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Raw Data Strategy | Database Marketing , Analysis , Predictive Modelling, Scorecard, Data Quality, Data Strategy, SAS Contractor

A marketing and credit risk consultancy, providing expert statistical advice on scorecard and segmentation development, and monitoring. Using market leading SAS software to analyse data to assess its quality and develop models and strategies to support risk management and database marketing activities.

http://www.rawdatastrategy.com/

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Robert Wishart

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s.●●●●●●●●●@btinternet.com

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Raw Data Strategy | Database Marketing , Analysis , Predictive Modelling, Scorecard, Data Quality, Data Strategy, SAS Contractor | rawdatastrategy.com Reviews
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A marketing and credit risk consultancy, providing expert statistical advice on scorecard and segmentation development, and monitoring. Using market leading SAS software to analyse data to assess its quality and develop models and strategies to support risk management and database marketing activities.
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1 Database Marketing
2 Analysis
3 Predictive Modelling
4 Scorecard
5 Data Quality
6 Data Strategy
7 SAS Contractor
8 Scorecard Monitoring
9 Statistical Support
10 SAS Programmer
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raw data strategy,skill transference,tight project deadlines,assessing,data,quality,data cleansing,analysis,predictive modelling,implementation and,monitoring,database marketing support
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Raw Data Strategy | Database Marketing , Analysis , Predictive Modelling, Scorecard, Data Quality, Data Strategy, SAS Contractor | rawdatastrategy.com Reviews

https://rawdatastrategy.com

A marketing and credit risk consultancy, providing expert statistical advice on scorecard and segmentation development, and monitoring. Using market leading SAS software to analyse data to assess its quality and develop models and strategies to support risk management and database marketing activities.

INTERNAL PAGES

rawdatastrategy.com rawdatastrategy.com
1

Predictive Modelling

http://www.rawdatastrategy.com/pro.html

Build separate models for each population split and then a combined model. Compare the development statistics of each model. Identify any outliers that may affect the final results. Create a list of variables for the final model. Last chance to remove any problem variables. Create model using small variable list. Ensure that all variables are sensible. Out of time validation. What to do if the model does not validate. Document Development include any policy rules that need to be implemented.

2

Data Analysis

http://www.rawdatastrategy.com/dataa.html

Third Party Analysis Tools. There are numerous analysis packages available to support the business analyst. Some need to have the data pre-. Processed before it can be analysed. Others will work based on the raw data. These tools can be useful but working from first principles can be more rewarding in terms of results. SAS has a number of tools for analysing data, Proc freq, proc summary, proc means, proc univariate,. Proc tabulate along with it graphical options and Statistical procedures. Balance compa...

3

Database Marketing Support

http://www.rawdatastrategy.com/database.html

Dealing with MPS, TPS, EPS and other customer opt outs. Product based opt outs. Campaign developed in SAS. Integration with Campaign Management Software. Email / Tele Marketing and Direct Marketing fulfilment. Data sourcing and timeliness. Risk / Response trade off. ROI, Take Rate, Cost per Click. Attrition / Acquisition balance. Cold Lists Purchase of 3rd party contact details. Applying relevant policy rules. Pre Screening to remove bad bureau data. Segmentation and Model Development.

4

Assessing Data Quality

http://www.rawdatastrategy.com/assessing.html

What is high quality data? Data is of high quality if it is fit for its intended uses in operations, decision making and planning. Data Quality Assurance is the process of verifying the reliability and effectiveness of data. Data Scrubbing I Data Cleansing, is the process of amending or removing data in a database that is incorrect, incomplete. Improperly formatted, or duplicated. Financial Reasons for Change. Poor data quality costs sales. Missing or incorrect data results in bad decisions. Identificati...

5

Data Cleansing

http://www.rawdatastrategy.com/data.html

Demographic and Transactional Data. As with core data not all demographic data changes. Some simple tests of accuracy can be applied e.g. does gender agree with title? Attitudinal information changes rapidly as customers are influenced by their environment. Often the problem with transactional data is the sheer amount of information. Summarisation of fields makes it easier to work with but are we missing key information? Experience with both customer and merchant transactions provides relevant insights.

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