Leveraging AI-Driven Dynamic Pricing to Deliver Holiday Cheer to All

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Anyone who’s been paying attention to the news in recent months has heard of ChatGPT, the ground-breaking artificial intelligence (AI) chatbot created by OpenAI. Like nothing else in the long-and-winding history of AI development, ChatGPT has seized the imagination of the general public. People recognize that the technology has immense promise, but there are definitely some kinks that still need to be worked out. Despite this, ChatGPT is a premier example of generative AI and is proving to be a game-changer.

But other forms of AI technology are starting to exert just as much influence on society, only in ways that are a bit less dramatic. Take AI’s growing impact on pricing. The cost of goods has always been a key driver for individuals, and businesses live or die on their ability to persuade consumers to buy. Shoppers are influenced by brand, by the reputation of the seller, by the season, and by a host of other factors, but price is the thing that ultimately pushes the shopper forward—or holds them back. Even during the holiday season, when consumers are supposedly shopping for “just the right thing” for friends and loved ones, price is often the determinative factor. This is the reason securing a new flat-screen TV for ninety dollars on a Black Friday is considered a major coup.

Even sporadic shoppers notice how online retailers adjust their prices, especially during the latter part of the year, known as “the golden quarter.” Promotional messaging touting “best price in 30 days” or “the lowest price this week” tends to proliferate even more during high-volume, high-profit shopping periods such as the key months of October to the end of December. After all, this is when most businesses make a huge percentage of their annual sales.

So why would a well-known online retailer bother, in mid-November, to slash the price of a gaming laptop by $33.23, raise the price of an electric bike by 13 percent, and/or cut the price of batteries by $1.84, and then generate an entirely new set of prices only a few days later? The answer is that the retailer in question didn’t bother to adjust these offers. Increasingly, retailers of all sizes do not have the time, energy, or intimate knowledge of complex economic and supply chain factors to make such nuanced adjustments on the fly. No human being does.

All of these fluctuating pricing adjustments are the product of AI-driven dynamic pricing. Used as a flexible online pricing strategy, AI-driven dynamic pricing leverages artificial intelligence to help identify the optimum price of a good or service at any particular point in time. The technology has been around for a while and is used by well-known online destinations like Expedia, rideshare apps like Uber, and all-powerful commercial entities like Amazon and Walmart.

Thanks to the addition of AI, dynamic pricing has reached a whole new level in terms of speed, power, and sophistication. Today’s dynamic pricing relies on the capture and analysis of enormous volumes of data, including sales and supply chain data, customer feedback, inventory levels, and the pricing activities of competitors. This is why prices are endlessly in flux: the system notes when one or more variables shifts; it then assesses elasticity and proceeds to recommend or adjust pricing in response.

Some analysts and observers have expressed fear that dynamic pricing can be abused to gouge customers, especially during peak shopping periods. This concern is not just theoretical. Uber has been criticized for its “surge pricing” model and sellers on Amazon were accused of jacking up prices unfairly during the recent pandemic. Just as often, however, consumers benefit from falling prices as businesses compete fiercely for market share. The answer to the question of whether dynamic pricing helps or hurts consumers has a lot to do with the behavior of those consumers and how interested they are in finding a deal.

For instance, imagine this all-too-familiar example. It’s mid-December and an online computer hardware retailer is facing intense demand for the latest version of a popular gaming console, yet inventories are low due to a series of transport-related disruptions. AI-based dynamic pricing software automatically senses these trends and recommends and implements a price increase in response to spiking demand, in real time. Simultaneously, the system generates discounts that will go into effect on Christmas Day and continue until the New Year, as the system is predicting that this is when inventories will rise. The system is also anticipating similar pricing strategies on the part of rivals and the likely impact of the fact that most of the buyers will be purchasing the gaming system for themselves and not for others. Though subtle, the ongoing pricing shifts generated by the system ensures that the retailer maximizes revenues at all points of the holiday season.

However, it’s worth mentioning that sometimes realizing maximum profit on each and every sale is not the top priority. For example, a business focused on highly-personalized engagement with customers over the long term may tweak its model to minimize price spikes, cutting into potential revenues in the short term in order to maximize brand loyalty and repeat business. Each and every business will adjust its AI-based pricing software as it sees fit based on its unique priorities and strategy in the marketplace.  

Even so, any business thinking about implementing AI-based dynamic pricing should be sure that the system it chooses is at a minimum capable of the following:

  1. Amplifying revenue and profits – The system has to be able to respond to shifting market data in real time, in ways that permit maximum generation of revenue. Efficient, hyper-accurate monitoring of supply and demand data, seasonal data, competitor pricing, and other factors is an absolute must.
  2. Reducing customer churn – The system should have the capacity to identify wavering customer loyalty and offer creative discounts and incentives to keep customers locked in.
  3. Responding dynamically to competitor price shifts – The technology must have the capacity to collect vast amounts of real-time, accurate, and reliable competitor pricing data. The more up-to-date the data, the more likely it is that the system responds appropriately.
  4. Maximizing customer loyalty via an improved customer experience – The system should be able to implement dynamic pricing in effective ways that are also customer-friendly and contribute to greater personalization, price transparency, and purchasing options.
  5. Aligning with business goals – The system should allow individual businesses to tweak the parameters to reflect their own priorities…i.e., market penetration vs. profit maximization.

The implications of dynamic pricing for businesses, markets, and the entire economy are enormous. Ultimately, dynamic pricing is just a more efficient way for businesses to do what they’ve always done—adjust prices in response to market conditions. But the efficiency made possible by dynamic pricing portends a better future for buyers and sellers alike. This coming holiday season, online shoppers may complain about rising prices for specific consumer goods. But they should remember that dynamic pricing flows in both directions, and if they shop strategically and do things just a little bit differently than most shoppers, they may be amazed at the deals that await them.

Mridula Saini
Mridula Saini, is Chief Revenue Officer at IKASI, a pioneer in custom, machine learning-based predictive models for business and marketing professionals enabling them to grow revenue by identifying the most at-risk and valuable customer relationships. For more information, follow them at www.ikasi.ai and on LinkedIn.

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