moving beyond attribution to omnichannel optimization
TRANSCRIPT
Moving beyond attribution to omnichannel optimization
Ian Thomas
Microsoft
About me
~25%1,300
12B 2.4B
• Run Marketing Operations &
Analytics team at Microsoft
• Responsible for RM Marketing
Ops for Windows, Surface,
Xbox, Bing, MSN, Microsoft
Rewards, Windows Store
• 16 years in Web Analytics, Big
Data & Digital Marketing
operations
The problem
How to maximize digital campaign effectiveness across
multiple channels?
Database Plan Deliver Analyze
Optimize
The traditional model (aka, the good old days)
Timescale: weeks
Ask for more budget
Today’s world: Scaling to multiple channels
Display
Social
Timescale: days/hours
Our problems
Multichannel digital attribution is a fool’s errand
Manually optimizing over multiple channels is incredibly
time-consuming & complicated
Traditional model of plan-deliver-analyze-adjust doesn’t
scale in an omnichannel world
The solution:
Think different(ly) about digital marketing
Digital marketing as an optimization problem
Inputs
User profile
Offer
Creative
Tactic/channel
Outputs
Views/clicks
Conversions
Engagement
Revenue/GM
Constraints
Budget
Inventory
Continuous optimization
The things you need
1 A comprehensive user profile
2 Integrated delivery systems using a common creative repository
3 A set of common objectives
4 Integrated Marketing Operations function
5 An optimization engine
A/B testing vs multi-armed bandits
Looking for definitive “winner” of
a number of options
“Explore” phase precedes
separate “exploit” phase
Good for relatively stable
environments (where the winner
stays the winner)
Quicker to get to statistical
significance in results
Looking for the “best” of a
number of options
“Explore” phase overlaps “exploit”
phase
Good when the conditions that
make “best” change over time
(i.e. continuous optimization)
Minimizes traffic to poorly-
performing alternatives
Dimensions of data
Audience Data
• Product ownership
• Product Engagement
• Marketing Engagement
• Attitudes
Offer Data
• Product info
• Price range
• Purchase model
Tactics
• Channel
• Creative
• Timing
• Format
• Cost
Too many vs too few attributes
Too few:Model optimizes quickly, but with low lift
Too many:Model optimizes too slowly, or never
Single-channel, single campaign optimization
User data Matching engine Ruleset Delivery Metrics
Creative library
Data feedback
Plan
Multi-channel/multi-offer optimization
Display
Social
User data Matching engine Ruleset
Creative
libraryOffer library
(“The hopper”)
Games with Gold
Experiment with offers, creative and timing
within Games with Gold email series
Also extending to in-product notifications
Goals: Increase lift, reduce effort associated
with putting this email together
In summary…
• Think of campaign optimization as a single space across channel / user / offer / creative
• Pick the crawl – walk –run that is right for your business
• Let me know how you get on so I can learn from you!
• Amplero
• Salesforce (Einstein)
• Optimove
• Kahuna
Companies in this space
