Name: Setting up Smart Insight with the Self-Service Tool in SAP Emarsys
Uploaded: 2025-6-18
Duration: 653 s
Description: In this video, we'll guide you through setting up Smart Insight using our new self-service tool. You'll learn how to configure the necessary settings for product, sales, and store data, as well as setting eRFM parameters to track customer engagement. This step-by-step process ensures your Smart Insight is ready to analyze and optimize your retail data effectively.

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<div><div><div><div><div><div><div><div><p>In this video, we'll guide you through setting up Smart Insight using our new self-service tool. You'll learn how to configure the necessary settings for product, sales, and store data, as well as setting eRFM parameters to track customer engagement. This step-by-step process ensures your Smart Insight is ready to analyze and optimize your retail data effectively.<br></p></div></div><p></p><div><p></p><br></div><p></p><div><p></p></div></div></div></div></div></div></div>

Setting up Smart Insight with the Self-Service Tool in SAP Emarsys

Hello! In this video, you'll learn how to set up Smart Insight with the SAP Emarsys self-service tool. To walk you through the Smart Insight configuration steps I'll use a simple example of onboarding Smart Insight for a retail account. I'll show you how the process differs depending on whether your account includes Predict, Loyalty or neither. If a step is specific to one of these cases, it will be tagged in the video. The Smart Insight self-service tool is a configuration wizard available directly in SAP Emarsys, that makes it possible for clients to set up and configure Smart Insight in order to simplify onboarding. What will you need before starting. Smart Insight enabled in your account and access to the related pages. If you don't have access, please change your access rule to BI Administrator in User Management or ask for assistance from your account owner. A sample of your product sales and store data files with max ten lines each, including the header, that conforms to our specifications. Before starting, please visit our related Help Portal pages. If you're using the Magento or Shopify plugin, sample files are not necessary. Please note that store data is only necessary if you have a retail business. A list of product sales and store data fields you would like to use for filtering and Smart Insight. eRFM parameters determine quantitatively the different stages of your contacts by examining their engagement level, recency, frequency, and monetary value of their purchases. If you have these parameters, you can apply it for Smart Insight. If you don't have them complete the configuration without it, then reach out to your SAP Emarsys contact person. For the Sales Data Load the Standard Sales Data feature has to be enabled. If you use Web Extend you need a completed implementation  of the Web Extend data collection scripts on your website. Let's start the configuration on an account that doesn't have Predict or Loyalty enabled. In this case, I begin at the Management > Sales Data Loads page. First I set up my SFTP account. Here I see the credentials needed to log in to the SFTP server to upload data files regularly. I copy the password immediately since it's only visible now. If I don't save it, I must regenerate it later. Next, I return to the setup steps and move on to configuring the sales data. At the File Setup step, I drag and drop a sample sales data file. I set the file name pattern, check the selected delimiter and quote settings, and move on to the next step: Field Mapping. Matching sales-related fields between the sales data file and the SAP Emarsys system happens automatically, but some fields may need adjustments. So, always review the mappings carefully. These fields can later be used for filtering in Smart Insight. Once everything looks good, I save the configuration. At this point, the Sales Data Loads page is empty, since no data has been loaded yet, later this page helps monitor load status, including history pending and failed uploads and the quality of the loaded data. To continue the setup, I go to the Smart Insights Settings page. If your account has Predict or Loyalty, this is actually where you'd begin. So let's take a look at that scenario next. On the Smart Insights Setup page a step by step integration checklist appears that guides you through the process. There are five major tasks you need to perform. You can pick the items in whatever order you want. The tool will save the state and you can get back to it later. I start at the top. The first item is to create an FTP account. Even if you want to upload your data through API, the FTP account has to be created. So I click on  “Set up your FTP account”. I click on“Create Account” to get the credentials to access a secure FTP server to upload my product and sales data on a regular basis. It is very important to save the username and password. After clicking Done, the password can no longer be retrieved. The next two steps are the same regardless of Predict and Loyalty enablement. Let's select the contact fields for filtering. On this screen, I can select the unique identifier to identify my contacts when they make a purchase and store their registration date. I also select the contact fields for filtering that lets me create segments later. Depending on how you want to upload your data, the unique identifier set up can go on different paths. If you have custom alphanumeric IDs  instead of email addresses in your file, select that from the dropdown list. In case you have the email addresses and you want to use the current Sales Data API via Predict, select the Predict user ID field. We highly recommend one of these options if you want to use the FTP and you only have email addresses, select the email field as the unique identifier. Keep email for import via Plugin. We can move on to the product data. I paste here the sample product data. Then I click next. The system automatically recognize the files delimiter and quote character. At this step, the product file name pattern can be modified. If the product catalog feed is enabled on the Predict Data Sources page of your account, we recommend using the default file pattern. Let's move to the next screen. In the Product field step, the system detects the fields you use for identifying your products, linking them to purchases, and storing category paths and product names. The values of these columns will be matched to the product identifier field values in your uploaded sales data files. Check the result of the auto detection and select the right fields and types if needed. Below, you can also select as many fields as you want for filtering. If you use Predict catalog or Google Product Feed (GPF) which is also synced through Predict, please contact  SAP Emarsys Support  to get a file sample from the final product CSV. Let's configure the sales data. If the account doesn't have Predict or Loyalty, this step was completed earlier. So, you just have to select to upload the sales data via the Sales Data Batch Load. If the account has Predict or Loyalty, follow these steps. First, select the data source you would like to use. You can choose from the following options: “Upload custom CSV” or “Upload via Plugin”. Choose Upload custom CSV if you would like to upload your sales data to the FTP and CSV files. Smart Insight will automatically fetch your sales data files daily. First, I insert the sample data. On the next step, the system has again automatically recognized the delimiter  and the quote character. Here you can change the file name pattern. If you are using the current Predict API, leave the file name pattern as it is. I leave the default name and I click on next. The fields for identifying contacts, products, orders, purchase date. The total price of the order and purchased quantity are recognized. In our retail use case. There are the sales channel of the sold products and store ID to identify your stores. If you use custom naming and it is mismatched, select the right unique identifier keys from the drop down menus and check if the right data types were selected for the filtering. In this phase, you need to select the fields from the sales file that you'd like to use for filtering and creating contact segments. Beware, changes after the first historical data load will require a full reload. Choose Upload via Plugin instead, if your sales data will be handled by the Emarsys Shopify, or Magento plug-in. With this option, you don't need to upload the first 5 to 10 rows of your sales data file, and SAP Emarsys will configure mandatory fields by default, using ems-user-id representing email for identification. Just make sure you have activated the order synchronization before. We are almost done with the data configuration. From this point on, the setup doesn't differ between Predict/Loyalty and non-Predict/Loyalty accounts. For my retail account there's an extra six step: I need to configure my store data. If you don't have retail sales your next step is the  eRFM parameter settings. At this stage, you have to give information about the format of your store data file and select the store data fields you want to use for customizing your Smart Insight reports and segments. So I add some sample rows of my store file. There is an auto recognition of the delimiter and the quote character. The name of each uploading file needs to be unique, so I leave the given wildcard character for the file name pattern. As I include timestamps in the store file name. Then let's move to the next step. Here are the following fields: Store ID to identify the stores and link them to purchases, Name, Opening date, Country code, City, Address, Postal code, GPS coordinates, Size and category information of your stores. By default, each field is available to create filters and segments. I check the selected fields and date types, then I click save. The last step is setting the eRFM parameters, which are the results of your eRFM analysis. I click on eRFM parameters. Then I select the currency I'd like to use. You can only have one currency for the purchases. eRFM parameter setting comes after the currency is set. In case you have eRFM parameters created externally and you'd like to use them in Smart insight, you need to define the range limits for the Lead Lifecycle Status,  Customer Lifecycle Status and Buyer Status. If you'd like us to create your eRFM profiling and configure the eRFM parameters, contact SAP Emarsys for further assistance. We'll analyze the purchase data of your contacts and provide the initial eRFM model for your customer life cycle screens. Click save. It will deploy the screens with some default settings, which can be changed any time after it's saved. All steps are done. I can finish the setup. In a few seconds the screens will be deployed. Here we go. After the first data load, my Smart Insight will be ready to use. And that's how you can set up your Smart Insight! I hope you enjoyed this session and thank you for your attention.