Making Sense of Dynamic Pricing in B2B eCommerce

If there has been one favorable outcome from the COVID-19 pandemic, it’s undoubtedly the rapid rise in ecommerce. According to the recent research by eMarketer, ecommerce sales increased by 32.4% in 2020 while brick-and-mortar fell by 3.2%. Amazon alone saw 39.1% sales growth in 2020, making Jeff Bezos the world’s wealthiest person. While Starbucks hands out free coffee to health workers and McDonald’s gives away 10,000 barbecue sandwiches for anyone who shaves in lockdown, Bezos hoards more and more wealth (+90.1 billion), sometimes at the expense of his employees. 

This article is not an argument about ethical aspects of the accumulation of wealth, however, rather how to benefit from the changing market dynamics and to take advantage of the readily available tools and strategies.

The pandemic has certainly changed the ecommerce landscape, which has been historically associated with B2C, and heavily impacted traditional sales processes. The disruption in supply chains and travel has led many B2B companies, even those who had been slow at adopting new technologies before, to scramble and shift to digital channels. According to ResearchAndMarkets.com, the global B2B ecommerce market size is estimated to reach $20.9 trillion by 2027, essentially making B2B ecommerce a hotbed sector that provides numerous growth opportunities. To take advantage of this “field of miracles,” business leaders need not only adapt their marketing strategies accordingly but make use of the many resources and tools that the digital world offers.

One of those advanced tools is automated dynamic pricing, steadily paving its way into sophisticated ecommerce solutions. As Virto Commerce research shows, several trends, such as increasingly volatile demand due to the COVID pandemic and proliferation of the readily available data, create compelling opportunities and arguments in favor of tinkering with AI and adopting a dynamic approach to pricing. Especially in companies with a high level of digital maturity, dynamic pricing can become a crucial competitive advantage. 

In this piece, we will break down dynamic pricing in B2B ecommerce into bitesize comprehensible chunks, cover all types of dynamic pricing and their pros and cons, and walk you through the implementation of your own dynamic pricing strategy. 

Dynamic pricing is a pricing strategy where prices are set and changed based on sophisticated algorithms that take into account supply and demand, competitor pricing, consumer behavior, and other market factors. 

Dynamic pricing is by no means an innovation – it has existed for most of human history. For some time, the traditional bargaining system was overshadowed by the Quaker’s idea of egalitarianism and fair prices for all, which materialized in the form of a price tag, but was resurrected by the airline industry in the 1980s. Seeing the success of the dynamic pricing in selling airline tickets, many other businesses followed suit and adopted multiple pricing strategies that took into account various market variables. The ecommerce sector, in particular, has become a playfield for experimentation with pricing. However, for some, adopting different pricing strategies has become a matter of survival due to the unprecedented influx of new competitors during the pandemic. 

Dynamic pricing is typically achieved with the help of an automatic repricing tool that monitors competitors and considers various other factors, from the time of day to consumer behavior, and adjusts the prices accordingly. Below we’ll look at the different types of dynamic pricing and how they differ from each other.

While you can slice the dynamic pricing to achieve the maximum possible benefit in many ways, typically, the most common approaches are as follows:

In the cost-plus pricing approach, a store will simply charge a customer based on the cost to produce a product plus the predetermined profit margin. Although this method is easy to execute, it considers only the internal factors. It doesn’t account for any external variables, which makes this approach the least preferable.

Competitor-based pricing is based on changing market prices according to the competition. Using the price-matching mechanism, ecommerce stores typically offer prices either matching those of the competitors or lower than the lowest. One can argue  that in a highly competitive market, online stores are compelled to cut down the prices, but instead of undercutting each other, the market players tend to cooperate on the prices.

Value-based pricing is based on the idea that different consumers are attaching different values to the same product. Even though a customer’s willingness-to-pay represents an intricate and elusive concept, it can be used as a proxy for the perceived value and calculated based on the elasticity of a product. Taking the price elasticity of products, online stores can calculate how much customers are willing to pay at any given price point. With other variables like margins, timing, and competition, price elasticity can be incorporated into the dynamic pricing engine to contribute to profit maximization strategies.

In conversion rate pricing, prices change according to website conversion rates. Dropping the prices when the conversion rates are low is a standard practice in a dynamic pricing strategy.

Time-based pricing is based on changing prices throughout the day, week, year, season, and other time-related variables. Sometimes online stores want to give customers an incentive to use or buy a product at a certain time of day, leading to a noticeable change in the product’s price.

Below, we will look at the obvious advantages and sometimes not as obvious disadvantages of the dynamic pricing approach.

• Dynamic pricing allows you to better understand and predict when to push prices higher or lower, thus improving the agility of the decision-making process and providing granular insights into what drives the market. 
• Dynamic pricing allows for self-discovery and testing of multiple hypotheses: by feeding win and loss information and factoring numerous criteria into the price recommendation mechanism (accounting for strategy, customer taxonomy, product mix, deal size, and so forth), sales teams can gradually and steadily improve its accuracy.
• Dynamic pricing allows for maximizing profits on every transaction. The difference between what a customer pays and is willing to pay can represent a tremendous surplus that’s often left overboard. By analyzing your customers’ behavior and segmenting them according to their willingness to pay, you can mop up the consumer surplus.
• Dynamic pricing helps with stock management. By pushing prices down on slow-moving SKUs and increasing prices on better selling products you can manipulate buyer behavior, even if short-term, so you have enough time to replenish the stocks for faster trading items.
• Dynamic pricing helps boost conversion rates. By catering to your consumers’ needs, offering seasonal deals, and lowering prices for consumers’ “favorite” items, you’ll deliver a better user experience, resulting in better conversion rates. You can also manipulate conversions and push prices higher when conversion rates are high and vice versa, lower – when the customer demand falls.
• Dynamic pricing helps remain competitive or nullify competition altogether. Closely monitoring your competitors and adjusting prices accordingly, you ensure you’ll never over or under-charge your consumers.
• Dynamic prices can help kickstart launches. “Penetration pricing,” a type of dynamic pricing where the price of a product is initially set to low to reach a wider fraction of the market quickly, can help you supercharge your new products and gradually push up the prices as demand grows. 

• Dynamic pricing is a complex strategy. Many B2B companies remain skeptical of dynamic pricing, considering it a high-tech overkill. Indeed, while the dynamic pricing approach requires the deployment of artificial intelligence (AI) and processing of the myriad of financial and commercial inputs, it is quite manageable with the help of professionals, considering its benefits far outweigh the costs (dynamic pricing in B2B ecommerce is proved to bring higher revenue, profits (25%), and margins). Part of the problem, and as a consequence, its solution, lies in the ecommerce platform you run – if it’s a monolithic structure that is incapable of extending, you’d want to switch to a more modern solution that can seamlessly incorporate your pricing engine along with other newer technologies. Virto Commerce with its one of the top b2b ecommerce platforms is one of those scalable and extensible solutions that can support your company as it grows or tries new business strategies, adds new features, integrates with other services, and so forth.
• Especially in its “price gouging” form, dynamic pricing is believed by some to be not entirely ethical. Although it’s typically driven by demand-supply dynamics, some instances may prove otherwise, for example, as a result of software malfunction. 
• Dynamic pricing can trigger price wars and lead to increased competition. Customers are more likely to shop around to see which website offers the best deal. Truth be told, buyers have been doing it for centuries, and whatever pricing approach you adopt is unlikely to change that behavior.

At the core of the dynamic pricing approach lies the pricing engine or algorithm. With that said, machine intelligence doesn’t entirely eradicate humans; instead, it forces both forms of intelligence to sync and work together. The key to designing the automated pricing engine is to involve users continually in the process of creating benchmarks, rules, and constraints, adoption of the engine, and transformation of its outputs into decisions. 

The adoption of the dynamic pricing approach is unique for every company and not something that can be prescribed as a medicine. However, there are essential components of a successful transition that are typical for every company. These necessary elements relate to the two fundamental processes of the implementation of any solution: the building of the engine and its execution and hardwiring in the organization. 

Before embarking on developing an engine, managers need to assess the available technology and data. The questions to answer here are 

• How much quality-data does your company have? 
• How automated are the processes of data collecting and processing?
• What data matters for the organization: Information on customers and competitors? What kind of information: Negotiation histories? Comments? Sales overrides?  
• How does the current sales pricing engine work? Can you add any other inputs to the existing mechanism that would benefit the computational logic and result in a better pricing recommendation? 

The pricing solution will largely depend on the type of business you’re operating; however, in terms of software architecture, there are typically two types of solutions available on the market. First, it’s a rule-based system, which operates on the knowledge base containing rules, and machine learning software, which mines data to find the approaches to solving an issue without direct programming. 

In a rule-based system, the rules are represented in the form of “if-then” statements. When software discerns a particular pattern, an inference engine defines the relationship between the rules and facts. When a rule gets triggered, the software acts accordingly. Because software relies on the “built-in” knowledge to respond to the environment’s current state, it’s pretty inflexible and cannot react appropriately to the changing circumstances. As an inventory grows or some other factors change, the rule-based system requires more and more maintenance by adding rules, modifying the existing ones, ensuring rules aren’t duplicated, and so forth. 

Software that’s powered by machine learning (ML), on the contrary, gains knowledge from data – the more data is fed into the system, the more it learns from it and improves its performance – it doesn’t need detailed instructions on decision-making. AI and ML-powered software allow for richer and more extensive analysis and hence, for broader functionality.  

Some of the typical features of the ML-based pricing solutions include

• Granular customer segmentation and cluster analysis, which can uncover subtle customer behavior patterns and determine customer personas with surprising degrees of accuracy.
• Incorporation of large amounts of variables (including both internal and external factors) and massive amounts of data.
• Real time data market analysis.
• Possibility to align pricing recommendations with internal performance metrics, such as margin and inventory optimization.
• Price elasticity evaluations, which determine if any given customer is prepared to pay a new price.

Whether you try to build an engine yourself or find a partner with a dynamic pricing solution, it’s always a good idea to start with a pilot project and use it to gain as many insights as you possibly can.   

As powerful as analytics can be, the successful outcome of your pricing endeavor largely depends on people working together and exercising control over the process. Ideally, the sales and marketing teams have to work hand in hand with data scientists and supplement their knowledge with the human side of pricing. The pricing team controls the model, and IT runs and enhances it. 

Before endorsing the new dynamic approach into your organization, ask yourself the following questions:

• How should the switch to dynamic pricing be implemented and hardwired into your everyday pricing routines?
• How much of a stronger mandate does the new pricing routine require from you as a manager? How should the leadership of your organization be involved in endorsing the new solution?
• How much and what kind of training do your teams require before being comfortable using the new systems?
• Does the change in the mindset of your team members is necessary?
• How will you create a feedback loop between your teams to ensure continuous improvement of your pricing engine and strategy?

There’s no such thing as perfect data. However, starting even with a small number of use cases and improving the capabilities of your pricing engine one use case at a time, you can successfully implement a dynamic pricing strategy. Agile approach with continuous delivery and self-assessment is essentially the right strategy. The switch to dynamic pricing cannot happen overnight but should be implemented gradually. First, you may start with an initial appraising of the market and your industry’s pricing standards. Then, introduce a loyalty program, which will allow you to perform an initial segmentation of your customers into groups and implement different dynamic pricing strategies accordingly. Add demand, perception pricing, and competition-based pricing into the mix one step at a time, and you’ll end up with a holistically dynamic approach to pricing.

Although the dynamic pricing technology has been around for a while, Virto Commerce sees the rekindled interest in it, especially with the rise of concurrent technological waves associated with the use of AI and ML. With that being said, the market penetration of the dynamic pricing technology by B2B ecommerce companies is still incredibly low. As mentioned above, the slow adoption is largely due to the complexity of the dynamic pricing and caution exercised by B2B ecommerce enterprises when confronted with new technology. 

The fact that digital commerce relates to the concept of self-service implies the following: customers interact with the pricing catalog along with other systems without the help of intermediaries, such as sales representatives, and business owners are able to observe their customer behavior and feedback directly while bypassing their company’s employees. While new technology obviously helps to save on labor costs, it also brings to new challenges – if customers can wait for a sales rep to look up the prices, they barely have the patience to wait for a page to load if it takes more than a few seconds. As dynamic pricing happens in real time, the engine needs to work as fast as possible. The keyword here is “real time,” which might be a challenge for businesses who still utilize monolithic ecommerce platforms of an older generation which are difficult to extend to incorporate new technologies, including pricing engines. Virto Commerce, being an innovative solution built on modern technologies, like asp.net ecommerce in this sense, offers a substantial advantage – it has a flexible modular architecture that allows for third party applications and IT components to seamlessly integrate into or on top of its platform. Such stretchability and extensibility guarantee that whatever pricing engine you come with will work across all parts of your system without compromising speed and affecting performance.

Virto Commerce, a powerful B2B ecommerce platform, ensures that whatever changes or additions your ecommerce website requires, including pricing engines, you will be able to keep up with the times and continuously implement new ideas and technologies without thinking of switching solutions or completely overhauling your existing systems. If you’re interested in trying Virto Commerce B2B ecommerce platform and work hand-in-hand with the team of professionals on transforming your B2B ecommerce website, don’t hesitate to reach out to us.