Getting the Most Out of Your Data: Using Tags, Soft Descriptors, and Metadata

At Pagos, we want you to get the most out of your transaction data. We know that transaction data tells you more about your business than simply how much money is moving around. It can tell you about your customers and their habits, or even about the health of your different business units. 

Collectively, your transaction data tells the story of your customers’ actual behavior—including when and where they decide to spend their money. As such, segmenting your data by customers, products, acquisition channels, or behaviors provides you with insight into who your most valuable customers are, what products and channels are the most successful, or the likelihood of retry success by processor. The thing is, none of these data labels come with standard transaction data. Instead you have to attach custom data labels to your transaction data.

Pagos makes using custom data attributes easy through three key fields and filters: tags, soft descriptors, and metadata. There is a distinction between these three fields; tags are Pagos-specific, custom labels for your data connections, while soft descriptors and metadata are custom labels for your transactions populated in collaboration with your processors.

Organize Your Data Connections with Tags

With tags, you can group your data sources and MIDs by your business’s internal hierarchy. In this way, tags liberate you from viewing your data connections the same way that your processors do. For example, if you are an international company, you may organize your business by region. Say your European region has ten MIDs on two processors; how can you combine the data of all of those data sources to see a singular view of European transactions data? With tags!

Here’s how it works: during your original onboarding with Pagos or anytime after you’ve established a data connection in your account, you can add a tag by editing the data source in your Pagos account settings. Every data source includes a Tags section where you can see what tags you’ve already added to your data source or add new ones. To add a tag, you must include both the tag category and the tag value. 

The tag category tells us which segment group the tag is in, and the tag value is the actual label that you would want on the data connections or MIDs. To continue the example above, the tag category could be something like Geolocation, while the tag label could be Europe. We provide two default tag categories—Geolocation and Payment Setup—for common use cases, but you can add any category you’d like by typing your custom value into the field and clicking Enter. You may want to divide your data sources by business platforms, business units, or anything else specific to your own company’s hierarchy.

Tags are a really helpful tool, but they only get you so far. If you want to add custom labels to your transactions themselves, we recommend using the soft descriptors and metadata fields.

Label Your Transactions with Soft Descriptors and Metadata 

Soft descriptors and metadata allow you to label your transactions with additional information about the sale. In general, you’ll establish this field with your processor via an optional API call; Pagos then ingests it through your data source connections. 

Soft Descriptors

A soft descriptor is a customizable label created in collaboration with your payment processor that contains dynamic data to identify a transaction on issuer statements. The soft descriptor field is most commonly used for “standard” payment data segments. A common use case for soft descriptors is the submerchants of a marketplace company. A marketplace may want to identify and track their submerchants’ transactions individually to evaluate transaction performance and potential for fraud. While submerchant is a standard data categorization, the submerchant naming convention ID is specific to every company with a submerchant. As such, the soft descriptor field is a perfect place for this dimension. 

Submerchant identification is not the only use for a soft descriptor. A company whose transaction volume is driven by events or releases, for example, may use soft descriptors to identify the transactions associated with individual events; doing so ensures they can later evaluate only those transactions when assessing the overall performance and effectiveness of the event in question. Similarly, an inventory-heavy company could use the soft descriptor field for SKUs to see which products are the most or least successful. The possibilities are endless!

Metadata

Metadata is similar to soft descriptors in that it’s a transaction-level label communicated via a merchant’s processor. However, while businesses typically use soft descriptors for standard payments data segmentation, they often use metadata for segmentation that’s much more company-specific. We see two generalized use cases for metadata: one is for segmentation that’s not achievable via data connections, while the other categorizes transactions based on behavior. 

For example, you may want to track transaction volume or segment sales traffic based on a product line, customer acquisition channel, or customer cohort. This custom segmentation is about areas of your business, but it’s not necessarily specific to any one MID or data connection. The metadata field is perfect for this. 

Behaviorally, the best use for metadata is retries. You can categorize your transactions by a First Attempt, Retry, Second Retry, etc. Then, you can track transaction approval performance or even customer churn via the number of transaction attempts.

Now that we have a better sense of why and how you can add custom labels to your transaction data, let’s take a look at how Pagos can help.

Custom Data Filters in Peacock

In Peacock by Pagos, Tag, Soft Descriptor, and Metadata are all filter options you can use to filter the data in any dashboard. That means you can filter any metric or dimension in Peacock by any of your custom fields! It’s important to note that because these fields are custom to you, you need to understand how the data works in order to use each filter. Let’s walk through it together. 

When you click the Tags filter, you’ll be prompted to enter your tag category first. Remember that you configured your tag categories and tag values in the Data Sources page of your Pagos account settings. To continue the example from the first section of this post, if you choose Geolocation as the tag category, you can then select one or multiple tag values before applying the filter. 

Soft descriptor is a standard payment field, so unlike tags, you don’t need to specify your soft descriptors before using the filter within Peacock. To use the Soft Descriptor filter, simply click the filter, then enter the value of the soft descriptor you want Peacock to filter for in the available field. For example, if you want to look at a submerchant, type the exact submerchant ID in the soft descriptor filter. 

Like soft descriptors, you aren’t required to tell Peacock you have metadata fields before using the Metadata filter. That being said, because metadata is a nonstandard payments field, you will need to specify the field name and field value in the filter. Let’s say that you have your metadata configured to categorize customer acquisition channels, and you want to filter a Peacock dashboard to see all customers that made a transaction after an organic search. You would type whatever your metadata field name is first—let’s say it’s acquisition_channel—and then the value you want to filter on—in this case, organic.

Custom data filters are one of the most valuable features of Peacock. With them, you can begin looking at transaction KPIs with the unique context of your business, thus gaining more actionable insight and clarity on the successes and challenges of your payments stack. 

Get Started with Tools Customizable to Your Business

Peacock is the easiest way to incorporate custom data attributes into your aggregated transaction data. It does the hard work for you, so that you can spend your time on activities more impactful than data cleaning and categorization. Ready to get started? Contact us today!