A real differentiator for BNPL


Marketers trying to respond to the WIN (Want It Now) mentality of today’s consumers have their work cut out for them. The average merchant site sees four in five visitors abandon their cart before making a purchase. Of these four, only one is likely to come back later to complete their purchase, while the other three continue to purchase their item or continue browsing. somewhere else. Blame can be attributed to unintegrated and unconvincing visuals and customer experience on the website or a lack of effective drip campaigns to ensure they attract buyers who are already determined. Yet while these factors certainly contribute, the real obstacle to closing the deal is the customer’s will. suboptimal – if not broken – Point of sale (PoSX) experience. All the journey mapping that was done, the countless hours of innovation, the ingenious marketing and lead generation, and even the best recommendation engine on the website,… would eventually fall under the sword of Payments.

As the 2020 lockdown cemented customers’ hyper-personalized expectations of their shopping experience, BNPL (Buy Now, Pay Later) is back in fashion. Yes, good ol ‘BNPL, which has seen many reincarnations as installment billing in developed markets and microcredit in emerging markets for decades, is now helping retailers remedy this oft-forgotten PoSX. This time around, with the playing field digital, customer reach is instantly global, meaning retailers get all the help they get to adapt to customer conversion in-store and online. At the same time, they mainly focus on attracting visitors to their websites. With stiff competition, merchants are even willing to pay more in transaction fees, as the conversion rates and purchase amounts that come with each transaction more than make up for such high fees. Banks, which previously provided finance directly to buyers, are equally happy to partner with BNPL systems and technology providers, as it also means greater market capture for them without having to raise the leverage. little finger to upgrade their typically giant computer systems to afford the momentum that today’s e-commerce demands. Still, some things remain broken:

Pavlov and his dogs are back, unnoticed

Take, for example, the customer’s buying journey. They go to the website, spend time looking at the items, personalize their items (if allowed), put them in their shopping cart, and – depending on how much they want their personalized item (reward) – they are willing to endure particular idiosyncrasies of the payment experience (stimuli). Yes indeed, this is the classic conditioning we all learned but forgot from Psych 101 in college. Simply put, consumers have trained themselves to expect boring identity and income verifications and credit decisions during their shopping trip. purchase experience, which they have learned to be disjointed and jerky from their purchases experience. Additionally, they need to optimize their response to figure out how to get their reward faster with the least amount of hassle (variable reward program). It is all this “discovery” and learning of the idiosyncratic credit decisions of BNPL’s systems that indicate the value the buyer places on the item they want to keep hanging on instead of hanging on. abandon his basket. This “value” is what is captured in psychography.

Psychography? What is that?

The consumer’s feelings, propensities, attitudes, relationships, and decision-making criteria make up his psychography on a certain subject, whether it is an item, situation, or other people. Psychography can quantify the value a consumer places on the things, experiences, and relationships around them. For BNPLs who experience the standard 80% cart abandonment rate mentioned earlier, learning to integrate these psychographic levers into their credit decision could be their survival differentiator. For the retailer choosing which BNPL to partner with, examining how it deals with cart abandonment through the prism of how it calculates buyer’s values ​​should separate the players who have been thinking more. deeply than those who rely solely on the digital scale. As part of the buyer’s ‘decipher’, psychography is a key contributor to ‘getting’ the buyer to hang on in the hopes of seeing messages, offers, and terms that work in their favor, such as shown in Figure 1 below. The retailer and its partner BNPL must crack this code together to remove the jerky nature of the customer experience across their systems.

Figure 1. The role of psychography in the value-driven customer experience.

Credit decision on intelligent event-driven infrastructure

While it’s important in and of itself to understand how to capture the psychographics of buyers in their value assignments and decision making, today’s competitive landscape also demands that BNPLs make credit decisions on the fly. This means pushing the credit decision more and more to the user interface (UI) and not elsewhere. Therefore, it also means faster retrieval of buyer information from anywhere in the BNPL-retailer data ecosystem on the user interface. In the past (and even today), the rules of judgment underlying a few UI qualifying questions made it seem like dynamic decision-making was happening. It doesn’t match today’s standards among WIN-minded consumers and the kind of hyper-personalization they demand from retailers and lenders. What scale is a data infrastructure that maps information into contextual chunks to enable faster retrieval. This is why knowledge graphs, as some would say, have crossed the Rubicon. Knowledge graphs are made intelligent by the AI-compatible schema that can be installed there. Ontologies and different types of deep learning via neural network can provide self-learning patterns to graphics databases. Even decentralized data schemas like blockchain are best rendered on charts. When all the information is also accessible on the knowledge graphs, the triggers (events) and causalities are easily observable and mapped.

In terms of analysis and scoring, there are methodologies that are also more user-friendly for knowledge graphs. Bayesian neural network, federated learning via genetic algorithms, and even swarm learning (where no synthesis of disparate data sets is needed) can accelerate real-time credit decision when applied to a knowledge graph data infrastructure.

Conclusion

Knowledge graphical data, where semantic layers based on deep learning provide context to the payment experience, not only facilitates access to the information necessary for live credit decisions, but can also integrate learning. automatic in the database itself, thus making self-learning credit scoring systems feasible. Additionally, when psychography is harnessed in real time, dynamic buyer profiling enables hyper-personalization and real-time credit decision making right on the user interface. Net-net, BNPLs capable of carrying out this kind of innovation will win, with clear advantages and value propositions to put forward to their future trading partners.

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About the Author: Maria Singson brings over 20 years of experience as a technical and business leader in analytics-driven Centers of Excellence (CoE) for clients aiming to be strategic and culture-conscious in their digital transformation. In her previous roles as Innovation Analytics Leader at Dun & Bradstreet, CEO of twoMS.co and Scientific Director at Genpact, Maria has created centers of excellence that have helped companies harness their data and reinvent their risk decision-making and their sales and marketing analyzes. She is also the founder of several startups in analytics and retail, where she leverages AI / ML to create economic opportunities for disadvantaged women and benefit children with disabilities. Maria teaches AI strategy and metrics to organizations to assess and forecast their adoption of AI at Rutgers Business School for Executive Education in her spare time. His human performance-centric approach to AI readiness and transformation is rooted in his PhD in Cognitive Science (UC Irvine) and BA in Psychology (University of Southern California).


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