Moving beyond attribution to omnichannel optimization
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Performing accurate multichannel digital campaign attribution is very hard. This presentation proposes an optimization-based approach to multichannel digital which focuses on outcomes to drive marketing ROI.
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Moving beyond attribution to omnichannel optimization
- 1. Moving beyond attribution to omnichannel optimization Ian Thomas Microsoft
- 2. 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
- 3. The problem How to maximize digital campaign effectiveness across multiple channels?
- 4. Database Plan Deliver Analyze Optimize The traditional model (aka, the good old days) Timescale: weeks Ask for more budget
- 5. Email Today’s world: Scaling to multiple channels Display Social Timescale: days/hours
- 6. 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
- 7. The solution: Think different(ly) about digital marketing
- 8. 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
- 9. 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
- 10. 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
- 11. 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
- 12. Too many vs too few attributes Too few: Model optimizes quickly, but with low lift Too many: Model optimizes too slowly, or never
- 13. Single-channel, single campaign optimization User data Matching engine Ruleset Delivery Metrics Creative library Data feedback Plan
- 15. Multi-channel/multi-offer optimization Email Display Social User data Matching engine Ruleset Creative library Offer library (“The hopper”)
- 16. 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
- 17. 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
Notas do Editor
- Graphic showing how people can’t work in channel-specific silos any more, and the effectiveness of their campaigns has to be understood in the context of all digital activity + They have to compete with others for scarce marketing budget + They are expected to execute more and more granular campaigns – running out of time for traditional campaign analysis
- You need to think about your users, your campaigns, your offers and your objectives as four sets of related data that you need to optimize over At any moment, you’re looking to find the optimal combination of user, offer, tactic/channel to generate the best outcomes Everything you do (and the results it generates) should be fed back into the machine to make it better
- The approach we're talking about here is basically a combination of multi-variate testing and multi-armed bandit experimentation. The various user, campaign & offer attributes are the multiple variables that go into a series of tests; each unique combination of the attributes becomes one of the "bandits", whose potential is constantly being assessed through experimentation. Also, new "bandits" are constantly being created as new offers and creatives are put into the system (and old ones are retired) - though you want to reduce bandit churn where you can (i.e. reduce the churn in your attributes and the values of those attributes). Key activity you need to undertake is to decide on the attributes you want to take into account.
- There are three major dimensions of data you can use in this – offer info, audience data, & tactics You can choose to optimize in one, two or all three of these areas Which features of these dimensions you choose will be determined by a number of factors: Offer data: If you have a large catalog of things to offer, you can experiment with lots of different offers, so long as you have the creative assets to represent them Audience data: If you know a lot about your audience, you can experiment with audience data Tactics: If your delivery systems are well-integrated, and you have a good creative repository, you can experiment across tactics (including channels)