Embed Size (px)
DESCRIPTION
New York UniversityWednesday, September 16, 2009
Citation preview
New York UniversityWednesday, September 16, 2009
Analytics, SAS, and 20+ years of optimal marketing decisions
Paul DavisVP, Analytics [x+1]
20+ years of using for Analytics
• 2/89-4/90
• 4/90-4/95
• 4/95-4/96
• 4/96-4/99
• 4/99-12/00
• 12/00-3/02
• 3/02-5/04
• 5/04-4/08
• 4/08-Present
ArbitronSAS/STAT
Objective:• Try to understand the relationship between TV advertising exposure and CPG purchases using regression
analysis (PROC REG).
Background:• Arbitron introduced its people meter, ScanAmerica, in late 1986, by testing it in Denver. • They were planning to introduce people meters for the 1988--89 TV season. • Meters were expected to provide more accurate information, as television viewership was becoming
fragmented between broadcast network, independent, syndication, and cable television. • Arbitron's ScanAmerica was designed to package national TV audience ratings with household purchase
data. • The service would require participants to scan the bar codes on purchased products with a wand. • Primary customers were expected to be packaged goods advertisers. • Critics argued that ScanAmerica was too ambitious, and that it would be too difficult to collect both
household product purchase information and national TV ratings. • Arbitron committed $125 million to develop ScanAmerica.
Results:• The demise of the only potential ratings rival, ScanAmerica, a national ratings and product-buying survey
run by the Arbitron Company, has left the business of national television audience measurement solely in the hands of the A. C. Nielsen Company, just where it has been for more than 40 years.
Avis Rent-a-Car SAS/OR
Objective:• Design a Yield Management system to rent the last car on the lot for the most amount of $.
Approach:• Optimization formulated as a Network Flow• Each Node was a rental station• Each Arc was demand for a specific car class, station pair, date• Constraints: Available Inventory, Corporate contracts, etc.
Results:• Improved Fleet Utilization• Increased Revenue
ACNielsen – a Philip Morris exampleSAS/ETS
Objective:• To increase cigarette sales at convenience stores
Constraints:• No TV or Radio ads allowed by law.• Strict Price Controls• Can use free goods (i.e. “Buy 2 Get 1 Free”)
Approach:• PROC AUTOREG – weekly time series
Results:• Cigarette Sales increased in the 18-24 category
Publishers Clearing House SAS/STAT
Objective:• Only mail to those customers, within the 160 MM Household file, that will perform to at least “break
even”
Approach1. Estimate order amount in $’s using PROC REG2. Estimate likelihood of payment (0-1) using PROC LOGISTIC3. (Order Amount) * Payment >= $0.50 (cost to print & mail)
1800flowers.comSAS Data Integration
Background:• 1-800-FLOWERS.COM has grown its family of gift brands to more than 14 thru development and strategic
acquisitions (i.e. Plow & Hearth, The Popcorn Factory, Cheryl&Co. and Fannie May Confections).
Objective:1. Need to aggregate information across multiple platforms to provide a 360-degree view of more than 30
million customers2. Help 15 separate business units derive the information they need to grow revenues and reduce operating
costs
Benefits:3. Increased customer retention by 10 percent, generating an additional $40 million in revenue.4. Increased retention of its most profitable customers to 80 percent by giving them extra, customized
attention when they order.5. Accurately forecasts the type of products that will appeal to customers and anticipates what they want
when they log in or call.
USA Networks – a NBA example Campaign Management with SAS® Marketing Automation
Objective:• To develop a campaign management system with email capabilities for the online stores of many sport
sites (NBA, PGA Tour, NHL, etc.).• Maximize sales and customer acquisitions• Minimize email opt-outs
Approach• Traditional response models using regression• New SAS “toys” for email scheduling and deployment
Results:• Poor economy and bad business decisions let to the ultimate demise of Electronic Commerce Solutions
Modem Media – a Kraft exampleSAS/QC - Design of Experiments
Most effective headline
Most effective call-to-action
Most effective opening image
Most effective unit version and size – 250x250 popup
Call to action B Standard Version Pop-up 250x250 Opening Image Std-3 Headline Std-2
MMA- a Clorox exampleSAS/IML
Marketing Mix Models
• What is the ROI of each marketing vehicle?• What is the impact of external factors such as the economy or competition?• What is the impact of operational factors?• Is marketing driving the desired consumer behavior?• What is the impact of marketing on various consumer segments?• Are there synergies between marketing vehicles?• What are the interactions across a portfolio of products (halo and cannibalization)? Periods (Weeks)
Marketing Vehicles(TV, Radio, Print, Direct Mail, Trade, Online)
Products•Hidden Valley•KC Masterpiece•Fresh Step•Scoop Away•Kingsford
What is the optimal spend for each Marketing Vehicle by week?
[x+1] – an American Express exampleSAS Enterprise Miner – (Decision Trees, Neural Networks, LOGIT)
Here are some constraints that have been or are currently applied:• Constraint 1: A unique card needs to be shown in each position• Constraint 2: Blue and Blue Cash can’t be shown together• Constraint 3: Jet Blue cannot be shown with a Delta card
24
But, the goal isn’t to find the best overall 5 card set out of 5,100,480 – it is to find the best 5 card set for each potential new customer… Now the problem gets interesting!
There are 5,100,480 possible five-card combinations that can be shown on the Prospect Home Page.
• Even the number of two-way interactions (552) makes estimating interaction effects difficult given the volume of conversions that occur on the page, making synchronization rule testing and constraint-based optimization important
