Using AI for Dynamic Pricing: The Smarking Example
Business + ManagementRetaildynamic pricingWest 2018posted by Elizabeth Wallace, ODSC September 20, 2019 Elizabeth Wallace, ODSC
What do airlines, hotels, parking, and cloud computing have in common? You invest in assets upfront and render them out as slices of time. While parking assets exist in the physical space, and cloud computing exists in a virtual space, the principle is the same. Dr. Maokai Lin of Smarking offers his take on dynamic pricing and how the AI revolution will aid companies operating on this model of business by looking at Smarking, his company’s work with parking prices. Let’s take a look.
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Dynamic Pricing: The Start
So what’s the ideal scenario for businesses that run on this model? To be sold out, of course. You’ve invested in the asset up front, and you want to use it as much as possible. If you’ve got an empty parking space for even an hour, that’s a loss of revenue. So how do you optimize the parking asset?
You modulate the price based on circumstances. In peak times, you want to keep prices high to make more money because people will rent the space regardless. Off-peak times, you want to lower the price to encourage demand. This isn’t a hard concept. The difficulty is knowing how high and how low to change prices before you see diminishing returns.
Dynamic pricing uses algorithms to find the ideal pricing for these situations, improving revenue outcomes, and lowering the guesswork for all this pricing madness. There aren’t many people besides Smarking doing parking like this, but Smarking estimates an additional 10 to 15% gain by using this method.
The simplest method is finding that balance between price and availability that offers the most profit. Other methods include user differentiation (which many consider unethical) and product differentiation, such as a hotel room with a good view versus one with a view of an alley.
So how is pricing done now? Pricing uses past performances, usually gleaned from quarterly reviews. This is a slow-moving process relying heavily on indicators that may or may not be true in the future. You have the potential for a loss of revenue if outside circumstances cause pricing mistakes, and you can’t pivot quickly.
When you do need to pivot, many businesses rely on gut feelings or other emotional reactions to handle the changes. While this may work for those with plenty of experience in the industry, overall, it doesn’t make for a long term effective strategy.
The human error is a massive factor in traditional pricing as well. It takes time away from your human team and creates environments where details are missed, and market reaction misses the mark. In today’s world, with so much computing power at our fingertips, companies that rely on traditional pricing may soon find themselves edged out by the competition.
How Does AI Help?
AI can make dynamic pricing easier and better. It facilitates continuous prediction, allowing for pivots for events in real-time. While you can predict some of these events with historical data, but not all. With AI, you identify what’s going on much faster, and you can adjust the price within minutes instead of once per quarter.
For human behavior, faster reaction times are better. AI can review numbers more quickly than humans, taking them out of the loop for faster results. For Smarking’s pilot project, they worked with three locations: Chicago, New York, and Boston. In all locations, Smarking’s dynamic pricing model increased revenues.
In Public Parking, dynamic pricing can also encourage a few other behaviors. For example, public parking would like to encourage turnovers for more revenue, but also to cut down on circling. 85 to 90% occupancy is ideal for maximum revenue without being full. It can also increase transparency for the public while increasing revenue overall for the city.
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Whether it’s private or public parking, AI can help change the way organizations think about pricing with better data and faster action. It can have a significant impact on revenue to the tune of billions of dollars by improving the way businesses use their assets and facilitate the reactive cycle for market conditions.
Humans are prone to errors in this type of analysis. You can save people time, have an impact on revenue, and increase market reaction time by using AI for dynamic pricing. Companies like Smarking are making it easier for businesses to utilize these new technologies. Instead of squeezing the last cent out of customers or resorting to unethical pricing strategies, companies can use these smart strategies to reduce waste and improve results.