Copyright © 2010 | DemandGen™
Data Analysis & Cleansing
Copyright © 2010 | DemandGen™
• World’s largest global marketing automation consulting firm
• Founded 2007 (US) / 2003 (Europe)• 60+ Employees Worldwide• Offices in USA, Germany, Vienna, France,
London• Supported Languages: German, English,
and French• 150+ Clients
– Apple, Siemens, NetApp, Novell, Nokia, Polycom, American Express, FICO, Dupont, Porsche, Standard and Poors, VMWare, Citrix, Riverbed, Successfactors, Taleo…
• Partners: Eloqua, Marketo, Salesforce, Oracle, Microsoft, MarketingSherpa
Global Marketing Automation Consulting and Services
Copyright © 2010 | DemandGen™
Today’s Challenges• Current database is not clean and contains duplicate data.• Some countries are working with their own local databases.• External sources (e.g. partners, agencies, address brokers,
etc.) are not using a standard data set for uploads.• No common upload process for data import including de-
duplication and normalization.• Field content for targeting and segmentation is not
standardized.
Copyright © 2010 | DemandGen™
Implications
Reporting is not accurate. ROI calculation is vague at best. Campaign success is difficult to measure. No trust in data accuracy -> no trust in Marketing results.
Copyright © 2010 | DemandGen™
Our approach - project scope• Integrate different data models from multiple
countries/sources into one data model.• Develop a common process to update different
data sets into a single format.• Define a common, international data dictionary
for all languages and countries.• Transform current data into new data format.• Turn on automation workflow for future data
cleansing.• Agree on a common process for importing new
data; provide related training.• Define a set of reports to measure data
accuracy.• Conduct regular database health checks.
Copyright © 2010 | DemandGen™
Our project approach
• Analysis of existing data base structures• Data cleansing
– Create a data dictionary– Define common data standards for
data standardization– Normalize data– Eliminate duplicates
• Data mapping and upload• Project documentation
Copyright © 2010 | DemandGen™
Data AnalysisAnalyze the existing database
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Data AnalysisDefine list of standard fields
Copyright © 2010 | DemandGen™
Data Dictionary
• As part of Data Cleansing, define and create a Data Dictionary containing a list of all fields and their selectable values for Contact Data, Company Data and Campaign Data.
Copyright © 2010 | DemandGen™
Data Standardization
• The Data Standardization Programcombines the different data modelsand standardizes the data into the global data model.
• The global data model is based on the data dictionary.
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Data Normalization
• Update Rules and Validation Rulesfor automated data cleansing anddata normalization
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Project documentation
Copyright © 2010 | DemandGen™
Benefits of a common data model
• One single clean database without duplication and data overlap
• Improve data quality to minimize faulty data • Easy segmentation and targeting based on standardized fields• Data standardization one key requirement for
explicit scoring to provide meaningful results• Improve customer experience and positively
impact campaign results
Copyright © 2010 | DemandGen™
Important reports
• Key segments• Utilization/Country• Database growth• Field completeness• And others
Copyright © 2010 | DemandGen™
Copyright © 2010 | DemandGen™