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In this video, we’ll show you the Quick Insights Panel in Customer Journey Analytics and how it helps you uncover insights quickly and easily without needing to manually build tables yourself. As a non-analyst or a new user of Analysis Workspace in Customer Journey Analytics, you might wonder what visualizations would be most useful for your business case, what metrics and dimensions to use, where to create segments, and more. The Quick Insights Panel helps you rapidly answer all these questions in an easy and intuitive way. It dynamically builds a freeform table and visualization for you based on the input information you provide. No need to build it manually from scratch. Let’s create your first Quick Insights panel and see it in action. In the Analysis Workspace interface, you can find all panels under the Panels tab in the left navigation. To begin, drag and drop the Quick Insights panel onto the canvas. If this is your first time working with Quick Insights, you can click this question mark icon to get a short overview of what this panel is. You can click Intro Tutorial, and it will walk you through the parts of the interface.

Here at the top, you can see the input fields. These are the building blocks that help you create a freeform table and a connected visualization that you can see below. The “Analyze” input field specifies the dimension which represents a specific characteristic of the metric data you want to view, like the name of a product or its SKU, or the user’s device type, and many other options, depending on your data. The “by” input field specifies the metric, which is the quantitative information about the person related activities or events that you’re tracking, such as purchases, sessions, page views, and so on. The “Filter by” input field helps you build segments to identify subsets of persons based on characteristics or website interactions. Finally, the “on” input field helps you set up the date range for your insights. For the visualization to function properly, you need to add at least one dimension and one metric. For example, let’s say you want to analyze your product SKUs by the number of views they’ve received in the past 90 days. You can start building your analysis in different ways. You can simply drag and drop components from the left panel or use the building block input fields. You can start typing the value directly in the field to narrow down the list, and the field will automatically populate with suggestions. Alternatively, you can browse all available values in the dropdown menu. Notice how some of the options in the dimension list have a “Popular” tag. The Quick Insights panel uses an algorithm that analyzes dimensions, metrics, segments, and date ranges your company uses and presents you with the most popular components. Since there’s so many dimensions and metrics to choose from, this can be very helpful to quickly identify a potential place to start. So, following our proposed scenario, we can use the product name or SKU as a dimension. For this case, let’s select “SKU” from the dimension list. Now you need to specify a metric, which, for our example, would be “Product Views”. Once you’ve selected both your dimension and metric, Quick Insights automatically loads the freeform table and the accompanying visualization. Right away you get a ready-made table and visualization without any need to know how Analysis Workspace functions, or spending time on building the table yourself. Now, let’s change the date range for these insights to complete our basic example. You have many options here. You can select one of the presets or create a custom date range.

For this example, select “Last 90 Days”. You can also add segments here to filter your data based on specific customer based criteria, like the customers marketing region or whether they purchased something in the last 90 days. This can help you understand overall traffic patterns and identify popular pages or features. For our example though, we’ll just stick with “All Data”, which doesn’t include any specific criteria or filters and covers all segments. Now let’s take a quick look at the output. The freeform table here presents the selected dimension, which is the “SKU”, and the selected metric, which is “Product Views”, segmented by “All Data” for the last 90 days. The corresponding visualization in this case is a bar chart. You can easily change it by clicking the visualization type and selecting any of the available options. For example, you can turn this into a donut chart since you’re comparing proportions of views for top products. A donut chart might make sense to visualize them as chunks of a whole, rather than a bar chart, which focuses on individual comparisons.

Now, let’s say as you’re looking at this data, you get curious about how these numbers might break down for each day of the week or by time spent per session. Using the building blocks at the top, you can refine your analysis further by including up to three layers of dimensions, metrics, or segments in the panel. What do we mean by layers? Well, right now, you have “SKU” as a dimension, and you can call that your first layer. If you click “Add Breakdown” under SKU and then select “Day of Week”, it gets added as a secondary dimension. You can see that the table immediately updates to show you the additional level of details you just added. Now, you have two layers of dimensions: SKU, broken down by day of the week. Now, when it comes to time spent per session, you could break this down at the second layer, if you just wanted to see product view trends based on the whole data set. But in this case, let’s say you’re interested in how the session data breaks down for each day of the week. To do that, you can go one layer deeper and add a breakdown for day of the week and select “Time Spent per Session” as a tertiary dimension. And again, the table will dynamically update to accommodate the changes. Same can be done with metrics. Let’s say you also want to see “Purchases” here to analyze how effective your online promotion was for specific products. The table and visualization will automatically update to include the new information. If you want to remove any of the added items, simply click the X icon next to the item in question. This will bring up the lower level and rebuild the freeform table and visualization for you. Alternatively, you can use the “Clear” button to return the Quick Insights panel to its initial blank state. More advanced analysts may want to dive in deeper and go beyond the three level limit set up by the building blocks. You can do so by dragging and dropping the additional components directly onto the freeform table. However, as you can see, when you do so, you get this dialog explaining that this action will render the output out of sync with the building block inputs you have at the top. You can undo this action or continue. If you continue, your table and visualization will dynamically update with the changes, but the input fields at the top will be grayed out. You can continue adding new dimensions and metrics to the table to build your in-depth analysis. The visualization is attached to the table and will continue to update as well. If at any point you want to get back to where you started, simply click “Resync Builder” to return to the last sync state you’d saved in the builder, and continue using the Quick Insights input fields as your governing feature.

You should now know how to use the Quick Insights panel in Analysis Workspace. We hope this will give you a good starting place to get a quick analysis without the need to build custom tables yourself. Thanks for watching.