Data and integrations FAQs

Does Ascent360 share data across clients?

No. Ascent360 does not share data across client databases without express written permission from the client. For more information, view Ascent360's Privacy Policy.

Can Ascent360 pull customer information from a credit card at point-of-sale?

No. It is not currently possible to pull customer information from a credit card at point-of-sale. Although there are some vendors and website that suggest that they are able to do this, none of the reputable Point of Sale vendors are selling this service.

Does Ascent360 store credit card information in the database?

Ascent360 will not store unsecured credit card information in the database. However, Ascent360 can store an encrypted version of a credit card such as a token or a seeded and hashed credit card. The encrypted version of the credit card can be very useful for matching.

How do I access the Ascent360 SFTP?

This article is for clients who have an existing FTP (file transfer protocol) folder set up by Ascent360.

When transferring Personally Identifiable Information (PII) to Ascent360, please do so via SFTP. Looking for information regarding the in-Platform File Management system? Please see Dropping and Picking Up Files with File Management.

Option 1: Access via an FTP Client

Access your FTP folder through a dedicated FTP client like FileZilla.

  • Hostname:
  • Connect using your FTP username and password, provided securely by the Ascent360 Team.

Option 2: Access through a Web Browser

Alternatively, access your FTP folder through an online browser.

  • Web Transfer Login Page:
  •  Login using your FTP username and password, provided securely by the Ascent360 Team.
  • Should you wish to reset your FTP account password, please do so through the Web Transfer Portal.

Note: Web Transfer is not optimal for files 2GB or larger. If the file fails to upload, try transferring the file through a dedicated client (Option 1)

How do I configure a dynamic date in an export?

If you are setting up an automated campaign and need the name of the file or contact list to display a dynamic date, insert the following into the file name: [[yyyyMMdd]]

This means that your file or list name will include the date on which the export was run.

For example, if a daily recurring file export needs the title "Purchasers" along with the date the file was generated, the name would be entered like this:

If the campaign runs on June 1st, 2021, the file name will be: Purchasers 20210601.

What is a RFM Score?

Ascent360 has an aggregate called RFM -- “Recency, Frequency and Monetary”. At its core, the RFM score is a tool to understand customer value by comparing individuals against one another. RFM has been used in database marketing for about 20+ years.

  • Recency - How recently did the customer purchase?
  • Frequency - How often does the customer purchase?
  • Monetary Value - How much does the customer spend?

How RFM is calculated

To calculate RFM, Ascent360 looks at all customers, records and transactions in the customer database, and divides the customer list into equal groups of 20% (known as Quintiles) for each dimension.   


The idea behind ‘Recency’ is that customers who have purchased from your business recently are much more likely to respond to a new offer than someone who had made a purchase in the distant past.

Recency quintiles are based on the date of most recent purchase at your business and are organized by the Min / Max number of days since last purchase. As a result, the more recent a record’s last transaction date is, the higher the quintile score will be assigned to that record. Thus, customers with the top 20% most recent transactions (in terms of Recency) are given a code of "5". The next 20% in terms of recent purchases is coded as "4", and so on until all records in your database are now assigned either a 5, 4, 3, 2, or 1 in terms of Recency.

Example of Recency quintile breakdown:

  • #5: 7 days – 60 days
  • #4: 60 days – 120 days
  • #3: 120 days – 240 days
  • #2: 240 days – 300 days
  • #1: 300 days – Oldest transaction date associated with a record in your database


Frequency quintiles are based on the sum quantity of total lifetime transactions each customer has made at your business.  

Customers are sorted in your the database by this number – from the most to least frequent, coding the top 20% as "5", and the less frequent quintiles as 4, 3, 2, and 1.  Note: brand new customers will have a Recency code of "5", but a frequency code of "1", resulting in the lowest frequency quintile containing your newest customers.

Example of Frequency quintile breakdown:

  • #5: 5 transactions – Maximum number of transactions associated with a record in your database
  • #4: 4 transactions
  • #3: 3 transactions
  • #2: 2 transactions
  • #1: 1 transaction
  • #0: 0 transactions


Monetary scores are organized into quintiles based on the sum of an individual's total lifetime spend. Your biggest spenders will be assigned a “5”, and the others, 4, 3, 2, and 1.  Customers are organized into your database by min / max amount of total dollars spent with your business.

Example of Monetary quintile breakdown:

  • #5: $400.00 – Highest lifetime total dollar sales associated with a record in your database
  • #4: $200.00 - $400.00
  • #3: $100.00 - $200.00
  • #2: $50.00 - $100.00
  • #1: $1.00 - $50.00
  • #0: $0.00

Putting The Three Scores Together

RFM analysis depends on Recency, Frequency, and Monetary measures, but the real power of the technique comes from combining them into a three digit RFM "cell code".

Using the quintile system explained above, all customers end up with three digits in their database records. They are either 555, 554, 553, 552, 551, 545,...down to 111. There are 125 different "cells" in all, with all cells containing approximately the same number of customers.

You may notice in your CDP database that not all RFM buckets are utilized AND that you don’t actually have an even 20% in each bucket. As an example, your RFM may only have customers assigned a 4 or 5 and not 1, 2, 3. This is because the frequency of known purchasers is not large enough to create a complete segment. If 80% of your customers have only purchased once, than it is impossible to break this 80% into different frequency buckets. The entire 80% will be assigned a frequency of 4. The other 20% that have more than 1 transaction will be assigned a 5.