Monetizing Your Enterprise Datasets

Who Should Attend?

Duration: 1 Day
This course prepares business professional ways to harness the advantages with ready available data from applications like CRM, ERP, POS, MRP and HRM. Relevant datasets for each application will be presented, along with the layman illustration of the step-by-step process in creating and deploying the predictive models. With the business objective to growth sales and reduce cost, this course will definitely beneficial to your organization.
  • Direct marketing via customer segmentation
  • Customer loyalty prediction
  • Product upsell/cross-sell recommendation engine
  • Sales and marketing forecasting engine
Introduction to Data Analytics

  • The Variety of Data
  • Types of Analytics
  • Importance of Data to Enterprises
  • Identifying Data Opportunities
  • Best Practices to Initiate Data Analytics Initiatives
Data Analytics for Retail/Wholesale Businesses
  1. Market Basket Analysis
    Identify upsell / cross-sell potential with historical purchase transaction data to generate extra revenue.
    Data sources: CRM, POS
  2. Customer Segmentation
    Identify groups of customers with similar behaviors to align respective marketing strategies and ultimately improve rates of successful sales.
    Data sources: CRM, MRM
  3. Sales Forecasting
    Accurately predict sales performance and profit margins based on seasonal trends and stages of sales funnels.
    Data sources: CRM, ERP, Accounting Systems
Data Analytics for Services Industry (e-Commerce, Telco, Banking)
  1. Customer Profiling
    Identify the key-drivers in customers’ decision-making process to improve customer experience and revenue.
    Data sources: CRM, POS, ERP, Machine Logs
  2. Churn Prediction
    Predict customer lifetime and implement preventive strategies to ensure high customer retention.
    Data sources: CRM, POS, ERP, Machine Logs
  3. Social Media Analytics
    Grasp the real-time trends in your business/industry and align your corporate strategy on the move.
    Data sources: Web, Media, Social Media

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