![]() ![]() ![]() Customer Journey Modeling is the most effective approach to identify the needs of customers.Ī customer journey model provides you with a new perspective. The best way to improve customer experience is to understand customer expectations, then align your business goals with them. The details of doing this is a whole training on its own.Why Are Customer Journeys Useful in Practice? So we need to determine the best way to stitch the different datasets together, by linking common IDs. A powerful feature of AEP is its ability to allow any type of data ID. This is where the identity strategy comes in. Then we choose how to stitch the datasets together. Then when we onboard the data, we know exactly what type of data we have. We need to model our data to the XDM schemas by mapping each field to the schema definition. Once we determine the data needs, we then decide where the data will be sourced from and how it will be onboarded. We want all authenticated and anonymous customer touch points. We want the online visits, the store purchases, the customer profile, the survey results, campaign interactions, you get the idea. So when we determine what data is needed for our analysis, our goal is to get a holistic view of our customer. These are usually performed by the data engineer. Now because CJA data is pulled from AEP, these tasks are mostly executed in AEP. They are, determined data needs, decide data sources, and choose identity strategy. From the data architecture standpoint, there are three primary tasks that need to be performed in CJA. But for unusually high volumes of data flow, this could take up to 24 hours. ![]() When a new data connection is set up with CJA, the processing under normal loads, takes less than an hour. Data that is onboarded into AEP is batched and uploaded every 15 minutes. This allows for very fast access, filtration, and queries, and multiple sets of data from the data lake, can be curated into a single data view, and you can have as many data views as you want. CJA uses a columnar format, which stores the data in columns, instead of rows. It then optimizes the data to its own customization pattern. When CJA accesses the data lake it actually pulls a copy of the data set. Here on the right CJA integrates with AEP through data connections. The XDM allows AEP data lake to act like a broker of raw data when it’s requested. Now the XDM also controls governance on how data is used so that it complies with internal policies, contractual restrictions, and government regulations. And then we see here at the bottom of AEP, when the data is onboarded, it’s organized into a common set of schemas and cataloged in the Experience Data Model, or XDM. The data is received through streaming or batch files. Can come from third party tools and other technologies. Starting on the left, the data collection can come through Adobe’s SDK or other Adobe solutions. Let’s walk through this diagram how CJA leverages the technology in AEP. ![]() Customer journey analytics is basically analysis workspace integrated with AEP. In this training we’re going to talk about, the architecture of the customer journey analytics, and how it integrates with the Adobe Experience Platform. Hi, welcome to this course on customer journey analytics, architecture, and integrations. ![]()
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