Data analysis and cleansingPresentation Transcript
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.
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 newdata; 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 anddatanormalization
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