Data #1
Buy Now Pay Later Isn’t Big (YET) When You Run The Data.
Why does this data matter?
The surge of BNPL has raised concerns among some outlets about its impact on consumer health and overall economic stability. These concerns suggest BNPL could be a significant threat. However, the bigger picture might be a shift in the credit landscape. BNPL platforms have transformed from simple payment options to comprehensive commerce platforms, offering various transaction methods and merchant discounts. Additionally, these platforms hold a unique advantage: SKU-level data. This data provides them with exceptional visibility into consumer behavior, allowing them to not only personalize the shopping experience but also potentially underwrite loans more effectively.
Focusing on the recent "phantom debt" headlines surrounding "Pay in 4" plans, it's important to consider the scale. While Pay in 4 transactions were prominent in 2023, with a total volume of $50 billion, the average payment cycle is only 3 weeks. This translates to a relatively small outstanding balance at any given time, estimated to be around $2-3 billion (calculated by dividing the annual volume by the number of weeks in a year). This represents a mere 0.3% of the significantly larger $1.12 trillion credit card market.
Data #2
Apple Users Continue to Delay Upgrades.
Why does this data matter?
It's been a while since I've personally upgraded my iPhone. This sentiment seems to be shared by many - a recent survey shows that 54% of respondents now expect to wait over a year before upgrading, compared to just 40% a year ago. While Apple successfully pushes services within the iPhone ecosystem, this trend towards longer upgrade cycles could pose a vulnerability.
As time goes on, if people hold onto their iPhones for longer, Apple becomes more susceptible to competition as new and potentially more innovative devices become increasingly widespread. This is definitely something to keep an eye on.
Data #3
Short Term Stock Moves Driven By Bots Battling It Out
Why does this data matter?
Why does a stock go up or down on a single day? Just ask the bots. Nearly 90% of all trading activity on a given day is bots buying and selling. This is why we focus on price trends over longer periods.
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