AOL Advertising on Price/Volume Analytics for Advanced Bid Optimization and Inventory Management

Understanding Price/Volume Curves

Speakers
Michael Kamprath
Sr. Technical Director, R&D Technology
Tracy La
Technical Manager, R&D
Brad Terrell
VP and General Manager, Digital Media, Netezza

agenda
Managing Ad Inventory
What is a P/V Curve?
How to Use a P/V Curve
How to Make a P/V Curve
Building P/V Curves: Challenges and Solutions

Online Advertising Network Modeling
The Advertising “Market”
Asset = Inventory
Supply = Publishers
Demand = Advertisers
Auction determines how assets are assigned.
Market-based ≠ Reservation-based

Market State
Understanding a market’s state is complicated by several factors
Not all advertisers target the same consumers, but there is overlap
Advertisers may not want all inventory that fits their targeting criteria
Dynamic markets
A P/V curve can provide insight.

What is a Price/Volume curve?
Represents market’s bidding environment.
Plots expected impression volume for a range of bids in the targeted market.
P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM IMPRESSIONS PER DAY

Understanding Bid Price and Volume
Questions a P/V curve can answer:
How to bid when entering a market
How to adjust bids when objectives change
How to find the bid that generates the most profit
P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM IMPRESSIONS PER DAY

GOAL 600K impressions
Using a P/V Curve Market A: East Coast $0.60 bid P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM IMPRESSIONS PER DAY Market B: West Coast P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM IMPRESSIONS PER DAY $2.00 bid In Market B, bid at $2.00 CPM In Market A, bid at $0.60 CPM

Using a P/V Curve Market A: East Coast $0.20 increase P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM IMPRESSIONS PER DAY Market B: West Coast P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM IMPRESSIONS PER DAY $0.03 increase Goal: increase impressions from 600K to 700K In Market A, increase bid by $0.20 to acquire 100K additional impressions In Market B, increase bid by $0.03 to acquire 100K additional impressions

Using a P/V Curve
Goal: Find the Optimal Bid
R = revenue derived from showing ad B = ad’s bid N(B) = impression volume for bid B as predicted by P/V Curve Market A: East Coast P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM Bid IMPRESSIONS PER DAY Profit Curve 0 500 1,000 1,500 2,000 2,500 3,000 3,500 0.00 1.00 2.00 3.00 4.00 5.00 CPM Bid PROFIT

Building a P/V Curve 0 2 4 6 8 10 12 0.01 0.02 0.03 0.04 0.05 Cumulative P/V Curve IMPRESSIONS PER DAY CPM Bid DATA IMP NO UNIQUE ID BID PRICE 1 A1 0.01 2 A3 0.01 3 A2 0.01 4 A2 0.02 5 A5 0.03 6 A1 0.03 7 A4 0.03 8 A4 0.04 9 A4 0.04 10 A1 0.05

Adjusting for Frequency Cap
Frequency Cap
Limits the number of times a unique viewer sees an advertiser
Reduces number of eligible impressions for a given target
Frequency Cap Availability
Proportion of frequency cap eligible impressions within the target to all impressions in the target
Availability is different at each price point
0 2 4 6 8 10 12 0.01 0.02 0.03 0.04 0.05 IMPRESSIONS CPM Unadjusted Curve P/V Curve

Adjusting for Frequency Cap 0 2 4 6 8 10 12 0.01 0.02 0.03 0.04 0.05 IMPRESSIONS CPM Unadjusted Curve P/V Curve 0 1 2 3 4 5 6 0.01 0.02 0.03 0.04 0.05 IMPRESSIONS PRICE Adjusted Curve Final P/V Curve DATA IMP NO UNIQUE ID BID PRICE 1 A1 0.01 2 A3 0.01 3 A2 0.01 4 A2 0.02 5 A5 0.03 6 A1 0.03 7 A4 0.03 8 A4 0.04 9 A4 0.04 10 A1 0.05 FC AVAILABILTY BID AVAILABILTY 0.01 1 0.02 0.75 0.03 0.714 0.04 0.556 0.05 0.5

Engineering P/V Curves

Engineering P/V Curves
Simple in theory, but difficult in practice
Large amounts of data
Complex Market Targeting
Frequency caps analysis
Time pressures

Engineering Challenge: Large Amounts of Data
The Challenge:
5TB-10TB impression data daily X 1-4 weeks

Engineering Solution: Large Amounts of Data
Data sampling reduces data size
Distributed computing clusters improve performance
AOL Advertising Selected Netezza data appliance
User-friendly yet customizable
Manageable learning curve
Proven

Netezza Data Appliance SMP host Massively Parallel Intelligent Storage Snippet Processing Unit (SPU) Processor & streaming DB logic High-performance database engine streaming joins, aggregations, sorts, etc. SQL Compiler Query Plan Optimize Admin

Engineering Challenge: Complex Market Targeting USER AGE 20’S 30’S GEOGRAPHY CA NY

Engineering Solution: Full Boolean Targeting
AOL Advertising algorithm plus Netezza OnStream
Fast matching of complex targets
Multiple curves with a single record scan
10x faster than SQL-only

Snippet Processing Unit (SPU) 1M Gate FPGA Enterprise SATA Disk Drive 400 GB 440GX Power PC AOL Advertising Matching Algorithm

Engineering Challenge: Frequency Availability Calculation
Frequency histogram = # users at given frequency at given price

Frequency Histogram Example SAMPLE IMPRESSIONS FREQUENCY CAP: ONE IMPRESSION PER UNIQUE VIEWER UNIQUE ID PRICE A $0.01 A $0.02 A $0.02 A $0.05 A $0.06 B $0.02 B $0.03 B $0.04 B $0.05 B $0.05 B $0.05 FREQ PRICE $0.01 $0.02 $0.03 $0.04 $0.05 $0.06 1 1 1 0 0 0 0 2 0 0 1 0 0 0 3 0 1 1 2 0 0 4 0 0 0 0 1 0 5 0 0 0 0 0 1 6 0 0 0 0 1 1 1.0 0.8 0.6 0.4 0.2 0.0 $0.01 $0.02 $0.03 $0.04 $0.05 $0.06 FREQUENCY AVAILABILITY

Engineering Challenge: Frequency Availability Calculation
The “Brute force” approach
Find all the impressions for an individual unique viewer
Sort according to price
Generate a frequency histogram specific to the unique viewer
Repeat for each unique user and aggregate results
“ Brute Force” approach does not scale well
Multiple TBs of data
100s of millions of unique viewers

Engineering Solution: Frequency Availability Calculation
Use map/reduce approach
Distribute data among SPUs
Generate frequency histograms
Aggregate

Frequency Histogram Processing in the Netezza Gigabit Ethernet Fabric Netezza Performance Server® System SPU C12 C13 F12 F13 G12 G13 F14 G14 C21 F21 F22 G21 G22 C23 F23 G23 C30 C31 F31 G30 G31 C39 F38 F39 G38 G39 C40 F40 G40 C6 C7 F6 F7 G6 G7 C14 F30 C38 C22 D B H F O L S Q U C A G E M J R P V T SPU SPU SPU SPU N K SMP HOST A1 A2 A3 A4 A5 A B C D E F G H 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 FPGA FPGA FPGA FPGA FPGA C6 F7 C12 F13 C38 F39 G13 G22

Demonstrated Performance
One curve per 1-5 minutes
Multiple curves simultaneously
Smooth campaign delivery

Conclusions
P/V curves provide the powerful analysis that Market model demands
Netezza data appliance supports generation of P/V curves in high volumes

http://www.datadrivesmedia.com	299
http://www.slideshare.net	29
http://smartdatacollective.com	19
http://feeds2.feedburner.com	8

AOL Advertising on Price/Volume Analytics for Advanced Bid Optimization and Inventory Management

by Brad Terrell on Dec 01, 2009

Accessibility

Categories

Tags

Upload Details

Usage Rights

4 Embeds 355

Statistics

AOL Advertising on Price/Volume Analytics for Advanced Bid Optimization and Inventory Management — Presentation Transcript