Use Journeys to move away from simple cross-channel engagement toward an organization-wide customer journey strategy. Journeys help you to perform journey analysis, testing, and orchestration in a single workflow:
Because customers interact with your brand in many different ways at many different times, you need to reach them based on their behaviors, and reach them with a cohesive voice to deliver a personalized experience across multiple marketing channels.
You want to design a multistep journey: starting with the users available with the inputs you select, you can apply sets of criteria called milestones. At each milestone a new audience is created, which you can then send downstream for further action.
You can create as many journeys as you need (up to your active audience limit), and delete empty journeys when they are no longer useful. You can also delete milestones and their related audiences and connections from a journey and add new milestones.
At the start of the journey, you have access to all the users available from all the inputs from all the workspaces in your account. You choose the workspaces and inputs you wish to select audience members from, and then build the journey:
The following diagram shows a simple journey with one milestone:
In this journey, all high-value customers are sent an email. Next, all of the high-value customers who are also long-term customers are sent to an internal Slack channel, and the audiences are also sent to additional outputs for further actions.
Notice the following:
Each step in the customer journey can be split into additional paths. For example, you could define a set of milestones for customers who buy handbags, shoes, or winter coats. Each milestone becomes the start of a new path.
You can also create a milestone for all audience members that haven’t fit any previous milestone criteria. This split is called a remaining user split.
The following three examples show the different ways you can use multiple splits.
Powered by Cortex, customer-centric teams can predict a user’s likelihood to churn. Using that prediction, teams can deliver a unique set of experiences for users who have a high likelihood to churn.
In this example, a brand engages high-churn risk users on multiple channels to ensure they get the message. In addition, the brand sets up a fail safe for the users who received an email but didn’t open it, engaging them over SMS.
One key piece of user preference is their consent status, what they’ve told the brand about how they want their data to be used for marketing purposes.
In this example, an eCommerce retailer delivers two different kinds of experiences based on a user’s consent to GDPR.
If users have consented, the retailer retargets the user on paid media, showcasing the abandoned product as a reminder to convert. For the remaining users, those who have not consented, the retailer triggers an onsite coupon if the user returns after several days.
For many retailers, a user’s purchase of a product is just the beginning of the relationship between the customer and the brand. After a purchase, the retailer can suggest to the customer many possible next steps to prolong and deepen that relationship: purchasing more products, leaving a review, referring a friend, posting on social media, and more.
In this example, retailers trigger the next best step based on a user’s lifetime value. For high LTV customers, brands can assume that they’re fans of the brand, and would be more willing to refer a friend. If the retailer knows a loyal customer’s preferred engagement channel, they can communicates with them there. For the remaining users, those who are not high LTV customers, a retailer can recommend products that pair well with the one that a customer just purchased.
Each audience that you create in a journey provides an estimated audience size immediately, so that you don’t have to wait for the audience calculation to complete. Once an audience has at least one active connection, audiences and all parent audiences in same path begin calculating the real size. When an audience begins calculating it no longer shows the estimated size.
To estimate the audience size quickly, mParticle samples the total number of users.
You see the estimated size of the audience with all criteria applied (as shown in the previous image). Estimated audience size per criteria is also displayed on the milestone.
Use the audience estimator’s immediate feedback to adjust criteria definitions and parameters if needed:
In some cases, you may see different symbols instead of an estimated size:
When an audience is actively connected, that audience is activated and consumes a real-time audience credit. Unlike the real-time audience experience, there is no explicit audience status of Draft or Active. The status is now derived from the connection status.
To view the number of audiences available to you, in mParticle go to Audiences > Journeys to display the list of journeys. The number of activated and available audiences is displayed under the New Journey button:
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