Follow Apple’s Lead?
Establish an Online Presence?
Product Differentiation
Offerings with Online Advantage
Offerings with neither Online nor Offline Advantage
Offerings with Offline Advantages
Conclusions
I wrote a previous blog entry on retailing competition between offline and online stores, Will Smartphone-Enabled on-the-Spot Price Comparisons “Upend” Stores’ Business Models?This blog entry takes the previous analysis a step further and considers more explicitly how offline stores might be able to compete with Internet providers. In particular, this examination considers the increasing tendency of consumers to use bricks-and-mortar stores to test out new products, but then buy the products at lower prices on the Internet. How can bricks-and-mortar stores prevent such free-riding or otherwise continue to sustain viable businesses despite the existence of lower prices on the Internet?
This brief analysis examines how retail sales have changed over the past 25 years, both in size and distribution.
As seen in Figure 1, total retail sales have grown by 2.7% - 2.8% per year, excluding the short drop in spending during the Financial Crisis (2007 – 2009).
Figure 1
Amazon Ecosystem Components
Amazon Segments
Amazon Sales, Income, and Margins
Amazon Methods of Competition
Anti-Trust Issues
Amazon is leaving a large pile of battered companies in its wake as it increasingly steals sales away from traditional bricks and mortar companies and decimates their market shares. Some of the better-known victims include: Barnes & Noble (books), Macy’s (clothes and home goods), Toys R Us (toys and baby products), Staples and OfficeMax (office supplies), Etsy (handmade products), and Best Buy (electronics).
In fact, there’s even an index, the Bespoke “Death By Amazon” index, that tracks the performance of 54 public companies most at risk from Amazon:
Bespoke publishes the “Death By Amazon” as a way to track performance of the companies most affected by the rise of AMZN. Companies included must be direct retailers with a limited online presence (or core business based on physical retailing locations), a member of either the Re- tail industry of the S&P 1500 Index or a member of the S&P Retail Select Index, and rely on third party brands. We view these attributes as the best expression of AMZN’s threat to traditional retail. The index is designed as both a performance benchmark and idea generation tool for our clients.
On July 17, 2017, Patti Domm reported in “Amazon's victims: These stocks have lost $70 billion so far this year” that the index is down 20% so far this year.
As Amazon’s seeks to dominate yet another market segment – the grocery business – through its plans to purchase Whole Foods, we must question once again whether or not Amazon is “too big” (however we choose to define bigness).
This analysis examines
- The various players in Amazon’s market ecosystem
- The extent to which Amazon covers its ecosystem
- How Amazon earns its money to finance its operation
- The methods Amazon uses to compete
- Other potential anti-trust issues
The Origin of Black Friday
What’s at Stake?
The Evolution of Patterns in Retail Sales
Some Issues Regarding the Black Friday Game
The Origin of Black Friday
The origin of the name Black Friday is described in “Black Friday (shopping) explained” as follows:
The day's name originated in Philadelphia, where it originally was used to describe the heavy and disruptive pedestrian and vehicle traffic which would occur on the day after Thanksgiving. Use of the term started before 1961 and began to see broader use outside Philadelphia around 1975. Later an alternative explanation began to be offered: that "Black Friday" indicates the point at which retailers begin to turn a profit, or are "in the black".
It has only been recently that Black Friday has been the busiest shopping day of the year. Previously, the busiest shopping day of the year had been the Saturday before Christmas. As Miranda Marquit describes it in “What Is Black Friday – History of the Holiday Shopping Phenomenon”
Interestingly, the day after Thanksgiving has only recently become the biggest shopping day of the year. Between 1993 and 2001, it ranked between fifth and tenth on the list of the busiest shopping days. In fact, for years, the busiest shopping day was usually the Saturday before Christmas.
But things changed in 2002. That was the year Black Friday took the lead, and it has remained the busiest shopping day of the year ever since, with the exception of 2004 when it was second.
Price discrimination may be defined as selling the same thing to different people for different prices. Price discrimination can take many forms, such as volume discounts, price premiums, or market segmentation. Suppliers regularly use many different forms of price discrimination, which people generally don’t object to.
Some suppliers use dynamic pricing, a sub-category of price discrimination, in which prices change over time with market conditions. Consumers have been used to the fact that prices for airline tickets and hotel rooms change constantly, and that different people end up paying different prices for a seat on the plane. While uncomfortable with the practice, consumers have generally come to accept this type of dynamic pricing (what choice do they have?).
Over the past few years, dynamic pricing has become more widely used by sellers as a means of supplementing shrinking margins in an increasingly competitive world. As more information becomes easily available in digital form, pricing algorithms used to support dynamic pricing systems have been able to draw upon more and more information to hone prices and increase profits.
A more controversial sub-category of dynamic pricing is personalized pricing, which uses personal information on each customer to tailor prices specifically to that customer.
This analysis examines the different types of price discrimination, how they increase profits, why they are becoming increasingly prevalent, and some emergent issues surrounding their use.
The e-Book Pricing Battle
The following is a brief history of the e-book pricing battle that has been taking place. The passage quotes heavily from three articles:
“Publishers, Amazon in Flux in e-Book Pricing Fray” by Phil Wahba and Alexandria Sage, Reuters
“Amazon Looking Foolish in e-Book Flap” by Therese Poletti, MarketWatch
“Cost of an E-Book Will Be Going Up” by Motoko Rich and Brad Stone, NYT
Being first to market, Amazon established a $9.99 e-book pricing model.
Amazon was first on the market with an electronic book (e-book) reader (e-reader), the Kindle. Being first to market and having a large market presence together provided Amazon with enough leverage vis-à-vis publishers as to be able to establish a low, fixed sales price of $9.99 for all sales of e-books to Kindle users. This single, low, fixed price for all books is analogous to Steve Job’s iTunes music store pricing model, which initially sold all songs for 99 cents each.
To download a pdf copy of this post, click on the attachment below.
History of Cable
Away from Cable and Toward Over-the-Top
The Over-the-Top Game
Issues and Strategies
The recent announcement of the AT&T – Time Warner merger has generated a flood of press. There is much skepticism about whether the companies will be allowed to proceed with the merger. Regardless, the underlying trend that spurred the desire of the two companies to merge – the increasing prevalence of over-the-top content – will continue.
Since the advent of broadband internet in the early 2000s, users have been increasingly dropping their cable TV services in favor of accessing à la carte content over the internet, from such providers as Hulu, Netflix, and Amazon Prime. Such over-the-top (OTT) content has been wreaking havoc on cable companies’ bottom lines. In response, cable companies have been increasingly buying up content providers and creating their own original content in order to better compete with OTT content providers.
This analysis examines the OTT Content Game, in particular,
(i) How will OTT content continue to evolve? and
(ii) How will cable companies respond to the increasing prevalence of OTT content?
Defining the Peer-to-Peer Economy
Description of the Peer-to-Peer Platform Game
Major Issues Surrounding the P2P Platform Game
Winners and Losers in the P2P Platform Game
The “sharing economy” is one of the hottest, new tech paradigms. How hot is it? I just Googled “sharing economy paradigm” and got 6,590,000 hits. Pretty hot.
In particular, Airbnb, Uber, and Lyft have been all over the media for challenging established regulations and driving traditional businesses into a tizzy for threatening their livelihoods.
Is the sharing economy really a new paradigm? Is it here to stay? If so, how will it impact the traditional economy? Who will be the winners and losers under the new paradigm?
The purpose of this analysis is to better understand the dynamics of internet platforms. The analysis considers the three basic types of platforms:
- Vendors (WalMart, Apple, Pandora, etc.)
- Social Media (Facebook, LinkedIn, YouTube, etc.)
- Matchmakers (eBay, Uber, etc.)
And will seek to address such issues as
- Who are the different players in each type of platform game?
- How do the players' actions combine to generate value in each type of game?
- Which types of platforms and configurations have the greatest value potential?
Remember when restaurant delivery options included Domino’s, Pizza Hut, or Papa John’s? No longer! These pizza restaurants still have a majority share of the restaurant delivery market. However, GrubHub, Eat24, DoorDash, UberEats, and other restaurant delivery platforms are expanding users’ choices beyond just pizza.
This analysis examines the Restaurant Delivery Game: Who are the Players and what are the issues?
Players
See Figure 1
Restaurants
- Traditional (Dine-In) Restaurants
- Takeout/Delivery Restaurants
♦ Takeout/Delivery Only (Virtual Restaurants)
♦ Dine-in or Takeout
A recent article in the NYT, "The Fight Over Who Sets Prices at the Online Mall" by Brad Stone, discusses that battle between manufacturers, who want retailers to abide by price floors, and retailers, who want the freedom to set prices as low as they please:
Wary of the Internet’s tendency to relentlessly drive down prices, major brands and manufacturers — and now, book publishers — are striking back, deploying a variety of tactics and tools to control how their products are presented and priced online…
A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.
Shopping Experiences: Bricks-and-Mortar vs. Online Stores
Let’s start with a comparison of shopping experiences in bricks-and-mortar versus online stores.
There are three major advantages associated with in-store versus online shopping experiences. First, buyers are able to handle the merchandise. This basic sensory experience reduces much of the risk associated with online shopping regarding not knowing exactly what you’re getting. Second, in-store shoppers are able to take immediate delivery of the items they buy – no shipping costs or delays. Finally, to the extent that shoppers need to return items, in-store returns don’t require any of the packaging or shipping costs associated with online returns.
A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.
In Playing the Same-Day Delivery Game Part 1, I discussed the differences between shopping experiences that take place in-stores vs. online, and I noted that same-day delivery services aim to provide shoppers with much of the convenience of online shopping, without the associated delays. I then discussed the Last Mile problem, which has historically been an impediment to the cost-effective provision of same-day delivery services.
In this part of my analysis, Part 2, I discuss various configurations of delivery networks.
Essential Components
The essential components required to operate any type of delivery network include (i) hubs, (ii) vehicles, and (iii) drivers. The minimization of costs associated with operating a delivery network entails several, simultaneous optimization problems involving these essential components.
A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.
In Playing the Same-Day Delivery Game Part 1, I discussed the differences between shopping experiences that take place in-stores vs. online, and I noted that same-day delivery services aim to provide shoppers with much of the convenience of online shopping, without the associated delays. I then discussed the Last Mile problem, which has historically been an impediment to the cost-effective provision of same-day delivery services.
In Playing the Same-Day Delivery Game Part 2, I discussed various configurations of delivery networks, including hub-and-spoke systems, aggregator systems, point-to-point aggregator systems, and point-to-point systems.
In this part of the analysis, I discuss the different options for delivery network operations and the barriers to adoption of same-day delivery services.
Delivery Network Operations
Suppliers who wish to provide delivery services to their customers currently have several options: (i) they can provide delivery services in-house; (ii) they can outsource delivery services to a third party; or (iii) they can provide modified delivery services, such as curbside delivery.
A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.
In Playing the Same-Day Delivery Game Part 1, I discussed the differences between shopping experiences that take place in-stores vs. online, and I noted that same-day delivery services aim to provide shoppers with much of the convenience of online shopping, without the associated delays. I then discussed the Last Mile problem, which has historically been an impediment to the cost-effective provision of same-day delivery services.
In Playing the Same-Day Delivery Game Part 2, I discussed various configurations of delivery networks, including hub-and-spoke systems, aggregator systems, point-to-point aggregator systems, and point-to-point systems.
In Playing the Same-Day Delivery Game Part 3, I discussed the different options for delivery network operations — in-house and outsourced, and others — and the barriers to adoption of same-day delivery services, namely, will enough customers and suppliers sign on?
In this part of the analysis, I discuss why same-day delivery services have just now (over the past several years) appeared in the marketplace.
The last mile problem has existed since the advent of transportation and communications systems. So why have same-day delivery start-ups suddenly been popping up now, over the past few years? In fact, what has changed since the late 1990s when Webvan, Kozmo, and other startups tried, but failed, to do the same thing?
There are two separate factors contributing to the recent renaissance of same-day delivery services. The first is the improvement in logistics technologies, which have vastly reduced the costs of operating delivery service networks. The second is the implementation of same-day delivery services by Amazon and Google as a means to other ends.
A copy of the full analysis can be downloaded by clicking on the link at the bottom of this blog entry.
In Playing the Same-Day Delivery Game Part 1, I discussed the differences between shopping experiences that take place in-stores vs. online, and I noted that same-day delivery services aim to provide shoppers with much of the convenience of online shopping, without the associated delays. I then discussed the Last Mile problem, which has historically been an impediment to the cost-effective provision of same-day delivery services.
In Playing the Same-Day Delivery Game Part 2, I discussed various configurations of delivery networks, including hub-and-spoke systems, aggregator systems, point-to-point aggregator systems, and point-to-point systems.
In Playing the Same-Day Delivery Game Part 3, I discussed the different options for delivery network operations — in-house and outsourced, and others — and the barriers to adoption of same-day delivery services, namely, will enough customers and suppliers sign on?
In Playing the Same-Day Delivery Game Part 4, I discussed why same-day delivery services have reappeared recently, after having been tried and failed in the late 1990s. In particular, I note that over the past decade, there have been tremendous advances in logistics technologies, which have significantly decreased the costs of providing same-day delivery services. I also note that Amazon's and Google's forays into the same-day delivery market are driven by more than just providing delivery services. Specifically, the two companies are seeking to (i) grab a share of the grocery market, (ii) increase their respective shares in the product search market, (iii) generate access to consumer use data, (iv) generate direct access to consumers, and (iv) generate spillover effects to other parts of their technology ecosystems.
In this part of the analysis, Part 5, I discuss how I think different aspects of the same-day delivery game will evolve.
Will Enough Consumers Sign Up?
Will enough people participate in same-day delivery services and use them frequently enough to support operations?
The Yelp Controversy
Yelp is a social media site that hosts information about local businesses and lets customers post ratings and reviews about their experiences with those businesses. Yelp uses a proprietary algorithm to sort through posted reviews and filter out those that might be fake or inauthentic. Reviews that do not pass muster are relegated to a separate folder and not used to calculate businesses’ average ratings scores, which are prominently displayed to users.
Yelp generates revenues to support its business primarily through sales of ads to local businesses that appear on Yelp’s website.
Business owners have alleged that in an attempt to coerce (extort) businesses into advertising on Yelp’s site, or in retaliation for ceasing to advertise on its site, Yelp manipulates those businesses’ reviews by either
i. Filtering out good reviews and relegating them to the “reviews not recommended” folder, thus hiding them and lowering businesses’ average star ratings,
ii. Highlighting bad reviews of the businesses, and/or
iii. Creating fake bad reviews.
Business owners allege that Yelp’s manipulations of the businesses’ reviews have resulted in lost sales for their businesses.
Yelp vehemently denies accusations that it extorts business owners or manipulates the filtering of reviews.
This analysis examines the Yelp Ratings Game. First, I describe the shift in customer relations that the advent of social media has engendered. Next, I describe how the Yelp site works. Then I discuss possible reactions by business owners to having ratings and reviews by users about their businesses posted online and perhaps manipulated to the business owners’ detriment. Finally, I discuss some other interesting issues related to the Game.
The Buy vs. Rent Decision
Actual vs. Potential Value of Durable Products
Characterization of Potentially Collaborative Products
There has been an increasing trend toward collaborative consumption (aka the sharing economy), which, which Wikipedia describes as follows:
The term collaborative consumption is used to describe the cultural and economic force away from 'hyper-consumption' to re-invented economic models of sharing, swapping, bartering, trading or renting that have been enabled by advances in social media and peer-to-peer online platforms.
Amazon
Apple
Amazon vs Apple
I just Googled “Apple” and “Amazon” and “war” and got 40,300,000 hits, with such headlines as
The Amazon-Apple War Is On
New $199 Amazon Kindle Stokes Fire in War With Apple
Amazon Ignites Tablet War with Fire, Takes on Apple
The Amazon-Apple War Is On - Kindle Fire, 7" Tablet for USD199
But is Amazon’s release of its new tablet really an indication that it is trying to take on the Apple ecosystem? I don’t think so.
Price Dispersion
Has the Internet Caused Price Dispersion to Decrease?
What Determines Retail Price?
How Big Is e-Commerce?
Conclusions
A recent article in the WSJ, “iPhone-Wielding Shoppers Strike Fear Into Retailers” by Miguel Bustillo and Ann Zimmerman, discusses the increasing incidence of in-store shoppers using their smartphones to compare in-store prices to prices available from other retailers on the Internet, and it claims that such consumer behavior “is threatening to upend the business models of the biggest store chains in America.”
