Programmatic is eating the world. With more ad formats, data signals, and competitors than ever, smart marketers need to have the right strategy to succeed in the next generation of digital advertising. Joe Luchs from Beeswax will discuss modern optimization and the tactics sophisticated marketers are using to win.
20. Standard
Strategies
Get all the features
of a traditional DSP
on Day 1
Bid
Modifiers
Adjust bids against
any RTB input to
drive results
Bring Your Own
Algorithm
Upload multivariate
models directly to
the bidder
The Optimization Landscape
22. Key Factors that Power Optimization
The who, the where, the when, and the $$$
User
Data
T H E W H O
How valuable are
your customers?
Inventory
Factors
T H E W H E R E
How valuable is
the inventory?
Time
Decay
T H E W H E N
How long
has it been?
Price
Data
T H E $ $ $
How much is every
impression worth?
23. User Classification:
"Good"
Bidding Strategy:
Bid always
Users as Segments
User Classification:
"Bad"
Bidding Strategy:
Bid never
Users as Scores
User Classification:
Score 1.67 for
offer ABC
Bidding Strategy:
Increase bid
by 67%
User Classification:
Score 0.21 for
offer ABC
Bidding Strategy:
Decrease bid
by 79%
VS.
24. Platform Exchange domain value
iOS Rubicon nytimes.com 1.50
iOS Index cnn.com 2.50
Android Pubmatic cnn.co.uk 3.50
... ... ...
Android AdX axios.com 4.50
Multivariate Inventory Factors
Set bid prices based on a specific, verified inventory source
25. Time Matters!
Adjust your bid price based on engagement recency
Visited 5 minutes ago
Increase
Bid Price by
48%
=
Time Decay Bidding StrategyUser
Visited 2 days ago
Decrease
Bid Price by
23%
=
26. Real World Example
3.1 X
User is worth
average.
A user with 3 past purchases
just put $150 worth of goods in
their shopping cart
2.4 X
Recency is worth
average.
This same user appears
in a programmatic
auction 7 minutes later
1.1X
Site/Exchange/OS
combo is worth
average.
They appear on a low quality
site, in an urban geolocation,
on an iOS device
0.062%
Likelihood of
for conversion.
Based on historical data, a
$6.24 bid is warranted.
Welcome to Tahoe everyone. I’m excited to be kicking off our content this week with a presentation on prgorammatic 2.0 and optimization for the next generation of digital advertising. Worth noting that I’m the opening act for Evan from T-Mobile who will be covering AdTech 2.0, so make sure you stick around for the headliner next.
My name is Joe Luchs and I lead the enterprise team over at Beeswax, working directly with marketers that want to maximize their transparency and control over their programmatic advertising. While transparency and control have become buzzwords in the industry, Beeswax specifically focus on replicating the functionality of owning your own tech stack, which ushers in many new possibilites for those that want true ownership without having to build from scratch.
Now before we dive into our topic for today, optimization in the programmatic 2.0 world, it’s worth a quick look back at the evolution of adtech.
In 1994 the first digital display ad was served by AT&T on hotwired.com. Today this would likely be considered clickbait, but back in the day, all was fair game. Anyone know what the CTR was? If you guessed 44%, you’re spot on. You’d also be correct in estimating that this is about 440x the current average industry CTR.
As digital advertising grew, ad networks came into the picture to aggregate inventory and sell it to agencies and marketers and help them scale.
However, greater efficiencies could be gained through an auction model, and thus the advent of programmatic. Programmatic took off and a whole ecosystem of DSP, SSP’s, data platforms, etc… launched in the next 5 years.
In 2013 we finally hit the year of mobile after more than one false start. Now mobile makes up over half of digital ad spend.
In 2016, video finally broke 10B, and now makes up about a quarter of digital spend, and is rapidly growing.
And backing out to the bigger picture, programmatic is eating the world. By the end of 2020, over 90% of digital ads will be transacted programmatically, with global spend approaching $150B.
And the world is only getting more complex. With 8 major formats and many more sub-formats, programmatic is evolving at a rapid pace, and the buying technology is racing to keep up.
SO why the history lesson? Well the reality is that most major platforms today were built for programmatic 1.0. Trying to retrofit their tech into the modern programmatic ecosystem is hard. And one of the hardest parts about it is optimization.
We at Beeswax believe that in the massive, complex, omni-channel modern marketing ecosystem, having the right optimization strategy is the key to success.
So let’s define optimization
Well let’s start with the foundation that 95% of programmatic stacks are the same. Features like exchange integrations, budgeting, frequency capping, and so forth are commodities. So how does one differentiate themselves. 1) Data 2) Optimization. Today we’re going to focus on optimization.
A/B Testing Graphic
Uber and Pandora Examples
Analogy of overpaying for something, or paying for something thats free
Many articles about our challenges
Fraud
Viewability
Hidden Fees
Bid caching
Data sharing
Can be distilled into a broader theme