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Objective

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. Roadmap. Data Mining “Meta” Analysis

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Objective

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Presentation Transcript

  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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