Billions and Billions: Machines, Algorithms, and Growing Business in Programamtic Markets
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The document discusses the evolution and complexity of digital advertising and programmatic markets, emphasizing the shift from audience-based to goal-based marketing strategies. It highlights the importance of predictive modeling and optimization in determining the right bids for impressions and which impressions should be purchased. The document also outlines necessary technologies and partner characteristics for successful programmatic advertising, concluding with a positive assessment of MediaMath's capabilities as a leading technology provider in this space.
Billions and Billions: Machines, Algorithms, and Growing Business in Programamtic Markets
- 1. Billions and Billions: Machines, Algorithms, and Growing Business in Programmatic Markets Ari Buchalter COO, MediaMath PhD, Astrophysics
- 2. What do these things have in common? The Digital Advertising Universe The Actual Universe • Both are complex systems • Math can be applied to understand both
- 5. The evolution of media decision making Past 60 years – “Audience-based” Today – “Goal-based” Describe your audience Figure out media they consume Buy placements, wait, and hope Get report, manually adjust Define your marketing goal Capture all the data (media, user) Model it to identify what works Automate the buying Humans making coarse decisions based on proxies, averages, and indexes Machines making exact decisions based on granular user data
- 6. We’ve seen this movie before…
- 7. We’ve seen this movie before…
- 8. …and we understand the benefits Buy in batch (wheat + chaff) Buy what you want (wheat only) Fixed price, regardless of value Variable bidding, aligned with value Little/no insight into true drivers Full insights into “what” & “why” Manual, labor-intensive (~5/FTE) Fully automated, scalable (~50/FTE) And the results are typically 10x better, BUT there’s a cost…. Analyze 10-20 buys weekly Analyze 1MM opps. per sec.
- 9. Let’s talk about Big Data in Programmatic ~100 BILLION impressions per day ~100 variables per impression ~100 values per variable EQUALS ~1,000,000,000,000,000 Possible combinations of data per day (1015 = ONE QUADRILLION)
- 10. Making sense of the chaos Algorithms Optimization Programmatic Automation Predictive Modeling Machine learning Decision engines
- 11. Getting inside the RTB transaction SSP or Exchange Publisher Consumer DSP Advertiser Agency or Trading Desk
- 12. The two (buyer) questions that matter What is the right bid for each impression? Which impressions should I buy?
- 13. Why does question #1 matter? What is the right bid for each impression? Too high Overpay & underperform Too low Lose out & underspend “Goldilocks” bid Maximize scale & performance
- 14. Why does question #2 matter? Which impressions should I buy? • ~$100MM/day of RTB supply • Typical campaign spends ~$1K/day (0.001% of total supply) • Not buying the RIGHT 0.001% is throwing money away
- 15. Answering the questions ain’t easy Data is large, and growing need technology at scale It’s called different things need to “normalize” data Data interactions are complex need sophisticated models Mix of goals (upper/lower funnel) need flexible methodology Supply & demand constantly changing need to remodel often Clients need to understand need intuitive, transparent output It’s all in real-time (100ms) need speed without latency Only a machine-learning algorithmic approach can handle the size, variability, complexity, and speed required
- 16. Question #1 – A simple exercise What is the right bid for each impression? $1 prize Flip a coin to win 1 dollar 50% chance $0.50 Bid Price Goal Value x Action Rate = Bid Price
- 17. Question #1 – The real thing What is the right bid for each impression? Goal Value x Action Rate = Bid Price 1% chance consumer takes desired action (purchase) $50 value to advertiser (CPA) Bid for an RTB ad $0.50 bid price (breakeven) YOUR AD HERE
- 18. Question #1 – The Goal Value (Input) (Prediction) (Output) Goal Value x Action Rate = Bid Price The goal can be anything at all: • Branding: positive survey response (awareness, intent, etc.) • Engagement: site visit, site action (locate store, post comment) • Conversion: signup, application, purchase, etc. • Retention: repeat purchase, renewal, upsell If it can be measured, it can be made better by math
- 19. Question #1 – The Action Rate (Input) (Prediction) (Output) Goal Value x Action Rate = Bid Price Predictive modeling: the process by which a mathematical model is created to predict the probability of an outcome, usually based on historical input data The model should base the prediction on all available data: • User: site activity (1p), interests & behaviors (3p), geo, TOD, DOW, etc. • Media: channel, publisher, page, ad size, above/below fold, etc. • Creative: image, offer, call to action, etc.
- 20. Answering question #1 Video Publisher: YouTube Unit: 15 sec pre roll Time: 16.46 – 17.00 Age: 25-34 Gender: Male Price: $15.76 CPM Social Publisher: FBX Unit: Newsfeed Day: Tuesday Time: 5.00pm – 5.15pm Price: $2.30 CPM Display Publisher: Rubicon Data: Rakuten Male Location: Tokyo Creative size: 160 x 600 Price: $0.63 CPM A different model for every creative in every campaign of every advertiser – all in real time!
- 21. Question #2 – Which ones to bid on? Optimization: the process of making the best choice among a set of options to achieve a desired goal, usually under some constraints Example – Shopping for food Constraints: fixed budget, nothing artificial Goal: Most mass of food? Most volume of food? Healthiest mix?
- 22. Question #2 – Two important concepts 1) Bid Price: How much the impression is worth to the buyer • Depends on who the publisher is and who the advertiser is • Is a measure of quality (i.e., what it’s worth to the buyer) 2) Market Price: The price the impression will clear for • Depends on the entire marketplace • Also obtained through predictive modeling
- 23. Question #2 – A meaty example Bid Price: $30 Bid Price: $30 $30 High (good quality) Bid Price Low (poor quality) Bid Price: $2 Bid Price: $2 $2 YES! Market Price High Eh, OK (not a deal) Low (a deal!) NO! Eh, OK $30 Selling for: $30 Selling for: $30 $2 Selling for: $2 Selling for: $2
- 24. Answering question #2 – Which to bid on? III IV Quality-driven performance <10% of impressions I II performance 40-70% of impressions Value-driven performance <5% of impressions Cost-driven performance 20-50% of impressions Relative Value Low High Non Low High Bid Price
- 25. Putting it all together 1) Use a predictive model to determine what each impression is worth 2) Use optimization to determine which impressions to bid for What is the right bid for each impression? Which impressions should I buy?
- 26. So where do I get me some of those? Find a partner who: Leverages robust technology – ask to see the scale & speed Has proven results – across verticals, geographies, over time Will expose the “black box” – transparency & insights are key! Has cross-channel capabilities – display, video, social, mobile, premium, BYO Has broad integrations – 3p data, surveys, viewability, attribution, etc. Can incorporate 1st party offline data – increasingly important Develops custom solutions – to suit your unique business needs Makes it easy – execution, workflow, reporting, testing, etc. Provides thought partnership & great service – it’s not just machines! (machines just enable people to do the REAL value-added work)
- 27. Forrester DSP Wave: MediaMath is #1 “MediaMath boasts excellent algorithmic optimization capabilities (including a multifaceted view of the decisioning engine’s output), and its multichannel media and data access is both broad and deep.” “MediaMath is a great all-around choice for buyers in market for a DSP.” “Its large employee base and diverse, well-tenured management team also provide the necessary foundation for it to execute effectively on its strategic vision: to empower marketing professionals with a flexible, easy-to-use, multichannel platform.”