B2C companies worldwide use dynamic pricing to price their products/services according to market demand. If you are booking your Uber during peak time or going on a vacation during the holiday, you are easily paying 2X-3X what you pay otherwise. While B2C companies have widely adopted this pricing strategy into their business, reaping benefits, B2B companies, especially wholesalers and distributors, have lagged behind.
Price Optimization is a hot topic in the distribution & wholesale industry. Your prices have the most significant impact on your margins. Moreover, determining the right pricing is a challenging aspect of any business. Overpricing can lead to a loss of customers, while underpricing may result in diminished revenue. Large wholesalers like Sonepar have started investing heavily in dynamic pricing technologies to find the optimal prices to boost revenue while not making a dent on their margins.
This blog will explore the concept of dynamic pricing and examine the potential for medium-sized wholesale businesses to enhance their pricing strategies by incorporating advanced AI technologies.
Dynamic pricing is an approach where businesses tailor their prices to meet the ever-changing market conditions.
You must be thinking that it's the nature of the market. Prices keep changing all the time. Your mango costs significantly less in summer than in autumn. Why do we need another term for it?
The answer is the degree of dynamism. While prices of mangoes change according to season, in dynamic pricing, the prices almost change in real-time.
Have you heard of Uber’s infamous surge prices when your phone battery is low? That's dynamic pricing. It's not an ethical example, but it's interesting nevertheless.
Dynamic pricing is a strategy where businesses use AI/ML-based algorithms to adjust their prices in a timely manner, taking into account various market conditions, customer behaviors, and other pertinent factors. This approach stands in contrast to the traditional fixed pricing model, allowing companies to set their prices based on factors like competitive landscape, market demand trends, and consumer behavior patterns.
Additionally, macro factors such as seasonality, geographical region, and prevailing economic conditions are also considered in dynamic pricing. The utilization of advanced algorithms and real-time data plays a crucial role in determining the most effective pricing strategies. This method enables businesses to not only optimize their pricing and revenue but also improve profitability and enhance customer satisfaction by offering prices that are in tune with the current market scenario.
Various methods and technological tools, particularly those rooted in machine learning—a branch of artificial intelligence—are available for optimizing pricing strategies. These technologies enhance the efficacy of pricing models significantly.
Advanced pricing systems can now forecast the effects of price changes even before they are implemented. This predictive capability is a significant leap forward in pricing strategy.
Large wholesalers have started investing heavily in AI infrastructure, with dynamic pricing or price optimization being one of the top use cases. These give them automated, tailored price suggestions for each customer and product, drawing on data about individual customer behaviors and specific circumstances.
These advanced pricing methods don't completely replace traditional strategies overnight. Instead, they complement them by incorporating customer-specific insights. For instance, when there's a price hike from the manufacturer, it's not uniformly applied to all customers. Instead, the increase is strategically targeted at those more likely to accept it.
B2B wholesalers, especially small and medium-sized ones, face significant challenges, including tight margins, supply chain issues, online competition, and increasing customer awareness. Therefore, it's crucial for them to move away from rigid, traditional pricing strategies. Adapting pricing based on well-informed, data-driven decisions, particularly for products and customers likely to respond positively, can be highly beneficial. Additionally, incorporating customer behavior into pricing decisions represents a shift away from one-size-fits-all approaches, opening up new potentials for profitability and market responsiveness.
As with most marketing strategies, pricing is as much an art as a science. Pairing human expertise with insights from AI gives you a holistic view that gives optimized results. Equipping individuals with the right tools leads to success. A pricing expert can give AI/ML models pricing frameworks and rules aligned with the company’s goals.