Ari Buchalter COO, MediaMathPhD, Astrophysics

Billions and Billions: Machines, Algorithms, and Growing Business in Programmatic Markets

What do these things have in common?

• Both are complex systems

• Math can be applied to understand both

The Digital Advertising Universe The Actual Universe

E = m c2

The evolution of media decision making

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

Past 60 years – “Audience-based” Today – “Goal-based”

We’ve seen this movie before…

We’ve seen this movie before…

…and we understand the benefits

And the results are typically 10x better, BUT there’s a cost….

Analyze 10-20 buys weekly Analyze 1MM opps. per sec.

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)

Let’s talk about Big Data in Programmatic

~100 BILLION impressions per day ~100 variables per impression ~100 values per variable

EQUALS ~1,000,000,000,000,000Possible combinations of data per day

(1015 = ONE QUADRILLION)

Making sense of the chaos

AlgorithmsOptimization

Programmatic

Automation

Predictive Modeling

Machine learning

Decision engines

Getting inside the RTB transaction

SSP orExchange

Publisher

Consumer

DSP

Advertiser

Agency or Trading Desk

The two (buyer) questions that matter

What is the right bid for each impression?

Which impressions should I buy?

Why does question #1 matter?

Too high

Overpay & underperform

Too low

Lose out & underspend

“Goldilocks” bid

Maximize scale & performance

What is the right bid for each impression?

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

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

$1 prize

Question #1 – A simple exercise

Flip a coin to win 1 dollar

50% chance $0.50Bid Price

xGoal Value Bid Price=Action Rate

What is the right bid for each impression?

1% chance consumer takes desired action

(purchase)

Question #1 – The real thing

$50 value to advertiser

(CPA)

Bid for an RTB ad

$0.50 bid price

(breakeven)

Bid Price=xGoal Value Action Rate

YOUR AD HERE

What is the right bid for each impression?

x Action Rate Bid Price=Goal Value

Question #1 – The Goal Value

Goal Value

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

(Prediction)(Input) (Output)

If it can be measured, it can be made better by math

x Bid Price=Goal Value Action Rate

Question #1 – The Action Rate

Action Rate

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.

(Prediction)(Input) (Output)

Answering question #1

VideoPublisher: YouTubeUnit: 15 sec pre rollTime: 16.46 – 17.00Age: 25-34Gender: MalePrice: $15.76 CPM

Social

Publisher: FBXUnit: NewsfeedDay: TuesdayTime: 5.00pm – 5.15pmPrice: $2.30 CPM

DisplayPublisher: RubiconData: Rakuten MaleLocation: TokyoCreative size: 160 x 600Price: $0.63 CPM

A different model for every creative in every campaign of every advertiser – all in real time!

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?

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

Question #2 – A meaty example

Bid Price: $30

Bid Price: $30

$30

Bid Price

High(good quality)

Low(poor quality)

Bid Price: $2

Bid Price: $2

$2

Market Price

High (not a deal)

Low(a deal!)

NO!

Eh, OK

Eh, OK

YES!

$30

Selling for:$30

Selling for:$30

$2

Selling for:$2

Selling for:$2

Answering question #2 – Which to bid on?

Relative Value

Low

Hig

h

Low High

Quality-driven performance

<10% of impressions

Nonperformance

40-70% of impressions

Value-driven performance

<5% of impressions

Cost-driven performance

20-50% of impressions

III IV

I IIBid Price

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?

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)

Forrester DSP Wave: MediaMath is #1

“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.”

“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.”