Marketing Optimization Using SAS

1. Marketing Optimization Using SAS Randy Sherrod rasherro@cisco.com March 2008

2. Discussion Topics • What is the impact of marketing investment on business metrics, e.g. sales? • How can we determine the level of marketing investment that optimizes return? • What data is required? • What techniques are available?

3. Data Collection: Historical Sales, Distribution Channel, Pricing, Marketing Investment, Competitor Behavior, International Macroeconomic Data Optimization: Use the econometric model as an input to an optimization engine that identifies optimal sales and marketing investment levels Data Collection Modeling Optimization Results Modeling: Develop Econometric Model(s) relating Historical Sales with important drivers of business Results: Compares optimized investment levels with actual, yielding insight into opportunity to increase sales through marketing reallocation Overview of Analytic Process Source: Cisco SMO

4. Outputs Econometric Model • Driver Elasticities • Predicted Sales Econometric Model Quantifies Relationship Between Bookings and Drivers – First Step to Driving Optimal Resource Allocation Inputs • Pricing • Distribution Channel: • Sales People, Resellers, etc. • Marketing Investment • Macroeconomics • Competitor Dynamics • etc Source: Cisco SMO

5. Definition Elasticity measures the responsiveness of Sales to changes in drivers, calculated as: %∆ sales / %∆ driver Diminishing Returns, Elasticity<1 Relevant Cases Elasticity<1 (inelastic): Percentage change in sales is less than percentage change in driver (ex. Increasing marketing investment by 1% leads to less than 1% increase in sales) Elasticity>1 (elastic): Percentage change in sales is more than percentage change in driver Elasticity Measurements that Quantify the Relationship Between Drivers and Bookings x x x x x x x x x x x Source: Cisco SMO

6. Background Observations • How to determine the optimal level of sales force and marketing? • Initial Values: Sales Force=$400M, Marketing=$50M, Total Sales=$1B. • Estimated Elasticities: Sales Force=0.40, Marketing=0.20 • Suppose there is an additional $40M to allocate, how do you split between Sales Force and Marketing to maximize Sales? • $40M=10% of Sales Force0.40*0.10*$1B=$40M increase in Sales • $40M=80% of Marketing0.20*0.80*$1B=$160M increase in Sales • What range of elasticities can we expect? • Sales Force (+) • Marketing (+) • TV (+) • Paid Search (+) • GDP (+) • What is the impact of GDP on marketing and sales? What might this mean for the optimal level of investment?

7. Modeling Possibilities • Framework • Log-linear models with SAS: • Proc GLM • Proc Reg • Proc Surveyreg • Proc Genmod • Proc Mixed • etc. Output from these procedures quantifies the impact of marketing on sales Source: Cisco SMO

8. Modeling Details Framework Log-linear model with customer-level fixed effects: Log Salesit=αi+β1log Competitor Advertisingt-1+ β2log Sales Forcet-1 + β3log Marketingt-1 + β5log Cust Satisfactionit-1 + β6log GDPt-1 + Seasonality Where: i=customer t=time (-1)=lag 1 QTR • 1. Imposes constant elasticity 2. Allows for many possible response curve shapes • 3. Explicitly accounts for synergies between drivers Source: Cisco SMO

9. Modeling Details cont. SAS Implementation proc surveyreg; class customer; model log_sales=customer log_comp_advertising_1 log_sales_1 log_marketing_1 log_cust_satisfaction_1 log_gdp_1 q4 /noint solution; cluster time; quit; Creates cluster-consistent standard errors Estimated model can then be solved for optimal levels using proc optmodel. Source: Cisco SMO

10. Questions?