Demand-Based Pricing, AI, and Big Data: A Love Story
Of all the numerous pricing strategies available to retailers, one has proven to stand the test of time: demand-based pricing. Otherwise known as consumer-based pricing, it has been at the core of many retail companies’ success. Its ability to hone-in on price elasticity and maximize profit is uncanny. There’s just one problem: to implement demand-based pricing, you need data; a lot of data, and you need a way to analyze that data a lot faster than the best mathematicians in the world can do.
To solve this issue and pave the way for successful demand-based pricing, a price strategy love story has occurred—the joining of Big Data and AI-powered pricing software. The retailers who figured this out first found themselves ahead of the competition by light years, and most savvy retailers have been adopting the technology in order to keep up and profit ever since. It’s not just the pioneers of retail pricing analytics that benefit from Big Data and AI in their strategy either. In fact, any business could adopt data-backed, demand-based pricing today and reap the benefits almost immediately.
Consumer-based pricing and pricing software managed to sweep retail off its feet thanks to its uncanny ability to consider price elasticity in ways store managers and product managers could have never dreamed of. Most retailers understand the ins and outs of price elasticity; the fact that some products have an unwavering demand while the demand of others can fluctuate dramatically. If you’re a gas station, it might not be that hard to figure out how inelastic your main product is. For retailers, however, figuring out the extent that certain products are inelastic or elastic is a lot easier said than done, and the standard equation doesn’t always lead to the right conclusions if you lack the proper data.
It is integral to figure this out though, as understanding price elasticity has several advantages for retailers. Figuring out exactly how much a customer is willing to pay for a certain product can allow the retailer to maximize sales without sacrificing profit margins as they find the perfect balance between demand and price. Some products are more inelastic than they may seem at first: Do you remember how much you paid for ground pepper last time you bought it? How about flour, or salt? Most retailers wouldn’t dare to double the prices of something so basic in fear of losing their sales, but the truth is, people will buy these products with little regard of what their price is because of how cheap the product inherently is. Compare that to a new car, in which doubling the price would immediately doom a dealer’s sales.
Traditionally speaking, many managers have struggled to see the relatively safe price hikes or decreases they could make in order to maximize profit. There’s only so much information a person could have to base their pricing decisions on, and a large retail enterprise needs to price thousands of products. Even worse, is that most modern retailers are utilizing dynamic pricing to some extent, so these products need to be re-priced every month, week, day, or if you’re Amazon, every couple of hours.
With Big Data, however, everything changes in this regard. Big Data can give retailers information on everything. Prices of every similar product on the market, promotions going on in a specific region, and even the weather on a particular day. Whatever it is, Big Data has it, and it’s useful for retailers to have to maintain a solid demand-based, dynamic pricing strategy.
If all of this sounds like something you want to start doing, the good news is that the process is relatively simple. In order to harness the power of Big Data and AI and profit from dynamic, demand-based pricing, there are three main steps every business needs to take:
As stated before, the more data you have, the better. That’s where Big Data comes in, filling in all of the gaps Collecting data, ensuring its quality, storing it and organizing it can take months to do. Generally, it’s suggested that retailers should have at least a year or two of structured, error-free data for their pricing analysis purposes.
Once enough high-quality data is available, AI-powered pricing optimization software can analyze it both quickly and thoroughly. Advanced dynamic algorithms can find relationships between prices, sales, and numerous factors that no human could manage within a reasonable timeframe. Anything from holidays to locality can be determined as factors for price changes. Though pricing optimization software only needs about one to two years of historical data to analyze, it can continue to process new information and become even more fine-tuned over time.
The AI-powered pricing analysis software will determine the right prices according to the data it processed in addition to factors like the retailer’s price positioning, uniqueness of items, etc. It is up to professionals to determine whether they will use the suggested price given by the software. As powerful as pricing optimization software can be, it is still not the ultimate deciding factor for pricing products. Pricing managers should take the suggested prices into consideration along with their experience, their strategy, and their business objectives in order to determine whether they will follow the suggested prices, alter them slightly, or ignore them. Regardless of what they choose, the pricing software is a tool that ensures each of their pricing decisions are data-backed science, optimized to encourage sales and maximize profits.
In the end, it is up to the retailer what pricing strategies they use in order to achieve their business goals. However, it is undeniable that dynamic, demand-based pricing remains one of the most powerful and profitable strategies out there for retailers to increase their sales and revenue within highly competitive and ever-changing markets. In order to utilize the best demand-based pricing strategy possible, AI-based pricing software backed by Big Data and supervised by experienced professionals is a must-have in order to compete with the likes of Amazon, Walmart, and other retail giants. Big Data, AI and demand-based pricing are a match made in heaven for retailers hoping to benefit from a solid pricing strategy, and they’re a trio that is now accessible to retailers across the globe.