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- 1. Understanding Price/Volume Curves
- 2. Speakers <ul><li>Michael Kamprath </li></ul><ul><ul><li>Sr. Technical Director, R&D Technology </li></ul></ul><ul><li>Tracy La </li></ul><ul><ul><li>Technical Manager, R&D </li></ul></ul><ul><li>Brad Terrell </li></ul><ul><ul><li>VP and General Manager, Digital Media, Netezza </li></ul></ul>
- 3. agenda <ul><ul><li>Managing Ad Inventory </li></ul></ul><ul><ul><li>What is a P/V Curve? </li></ul></ul><ul><ul><li>How to Use a P/V Curve </li></ul></ul><ul><ul><li>How to Make a P/V Curve </li></ul></ul><ul><ul><li>Building P/V Curves: Challenges and Solutions </li></ul></ul>
- 4. Online Advertising Network Modeling <ul><li>The Advertising “Market” </li></ul><ul><ul><li>Asset = Inventory </li></ul></ul><ul><ul><li>Supply = Publishers </li></ul></ul><ul><ul><li>Demand = Advertisers </li></ul></ul><ul><li>Auction determines how assets are assigned. </li></ul><ul><li>Market-based ≠ Reservation-based </li></ul>
- 5. Market State <ul><li>Understanding a market’s state is complicated by several factors </li></ul><ul><ul><li>Not all advertisers target the same consumers, but there is overlap </li></ul></ul><ul><ul><li>Advertisers may not want all inventory that fits their targeting criteria </li></ul></ul><ul><ul><li>Dynamic markets </li></ul></ul><ul><li>A P/V curve can provide insight. </li></ul>
- 6. What is a Price/Volume curve? <ul><li>Represents market’s bidding environment. </li></ul><ul><li>Plots expected impression volume for a range of bids in the targeted market. </li></ul>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
- 7. Understanding Bid Price and Volume <ul><li>Questions a P/V curve can answer: </li></ul><ul><ul><li>How to bid when entering a market </li></ul></ul><ul><ul><li>How to adjust bids when objectives change </li></ul></ul><ul><ul><li>How to find the bid that generates the most profit </li></ul></ul>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
- 8. <ul><li>GOAL 600K impressions </li></ul>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
- 9. 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
- 10. Using a P/V Curve <ul><li>Goal: Find the Optimal Bid </li></ul>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
- 11. 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
- 12. Adjusting for Frequency Cap <ul><li>Frequency Cap </li></ul><ul><ul><li>Limits the number of times a unique viewer sees an advertiser </li></ul></ul><ul><ul><li>Reduces number of eligible impressions for a given target </li></ul></ul><ul><li>Frequency Cap Availability </li></ul><ul><ul><li>Proportion of frequency cap eligible impressions within the target to all impressions in the target </li></ul></ul><ul><ul><li>Availability is different at each price point </li></ul></ul>0 2 4 6 8 10 12 0.01 0.02 0.03 0.04 0.05 IMPRESSIONS CPM Unadjusted Curve P/V Curve
- 13. 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
- 14. Engineering P/V Curves
- 15. Engineering P/V Curves <ul><li>Simple in theory, but difficult in practice </li></ul><ul><ul><li>Large amounts of data </li></ul></ul><ul><ul><li>Complex Market Targeting </li></ul></ul><ul><ul><li>Frequency caps analysis </li></ul></ul><ul><ul><li>Time pressures </li></ul></ul>
- 16. Engineering Challenge: Large Amounts of Data <ul><li>The Challenge: </li></ul><ul><ul><li>5TB-10TB impression data daily X 1-4 weeks </li></ul></ul>
- 17. Engineering Solution: Large Amounts of Data <ul><li>Data sampling reduces data size </li></ul><ul><li>Distributed computing clusters improve performance </li></ul><ul><ul><li>AOL Advertising Selected Netezza data appliance </li></ul></ul><ul><ul><ul><li>User-friendly yet customizable </li></ul></ul></ul><ul><ul><ul><li>Manageable learning curve </li></ul></ul></ul><ul><ul><ul><li>Proven </li></ul></ul></ul>
- 18. 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
- 19. Engineering Challenge: Complex Market Targeting USER AGE 20’S 30’S GEOGRAPHY CA NY
- 20. Engineering Solution: Full Boolean Targeting <ul><li>AOL Advertising algorithm plus Netezza OnStream </li></ul><ul><ul><li>Fast matching of complex targets </li></ul></ul><ul><ul><li>Multiple curves with a single record scan </li></ul></ul><ul><ul><li>10x faster than SQL-only </li></ul></ul>
- 21. Snippet Processing Unit (SPU) 1M Gate FPGA Enterprise SATA Disk Drive 400 GB 440GX Power PC AOL Advertising Matching Algorithm
- 22. Engineering Challenge: Frequency Availability Calculation <ul><li>Frequency histogram = # users at given frequency at given price </li></ul>
- 23. 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
- 24. Engineering Challenge: Frequency Availability Calculation <ul><li>The “Brute force” approach </li></ul><ul><ul><li>Find all the impressions for an individual unique viewer </li></ul></ul><ul><ul><li>Sort according to price </li></ul></ul><ul><ul><li>Generate a frequency histogram specific to the unique viewer </li></ul></ul><ul><ul><li>Repeat for each unique user and aggregate results </li></ul></ul><ul><li>“ Brute Force” approach does not scale well </li></ul><ul><ul><li>Multiple TBs of data </li></ul></ul><ul><ul><li>100s of millions of unique viewers </li></ul></ul>
- 25. Engineering Solution: Frequency Availability Calculation <ul><li>Use map/reduce approach </li></ul><ul><ul><li>Distribute data among SPUs </li></ul></ul><ul><ul><li>Generate frequency histograms </li></ul></ul><ul><ul><li>Aggregate </li></ul></ul>
- 26. 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
- 27. Demonstrated Performance <ul><li>One curve per 1-5 minutes </li></ul><ul><ul><li>Multiple curves simultaneously </li></ul></ul><ul><li>Smooth campaign delivery </li></ul>
- 28. Conclusions <ul><li>P/V curves provide the powerful analysis that Market model demands </li></ul><ul><li>Netezza data appliance supports generation of P/V curves in high volumes </li></ul>
- 29. Q & A

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