Thursday, August 23, 2012

For Banks and Payment Processors, the Task is "Avoiding Zero"

In the start-up business, there is an aphorism that suggests the most important thing for a start-up team is to "avoid zero," a way of warning teams that, no matter what, the first objective is not so much to execute fully on the original plan, but avoid wiping out the investment by failing completely.

In more established businesses under attack by disruptive competitors, the task is somewhat similarly to "avoid zero" in the sense of the underlying revenue model withering to "near zero" levels. 

That might not be a huge concern for most in the payment processing business, but the danger of downward pressure is obvious. And most observers would agree the pressure will have some effect. 

Graphic Style EmbedsOne of the most logical competitive positioning statements a company can offer a merchant is "lower transaction fees." And even though many would argue there is precious little wiggle room on that score, some contestants already are offering to attack the payment transaction fees in a major way. 

LevelUp, for example, is trying to beat competitors by jumping straight to a zero-percent charge for processing a transaction.


Though it used to charge businesses two percent per transaction, LevelUp has decided to "eat" the transaction fee, LevelUp still will have to pay its partner payment networks their customary fees. So how is LevelUp looking to modify its revenue model?

Basically, instead of charging for transactions, LevelUp will instead try to create revenue through special campaigns operated on behalf of its merchants. For instance, a local retailer could offer $2 off a $10 item for anyone using LevelUp. The customer would pay $8, and LevelUp would take 35 cents per dollar from the campaign. 

That proposition isn't riskless, either. But if successful, LevelUp would start to create pressure within the ecosystem for lower transaction fees, at the very least. 

No comments:

Costs of Creating Machine Learning Models is Up Sharply

With the caveat that we must be careful about making linear extrapolations into the future, training costs of state-of-the-art AI models hav...