machine learning and predictive marketing
TRANSCRIPT
1
Machine Learning and Predictive
MarketingReality, Pitfalls & Potential
Claudia PerlichChief Scientist
@claudia_perlich
22
Programmatic Advertising
33
Work with Brand
300 Million (US) consumer
100 Billion
bid requests per day
AdExchange
Measurement
Conversion
44
Machine Learning & Predictive Modeling:
Algorithms that Learn from Data
55
Historical Data is the Input for Predictive Modeling
Income Age Buy
123,000 30 Yes
51,100 40 Yes
68,000 55 No
74,000 46 No
23,000 47 Yes
100,000 49 No
66
Machine Learning: Who will buy?
Income
Age
No purchase
Insurance50K
45
Logistic Regression
p(Insurance|37,78000) = 0.53
p(+|x)=
β0 = 3.7
β1 = 0.00013
77
To whom should we show an Mercedes ad?
88
Whatever metric the marketer chooses is what I will
teach the machine to predict …
99
Bid on individuals with high predictions by the modelOnly reach audiences that consistently demonstrate interest in your brand.
SC
OR
ING
CO
NN
EC
T
CAMPAIGN PROGRESS
Conversion
++
+
+
No Longer
in Market
XXAdd to
Prospect Pool
and Serve Ad
+
+
-
-
+
+
1010
Predict who will click on the ad
1111
Predict who will sign up for a test drive
1212
Predict who looks up a dealer location
1313
Data in Programmatic Advertising ?
Income Age Buy
123,000 30 Yes
51,100 40 Yes
68,000 55 No
74,000 46 No
23,000 47 Yes
100,000 49 No
1414
Digital traces of everything: 100 Billion events per day (US)
amazon.com
4/11/16
nytimes.com
2/15/16
50.240.135.41
166. 216.165.92
207.246.152.60
108.49.133.218
38.104.253.134
208.76.113.13
Mlb.com
3/15/16
Etrade.com
4/25/16
mercedes.com
4/10/16
Zip 4
10023-4592
04/15/16
Zip 4
10023-6924
04/20/16
Zip 4
10016-2324
03/10/16
03/14/16
Web App
Phone/Tablet
Desktop
Phone/Tablet
com.mlb.atbat
04/20/16
com.rovio.angry
02/26/16
com.myfitnesspal.android
3/25/16
4/18/16
IDFA
31AB-26FC-
94AE-756B
amazon.com
4/11/16
1515
Predict actions to be taken on site base on all else …
amazon.com
4/11/16
nytimes.com
2/15/16
50.240.135.41
166. 216.165.92
207.246.152.60
108.49.133.218
38.104.253.134
208.76.113.13
Mlb.com
3/15/16
Etrade.com
4/25/16
mercedes.com
4/10/16
Zip 4
10023-4592
04/15/16
Zip 4
10023-6924
04/20/16
Zip 4
10016-2324
03/10/16
03/14/16
Web App
Phone/Tablet
Desktop
Phone/Tablet
com.mlb.atbat
04/20/16
com.rovio.angry
02/26/16
com.myfitnesspal.android
3/25/16
4/18/16
IDFA
31AB-26FC-
94AE-756B
amazon.com
4/11/16
1616
Predict who will use an app
amazon.com
4/11/16
nytimes.com
2/15/16
50.240.135.41
166. 216.165.92
207.246.152.60
108.49.133.218
38.104.253.134
208.76.113.13
Mlb.com
3/15/16
Etrade.com
4/25/16
mercedes.com
4/10/16
Zip 4
10023-4592
04/15/16
Zip 4
10023-6924
04/20/16
Zip 4
10016-2324
03/10/16
03/14/16
Web App
Phone/Tablet
Desktop
Phone/Tablet
com.mlb.atbat
04/20/16
com.rovio.angry
02/26/16
com.myfitnesspal.android
3/25/16
4/18/16
IDFA
31AB-26FC-
94AE-756B
amazon.com
4/11/16
1717
Predict who will go to the dealership
amazon.com
4/11/16
nytimes.com
2/15/16
50.240.135.41
166. 216.165.92
207.246.152.60
108.49.133.218
38.104.253.134
208.76.113.13
Mlb.com
3/15/16
Etrade.com
4/25/16
mercedes.com
4/10/16
Zip 4
10023-4592
04/15/16
Zip 4
10023-6924
04/20/16
Zip 4
10016-2324
03/10/16
03/14/16
Web App
Phone/Tablet
Desktop
Phone/Tablet
com.mlb.atbat
04/20/16
com.rovio.angry
02/26/16
com.myfitnesspal.android
3/25/16
4/18/16
IDFA
31AB-26FC-
94AE-756B
amazon.com
4/11/16
1818
Not interested
Brand action
Logistic Regression
p(sv|x)=
β0 = 0.54
β1 = 0.13
…
β10000000 = -0.82
+ …
Machine learning with ‘Big Data’ …
1919
URL’s indicating a visit
of Mercedes dealership
2020
‘In the market’ signal
2121
Real Estate
2222
High-end fitness ..
2323
TECH-SAVVY FAMILY-ORIENTED
THRIFTY SHOPPERS
• Prepaid Smartphone Shoppers
• Budget Wireless Shoppers
• Holiday Deals Shoppers
• Sweepstakes Enthusiasts
MUSIC FANS
SPORTS FANS
• MLB Fans
• NBA Fans
• Soccer Fans
• NFL Fans
• NCAA Fans
• IT Professionals
• Mac & PC
Shoppers
• Country Music
• Hip Hop
• Streaming Radio
Listeners• Military Families
• Working Parents
• Family Values
Advocates
Insights from models: BMW vs. Audi
CAR & MOTORCYCLE ENTHUSIASTSLUXURY SHOPPER
HEALTH CONSCIOUS
• Luxury Retail Researchers
• High End Camera
• Jewelry & Watch
• Furniture Shoppers
• HDTV Shoppers
• Energy Savers
• Lawn & Garden Enthusiasts
• Nutrition Conscious Eaters
• Heartburn Suffers
• Food Allergies Researchers
HOME DECORATOR
• Motorcycle Enthusiasts
• Aftermarket Accessories
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Pitfalls:
Things we can predict but should not!
2525
Good prediction can be harmful in an industry
focused on bad metrics
2626
Click Reality
2727
Model learned when people click accidentally:
Fumbling in the dark …
2828
Bots/ ‘Non-Human traffic’
6%
2011
36%
2015
2929
Anything that can be faked will be faked:
bot penetration
3030
Viewabilty?
31
Potential
Programmatic as the new digital focus
group!
3232
amazon.com
4/11/16
nytimes.com
2/15/16
50.240.135.41
166. 216.165.92
207.246.152.60
108.49.133.218 208.76.113.13
Mlb.com
3/15/16
Etrade.com
4/25/16
mercedes.com
4/10/16
Zip 4
10023-4592
04/15/16
Zip 4
10023-6924
04/20/16
Zip 4
10016-2324
03/10/16
03/14/16
Web App
Phone/Tablet
Desktop
Phone/Tablet
com.mlb.atbat
04/20/16
com.rovio.angry
02/26/16
com.myfitnesspal.android
3/25/16
4/18/16
IDFA
31AB-26FC-
94AE-756B
amazon.com
4/11/16
Linking digital and purchase behavior
3333
Measuring incremental impact by creative
3434
Use Predictive modeling to ‘look under the hood’:
People who go to Fast Food restaurants
Delicious:
3535
Use Predictive modeling to ‘look under the hood’:
Gym rats
Self-improvement:
3636
Deep view
into
self
improvement
3737
I can predict which creative is most effective for YOU!
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@Claudia_Perlich