Job Summary
As part of the Customer Value Management team, the role holder will be responsible for developing and conducting advanced modelling and analytics of customer data available across the enterprise by providing strategic actionable insights that are turned into campaign actions and results.
Leveraging internal data as well as external market data to develop quantitative and predictive models while conducting analyses in support of the customer value management team.
Provide a stream of practical actionable insights to the rest of the business covering analysis of customer behavioural patterns and potential campaign and recommend hidden opportunities through data insights.
Perform advanced micro analysis of customer value bands within the database with practical insights and recommendations on how to grow value and extend customer lifetime value by turning customer insights into tangible campaigns and actions that will drive revenue.
Leveraging on advanced statistical analyses with a detailed understanding of data mining techniques e.g. predictive modeling, segmentations and providing strategic recommendations and insights into key areas such as: retentions, churn, LTV, CVM, Portfolio Management and Product Management.
Design advanced analytics to address customer behavior associated with customer identification attraction, retention and customer developments.
Use data mining tools in interpreting and analyzing large data sets through cluster analysis, CHAID/CART, latent class, or other segmentation methods.
Design models and advanced analytics to address customer behavior associated with customer identification, attraction, retention and customer development.
Educational Requirements
A first degree in relevant numerate discipline.
Industry Certification(s) and or Postgraduate/Professional qualification(s) in a related field (an added advantage)
Expertise in data mining, transformation, and analysis
Expertise in building customized models in SAS, SQL, or other data-mining / ETL tools
Ability to use business judgment to guide analysis, draws implications from analysis, and synthesize into clear communications.
Excellent understanding of data manipulation and interrogation techniques, data mining and statistical techniques such as linear and logistical regression, CHAID and clustering
Six (6) to Eight (8) years relevant work experience with at least three (3) years in a supervisory role.
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