Are Card-linked Offers the solution to a decreasing interchange? (part 2)

16 September 2015

Part 1 of this post focused on the context for emerging card-linked offers and their definitions

Part 2 below looks at the two main capabilities that card-linked offer solutions should bring to the market:

  •     The analytical approach
  •     The sourcing approach

Card-linked offers: the analytical approach

When banks want to make card-linked offers or card-linked promotions available to their cardholders, they can do it in two ways.

The first way consists of publishing a list of offers on channels accessible by any customer of the bank, regardless of their preferences. In this case, a good search function is highly recommended so that cardholders quickly find the offers that matter for them. Consistency of the offers displayed in the various channels is critical while some segments interact better with some channels than others.

The second way means communicating to specific banking customers the right offer at the right moment. This is much more effective in terms of take-up and consumption of the offers and can be in the form of emails, sms, mobile notifications and messages. But banks here need to avoid spamming their customers at all costs and this is where analytical tools play a key role.

Analytics tools allow sending the right offer to the right customer at the right moment.

There are mainly three types of data analysis:

  •     Descriptive analytics: looking at the past, in other words, what happened and why did it happen
  •     Predictive analytics: betting on the future, or assessing the likelihood of an event or a situation to happen in the future
  •     Prescriptive analytics foresees what will happen but also when and why an event will happen

Predictive analytics are already known by the financial industry through credit scoring or the ability to assess the probability of a credit cardholder to become delinquent.

Applying predictive analytics to card-linked offers will help assessing an appetence scoring or the probability for cardholders to take up a specific card-linked promotion (based on its value), to visit a merchant (based on the list of offers he provides), to spend a certain amount (to get an offer).  

Predicting future spend behavior will help define the type and size of offers that drive incremental behavior from specific segments.

Banks who search accurate ways to target their cardholders with card-linked offers will greatly benefit from descriptive and predictive analytics.

Card-linked offers: the sourcing approach

While analytics tools provide the technology part of a card-linked offers solution, the sourcing approach provides the content part of that same solution.

Card-linked offers (CLO) are merchant-funded offers (MFO) linked to the usage of a specific card portfolio. So banks greatly appreciate a solution that give them access to a network of merchants willing to fund offers in exchange for more traffic from more relevant customers, higher ticket size and an increase of repeat visits.

Indeed building such a network of funding merchants takes time and efforts. A decent network starts with twenty merchants and should ideally increase to hundred to two hundred merchants after a couple of years. The type of merchants varies whether the network is online or offline.

Merchants join such network and fund offers if they can see incremental results and avoid paying extra to customers already planning to visit them. They must also commit to periodically propose new offers.

Part 3 will focus on the business model related to Card-linked offers / card-linked promotions solutions.