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Transcript

In this video, I’ll show you how to create an instance of Customer AI, an AI EMO service that’s part of ÃÛ¶¹ÊÓƵ Sensei. After watching this video, you should be ready to dive in and start using this powerful AI driven service to create your own predictions without the need for data science expertise. First, let’s define a problem. Luma, an athletic apparel retailer wants to increase sales of watches. They want to know which customers are more likely to purchase a watch so they can optimize their marketing spend. Customer AI will generate prediction scores, which will be added and synchronized with real-time customer profiles so you can use them in marketing actions across ÃÛ¶¹ÊÓƵ Experience platform. Before we dive into the interface, let’s understand the basic requirements for a Customer AI instance. First, Customer AI is a predictive algorithm, which predicts the likelihood of and rationale behind a future action. Before you can create an instance, you need to know what customer action you want to predict. And in our case, we’d like to predict whether a visitor to the website would purchase a watch. Next, we need to know what the timeframe is for the action. For example, do we want to predict the likelihood a customer will purchase a watch in the next 30 days or in the next week? Lastly, by default, Customer AI scores all profiles. Need to determine whether you want to narrow your eligible population, for example, do you want to predict that recent website visitors within a specific timeframe will purchase a watch? Also, Customer AI requires a minimum amount of historical data. You can learn more about how to prepare your data and what kind of historical data is needed in the documentation. Now let’s go to the Customer AI interface. And ÃÛ¶¹ÊÓƵ Experience platform navigate to the data science tab using the left rail. From here, select services, and then select open from within the Customer AI card. The Customer AI screen appears and displays all your service instances. You can select any existing service instance to edit, clone, delete, and view details about that instance. We’re interested in creating a new instance though, so let’s select the create instance button. The instance creation workflow appears starting on the setup step. First, we enter the instance name. This name is used in every place where a Customer AI score is displayed. So let’s give it a good descriptive name. For propensity type, we can choose between churn or conversion. In this case, we want conversion. Select next to proceed to the select data step. For this step, we select the datasets which will be used to predict scores. By design, only datasets compatible with Customer AI are listed. Currently, those are experience event datasets, data coming from the ÃÛ¶¹ÊÓƵ Analytics source connector or data coming from the Audience Manager source connector. If you don’t see your dataset, double check that it’s compatible with Customer AI. If you’re not sure which datasets you need, click the info icon to load a preview. In the modal, you can see various details of the dataset as well as some sample records. You can also select multiple datasets to use in a model. The one requirement is that the datasets share a common identity. The interface will display the primary identity namespace of the datasets underlying schema. However, you can change this to use any of the other identity namespaces defined in this schema. The main requirement when you use multiple datasets is that you select the same identity namespace for each dataset. When you use multiple datasets, all columns with the same name will be merged, even if the data is different. The dataset preview area is a great way to confirm consistency across your datasets. Each dataset will display 10 rows in the preview. Once you’re done selecting your datasets, select next. Now we define our goal, the action we want to predict by selecting the corresponding event from our dataset. We’re given a lot of options in the UI that we can use to customize our goal. To learn more about the available options, visit the documentation. In this example, we want to use multiple events joined by and conditions. Notice that depending on the data type of the event field, we’ll see relevant options in the next dropdown. Note that there are two operators above the conditions. The first, all of means both of the conditions must be true. There must be a purchase ID and this queue must contain watch. Alternatively, I can create an or condition by selecting any of. Just above, I can choose between will occur and will not occur. Will occur will predict that the customer will buy a watch, and will not occur will predict that they won’t buy a watch. You can then specify the timeframe for the prediction. In this example, we’re predicting the likelihood that the customer will purchase a watch in the next 30 days. Customer AI looks for a list of standard events as defined in the documentation. If there are additional events that you’d like the model to take into consideration, you can specify that in the custom events section. This could improve model quality and provide more accurate results. Similar to custom event definitions, you can specify profile attributes that you’d like the model to take into account. For example, there are two profile attributes that you’d like the model to consider such as city and membership type. You can specify that here. By default, propensity scores are generated for all profiles. However, you’re welcome to limit this eligible population to a smaller subset. If we want to get predictive scores for only those who have visited the website in the last 30 days, this is specified under eligible population. You can also include or exclude profiles based on the conditions you set. Select next to continue to the final step, set options. We need to set up our scoring schedule depending on how often we want our prediction scores to be generated. Prediction runs can be scheduled to run on either a weekly or monthly basis. If we want, we can exclude any event from this prediction that may be an anomaly and skew our results, such as an event that was configured for testing purposes.

Once the setup is complete, ÃÛ¶¹ÊÓƵ Sensei will begin scoring each customer on their likelihood to purchase a watch. The profile toggle allows Customer AI to disable syncing to the real-time customer profile. By default, scores are synced to profile. Click finish to create your Customer AI instance. Initially, the hyperlink on our instance name is disabled since the scores haven’t been generated. Customer AI will immediately start to train and score, but the entire process can take up to 24 hours to complete. After the initial training and scoring, scores will be updated according to the scoring schedule we specified when creating our instance. If there were any issues with our training or scoring, the status will show as failed. We can click anywhere on the row and the right rail will give us additional details on what went wrong. So we walked through some of the basics of creating a Customer AI instance. You should now feel comfortable creating your own instances of Customer AI to predict customer behavior. -