Introduction to Orchestrated Campaigns in Adobe Journey Optimizer

Transcript

Welcome to the demo of Campaign Orchestration in Adobe Journey Optimizer. We begin here in the Campaigns tab, where we can see the full list of all campaigns we’re running. We can filter, search, and add tags to find the campaigns we need, and use quick actions to duplicate campaigns to get started fast.

Opening up a campaign takes us to the canvas, an intuitive drag-and-drop interface where we can craft our campaign. We can create campaigns for immediate then, or schedule at whatever cadence we need. Let’s build a campaign, starting with customer re-engagement. More specifically, we’ll remind signed-in shoppers of products that they’ve added to their wishlist, with the goal of driving re-engagement and conversion.

With Campaign Orchestration, we can define our targets using any dimension, whether it’s recipients, wishlists, abandoned carts, products, or any other dataset. For this campaign, let’s create an audience by building a condition. We can build this audience using customer attributes, or we can start with other data types and back into the set of customers in our target audience. In this case, we’ll start with all recipients who have a wishlist, who have items in their wishlist, and where there are specific products with product images in the wishlist. Just like that, we’ve built a query, and we can calculate the exact number of recipients. We’re also able to save the query or apply sets of conditions we’ve created previously, so that we can easily apply targeting that we use often. Here we can see the recipient counts, including counts for each part of the condition set. After setting the conditions, we can view the resulting audience, and add in any attributes that we want to see, for a more detailed preview of our target audience and specific recipients.

Now that we’ve built our audience for this re-engagement campaign, we can personalize this campaign by enriching it with wishlist and product information. We can bring in recipient data, like their first name or preferred brand, and we can bring in non-recipient business data, such as the wishlist and product information. In this case, we can gather all wishlist item data.

By default, we are retrieving three items per wishlist. We are also able to manipulate the data that’s brought into this campaign by creating a filter, and sorting.

For our campaign, we want to pull a few other attributes. We can pull in the product description, and we can pull in the product price. Lastly, we can pull in the image URL for each of the products. With that, we have all the data that we need to create an individually personalized re-engagement message for each customer.

Next, let’s create the email. We can start by personalizing the subject line of our email. We’ll pull in first name from each recipient’s profile attributes, and can access the enrichment data, our wishlist items that we brought into the campaign, in case we’d like to add that to the subject line as well. We’ll start with an email template in the message designer, where we have access to all of our existing content fragments, and content for the brand. Let’s drag and drop a content fragment that we’ve saved previously.

And if we open that fragment up, you can see we’ve inserted the personalized set of wishlist items for each customer. Once we’re happy with our email, we’ll save and run our campaign in draft mode. We can see how the campaign will run before sending, and preview the results. We see the exact recipients we’re targeting, and all of the relevant information, such as their wishlist items. Further, we can run a test to evaluate how the message will land. We can see who was targeted, who was excluded, and why. Next, let’s proof our message content. We can jump into the email designer, and simulate the content to see how it will appear for test profiles. We can see that while more formatting is needed, we are getting the fully personalized three wishlist items in the message, with the right information, images, and prices for this profile. We can additionally send proofs to others for reviews, finalization, and publishing the campaign.

After the campaign has run, we can explore our reports, which gives us a robust set of data and KPIs about how our campaign is performing.

Next, let’s expand this campaign to include customers who have shown interest in our exercise equipment category, based on their web browsing behaviors.

We can begin by selecting an audience from Adobe Experience Platform, of all customers who have browsed our site in the equipment category. We can add this as a read audience, and identify their recipient email addresses. Next, we’ll clean up our audience by deduplicating any email addresses, and we’ll segment our customers by likelihood of churn, to deliver personalized experiences to each of those churn risk segments.

For our customers that are at high risk of churn, we’ll save that audience to Adobe Experience Platform for a separate targeted communication. And we can specify the exact attributes we want to save as part of that audience. For our low and medium churn risk customers, we’ll send a multi-step campaign. We’ll begin with our wishlist re-engagement email we created earlier, then wait for one day, then create a new audience for retargeting based on engagement with that first message. Here we’ll create an audience based on email click events, to retarget all of the people who interacted with the previous message.

And more specifically, clicked on a link within that message. We’ll enhance this campaign further by splitting engagement by channel preference, and delivering a follow-up via their preferred channel, SMS, or push notification, with the goal of driving conversion.

The content for messages in each of these channels is created in the message designer, just as we did for email. And we can bring in profile attributes, such as the recipient’s name, and all of that enrichment data, in this case, the wishlist items, to include in those messages. For the final branch of our campaign, let’s design a notification to let our customers know when their wishlist items are back in stock. First, we’ll create a query on the wishlist, to only include wishlists created over the last 36 months.

From there, we can use a change dimension activity, using our relational dataset to shift from wishlists back to the respective customer set for targeting. Here we can see the data on both our recipients and the wishlist items.

We can add the email address from the relevant wishlist, and preview the data. We can see several customers who have multiple wishlist items, and thus, using multi-level sending, will receive a separate email for each relevant item. Customers may have input different email addresses for their various back in stock requests. For example, here you can see six different wishlist items that map to six different email addresses, all for the same customer. To make this happen, inside the email configuration, we’ve set this to send one message per recipient, and wishlist as a secondary dimension.

So, we’ll send one email per wishlist item, and it will have an address coming from the secondary dimension, which is from the wishlist itself.

To show you an example of how this might look, as the recipient, I’ve received an email for each of my six different wishlist items. Each email is sent on a specific email address that’s different and belonging to that wishlist, with personalization coming from each wishlist for that message. With that, using campaign orchestration in Adobe Journey Optimizer, we’ve created a robust re-engagement campaign, including a signed-in shopper re-engagement, a multi-step, multi-channel re-engagement based on web browsing data, and a multi-level sending re-engagement, with unique messages for each back-and-stock notification request.