Marketing optimization using sas
1 / 10

Marketing Optimization Using SAS - PowerPoint PPT Presentation

  • Updated On :

Marketing Optimization Using SAS. Randy Sherrod [email protected] March 2008. 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?

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Marketing Optimization Using SAS' - ros

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Marketing optimization using sas l.jpg

Marketing Optimization Using SAS

Randy Sherrod

[email protected]

March 2008

Discussion topics l.jpg
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?

Slide3 l.jpg

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: 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

Slide4 l.jpg




  • Driver Elasticities

  • Predicted Sales

Econometric Model Quantifies Relationship Between Bookings and Drivers – First Step to Driving Optimal Resource Allocation


  • Pricing

  • Distribution Channel:

    • Sales People, Resellers, etc.

  • Marketing Investment

  • Macroeconomics

  • Competitor Dynamics

  • etc

Source: Cisco SMO

Slide5 l.jpg


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












Source: Cisco SMO

Background observations l.jpg
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?

Slide7 l.jpg

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

Slide8 l.jpg

Modeling Details


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




(-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

Slide9 l.jpg

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;


Creates cluster-consistent standard errors

Estimated model can then be solved for optimal levels using proc optmodel.

Source: Cisco SMO