Here's a statement of the obvious: The opinions expressed here are those of the participants, not those of the Mutual Fund Observer. We cannot vouch for the accuracy or appropriateness of any of it, though we do encourage civility and good humor.
Use of AI to price has been in place for a few years now but might be gaining wider momentum. Amazon is a pretty sophisticated player in this space and reportedly has done several trials around dynamic pricing based on buying patterns, device(iOS vs. Android), etc..
@Puddnhead- Yes sir, the link works just fine, but we are subscribers to The Economist, so that may be a factor. The worrisome thing is that the "give this link" item on the page for this article says it is only for one viewer for a limited time, so you'll need some other posters to test this for you.
I managed to parse this from my digital subscription. I value their content and feel compelled to properly cite and gracefully share:
The Economist Business Mar 26th 2022 edition Surge pricing Mar 26th 2022 How companies use AI to set prices The pricing of products is turning from art into science Few american business tactics are as peculiar in a freewheeling capitalist society as the manufacturer’s suggested retail price. P.H. Hanes, founder of the textile mill that would eventually become HanesBrands, came up with it in the 1920s. That allowed him to use adverts in publications across America to deter distributors from gouging buyers of his knitted under garments. Even today many American shopkeepers hew to manufacturers’ recommended prices, as much as they would love to raise them to offset the inflationary pressures on their other costs. A growing number, though, resort to more sophisticated pricing techniques. A seminal study from 2010 by McKinsey, a consultancy, estimated that raising prices by 1% without losing sales can boost operating profits by 8.7%, on average. Getting this right can be tricky. Set prices too high and you risk losing customers; set them too low and you leave money on the table. Retailers have historically used rules of thumb, such as adding a fixed margin on top of costs or matching what competitors charge. As energy, labour and other inputs go through the roof, they can no longer afford to treat pricing as an afterthought. To gain an edge, shopkeepers have been turning to price-optimisation systems. These predict how customers will respond to different pricing scenarios, and recommend those that maximise sales or profits. At their core are mathematical models that use oodles of transaction data to estimate price elasticities—how much demand increases as the price falls and vice versa—for thousands of products. Price-sensitive items can then be discounted and price-insensitive ones marked up. Merchants can fine-tune the algorithms to prevent undesirable outcomes, such as double-digit price surges or larger packages costing more by unit of weight than smaller ones. These systems are becoming cleverer thanks to advances in artificial intelligence (ai). Whereas older models used historical sales data to estimate price elasticities for individual items, the latest crop of ai-powered ones can spot patterns and relationships between multiple items. Makers of pricing software are incorporating new data sources into their models, from customers’ tweets to online product reviews, says Doug Fuehne of Pricefx, one such firm. The cloud-based platform developed by Eversight, another provider, allows retailers to test how slight increases or decreases in the price of, say, Heinz ketchup at different stores affect sales not just of that specific condiment but across the category. It is used by big manufacturers such as Coca-Cola and Johnson & Johnson, as well as some supermarkets (Raley’s) and clothes- sellers (JCPenney).
All this makes pricing systems “much more three-dimensional”, observes Chad Yoes, a former executive at Walmart who oversaw pricing at the retail behemoth. Retail bosses are keen to promote this sophistication to investors, who value firms’ pricing power at a time of high inflation. In February Starbucks, a chain of coffee shops, boasted about its use of analytics and ai to model pricing “on an ongoing basis”. us Foods, a food distributor, has touted its pricing system’s ability to use “over a dozen different inputs” to boost sales and profits. Price-optimisation may make prices more volatile. “Retailers are pricing faster today than they ever have before,” says Matt Pavich of Revionics, another pricing-software firm. That is especially true in the fast-moving world of e-commerce. But even Walmart reviews the prices of many items in its stores 2-4 times a year, says Mr Yoes, up from once or twice a few years ago. What pricing systems do not do is lead inexorably to higher prices. Mr Pavich calls this misconception “one of the biggest myths” about products like his. Sysco, a big food distributor which rolled out new pricing software last year, is a case in point. The firm says the system allows it to lower prices on “key value items”—as price-sensitive bestsellers are known in the trade—and raise them on other products. It can thus increase profits by expanding sales while maintaining margins. That keeps investors content and shoppers sweet. ■ For more expert analysis of the biggest stories in economics, business and markets, sign up to Money Talks, our weekly newsletter. This article appeared in the Business section of the print edition under the headline "Artificial prices" Published since September 1843 to take part in “a severe contest between intelligence, which presses forward, and an unworthy, timid ignorance obstructing our progress.”
Thanks @pudd, we should respect the copyright of Economist’s publications. Thus, posing the entire article may not be the best. Another way to share the information with the board is to post a summary of the article in bullet points as @yogibb has done.
My local library does not subscribe to Economists, but I get few free articles from Apple News that I subscribe to.
@Puddnhead- I'd have to agree with Sven regarding copying the complete text from any source that charges for it's material. I've posted excerpts from the Economist and other sources here from time to time, and I try to minimize the purloined content by summarizing just the most important parts of the material. It's a fine line for sure, but we need to keep MFO off of the lawyer's radar.
Comments
The experiment didn't work. But thanks for playing
God bless
the Pudd
I don't subscribe to The Economist but usually read it at the library.
I managed to parse this from my digital subscription. I value their content and feel compelled to properly cite and gracefully share:
The Economist Business
Mar 26th 2022 edition Surge pricing
Mar 26th 2022
How companies use AI to set prices
The pricing of products is turning from art into science
Few american business tactics are as peculiar in a freewheeling capitalist society as the manufacturer’s suggested retail price. P.H. Hanes, founder of the textile mill that would eventually become HanesBrands, came up with it in the 1920s. That allowed him to use adverts in publications across America to deter distributors from gouging buyers of his knitted under garments. Even today many American shopkeepers hew to manufacturers’ recommended prices, as much as they would love to raise them to offset the inflationary pressures on their other costs. A growing number, though, resort to more sophisticated pricing techniques.
A seminal study from 2010 by McKinsey, a consultancy, estimated that raising prices by 1% without losing sales can boost operating profits by 8.7%, on average. Getting this right can be tricky. Set prices too high and you risk losing customers; set them too low and you leave money on the table. Retailers have historically used rules of thumb, such as adding a fixed margin on top of costs or matching what competitors charge. As energy, labour and other inputs go through the roof, they can no longer afford to treat pricing as an afterthought.
To gain an edge, shopkeepers have been turning to price-optimisation systems. These predict how customers will respond to different pricing scenarios, and recommend those that maximise sales or profits. At their core are mathematical models that use oodles of transaction data to estimate price elasticities—how much demand increases as the price falls and vice versa—for thousands of products. Price-sensitive items can then be discounted and price-insensitive ones marked up. Merchants can fine-tune the algorithms to prevent undesirable outcomes, such as double-digit price surges or larger packages costing more by unit of weight than smaller ones.
These systems are becoming cleverer thanks to advances in artificial intelligence (ai). Whereas older models used historical sales data to estimate price elasticities for individual items, the latest crop of ai-powered ones can spot patterns and relationships between multiple items. Makers of pricing software are incorporating new data sources into their models, from customers’ tweets to online product reviews, says Doug Fuehne of Pricefx, one such firm. The cloud-based platform developed by Eversight, another provider, allows retailers to test how slight increases or decreases in the price of, say, Heinz ketchup at different stores affect sales not just of that specific condiment but across the category. It is used by big manufacturers such as Coca-Cola and Johnson & Johnson, as well as some supermarkets (Raley’s) and clothes- sellers (JCPenney).
All this makes pricing systems “much more three-dimensional”, observes Chad Yoes, a former executive at Walmart who oversaw pricing at the retail behemoth. Retail bosses are keen to promote this sophistication to investors, who value firms’ pricing power at a time of high inflation. In February Starbucks, a chain of coffee shops, boasted about its use of analytics and ai to model pricing “on an ongoing basis”. us Foods, a food distributor, has touted its pricing system’s ability to use “over a dozen different inputs” to boost sales and profits.
Price-optimisation may make prices more volatile. “Retailers are pricing faster today than they ever have before,” says Matt Pavich of Revionics, another pricing-software firm. That is especially true in the fast-moving world of e-commerce. But even Walmart reviews the prices of many items in its stores 2-4 times a year, says Mr Yoes, up from once or twice a few years ago.
What pricing systems do not do is lead inexorably to higher prices. Mr Pavich calls this misconception “one of the biggest myths” about products like his. Sysco, a big food distributor which rolled out new pricing software last year, is a case in point. The firm says the system allows it to lower prices on “key value items”—as price-sensitive bestsellers are known in the trade—and raise them on other products. It can thus increase profits by expanding sales while maintaining margins. That keeps investors content and shoppers sweet. ■
For more expert analysis of the biggest stories in economics, business and markets, sign up to Money Talks, our weekly newsletter.
This article appeared in the Business section of the print edition under the headline "Artificial prices"
Published since September 1843 to take part in “a severe contest between intelligence, which presses forward, and an unworthy, timid ignorance obstructing our progress.”
God bless
the Pudd
My local library does not subscribe to Economists, but I get few free articles from Apple News that I subscribe to.