data analysis and cleansing

Copyright © 2010 | DemandGen™

Data Analysis & Cleansing


• 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


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.


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.


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.


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


Data AnalysisAnalyze the existing database


Data AnalysisDefine list of standard fields


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.


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.


Data Normalization

• Update Rules and Validation Rulesfor automated data cleansing anddata normalization


Project documentation


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


Important reports

• Key segments• Utilization/Country• Database growth• Field completeness• And others

data analysis and cleansing

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