Defying Cost Volatility

 In an era where pricing is important. 

Defying cost volatility: A strategic pricing response

Input cost increases provide an opportunity to restructure and improve pricing while also institutionalizing best practices. Yet margins will suffer if they are not done carefully.

Key takeaways

- The ongoing input cost increases and volatility, while representing a difficult challenge, provide an opportunity to improve pricing through adoption of best practices.

- Organizations that adopt a strategic approach to pricing actions can significantly minimize margin leakage during a pricing action.

- Our four-step approach can put your organization on a path to pricing excellence, allowing you to recover cost increases and minimize negative impacts to financial performance in a responsible, transparent, and customer-centric manner.  ... '

The Retail Power of .99?

 A bit of retail knowledge from the research lab at Ohio State. 

This is a very insightful finding. It would be interesting to find out the effect of the price difference (99 cents vs. $1) on volume though.  Xavier Lederer

Ending prices that end in 99 cents  by Al McClain in Retailwire. With further expert comment.   

Retailers might want to rethink doing away with prices that end with “.99” if they believe the results of new research from researchers at The Ohio State University’s Fisher College of Business.  

The study found that setting prices “just below” round numbers (i.e., $19.95, $19.97 or $19.99 instead of $20) can make consumers less likely to spend to upgrade to a more expensive version or size of the product or service.

In a coffee stand experiment done on campus, the researchers changed prices hourly, offering a small coffee for 95 cents, or a larger cup for $1.20. Every other hour they would change the offering to $1 for a small cup or a larger cup for $1.25, so both sizes of coffee cost more. When using the latter pricing scheme, 56 percent of customers upgraded to the larger size, versus 29 percent who did so with the first pricing scheme.

The researchers concluded that while the just-below price makes a product seem like a bargain, it also makes the step up to the premium product seem too expensive.   ... ' 

Competing Pricing Algorithms

In HBSWK, interesting article on competing pricing algorithms, below the intro, reading:

Warring Algorithms Could be Driving Up Prices

Companies increasingly use software to conduct rapid price changes. Alexander MacKay explains why firms might benefit but consumers should be worried.

The widespread use of pricing algorithms is reshaping the nature of competition in online markets and potentially driving up the prices of retail goods, according to recent research.
These automated, price-adjusting software programs may also be catching the eye of government regulators and antitrust authorities, who fear they could ultimately harm consumers by raising prices above typical competitive levels.

It doesn’t seem too long ago when a price change was a major strategic decision for companies, requiring extensive data analysis, management consensus, coordination with advertising schedules, and other factors in an era when computers were helpful but not critical to pricing strategy. The result: A price change was more an annual or semiannual event. But these days, when companies can analyze consumer data and use technology to raise or lower prices in the blink of an eye, changes can be made not just once a year but multiple times daily.

“WHAT WE SHOW, THEORETICALLY, IS THAT (ALGORITHMIC COMPETITION) LEADS TO HIGHER PROFITS FOR BOTH FIRMS.”

Enter the rise of pricing algorithms, where software monitors prices posted by competitors and makes adjustments using parameters developed by the company’s marketers and strategists.

“They want to react to changing demand and supply conditions,” says study author Alexander J. MacKay, an assistant professor of business administration at Harvard Business School who studies competition, including pricing, demand, and market structure.  .... "

Competition in Retail Pricing Algorithms

Algorithms don't just come from the suggestion of AI methods,  we worked with many, over many years.   Here an attempt to infer retail pricing strategies from data, and implications.  Note increased data from online.  Also note the implication of action and reaction,  what actions are being caused by competitive actions.   Can be taken all the way to game theory, which we examined as well.

Competition in Pricing Algorithms
by Zach Y. Brown and Alexander MacKay

The adoption of pricing technology can lead to higher prices, by increasing the frequency of price changes and/or encoding pricing strategies in algorithms. This raises new antitrust questions for policymakers, as firms do not need to coordinate or collude to raise prices.
Author Abstract

Increasingly, retailers have access to better pricing technology, especially in online markets. Through pricing algorithms, firms can automate their response to rivals’ prices. What are the implications for price competition? We develop a model in which firms choose algorithms, rather than prices. Even with simple (i.e., linear) algorithms, competitive equilibria can have higher prices than in the standard simultaneous Bertrand pricing game. Using hourly prices of over-the-counter drugs from five major online retailers, we document evidence that these retailers possess different pricing technologies. In addition, we find pricing patterns consistent with competition in pricing algorithms. A simple calibration of the model suggests that pricing algorithms lead to meaningful increases in markups, especially for firms with superior pricing technology.

Paper Information
Full Working Paper Text (pdf)
Working Paper Publication Date: November 2019
HBS Working Paper Number: HBS Working Paper #20-067
Faculty Unit(s): Strategy

Michael Goodkin on Getting Answers Faster

Recent reading inspires some thoughts.

Struck me that such methods what we are doing today.  Is faster always better?  And may be further advanced by technologies like 5G.   Depends on the risk involved and how it is managed.

The Wrong Answer Faster: The Inside Story of Making the Machine that Trades Trillions

(Michael Goodkin).   His company's original investment techniques became known as statistical and quantitative arbitrage. By 1996, these techniques accounted for most of the volume on the global exchanges and the financial derivatives market. Having resettled in Chicago, Goodkin then set out to make the market less risky by introducing computational physics to derivatives risk management.[1]

Goodkin’s most successful start-up was Numerix. Recruiting a group of academic physicists, including Mitchell Feigenbaum, winner of the MacArthur grant and the Wolf Prize in Physics for his pioneering work in Chaos Theory, Numerix was founded in 1996. The company’s initial product was a software algorithm that dramatically reduced the time required for Monte Carlo pricing of exotic financial derivatives and structured products. Numerix remains one of the leading software providers to financial market participants.[3]   .... "

Pricing Smart Products

Towards business models of smart products.

5 Questions to Consider When Pricing Smart Products
Nicolaj Siggelkow, Christian Terwiesch  in HBR

Imagine you are the CEO of an oral care company and you have been selling a product called the Power Brush 2000, a good electric toothbrush. Your revenue model was likely focused on profiting from selling the toothbrush with some creative pricing coming from the replacement heads (a typical “razor-razorblade” model perhaps with a subscription plan à la Dollar Shave Club). Now, your R&D group has a new product ready for launch, a toothbrush for the 21st century. The toothbrush is smart (it has built-in sensors and AI to detect plaques and cavities) and is connected via Bluetooth to the Internet. So, let’s call it the Smart Connect XL3000. Your job now is to price the Smart Connect XL3000, or, more broadly speaking, to articulate a revenue model.

The revenue model is one of the most important elements of a firm’s strategy. It defines the ways in which a firm gets compensated for the value that its products or services generate. In the old days, revenue models primarily consisted of picking a “good” price. Connected, smart devices are changing this paradigm.

Companies that pursue what we call a connected strategy (ie., those that transform their connection to customers from episodic interactions to a more frequent and data-driven relationship) have a bigger set of revenue models to choose from. In other words, the price can now depend on factors that previously could not be used to influence the pricing decision. To think systematically about revenue models and to spot opportunities for improvement, we find it helpful to ask the following five questions .... ' 

Price and Demand

A simple yet useful example of revenue  -  price elasticity.  With a full Python code example   We used to run these models in SAS in big CPG for every product we sold, advertised or proposed.  Despite all the fancy things being done these days, this is still a useful to do.   Also a good simple Python example.

Optimizing price, maximizing revenue

Posted by Mab Alam in DSC

Problem statement

Price and quantity sold are the two determinants of business revenue/profit. At higher price the revenue is expected to be high. But this is not the case all the time. We know from our everyday experience, as price of something goes up, people have less tendency to buy it.

The reverse is also true, that is, as price is down, sales goes up (think what happens in a block buster sales event in a nearby shopping mall). But sales going up doesn’t always mean that the revenue will also go up, because of the trade-off created by price drop.

Setting a right price of products/services is one of the most important decisions a business can make. Under-pricing and over-pricing both can hurt a company’s bottom line. So where is the sweet spot, the right price, that maximizes revenue and profit?

With a simple example let’s examine how to optimization price to maximize revenue/profit. ... " 

The Amazon Pricing Effect

Some good data and analysis here.   How much is the effect and in what business contexts?  Striking how much private label development is going on now in traditional Grocery, price competition there too. 

The 'Amazon Effect' Is Changing Online Price Competition—and the Fed Needs to Pay Attention
by Roberta Holland  in HBSWK

Amazon's power in the retail sector puts price pressure on what competitors charge, with implications for how federal regulators govern inflation, says Alberto F. Cavallo.

It’s no secret that fierce competition from Amazon puts downward pressure on prices charged by Walmart and other big multichannel retailers for the same items. However, the bigger “Amazon effect” relates not to the prices themselves but to the pricing behaviors of these more traditional retailers, according to Alberto Cavallo, the Edgerley Family Associate Professor at Harvard Business School.

Cavallo, who bases his findings on a decade’s worth of pricing data, sees two notable changes with large multichannel retailers: faster price increases and more uniform pricing between disparate locations.

“It’s not about just the markup, which, to some extent, is just a temporary effect,” Cavallo says. “If competition with Amazon changes the way firms such as Walmart or Best Buy make pricing decisions, it can have much longer-lasting effects on inflation dynamics and other macroeconomic phenomena.” .... " 

Illegal Pricing Algorithms

Warnings to developers of algorithms, the data they use, and even the smartest of contracts.

Law and Technology: Illegal Pricing Algorithms  By Michal S. Gal 
Communications of the ACM, January 2019, Vol. 62 No. 1, Pages 18-20  10.1145/3292515

 On June 6, 2015, the U.S. Department of Justice brought the first-ever online market-place prosecution against a price-fixing cartel. One of the special features of the case was that prices were set by algorithms. Topkins and his competitors designed and shared dynamic pricing algorithms that were programmed to act in conformity with their agreement to set coordinated prices for posters sold online. They were found to engage in an illegal cartel. Following the case, the Assistant Attorney General stated that "[w]e will not tolerate anticompetitive conduct, [even if] it occurs...over the Internet using complex pricing algorithms." The European Commissioner for Competition endorsed a similar position, stating that "companies can't escape responsibility for collusion by hiding behind a computer program."

Competition laws forbid market players from engaging in cartels, loosely defined as agreements among market players to restrict competition, without offsetting benefits to the public. This prohibition is based on the idea that competition generally increases welfare, and that for competition to exist, competitors must make independent decisions. Accordingly, price-fixing agreements among competitors are considered the "ultimate evil" and may result in a jail sentence in the U.S., as well as in other jurisdictions, unless the agreement increases consumers' well-being.

Until recently, formation of a cartel necessitated human intent, engagement, and facilitation. But with the advent of algorithms and the digital economy, it is becoming technologically possible for computer programs to autonomously coordinate prices and trade terms. Indeed, algorithms can make coordination of prices much easier and faster than ever before, at least under some market conditions. Their speed and sophistication can help calculate a high price that reacts to changing market conditions and benefits all competitors; the speed at which they can detect and respond to deviations from a coordinated high price equilibrium reduces the incentives of competitors to offer lower prices. Indeed, if one algorithm sets a lower price in an attempt to lure more consumers, a competitor's algorithm may be designed to immediately respond by lowering its price, thereby shrinking the benefits to be had from lowering the price in the first place. Moreover, as John von Neumann suggested, algorithms serve a dual purpose: as a set of instructions, and as a file to be read by other programs. Accordingly, by reading another algorithm's accessible source code, algorithms, unlike humans, can determine how other algorithms will react to their own actions, even before any action is performed by the other side. This enables competitors to design their coordinated reactions, even before any price is set.

The questions thus arise when the use of pricing algorithms constitutes an illegal cartel, and whether legal liability could be imposed on those who employ algorithms, as well as on those who design them. The stakes are high: if we cast the net too narrowly and algorithmic-facilitated coordination falls under the radar, market competition may be harmed and prices may be raised; if we cast the net too widely, we might chill the many instances in which algorithms bring about significant benefits. .... " 

Pricing and Data Science

More regarding pricing.  Large amount of data included is interesting. 

Using Data Science to Avoid Global Pricing Chaos    By Andrea Marron
What every industry can learn from luxury fashion.

 Luxury Fashion Global Pricing Business Omniretail

Technology and e-commerce have revolutionized the way consumers buy everyday products. While this often benefits consumers, many industries face challenges that never existed before. Take “showrooming,” for example. A customer wants to buy something — a piece of furniture, perhaps. It looks good online, but he wants to try it out first. So, the customer finds a nearby store that carries the item, looks at it in person, and decides he’d like to purchase it. Then he picks up his phone, and in a few clicks, finds that same model available at a cheaper price from an e-commerce site in another country, even with shipping costs.

This problem pervades many industries, and while organizations have learned to watch out for showrooming and other new obstacles, many still struggle with substantial price variations across markets.

One powerful example of this problem is the luxury and contemporary fashion industry, in which pricing variations create operational problems for retailers and e-commerce sites in some markets.

My startup, Ragtrades, used big data to explore just how big a problem this is. We aggregated figures from 20 major luxury and contemporary fashion brands and 50 retailers. We assessed prices across 12 countries, using local versions of each website and each country’s currency. This analysis included more than 300,000 data points, using our proprietary algorithms to identify exact matches. Our analysis found that prices in Russia and East Asia are the most misaligned, leaving the same item available at a wide range of prices. Western Europe, meanwhile, had the most aligned prices.  .... "

Downsides of Dynamic Pricing

Intriguing abstract and link to full PDF of technical paper:

Opportunistic Returns and Dynamic Pricing: Empirical Evidence from Online Retailing in Emerging Markets  by Chaithanya Bandi, Antonio Moreno, Donald Ngwe, and Zhiji Xu  in HBRWorkingKnowledge.

OVERVIEW — Dynamic pricing is widely applied in industries like airline ticketing, ride-sharing, and online retailing. This paper identifies two downsides of dynamic pricing: opportunistic returns and strategic choice of payment method. The impact can be significant and has implications for managers and researchers.  .... " 

Simple Sales Conversations with Considerable Profit

If we consider a conversation to be of multiple interactions,  and using multiple channels, the object of the seller is to maximize your returns, over this and often future interactions.   Conversations might include marketing of many kinds,  human tastes, advertising signage and the complete context of how and when a sale is made.  Now with the placenent of assistants in the home and elsewhere, we have the opportunity to manage conversations in completely new ways.

This Inc article shows a specific example, of how the US fast food company 'Burger King' uses this to maximize profit and loyalty.    The article is entitled " How does Burger King make a 240 percent  profit when you say Yes to this quick question".    So here is at least one question in a multi-turn conversation.  Not criticizing Burger King here,  but noticing elements of conversation that influence.

Burger King Makes a 240% Profit When You Say "Yes" to this Quick Question
This brilliant pricing scheme extorts huge margins while simultaneously increasing customer loyalty. ....  In Inc by Geoffrey James, Contributing editor, Inc.com@Sales_Source  .... " 

Wal-Mart Asks for Higher Priced Products

Not a surprise, different competitive positioning, margins.

No joke – Walmart asks CPGs for higher priced products
by Matthew Stern  in Retailwire with expert comments.

Walmart is known for its commitment to low prices, but the company is discovering that shipping the lowest-priced products is making it tough to turn a profit with e-commerce. So, the chain has begun encouraging vendors to provide higher-priced items to sell on Walmart.com.

Last week, Walmart CEO Marc Lore informed big-name CPG companies like Proctor & Gamble, Unilever and others that Walmart.com wants to focus on selling items that cost at least $5 and preferably more than $10, according to Reuters. .... " 

Panel: Retail and Machine learning

An interesting panel I am attending.  Like to see the idea of machine learning applied to retail, via Retailwire.

Machine Learning is Teaching Retailers How to Compete Again

Wednesday, April 18th, 1 pm Eastern / 10 am Pacific

Register and more information.  If you register you will get the panel recording.

A stellar panel, moderated by RetailWire's Al McClain.

Jared Brown    VP Data & Analytics, Tallan

Julie Bernard    Chief Marketing Officer, Verve

Phil Masiello      Founder and CEO, Hound Dog Digital Agency

Matt Kruczek     VP Web, Mobile and AI, Tallan

It's virtually impossible for retailers of any size to compete with the dominant online platforms based on pricing.

To maintain margins, SMBs are looking to differentiate in smarter ways — to carve out their own territory in areas rivals can't match. 

Recent advancements have made Machine Learning suites available to SMBs that can help them fortify their competitive strengths. We’ll cover ways retailers can start applying Machine Learning to:

Personalize recommendations based on learned intelligence 
Improve customer service through chatbot technology
Make customer "churn" predictions

Join us for a "humanizing" look at Machine Learning, including an engaging panel discussion exploring how this emerging tech can offer your business greater competitive opportunities.  .... " 

Walmart asks CPGs for higher priced products

No joke – Walmart asks CPGs for higher priced products   by Matthew Stern in Retailwire with discussion. 

Walmart is known for its commitment to low prices, but the company is discovering that shipping the lowest-priced products is making it tough to turn a profit with e-commerce. So, the chain has begun encouraging vendors to provide higher-priced items to sell on Walmart.com.

Last week, Walmart CEO Marc Lore informed big-name CPG companies like Procter & Gamble, Unilever and others that Walmart.com wants to focus on selling items that cost at least $5 and preferably more than $10, according to Reuters. .... "

Pricing Models, Uber and Beyond

Some of our earliest analytics were to understand how products and services should be priced.   Not to set prices, but to understand the overall pricing effect.   Fixed, Dynamic, Demand-based?     So the data science of this is particularly interesting.

Uber Starts Charging What It Thinks You’re Willing to Pay
The ride-hailing giant is using data science to engineer a more sustainable business model, but it’s cutting drivers out from some gains.    by Eric Newcomer  .... 

Pricing Bots

 Saw this experimented with.    With electronic shelf labels.

Are pricing bots a boon or bane for consumers?  by George Anderson   and Discussion.

It’s a common plot twist — a scientist invents a new medicine or technology intended to help humankind, but in the end its original intent is perverted to cause harm. Such may be the case with so-called pricing or shopping bots.

The idea behind the tech is pretty straightforward: an application is created that automates price checks of competitive websites. This allows a retailer to respond almost instantly to price changes made by rivals that could put the company at a competitive disadvantage. ....  "

Tableau Changes Pricing Structure

Looks good, but seems expensive for the small business.  Though I was a long time user in the enterprise, have not looked at how they structure their pricing for some time.  A popular direction, but I think it gives smaller business pause to get into subscription costs.  There are now many options available for these services.

In Computerworld: Tableau moves to subscription pricing for its BI products. ... The data visualization company will let all businesses pay as they go  ...   "

Price Optimization Using Decision Trees

 Interesting example, a technique I also experimented with for Retail.  Good because it relates to specific business process, and be can tested in that context.  Easy to understand the idea of a decision tree.  And building or using the model is also training in the business process.  But the methods involved are not new data science, and have been  around for decades.

Price Optimisation Using Decision Tree (Regression Tree) - Machine Learning  by Bernard Antwi Adabankah  

The research was conducted to find out what price  maximises profit without sacrificing the high demand for the product due to the price being too high nor sacrificing the margins on the product due to the price being too low. 

The goal is to experiment with different price levels for the same product in one market place and country to see how sales volumes change with prices and which volume level of products we can be sold for that optimal price range.  ... " 

Crowdsourcing Price Data

In Retailwire:  With discussion.

Can crowdsourced price data change shopping habits?

Comparing prices between brick-and-mortar grocery stores isn’t an exact science for even the most fastidious shopper. Even if someone is willing to drive from store to store to find a discount, it’s hard to imagine that person always getting the lowest price on every item they buy. The creators of a new app want to make finding the lowest price far easier.

Basket is a shopping app that aims to increase price transparency by using crowdsourced data to determine where a user can get the lowest price on the items on their shopping list, according to CNNMoney. Basket began its life, quite cleverly, as an app called StockUp, which gamified taking pictures of products and rewarded users with points and cash. The game allowed the creators to build out a database of 900,000 SKUs, which acted as the foundation of Basket. The app still enlists 5,000 “power shoppers” who continue to provide the app with SKU information from stores and uses an algorithm to predict impending price changes. ... "