Get an overview of the five key use cases of Experience Platform—Intelligent Re-engagement, “Don’t Lose” Campaigns, Customer Conversion Optimization, Contextual Recognition, and One-Time Value to Lifetime Value.
In this video, you will learn what the key use cases are, the personas and the customer value of those use cases. There are four key use cases that can be fulfilled with experience platform. However, that doesn’t mean that experience platform is only limited to these use cases. You will see other uses of platform in the customer landscape, in partner landscape, and as time evolves, other new use cases might be added to this list. The four use cases that we will cover in this video are described as real-time customer data platform, the ability to use it for customer journey intelligence, delivery and cross channel experience, and finally, customer application development. So let’s look at each of these one by one. First of all, enterprises have a challenge to bring data together around their customer. Whether they are individual consumers or whether they are enterprises, or whether we talk about households. Experience platform is looking to tackle all of these type of what we call profiles now or in the future. We will start with use cases around individuals which also can be applied to enterprises bringing together the data both from offline and online. This would be first party data combined with behavioral data augmented with third party data set. For example, in this case, we can use experience platform Launch to capture information directly in the digital channels on a website or mobile app or on an application and feed that information either through Adobe Analytic or directly into experience platform. These data sets can be brought together and are available for machine learning use cases and by using identity stitching can also be brought into the real-time customer profile, which provides a single view of the customer, of its attributes as well as recent behavior. And by doing this, the data is brought together in a governed way. So as a marketer, you have complete control to describe where your data is coming from, how it can be used, and whether it can be exported, yes or no. Adobe’s Experience platform is integrated with both Adobe applications for data ingestion, as well as data coming from other sources. For example, with tools like Unify, TMS data, SnapLogic or Informatica. And at the core of this data is the experience data model and industry standard data model to represent consumers and their behavior as well as the activities to take in the digital channels. Furthermore, centralized segmentation service allows a customer to build new segments for activation. For this very use case, Adobe’s real-time customer data platform is an application service built on experience platform that provides additional features to bring together this known and unknown data to activate customer profiles with intelligent decisioning throughout the customer journey. Real-time CDP combines multiple enterprise data sources to create unified profiles in real-time that can be used to provide one-to-one personalized customer experiences across all channels and devices. With the destination service, Adobe connects to a large ecosystem of partners, not to mention native integrations with Adobe Experience cloud. So you can seamlessly activate these audiences and deliver great customer experiences across all channels. From on-site or in-app personalization to email, paid media, call centers, connected devices and more. The typical personas that would be interested in this are of course, the marketer. He wants to bring together the data to better understand but also to better activate his audiences. But at the same time, this is also of interest to the CIO because working with data and central data stores is typically CIO territory. The operational people are typically the data engineer that could set up the data flows, the marketer that would use the data, describe the data and work with the segments and the resulting customer profiles. The data analyst to understand what is going on and the data steward to make sure everything happens in compliance with local regulations. The second use case is around customer journey intelligence. How do I understand the data and the journey that the customer has gone through to either take a decision and transact or maybe not take a decision and then understand how you bring the result of that, again, into the customer profile for activation. Key to this, is of course, all the data that we have provided in experience platform that is being stitched together using an Identity Service or either through a deterministic graph known as a private graph through a co-op graph, or maybe through a probabilistic stitching or third party graph provided by customers. This enables the real time customer profile benefits that experience platform provides. Also using query services to understand patterns as well as connecting with visualization tools and using data science workspace to develop models as well as real-time customer profile to help a customer build a single view but also to analyze that single view over time. For example, with attribution use cases, Adobe’s customer journey analytics is another application service built on top of experience platform. It brings the rich analysis tool known as analysis workspace into platform to allow customers to do multi-channel attribution, segmentation, flow, fallout and other analysis on any platform data set both online and offline. Intelligence services, also known as Adobe Sensei services is built on top of platform just like real-time CDP and customer journey analytics. Intelligence services leverages platform to manage data and activate insights, which simplifies everything for marketers as data is all consolidated into a single data lake normalized into a common language with the appropriate governance in place. The key difference in innovation with intelligence services is that it sits horizontally across the experience cloud to power a specific use case that is agnostic to any application or application service. These AI services naturally align and complement experience cloud applications and app services. For example, Journey AI with Campaign and Marketo. Customer AI capabilities will be integrated with real-time CDP, which will make Adobe CDP offering more differentiated in the market. Similarly, Attribution AI will complement Adobe analytics and more intelligence services as future integrations become available over time. The key operators would be a marketer, a data analyst, a data scientist and a data steward. And this work is interested by a CMO because it talks about her marketing activities and gives her the ability to gain insights and basically do this in a very optimal way. As well as the CIO, it’s a turnkey infrastructure to analyze and understand customer journey intelligence. The ability to deliver a cross-channel experience starting with real-time customer profile, a customer can start to build experiences that are consistent, independent of which channel he is using. Over time, Adobe solutions are working to integrate to consume the experience platform real-time customer profile. For example, using Adobe Campaign, you can set up transactional events with Journey Orchestration anytime something changes in the profile or any other condition is met, or really having one singular store of behavior and attributes of a consumer. Journey Orchestration is an application service integrated with experience platform. It provides an intelligent and open ecosystem to activate live data through scalable event-based engagement across any channel your business requires from marketing to operations, to service. Using Offer Decisioning, a customer can define rules about what offer to present and when to send an offer or when to suppress an offer. And with a rich set of API’s, a customer or partner can integrate directly with experience platform’s real-time customer profile. Similarly, by leveraging destinations for pre-built integrations with destination platforms, supported by Adobe’s real-time customer data platform, customers activate data and segments to those partners in a seamless way. For example, integration with an interactive voice response system. When a customer calls into a call center, identify that customer through her phone number, understand recent actions and route a call in an appropriate way. Or when you enter a shop and you are about to checkout, experience platform gives the sales clerk the ability to pull up your loyalty information and present you with offers on the fly. And this is the territory of the CMO, he wants to deliver a consistent experience while respecting the consumer and do that across all channels. The data engineer will be supporting here as well as the application engineer to make this possible. And finally, customer experience application development, taking the core experience platform and how Adobe is using that and leveraging the rich API ecosystem of both rest-based API’s as well as event driven systems to respond and build new customer experience applications. Whether Adobe solutions are covering it or where our customer is not using an Adobe solution. It could be API’s to ingest data into the real-time customer profile, as well as data to consume that from the real-time customer profile using data science to transform the data providing capabilities and decisioning service, and to provide a low latency access to the real-time customer profile, to make the decision for the customer application. The CMO is interested in this because it expands his control to non-Adobe solutions as well as the CIO, because it provides a single framework for both working with the data as well as allowing that data to be activated and be used across channels. -