Adobe Summit 2020 was a landmark moment for many digital marketers in many ways. Not only did it serve as a reference to where we are during the global coronavirus pandemic, but it also marked a transformative moment in digital technology history. Adobe accomplished the immense task of consolidating a 5-day in-person conference into a global digital video conference session that was then shared as an on-demand video session archive.
Of course, those who were planning to be in Las Vegas this week wish they were physically interacting in sessions and networking events, but we’re looking forward to next year. We have to appreciate how far digital technology and familiarity has come that allowed the Adobe team to pull this off in less than a month.
We think the execution of a virtual Adobe Summit resembles what Adobe Experience Cloud is hoping to enable for all of us – and that is to leverage your available tools and assets to deliver the best possible customer experience you can. This will become even more important as 3rd-party cookies continue to be deprecated, thus disrupting the digital marketing landscape forever.
With all that said, our team at 85SIXTY wanted to share some of our favorite industry-relevant takeaways from Adobe Summit 2020. We hope you enjoy it!
The power of a real-time CDP supports the personalization we’ve all been looking for
Powering AI-driven Real-time Customer Experiences // Pari Sawant (Adobe) and Matthew Fritz (Samsung)
Whether we realize it or not, machine learning and AI are prevalent across each touchpoint of a customer journey. This includes look-alike modeling, dynamic creative optimization, dynamic remarketing, product recommendations, chatbots, etc. A lot of these features are fully automated and can be used with a click of a button.
The Adobe Experience Platform (AEP) is integrating more of these capabilities into customer journey orchestration fueled by Sensei and Custom models in Data Science workspace. AEP is enabling an operationalized open service to enrich customer profiles in real-time which can unlock really amazing capabilities such as real-time propensity and churn prediction which can be used as triggers to deliver impactful experiences. We’re very excited to see what sort of data-driven orchestrations we can create for our clients.
Adobe Audience Manager is trimming down the walled gardens with People-based Targeting
Breaking Down the Walls: DMPs and Walled Gardens // Terry Chen (Adobe) and Tim Cook (Activision)
People-based Destinations was a feature announced at Adobe Summit last year that enables pushing ‘people-based’ identifiers to walled-garden environments. The first Destination available is Facebook, with others in Beta testing today (shh).
Now, nearly all brands already have some sort-of connection for loading their customer data into Facebook. The benefits of leveraging Audience Manager’s integration are:
- You only need to send one CRM file into AAM, instead of cutting a separate file for each audience to send to Facebook. This reduces manual ETL procedures which can be labor intensive and increase data security risks.
- Ability to combine CRM data with other authenticated Traits for smarter segments to be distributed over to Facebook for various use cases.
- Not be charged by the number of audiences you have automatically distributing to Facebook, unlike other integration platforms.
- You’re set up for future People Based Destinations as they are released.
As 3rd-party cookies crumble, so does the traditional programmatic ecosystem – and that’s a good thing
As the Cookie Crumbles: Advertising in a cookie-less world // Justin Merickel (Adobe)
Much of the programmatic ecosystem has been built on 3rd-party cookies, and as we all know, they will be blocked by nearly all browsers by 2022. With this policy change, we’ll lose the antiquated method for transferring data between domains and data partners which threatens many data sharing and measurement technologies that have fueled the ecosystem and attribution reporting. Although 3rd-party data targeting may go away, we can still get our message in front of the right people at the right time through some of the following privacy compliant tactics:
- Contextual advertising
- Private premium publisher deals
- Target with publisher 1P data
- Leverage more robust global device identifiers (IDFA, GAID, CTV IDs)
- Use authenticated-matching technologies for known audience targeting
- Leverage People-based targeting with hashed customer identifiers and PII
- Operate within a single web-domain to cohesively collect your customer data
- Consolidate all owned data into a usable data platform
- Implement a durable ID architecture and capture it everywhere possible
- Ensure all of your data collection and usage is fully privacy compliant
- Test how contextual targeting works for your brand
- Start developing deeper relationships with publishers
Machine Learning & Automation as a replacement for third-party data in Experience Personalization
Using Data to Scale the Experience Business of Travel // Julie Hoffman (Adobe) & Guy Cierzan (ICF Next)
Today, the demand for highly personalized omni-channel experiences has never been greater, but so too are consumer expectations of data privacy and governance. However, without access to large-scale third-party data that provides key insights into consumer preferences and behavior, how can businesses continue to provide the degree of personalization that consumers have come to expect?
In this breakout session, Julie Hoffman from Adobe highlights how Travel & Hospitality brands are leveraging Automation and Machine Learning within Customer Experience Management. Not only does this approach enable the delivery of significantly more relevant experiences, it also does so dynamically in real-time. As a result, the need for third-party data is dramatically reduced. Instead, through Machine Learning we are able to develop algorithmic models that predict user behavior based on both historical and real-time data. This happens continuously and iteratively as the user engages with the website, allowing the Machine Learning algorithm to better understand the wants and needs of the user.
Once the Machine Learning component has identified the optimal experience for the user, it passes that information off to the Automation component which dynamically modifies the experience based on real-time user behavior. As this process continues, the Machine Learning algorithm improves, learning more about how users engage with the website, and delivering better recommendations for experiences that will maximize value. And since the deployment of these experiences is automated, the scale at which these hyper-personalized experiences can be deployed increases exponentially.
As third-party data is progressively phased out, we expect to see newer technologies like Machine Learning and Automation take its place as a way to effectively speak and engage with our customers.
With Alloy.js, never tag for an eVar or Mbox again // Corey Spencer (Adobe) & Brandon Pack (Adobe)
- Another amazing feature surrounding this announcement is that there’s no need to configure eVars within the App Measurement code anymore. That’s now all handled within Analytics processing rules, further making the code lightweight and efficient. This is a game changer since performance improvements are measured on the millisecond level.
Customer Journey Analytics changes the game and unlocks the power of true multi-channel analytics
Customer Journey Analytics: The Omnichannel Future of Adobe Analytics // Trevor Paulsen, Jen Lasser — Adobe
Customer Journey experiences can be difficult enough to strategically design and deliver across channels, but analyzing them has always been seemingly impossible. It’s been very difficult to get all of your customer experience data in one place, stitch it together in an organized way, and then find an analyst who can make sense of it all with advanced querying and scripting experience. Most companies have found it impossible to source and organize your data properly, nonetheless finding an analyst to make sense of it.
With the help of Adobe Experience Platform you can now organize your online and offline data in the Experience Data Model. Once ingested, you can create a view which will be best for your analysis by connecting any ID and doing things like customizing sessions, persistence, and attribution of your data fields (imagine the pain of writing SQL to do this!). Once this new data is organized, you can now push over to Adobe Analytics Workspace where a new universe of capabilities are unlocked by seeing customer pathing across online and offline touchpoints and how each contributes to revenue. There are some additional benefits that Adobe Analytics power users will totally appreciate by using the XDM:
- No longer are you using rigid props and eVars, but defined data fields
- Unlimited unique values, so you’ll no longer have to deal with ‘Uniques Exceeded’
- You can easily alter and correct historical data
- Analyze and segment individual cookie or device-ids
- Combine data across individual report suites and offline data sources
- Create customer pathing of user experience touchpoints
- Pull in models for predictive analytics
The list of new analysis opportunities goes on and on and we’re excited to have our hands full.
Algorithmic Attribution is here for your business
Ignite Experiences through Real-time Customer Intelligence // Anjul Bhambhri — (Adobe)
Marketing attribution helps us understand what our customer journey is by giving different touchpoints credit along the way. Adobe Analytics provides a variety of Attribution models for our day-to-day analysis. Adobe has finally released their version of “Algorithmic Attribution” in the ‘Labs’ prototyping section of Adobe Analytics. This type of attribution leverages statistical significance to generate a custom attribution model based on performance. We find it helpful to use this data-driven model in conjunction with other more traditional models (linear, time decay, first/last touch) to better understand the impact of our marketing channel efforts. The really cool thing here is that you can also use the attribution features with other dimensions outside of marketing channels to get a picture of the relative impact of user or website dimensions (product categories, pages visited, device types, etc) on conversions.
Algorithmic Attribution is just one really cool feature bundled within the Labs section of Adobe Analytics. Check it out today to see potential upcoming features.
Questions? Don’t hesitate to reach out! firstname.lastname@example.org
About // 85SIXTY is a data-focused, integrated marketing agency that executes game-changing business strategies for ambitious clients in highly competitive industries. We combine advanced technical capabilities with a consulting mindset to speed time to revenue and increase ROI from clients’ marketing investments including those made in platforms such as the Adobe Marketing & Experience Clouds.