However, considering the smart millennials that we are, we’re going to do all of that at the best available price. That is to say, avoid surging fares on cab rides, order stuff online on the cheapest days, and compare prices of hotel rooms and air tickets to get nothing but the best deals.
The algorithmic pricing model we know
We customers owe such intelligent spending to a path breaking business model transformation – the algorithmic pricing model!
At its foundation, an algorithmic pricing model, also known as the dynamic pricing model, involves complex algorithms that take into consideration factors like time, competitor prices, demand and supply, and other external factors in the market to finally ascertain the price of a product or service. We have all, at one point or the other, experienced this pricing model in industries like hospitality, entertainment, travel and tourism, retail, and e-commerce.
Algorithmic pricing in BFSI?
But how about the application of the algorithmic pricing model in the financial services industry? Can banks and other financial service providers, like the other industries above, apply complex algorithms to offer dynamic prices for its products and services?
Algorithmic pricing in the BFSI sector is a completely different story, thanks to the nuances typical to this sector. Products offered by BFSI factor in a completely unique set of parameters such as operational costs, demand, and profit margins predominantly constructed around risk.
When a bank offers a loan to a borrower, it comes up with a lending rate that zeroes in the risk associated with the borrower and the cost of capital that the bank incurs. The only variable that the bank can fluctuate is its own profit margin. The floor on that margin however, has to align with liquidity coverage requirements.
Given the above constraints in the way of true pricing elasticity, algorithmic pricing still exists in the BFSI segment, although it looks a little different. For BFSI algorithmic pricing is predicated on individual customer relationship, rather than being product centric. A shift to algorithmic pricing in BFSI would mean a fundamental shift in how banks look at overall customer profitability.
How will it work?
This means banks would need to look at the whole portfolio of products and services that their customers avail from them, rather than the price of a single product, as their revenue drivers. This also means, that in the above example, if the borrower was a customer holding a savings account with the same bank it applied to for a loan or held an insurance policy with it, then the algorithmic price for such a customer would be viewed along two vectors – Relationship Pricing and Price Execution.
Relationship Pricing
The term relationship pricing takes away the focus from an individual product or service as the basis for pricing. In relationship pricing, customers are charged depending upon the bundle of products and services that they already hold with the bank. Therefore, better offers, longer offer periods, lower rates for products and services, etc. are offered to individual customers with a big wallet share with the bank, as opposed to those individuals who have a smaller wallet share or fewer products/services.
Price Execution
Price execution, on the other hand, is about how banks and other traditional financial institutions overcome the legacy core systems. Price execution calls for a shift in the architecture, deployment, and usage of technology in the BFSI sector. The price execution part of the algorithmic pricing model means that the price of a product or bundle of products can be dynamically changed in real time throughout the internal IT system of banks, i.e. from the CRM all the way through to the billing system, requiring only a single data entry point for such a change across all systems.
While most players in the BFSI segment have already adopted the relationship pricing strategy, pricing execution is still a challenge. This is owing to the cost involved in hardcoding the real time changes in prices across all systems, from CRM through to the billing system.
However, all major players in BFSI are well aware that algorithmic pricing models increase customer loyalty, improves customer experience, provides opportunities to cross-sell, and meets contextual needs of customers. But most importantly, algorithmic pricing models are helping the old players of this area stave off competition from the fintech startups that are offering new products at flexible rates. At a time when all banks seem equal and all offerings look identical, algorithmic pricing models and their bespoke pricing becomes the crucial differentiator.