analytics insights CPG Companies 27-7-12
Accenture: Commercial analytics insights CPG Companies 27-7-12 Brian Crotty
 
Big data Whitepaper
Big data WhitepaperBig data Whitepaper
Big data WhitepaperRahul Rathi
 
Actionable Analytics: Using Better Data Better
Actionable Analytics: Using Better Data BetterActionable Analytics: Using Better Data Better
Actionable Analytics: Using Better Data BetterAmazon Appstore Developers
 
Consumer Packaged Goods (CPG) Industry - 5 Digital Transformations
Consumer Packaged Goods (CPG) Industry - 5 Digital TransformationsConsumer Packaged Goods (CPG) Industry - 5 Digital Transformations
Consumer Packaged Goods (CPG) Industry - 5 Digital TransformationsNitin Jain
 
Role of Analytics in Consumer Packaged Goods Industry
Role of Analytics in Consumer Packaged Goods IndustryRole of Analytics in Consumer Packaged Goods Industry
Role of Analytics in Consumer Packaged Goods IndustryPerceptive Analytics
 
Marketing Mix Models In a Changing Environment
Marketing Mix Models In a Changing EnvironmentMarketing Mix Models In a Changing Environment
Marketing Mix Models In a Changing EnvironmentAquent
 
SAP HANA & HADOOP Implementation - Predictive Analytics – CPG and Retail on U...
SAP HANA & HADOOP Implementation - Predictive Analytics – CPG and Retail on U...SAP HANA & HADOOP Implementation - Predictive Analytics – CPG and Retail on U...
SAP HANA & HADOOP Implementation - Predictive Analytics – CPG and Retail on U...Cloneskills
 

Viewers also liked (9)

Advanced Operating Models Research Insights: Marketing
Advanced Operating Models  Research Insights: MarketingAdvanced Operating Models  Research Insights: Marketing
Advanced Operating Models Research Insights: Marketing
 
Accenture: Commercial analytics insights CPG Companies 27-7-12
Accenture: Commercial analytics insights CPG Companies 27-7-12 Accenture: Commercial analytics insights CPG Companies 27-7-12
Accenture: Commercial analytics insights CPG Companies 27-7-12
 
Big data Whitepaper
Big data WhitepaperBig data Whitepaper
Big data Whitepaper
 
Using web analytics and actionable insights
Using web analytics and actionable insightsUsing web analytics and actionable insights
Using web analytics and actionable insights
 
Actionable Analytics: Using Better Data Better
Actionable Analytics: Using Better Data BetterActionable Analytics: Using Better Data Better
Actionable Analytics: Using Better Data Better
 
Consumer Packaged Goods (CPG) Industry - 5 Digital Transformations
Consumer Packaged Goods (CPG) Industry - 5 Digital TransformationsConsumer Packaged Goods (CPG) Industry - 5 Digital Transformations
Consumer Packaged Goods (CPG) Industry - 5 Digital Transformations
 
Role of Analytics in Consumer Packaged Goods Industry
Role of Analytics in Consumer Packaged Goods IndustryRole of Analytics in Consumer Packaged Goods Industry
Role of Analytics in Consumer Packaged Goods Industry
 
Marketing Mix Models In a Changing Environment
Marketing Mix Models In a Changing EnvironmentMarketing Mix Models In a Changing Environment
Marketing Mix Models In a Changing Environment
 
SAP HANA & HADOOP Implementation - Predictive Analytics – CPG and Retail on U...
SAP HANA & HADOOP Implementation - Predictive Analytics – CPG and Retail on U...SAP HANA & HADOOP Implementation - Predictive Analytics – CPG and Retail on U...
SAP HANA & HADOOP Implementation - Predictive Analytics – CPG and Retail on U...
 

Recently uploaded

EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWERMadyBayot
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CVKhem
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDropbox
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...apidays
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024The Digital Insurer
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024The Digital Insurer
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoffsammart93
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAndrey Devyatkin
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...DianaGray10
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsNanddeep Nachan
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...apidays
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherRemote DBA Services
 
A Beginners Guide to Building a RAG App Using Open Source Milvus
A Beginners Guide to Building a RAG App Using Open Source MilvusA Beginners Guide to Building a RAG App Using Open Source Milvus
A Beginners Guide to Building a RAG App Using Open Source MilvusZilliz
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdflior mazor
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 

Recently uploaded (20)

EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
A Beginners Guide to Building a RAG App Using Open Source Milvus
A Beginners Guide to Building a RAG App Using Open Source MilvusA Beginners Guide to Building a RAG App Using Open Source Milvus
A Beginners Guide to Building a RAG App Using Open Source Milvus
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 

CPG Marketing data integration white paper

  • 1. Information Technology Consumer Packaged Goods Kneebone Cross-Marketing Performance Technology Kneebone 3.0 is a Software Platform built exclusively for marketing that integrates and automates data collection and delivery from all marketing data sources. Kneebone is purpose-built for marketing performance management. Kneebone offers a suite of Software as a Service (SaaS) Applications designed to provide marketers with the capability to analyze, visualize, collaborate and plan resulting in better decision making. The Client: This client is a multi-national consumer packaged Challenges: To integrate all relevant marketing information in a goods company with many Brands crossing multiple lines of seamless way to give a single view of sales and marketing business. Kneebone was used to track and model all brands across initiatives. two lines of business. A major need was to also track and model Success: For the first time, the client could track and plan competitor brands. The project was restricted to a single national marketing campaigns against a live unified view of sales data. division of the company. Marketing Data Sales Data All Media Events and Spend: All Coupons: AC Nielsen: Weekly SKU- Walmart: Regional and  TV/Radio Spots  Coupon Details Level sales data collected by Metropolitan SKU-Level  Print Inserts with vendor  Coupon Redemptions Region, Channel, and Brand weekly sales data. To be  Outdoor Descriptors for Client and combined with AC Nielsen  Online and Social Media Flyer Coop: Competitor products. sales data for a complete  Direct Mail  Flyer Details view.  PDF Flyer Images This is the main dataset for All data is date stamped with the client and will be fully Shipments: All SKU-Level distributions and spend. All data is date stamped with base lined for both client and date-stamped shipments from distributions and spend. competitor sales tracking. Client to Stores. Conditions Indices: All Index conditions Weather: All weather data is Consumer Feedback: All Online: All online conditions are date-stamped and can be date-stamped and aligned conditions are date-stamped are date-stamped and have overlaid with all other data. with Client regional and have a tree descriptor and key web site page descriptors definitions. type hierarchy. and referrers (Google etc,)  Stock price  Average Temperature defined.  Consumer Price Index  Total Rain Fall  Consumer feedback  Total Snow Fall  Call to consumer TFN  Web Site Activity  Consumer complaints  Web Site Referrals
  • 2. Media Data: The client provided permission for their Media Walmart: The client passes on their Walmart data to Kneebone vendor to send standardized order Insert reports to Kneebone. for inclusion in the platform. The data is in Excel cross-tab format Kneebone is experienced with all of the standardized media and adheres to the Walmart calendar year definitions. Kneebone reporting systems including DDS, AdTrak, and Fumius. has custom ETL processes to clean and normalize the data into the client’s fiscal year definitions and use the same terminology as all The reports come in Excel format and include all print and other sales and marketing data in the platform (AC Nielsen etc.). broadcast spot and ad level details with insert dates, distributions, and costs. Coupon Data: A third party vendor manages coupon program for the client. Kneebone receives detailed PDF reports on coupon Kneebone has developed automated ETL processes for cleaning details and redemptions. Kneebone has custom ETL process to rip and normalizing media data. This is a very important function since data from these reports, clean, and normalize it into the common media data is typically freely entered and contains many variations client platform. and misspellings for the same entity. Flyer Coop Data: The client uses online service that collects Unlike consultative or statistical modeling approaches to details on all flyers on a national basis for all brands and all marketing performance management, Kneebone uses granular competitive brands. The data includes all aspects of the flyer date “per event” marketing data. We don’t just capture aggregated and duration, the region and channel, the product, price point, and spending against media classifications; we capture each and every special offers. event to the individual TV spot or print ad. Hence, as an added benefit, the client can now use Kneebone as a record of their The data also includes scanned images of individual flyer pages. media purchasing without having to go through the vendor for Kneebone custom ETL processes to download data and assemble every report. concatenated Flyers. The flyer images are visible within Kneebone and can be downloaded in their assembled state. The flyer data is AC Nielsen: The client provided a license to access their Inf*Act repurposed to become useful within a broader context. OLAP national sales data for all brands. Kneebone pulls the data from a multi-processor server based installation and ports the data Shipments: The client provides all SKU-Level date-stamped into our custom AC Nielsen ETL process where it is cleaned, shipments to Stores. These shipments are modeled for historical normalized and combined with all other data resident in behaviour and base lines for them can be used in predictive supply Kneebone. modeling. Now the client can see instantly data that used to take hours to Conditions: All conditional data is date-stamped and provided in compile from desktop reporting applications. Moreover, they can a classification hierarchy enabling it be overlaid with all other data. see it in conjunction with other sales data (Walmart) and all Conditions of note include: marketing events and conditions. And the AC Nielsen data is now fully base lined with learned history that can be used in forward  Stock price & Consumer Price Index looking analytics. This repurposing of existing data is a huge  Average daily temperature benefit of the Kneebone platform.  Total daily rain & snow Fall  Consumer feedback calls to call center Online: Kneebone has automated ETL tools to extract data from a variety of Online Analytic system including Google Analytics, Proximity and raw web logs. All online data is date-stamped and has key web site, page, and referrers (Google etc,) defined.  Web site daily site and page activity  Web site daily referrals When a URL is used in a marketing event (print, Online, etc.), activity on the URL can be attached to the event as performance and full base line technology on on tap. 322 King St. W., Suite 400 Tel: 416 599-4001 Toronto, ON Fax: 416 637-9300 Canada M5V 1J2 www.kneebone.com