Name: Browsing Contact Origin Data in SAP Marketing Cloud
Uploaded: 2019-4-2
Duration: 169 s
Description: Based on the example of the Browse Contact Origin Data app, explore what Analytical List Pages can do for you. ALPs are highly configurable, interactive graphical tools for viewing and analyzing marketing data. Data can be flexibly sliced and diced, drilled down, filtered and sorted to provide high value insight and transparency.

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<div><div><div>Based on the example of the Browse Contact Origin Data app, explore what Analytical List Pages can do for you. ALPs are highly configurable,  interactive graphical tools for viewing and analyzing marketing data. Data can be flexibly sliced and diced, drilled down, filtered and sorted to provide high value insight and transparency.<br></div></div></div>

Browsing Contact Origin Data in SAP Marketing Cloud

Do you need to ensure that data flows into the SAP marketing Cloud system correctly and efficiently, or analyze data sets to clutter your system of old or stale data? Adam, a marketing administrator at Hana Company, has received complaints from users about the high volume of unidentified contacts in the system. Adam decides to investigate and make a proposal for cleaning up the data. He knows that the app browse contact origin data is the best place to start. Adam can immediately see some key aspects of the data in the visual filter charts at the top of the page. For example, he notes that the majority of the contacts are people. He notes in passing that a large number of contacts have no country information available. However, the number of anonymous contacts shown in the third visual filter is higher than he expects. So he clicks that item in the legend to focus on just that subset. He wants to find out how many of these contacts have not been updated in the last three months, and are too old to be useful to the marketers. So he switches to compact filters so he can set the appropriate filter values for last change time stamp he selects before and a date three months previous. He now knows the number of anonymous contacts older than three months. Quite a large number. I'll look at the smart chart. Helps him identify the top sources of this data. He wants to know exactly how many of these contacts come from the top three sources listed. So he clicks each one in the chart to filter them. In fact, the majority of the anonymous data comes from the top three items. Under view by. He can break down the Smart Chart data by country to find out whether most of this data contributed to the high number of missing country data, he noted earlier. Perhaps this analysis will help contribute to data quality as well as cleaning up old data. He now wants to spot check the raw data in the table. So he toggles to table view, where you can reorganize the columns, source, filter and export as his needs require. By switching to show all items, he can see how the filtered data of the top three items highlighted in blue fits in with the rest of the data. Adam is now confident he has found a set of data that should be removed from the system to clean things up.