1. 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. 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. 3 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 Case Study 1: Predictive Modeling

4. 4 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 Case Study 2: Sample Evaluation

5. 5 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 Case Study 3: Post-Interest Purchase Behavior

6. 6 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 Case Study 4: Cross-sell Investigation

7. 7 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 Case Study 5: Benchmark Distributors