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Enhancing Marketing Strategy through Data Mining for Analog Semiconductor Components Division

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  1. Objective • Investigate the feasibility and added value of data mining to Analog Semiconductor Components division of ADI • Use data mining to find unique characteristics of the customer base which can be used to enhance ADI’s marketing strategy

  2. Roadmap • Data Mining • “Meta” Analysis • Summary of Case Studies • Case Studies in Detail • Predictive Modeling • Sample Evaluation • Post-interest Purchase Behavior • Cross-sell Investigation • Benchmarking Analysis • Concluding Remarks

  3. Case Study 1: Predictive Modeling • Findings • The same variables are significant predictors throughout the time horizon • Approximately 50% of the variation in sales can be explained by these variables • Conclusions • Annual sales variations can be captured in a linear regression model • With proper adjustments the sales models can be used to predict annual sales in subsequent years • Recommendation • Use time-adjusted annual sales regression model to predict future annual sales for customers

  4. Case Study 2: Sample Evaluation • Findings • Characteristics of sample-ordering customers with different follow up purchase behavior have been identified • Conclusions • Data mining tools can be used to classify those customers based on their characteristics • Recommendation • Send samples primarily to customers who are most likely to follow up with a purchase

  5. Case Study 3: Post-Interest Purchase Behavior • Findings • Most customers who show interest in a certain product do not buy that product within a period of one year • Conclusions • Product interest is not a good short term predictor of customer purchase behaviors • Recommendation • Further investigate post-interest purchase behavior

  6. Case Study 4: Cross-sell Investigation • Findings • Various strong relationships exist among different product level categories over different regions • Conclusions • Cross-sell opportunities exist within and between regions • Recommendation • Target certain products to specific regions

  7. Case Study 5: Benchmark Distributors • Findings • Distributor performance varies across different sites and regions • Conclusions • If ADI know the ideal ratio of amplifier sales to data converter sales (A/D), sites that underperform can be detected using actual A/D ratios • Recommendation • Target the underperformers defined by A/D ratios to generate potential sales • Apply similar ratio technique to other products with strong relationships

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