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How many times have we spent minutes, or even hours browsing through an e-commerce website, filling our carts with items that caught our eye, only to abandon the process right before payment?
It’s safe to assume that this happens quite often. And it’s more common than one would think. Research has shown that the average online shopping cart abandonment rate is close to 70%. Reasons? Extra costs like taxes, shipping, and convenience fee being added at this last stage. This usually deters shoppers from completing the purchase and leaves to abandoned carts.
Another survey has shown that 87% of online shoppers will abandon their carts if checkout is too complex, and 55% would abandon the retailer completely. Admittedly, these statistics are based on B2C transactions. But one can safely assume the numbers aren’t too different in a B2B context - after all, it’s still a human on the other side of the screen.
There are of course certain things that are out of an e-commerce retailer’s control. Perhaps the potential buyer never had the intention to buy, or they simply changed their minds. But there are several parts of this experience that are in their control - payments being a major one.
How can retailers combat this? By opting for adaptive checkout - an innovative way to reduce cart abandonment and improve conversions by using dynamic capabilities to offer the best payment or credit product options to the buyers.
What is FinBox adaptive checkout?
Using our proprietary underwriting algorithms, we assess a user’s risk profile and cart value to offer the most relevant and convenient payment options at checkout - be it buy now, pay later (BNPL), credit line, or a loan. It brings an unprecedented level of personalization to the checkout experience, going beyond just payment. For example, in case of a returning user, their information is already pre-filled to make for a smoother UX. Messaging too is dynamic, adapting to the shopper’s preferences and online behavior.
What are the benefits of offering adaptive checkout?
It’s been proven to significantly lift conversion rates. Often, shoppers abandon their carts once they see the total amount to pay - it can feel like too much cash to cough up in one go. With adaptive checkout, they’re presented with several payment options that allow them to break up the total amount into bite-sized monthly payments, or pay at a later date. It goes a step further by also optimizing payment terms (such as interest rates and payment period or loan tenure) in accordance with the buyer’s financial risk profiles
Naturally, when buyers are presented with the option of paying for their purchases in convenient installments, they are also likely to purchase a higher number of products and / or higher value products in each session. This will help e-commerce retailers improve crucial metrics such as average order value (AOV), sales volumes, and annual revenue.
How does FinBox’s adaptive checkout solution work?
FinBox’s adaptive checkout solution has been proven to increase AOV by almost 80% and repeat purchase rates by 20%.
It is able to tailor the checkout experience across multiple variables such as total cart value, borrower profile, the nature of goods being purchased and other promotional/business priorities.
Let’s look at a hypothetical example of a cart value-driven checkout experience.
For cart values up to INR 10,000, it recommends interest-free payments at the end of the month - great for everyday, small-value purchases. For purchases above INR 10,000, it recommends installments to be paid off between 3-24 months at an optimal interest rate.
Similarly, if the platform wants to boost sign ups for its own BNPL/Credit Line, the adaptive checkout can automatically prioritise those options for borrowers who are yet to sign up.
There are several ways in which tailored credit offerings can be generated, promoted and targeted to users across platforms and FinBox Adaptive Checkout utilizes deep machine learning capabilities to curate the most optimum experience for a buyer cohort.
To learn more, get in touch here.