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The pitfalls of BIN level analytics and other options to consider

In the era of Amazon and convenience shopping, offering a personalized experience informed by a deep customer understanding is paramount. One of your key allies in this journey is payments intelligence.

Payments can explain a lot about the person buying a product or signing up for a free trial offer. The insights that come from combining the attributes of payments with internal purchase data can be used to boost sales and improve customer lifetime value.

Traditionally, payment analytics awareness has been low and the more savvy merchants have relied on simple reports organized around Bank Identification Numbers (BIN files), the first six digits of a card number. BIN files provide card characteristics at an issuer level, which can be mapped to transactions and used to segment customers.

While BIN files are a good place to start, they can often result in inaccurate—or even misleading—conclusions. This is because a BIN number only uniquely identifies the issuer, not the card product.

A card product is denoted by the Account Range which is the first nine or eleven digits of the card number. For example, a Capital One Visa Traditional card can be denoted by Account Range as follows

Range Start Range End
414709000 414709999

Some of the issues merchants can run into with BIN files include the following.

1. Inaccuracies with overlap within BIN files. There are two things to consider here.

First, the funding source is unique for a particular account range among credit, debit, and prepaid card types. For example, BIN 511137 can be mapped to a prepaid card or an FSA card, depending on the account range. This could affect merchants who filter cards based on BIN type or restrict purchase with certain products.

Second, tracking performance at the BIN level can lead to errors when averaging card products and a lack of granularity will make it much harder to understand the root cause of issue. For example, if approvals are low for a particular BIN, there might be a single prepaid card under that BIN that is driving all the declines with insufficient funds.

2. Incorrect data: In some cases, the BIN and the first six digits of the card number may not match because of the card brands’ card number optimization policies. As card numbers run out, the card brands are asking issuers for unutilized account ranges which results in different BINs and account ranges. For example, if a card number starts with 405413, you might assume that this number is the BIN, but instead, the card applies to BIN 414720.

On average, a single issuer can have up to four different card products. There are over 270K account ranges (aka card products) for 70K BINSs or issuers1.This illustrates the need to go a level deeper in analyzing card data. Even networks recommend tracking card data at an account range level.

At Worldpay, we track data at an account range level and allow our merchants to do the same. To find out more, watch this video.

Issuer Insights for ecommerce

1.Worldpay Issuer Insights data

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