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Transcript

Hi, this is Derek Tangren from the Ă۶ąĘÓƵ Analytics product team. I’m going to walk through a powerful new feature that we recently released inside Customer Journey Analytics called Binding Dimensions. Some of you may be familiar with a similar concept that exists in traditional Ă۶ąĘÓƵ Analytics. And I’m excited to show you how we’ve brought that to CJA. The concept of binding dimensions is the ability to take one dimension and connect it to another dimension for the purpose of more refined persistence allocation. We’re going to look at a few examples to help highlight how we think this can be beneficial for you and your organization. In our first example, a user looks at a page for a washing machine. You’ll notice that it has a color attribute of white. They add it to their cart. Next, they look at a dryer which is neon orange in color and also add that to their cart. Finally, they purchased both items, the white washing machine for 1600 and the neon orange dryer for 499. Note that we aren’t setting the color on the confirmation page. If we head into Customer Journey Analytics, let’s take a look at our product color report. Unfortunately, we can see that all the revenue is being attributed to neon orange, but our washer was actually white. We are attributing the last value seen, in this case, neon orange and applying it to everything after. As you can imagine, this could create confusion or misrepresent what is actually happening. Maybe we don’t even sell a neon orange washer. We need a way to appropriately associate color with our product name instead of simply taking the last value seen during a user session. Otherwise, this starts to create some concern about whether or not my data is showing me accurate information. This is an area where binding dimensions can help. To use this, we head into our Data views configuration and we’ll select our product color dimension. We can see, under PERSISTENCE, we have an option to select a binding dimension. In this scenario, we’ll want to select Product Name as the binding dimension. This will allow the value of color to persist such as white, bound to a specific value of a product name such as washing machine 2000. We then save and close the configuration window.

Almost like magic we see that now the 1600 is properly attributed to white and 499 is properly attributed to neon orange. Next, we are going to look at something a little more complex. We’ll follow a user who is searching for a few different things to buy. Our user starts by searching for boxing gloves, where they are presented with three different options: beginner gloves, tier three gloves, and professional gloves. They select the tier three gloves and add it to their cart. Next, the user searches for a tennis racket, where again they’re presented with three different options: shock absorb racket, a women’s open racket, and an extreme racket. They select the shock absorb bracket and add it to their current. Finally, the user searches for shoes, where they are again presented with three different options: men’s walking shoes, tennis shoes, and skate shoes. They select skate shoes and add them to their current. Finally, they then go through the checkout process and purchase the gloves for 89.99, the racket for 34.99, and the shoes for 79.99. Now, let’s head over to Customer Journey Analytics and see what insights we can derive from this user’s experience. We are particularly interested in looking at the search terms and how they help contribute to the overall revenue. When we look at our search term report, we can immediately see that we have a problem even though we know that the user searched for three different items, only the shoes are getting credit for the revenue. It looks like we are using the most recent value to allocate our revenue. That works in some situations, but in this case, it doesn’t give us the right insight into what is actually happening. Thankfully, we know that binding dimensions was recently added to Customer Journey Analytics. Again, we’re going to use this magic to allow us to properly allocate the revenue amounts back to the right search terms. We do this by heading into our Data views configuration and finding our search term dimension. Again, under PERSISTENCE, we select a dimension to bind our search term to. In this case, we want to select Product Name as the binding dimension. This allows us to be specific about what situations the search term persists, specifically just in those instances where the same product name is present. Because our binding dimension is in an object array, in this case, the product object array, and our search term is in a higher part of our schema hierarchy, we need to select a binding metric. CJA automatically detects this relationship. If everything is happening at the same level of the schema hierarchy, like our previous example, a binding event will not be required. In this case, we select searches as the binding metric to tell the system when a search happens, the value of the search term should be bound to the product or products set at that time and persist when the value of product name is present. Now we can save and close our Data views configuration. Again, like magic, we can see that revenue is now properly attributed to the appropriate search terms that contributed to it. You might be saying, “Derek, this is great but I don’t work in retail. Combining dimensions still help me?” Absolutely. We’ll look at one more example. This time in the media space. We have a scenario in which we have a video streaming account with two different profiles, presumably for a parent and a child. Currently, the profile selected is the child’s profile denoted by a profile value of two. While the other profile will be denoted with a value of one. Our example starts out with a user on the child profile, searching for the term kids show. They then proceed to start watching a show named “Orangey” and a Video Start event is captured. Later, someone changes the profile to the parent profile and does a search for grownup movie. They find a movie called “Analytics After Hours” and start watching it. Again, a Video Start event is collected. Finally later, the profile is then switched back to the child profile. That user watches another episode of "Orangey’ presumably not needing to search since they already found a show they like. A final third Video Star event is collected. Once again, let’s head into Customer Journey Analytics and see what the reports are telling us. We can see the two searches, one for kid show and one for grownup movie is showing up properly. But again, we seem to have a problem with the data. We see that the search term grownup movie is getting credit for two Video Starts, the “Analytics After Hours” and the second “Orangey” show since grownup movie was the last term searched. This doesn’t really tell the complete story. And if we choose to adjust the user experience based on what we’re seeing, this could have an unintended negative impact on our customers. Luckily, this is another area where we can use the power of binding dimensions to more accurately represent what is contributing to Video Starts. We’ll head back into our Data views configuration with CJA to work our magic. In this case, we want to bind our search term to the profile ID. This means that our search term will persist based on the unique values for the profile ID. Since within our schema, profile ID and search term are at the same level of our hierarchy. CJA recognizes that we don’t need a binding event and only provides an option to select a binding dimension. We select Profile ID as our binding dimension. Save it and head back to our report. We can now see that kid show is being appropriately credited with both Video Starts of “Orangey”. I hope these examples have helped give you a better understanding of how you can use binding dimensions with your data and Customer Journey Analytics. Keep in mind, this is one of the more complex concepts within CJA, so it’s totally fine if you need to go back and watch that again. Thanks so much for watching. -