By Katerina Jones, Chief Marketing Officer, Fleet Advantage
August 7, 2024
Earlier this year, fast-food chain Wendy’s received significant backlash over its announced use of surge pricing on menu items. Part of the national conversation that ensued centered around distinguishing between surge pricing versus dynamic pricing.
However, it is important to know that dynamic pricing is different from surge pricing. In fact, contrary to popular misunderstanding, dynamic pricing isn’t just about raising prices when demand exceeds supply. That’s “surge pricing.” Dynamic pricing also includes lowering prices, both when demand declines and during supply surplus. Dynamic pricing essentially responds to the ebbs and flows of a free market economy.
It is also important to understand that Wendy’s new pricing strategies weren’t all that new, and many other industries for years have been experiencing strategies such as dynamic pricing. Businesses that procure heavy-duty trucks such as private fleets, for-hire trucking companies and other vendors, have been placing purchase orders for trucks for many years with prices based on many economic factors that contribute to “dynamic” prices.
However, technology algorithms today are making it much easier for fleets to understand the pricing fluctuations in real time. The key is knowing that not all algorithms are built alike, so it’s important for companies to understand if they are leveraging accurate, real-time data to determine a procurement decision suited for their organization.
Understanding the Use Of Data Algorithms for Fleets
Many fleets today are leveraging a wealth of data for their operations, helping to better understand and manage the operations of their vehicles with much greater efficiency. Data is also being leveraged by finance and analytics departments to monitor and assess the optimal time to replace an aging truck. Today’s real-time sophisticated data, which includes the use of price-setting, can also leverage artificial intelligence and predictive modeling algorithms to analyze large amounts of data and make pricing decisions based on various analysis.
This type of pricing uses data-driven insights and predictive analytics to determine the price for a heavy-duty truck by the manufacturers, based on many different economic and industry-driven factors. Independent truck lessors also base their lease payment calculations on costs set by the manufacturer and must also leverage algorithms to account for these varying price fluctuations.
The Use of Dynamic Pricing by OEMs
Sure, dynamic pricing made many headlines earlier this year, but it has been used by the heavy-duty truck industry for years. Everything from geopolitical tensions affecting the supply of parts and commodity prices to regulatory changes shaping emission standards, as well as demand from large fleets, many factors contribute to price adjustments. Traditionally, fixed pricing models struggled to account for these fluctuations effectively. However, dynamic pricing emerged over the years to help OEMs adjust for changing market dynamics, further exacerbated by the rapidly changing supply during and post Covid.
One of the primary drivers behind the adoption of dynamic pricing is its ability to capture the true value of heavy-duty trucks in real-time. Unlike static pricing models that rely on historical data and gut instincts, dynamic pricing leverages real-time data that includes market trends, competitor pricing, raw materials prices and availability, macro-economic trends, and customer behavior. This data-driven approach enables manufacturers to set prices dynamically, reflecting the ever-changing market conditions accurately.
Moreover, dynamic pricing empowers manufacturers to respond to shifts in supply and demand, such as economic expansions or seasonal peaks. Conversely, during downturns or when facing surplus inventory, prices can be lowered to stimulate demand and clear excess stock.
Understanding How Advanced Data Plays a Role in Remarketing
Truck remarketing has also benefitted from dynamic pricing over the years. The collection and analysis of real-time data and algorithms today have become central pillars of optimizing the remarketing of used trucks.
Understanding market trends and residual values, buyer behavior, and past price/demand performance metrics enables sellers to adjust their strategy precisely and effectively. Advanced data algorithms today are being utilized to recognize specific truck characteristics (brand, model, condition, equipment). These algorithms can also prove invaluable in estimating refurbishment costs, secondary finance options, and truck trade-in values, based on historical data and local market trends.
Heavy duty fleets today are partnering with strategic asset management providers with an analytics technology stack, which includes Fleet Modernization Studies, Emission Studies, and Comprehensive Emission studies with analytics software, which monitors and analyzes each truck’s raw data from their ELDs. This advanced real-time data, along with predictive modeling algorithms is helping fleets to better understand dynamic pricing, and to properly plan for the upgrade and acquisition of new equipment three, four, and five years down the road.
By harnessing the partnership of asset management providers that leverage the powers of data-driven insights and adaptive pricing strategies, these partners can help fleets see a holistic view of their life cycle, from beginning to end so they are fully optimized from planning to procurement to exchange. This way fleets can navigate the next several years with confidence, maximizing their Total Cost of Ownership while delivering greater value to customers regardless of the fluctuating change in equipment prices.
About The Author
Katerina Jones is Chief Marketing Officer for Fleet Advantage, a leading innovator in truck fleet business analytics, equipment financing and life cycle cost management. For more information visit www.FleetAdvantage.com.
