A Guide to How Dynamic Pricing Algorithms are Used in Finance

The world is driven by change. It keeps humans innovating and improving upon existing foundations so that we may evolve into the future. It is the constant variable which we should all remember, regardless of who you are and what you do. Change is also a critical element in any theory to be philosophized, data science model or program first being developed or attuned over the test of time.

Whilst holding the promise of transformation, change often meets resistance until its challenges outweigh the risk of nonaction. However, with a global recession looming on the horizon, innovating in your approach to finance can assure business longevity. Many of us in B2B or business-to-business affairs, especially amongst small and medium sized businesses, already understand.

As massive technological advances of this generation square a struggling economy, history proves that early adopters will reap rewards. Industries such Airlines, Rental Cars, Biotech, Energy, and Chemicals have already started to innovate by applying technology in their banking and financial services strategy. Meanwhile, small and medium sized businesses who prioritize other business functions will continue struggling as the metaphorical chasm widens into the digital era by the actions of their larger or more resourceful competitors.

In this article, we explore how AI and ML are being deployed to expand markets, increase sales, and improve the options for both sellers and buyers regardless of the size of your business.

Traditional Financing Strategies and its Impact on Business

A traditional finance approach suggests that prices reflect current market dynamics, and that money is raised to fund ideas with a venture debt, banking financing, loans or fundraising in exchange for equity. The brutal truth is that this model is outdated: it hurts your cash flow, leaves money on the table, and outsources the hard-earned value of your business venture to third party stakeholders.

With this strategy, price models are fixed based on some assumptions and the then-current economics. Buyers and sellers feel burdens of financing long after the point of sale, and default risk increases as the time to collect on the contract value comes due. Especially with SaaS or tech companies, all parties suffer as the true sale price ventures through the revenue lifecycle, from quoting and proposals, to subscription management or customer success, back to collections and finance. In this primordial context and as the slowing economy may suggest, the cost of executing business with traditional financing or monthly subscription management yields a juice that is not worth the squeeze.

Thankfully, innovative finance strategies allow businesses options for better performance. Dynamic pricing and proprietary algorithms from B2B BNPL companies enable businesses to offer flexible payments and financing options to their buyers in the most efficient and effective way.

How Dynamic Pricing Works in B2B BNPL

In a sophisticated business landscape, machine learning or ML Models are developed to customize pricing based on input factors such as demand fluctuations, market conditions, and competition pricing. Pricing strategies realized this benefit over static approaches, so that sellers can collect the most money for the then-current economics and perceived value that their goods or services to the end-customer or buyer. This sort of model is popular amongst common consumer pricing models.

For example, ride-sharing companies like Uber or Lyft leverage changing or dynamic pricing models to calculate the rate of individual rides. “Surge rates” are an application of dynamic pricing algorithms, where it is common to increase the price of rides when there is an increase in need or demand such as commuting after an event or during the dinner rush on Friday night.

Beyond using dynamic pricing to compute your sales price, these proprietary algorithms can also be introduced at the point of sale as a part of the financing terms between transacting parties. In this application, ML is deployed in the sales closing process to calculate the optimized price for both buyer and seller once again.

The buyer is able to purchase the goods and services with flexible payment options at a customized rate based on their individualized risk assessment of financial standing, without spending lots of time or energy uploading and analyzing financials as expected in a traditional transaction

The seller is able to receive revenue up front and reduce the risk of default when collecting dues, as the underwriting finance vehicle of the contract assumes the risk of the transaction and payment from the buyer

Integrating B2B BNPL into the Sales Closing Process

In many cases, BNPL is an embedded finance tool integrated seamlessly with B2B CRMs and accounting systems, so that a transaction can be completed within a few clicks and minutes between parties.

The benefits of BNPL for B2B, powered by dynamic pricing algorithms for underwriting, enhances the sales experience and results. This includes:

Reducing sales cycle times

Maximizing revenue and profit

Minimizing customer churn

Competing with competitor prices and attracting their customers

Improving customer experience and maintaining loyalty

Optionality for flexible payments without overtly discounting or involving finance professionals

Beyond buyers and sellers, there are other beneficiaries and stakeholders who should familiarize themselves with the benefits of the algorithms placed in the heart of embedded financing.

Financial Services Professionals: Individuals working in the financial services industry, such as bankers, financial analysts, investment managers, and insurance professionals, who are interested in understanding how dynamic pricing can impact their business operations and client offerings.

Data Scientists and Quantitative Analysts: Professionals involved in data analysis, algorithm development, and quantitative modeling who seek to apply or improve dynamic pricing strategies within financial services.

Financial Technology (FinTech) Enthusiasts: Individuals and professionals interested in the intersection of finance and technology, including entrepreneurs, start-up founders, and technology consultants working in or exploring FinTech innovations.

Risk Management Professionals: Those involved in managing financial risks, such as risk analysts and actuaries, who need to understand how dynamic pricing algorithms can influence risk assessment and mitigation strategies.

Regulators and Policymakers: Government officials and regulators overseeing financial markets and institutions who need to be aware of the technological advancements and their implications for market stability and consumer protection.

Academic and Research Communities: Scholars, researchers, and students in finance, economics, and data science studying the applications and implications of dynamic pricing in financial services.

Financial Consultants and Advisors: Professionals providing consulting and advisory services to financial institutions on pricing strategies, market competitiveness, and technological adoption.

BNPL for B2B User Stories & Case Studies

Early adopters of embedded BNPL are seeing impressive results. Ratio Tech, a B2B BNPL company who has raised over $411 Million for their initiative, has created the pathway for businesses to easily integrate and deploy this solution. Joe Brown, CEO of DearDoc and early adopter, shared the business impact of leveraging Ratio Tech Boost as his integrated financing platform in this interview:

“With Ratio Tech, we've streamlined our sales process by integrating flexible payment options and consolidating our agreement and payment systems into one seamless platform. This has not only boosted our sales velocity but also improved our customer satisfaction significantly."

Rachel Cunico, Lead Product Manager of Ratio Technologies, validates the demonstrated business value of their B2B Buy Now Pay Later (BNPL) clients in this interview:

Question: "For the CEOs, finance folks or even sales teams who are looking to leverage the benefits of B2B buy now pay later, can you tell me how easy is it to get started? Technically, what does somebody need; what does the normal implementation process look like?"

Answer: "It's really easy to get started. One of the founders that I work with on a consistent basis have been raving about it because we 3 x’ed their sales in just one quarter. Huge increase in revenue due to the amount they're able to charge to their customers now that payments are made on a monthly basis. Definitely worthwhile to get started and the way to do it is really easy. You go to ratiotech.com to start the process without actually needing to talk to anybody. You fill out some basic information like what is the ARR of your company, what are you trying to buy now and pay later? Once the application is started, everyone wants to know the integration and the engineering effort. Depending on which pieces you want to connect will determine how much effort it'll take, but the whole goal is to be established with everything within a week or two"

Future Trends & Adoption of Financial Technologies

When evaluating the importance of dynamic pricing algorithms in your finance strategy, the thing to remember is that the objective of the technology is to improve your business results with enhanced options and user experiences. With this application, embedded BNPL for B2B can significantly assist in the scaling and sustaining of your business, while better serving your customers and business partners.

Alternatively, those choosing stability over the cost of change will likely suffer in lost opportunities, lower sales prices, and declining results. Luckily, the technology products and providers are here for SMB to join the enterprise movement of AI and ML in finance, and to finally cross the chasm of transactional transformation.