Winning the Hardware Software Game Winning the Hardware-Software Game - 2nd Edition

Using Game Theory to Optimize the Pace of New Technology Adoption
  • How do you encourage speedier adoption of your product or service?
  • How do you increase the value your product or service creates for your customers?
  • How do you extract more of the value created by your product or service for yourself?

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  • Anonymous said More
    Well written. Well constructed. Tuesday, 13 August 2019
  • Ron Giuntini said More
    As always a good read.
    I have always... Thursday, 25 January 2018

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.”

Until recently, retailers could reasonably assume that if they just lured shoppers to stores with enticing specials, the customers could be coaxed into buying more profitable stuff, too.  Now, marketers must contend with shoppers who can use their smartphones inside stores to check whether the specials are really so special, and if the rest of the merchandise is reasonably priced…

Some of the most vulnerable merchants: sellers of branded, big-ticket items like electronics and appliances, which often prompt buyers to comparison shop. Best Buy, the nation's largest electronics chain, said Tuesday that it may lose market share this year, a downward trend that some analysts are attributing in part to pressure from price comparison apps…

Smartphone fans … are still a small subset of shoppers. It remains unclear whether large numbers of Americans will be willing to take the extra time to compare offers with mobile programs. Some consumers may want to deploy the technology only when buying expensive or unusual items…

"Only a couple of retailers can play the lowest-price game … This is going to accelerate the demise of retailers who do not have either competitive pricing" or a standout store experience…

Do in-store price comparisons via smartphones represent the dire threat the authors contend they do?

More specifically, to what extent will on-the-spot price comparisons cause either/or

  • Retailers to lose sales?
  • In-store prices to drop?

 

Price Dispersion

As of the week of December 26, 2010, 4th of July by James Pattersons’ and Maxine Paetro is at the top of the NYT Hardcover Fiction bestseller list.  A new, hardcover copy of this book is about as standardized or homogenous a product as you can get.  One would think, then, that the price different sellers charge for the book would have to be the same – who would buy a higher-priced copy when lower-priced copies exist?

An Internet comparison of prices for this book on December 28, 2010 yielded the following information:

4th_of_July_Ps

The price dispersion, that is, the variation in prices across sellers for the same item at the same point in time, for the book is enormous!

 

Has the Internet Caused Price Dispersion to Decrease?

The cost to consumers of finding information as to who is selling what and for what price has been considered a significant contributor of the price dispersion that has traditionally existed in the marketplace.  Since the Internet greatly reduces these cost of consumer search, it was predicted that the Internet would have a large impact on causing sellers’ prices to converge around the lowest price offered.  As such, there has been a great deal of research examining how price dispersion has changed in bricks-and-mortar stores and in on-line stores with the increasing penetration of the internet.

A review of empirical research on price dispersion was published in the Journal of Interactive Marketing in 2004, “Price Dispersion on the Internet: A Review and Directions for Future Research,” by Xing Pan, Brian T. Ratchford, and Venkatesh Shankar.

Here’s a summary of the research comparing price dispersion online versus off-line:

In summary, substantial price dispersion has been observed on the Internet. In general, Internet markets exhibit no smaller (and in many cases larger) price dispersion than traditional markets. It appears that greater information flow and easier consumer search facilitated by the Internet has not made online markets more competitive and “frictionless” as predicted by theory. However, as cautioned by authors of many of these studies, the findings may be a result of the immaturity of Internet market and due to the lack of stable market equilibrium in prices.

Here’s a summary of the research examining the behavior of online price dispersion over time:

Based on the findings from these studies, we conclude that online price dispersion is a persistent phenomenon across categories and over time, regardless of the number of retailers in an online market. Although the magnitude of price dispersion has declined somewhat as Internet markets have grown over time, it continues to be substantial.

 

What Determines Retail Price?

To better understand the impact that on-the-spot (smartphone) price comparisons will have on in-store retail prices, I need to understand what determines the price different retailers charge for similar products.  If the prices offered by competing retailers serve to significantly constrain each others’ prices, then the ability of shoppers with smartphones to easily compare prices while in stores might very well put downward pressure on in-store prices.

Each product available for sale, even the most seemingly homogenous of products, can be considered a bundle of characteristics, which include

  • the specifications and features of the product itself;
  • the physical location of the seller (distance from the buyer);
  • the total price charged by the seller (including any associated sales tax, shipping, and handling charges);
  • terms of payment (cash only, credit card, financing, etc.);
  • terms of return (full refund, re-stocking fee, store credit only, shipping/delivery charges, etc.);
  • timing of delivery (in stock, on backorder, delivery taken on-site by consumer, mail-order, etc.);
  • ability of consumers to touch and/or demo the product;
  • availability of sales assistance (information);
  • etc.

Even if the product itself is the same, the bundle of associated offerings with which it is sold will vary across sellers.

The price any given seller charges for the product will depend on

  • the cost of providing the bundle of characteristics, where higher costs mean the seller must charge a higher price to earn a profit;
  • the number of different potential consumers of the product bundle, where more potential buyers enable sellers to charge higher prices;
  • the value buyers place on the bundle of characteristics, where higher consumer values will enable sellers to charge higher prices;
  • the prices charge by other sellers for similar product bundles, where lower prices for available alternatives will put downward pressure on the price the seller can charge for his product bundle;
  • the selling strategy or reputation of the seller; prices may be lower, for example, if the seller is trying to be a low price leader or if he uses the product bundle at issue as a loss leader; alternatively, prices may be higher if the seller offers valuable associated services or has otherwise established a good reputation as a high quality seller.

The price any buyer will be willing to pay for a given product bundle will depend on

  • the value the consumer puts of the specific bundle of characteristics;
  • the cost of finding information on which other sellers have similar product bundles, together with the associated prices;
  • The relative net values (value of product bundle less cost of product bundle less cost of finding product bundle) of other similar product bundles.

The persistence of significance dispersion of prices, even for homogenous products, even after the has decreased search costs, suggests that (1) retailers differentiate themselves by offering different bundles of services and/or pursue different selling strategies, and (2) customers differ in the values they place on the various bundled services; otherwise, prices would converge over time to the “agreed upon” value.

 

How Big Is e-Commerce?

With the advent of the Internet, retail sales now come from different types of sellers:

  • Pure bricks-and-mortar (“off-line”) sellers
  • Pure on-line sellers (e.g., Amazon, eBay, etc.)
  • Multi-channel retailers (retailers who sell both off-line and on-line) (Barnes and Noble, Wal-Mart, Sears, etc.)

Only if the on-line market is “big enough” can the existence of on-line retailers (either pure-play on-line sellers or multi-channels sellers) have a significant impact on off-line retail sales prices.  In other words, if a million people want copies of that latest New York Times bestseller mentioned earlier, but there are only 100 copies available on-line, then the price that on-line sellers are charging cannot have a meaningful impact on off-line sales prices.

To see how large a portion of total retail sales the on-line segment constitutes, I looked at census data on US retail sales.

The category Retail Trade (NAICS #44) is divided into 12 segments.  The census data also include Food Services and Drinking Places (NAICS #722) in the retail sales category.  The 13 categories included in the census data are:

 

Note: A specific description of NAICS codes for Electronic Shopping and Mail-Order Houses contained in the 454 Nonstore Retailer category indicates: “Store retailing or a combination of store retailing and non-store retailing in the same establishment--are classified in, Sector 44-45--Retail Trade, based on the classification of the store portion of the activity.”  I interpret this to mean that for multi-channel retailers, their on-line portions of sales are included in the 454 category.

Census data for US retail sales were available for 1992 through November 2010.  I used the CPI to convert 1992 dollars into 2010 dollars and compared the real 1992 dollars per category to those in 2010.  I sorted the categories by size of 2010 sales and stacked sales for the categories, putting sales for the largest category in 2010 on the bottom.  US retail sales grew from $2.7 trillion in 1992 to a little over 4 trillion in 2010, a 46.7% total increase, or an average annual increase of 2.1%.  Here’s what the numbers look like:

 

The number in parenthesis after the category name in the chart is the total 1992 to 2010 growth in sales for that category.  Since total sales grew at 47% over the period, any category that grew less than 47% grew at a slower rate than average, and any category that grew more than 47% grew at a faster rate than average.

Unsurprisingly enough, sales for Nonstore Retailer grew the fastest by far during the period, by a total of 251%.

Other categories with large growth include:

  • Health and Personal Care Stores with 98% growth
  • Gasoline Stations with 89% growth
  • General Merchandise Stores (department stores, warehouse clubs, and supercenters) with 85% growth
  • Food Services with 85% growth

Categories with relatively low growth include:

  • Food (Grocery) Stores with 5% growth
  • Motor Vehicles with 6% growth
  • Furniture and Home Furnishing Stores with 6% growth

I checked the breakout in sales for the Nonstore Retailer category to verify that most of the growth has been in mail-order and e-commerce, rather than vending machines and direct selling, all of which are included in this category.  Sure enough, most of the sales growth for Nonstore Retailers during the 1992 – 2010 period was, indeed, for mail-order and e-commerce:

 

HOWEVER, while mail-order and e-commerce sales have grown very rapidly over the past two decades, and much faster than any another retail category, the Nonstore Retailers category still only accounts for a small portion, 8.1%, of total 2010 US retail sales.  If you take out Vending Machines and Direct Selling from sales for Nonstore Retailers, your down to about 5.6% of total retail sales accounted for by mail-order and e-commerce.  And if you take out mail-order that’s not e-commerce, that is, sales generated from mail-order catalogues, toll-free telephone numbers, and television advertisements, then nonstore e-commerce sales would probably account for only a few percent of total US retail sales.

 

Conclusions

In summary, I have determined that

  • There is a substantial amount of price dispersion in the marketplace;
  • Even though the Internet has decreased search costs, the amount of price dispersion even on the Internet “continues to be substantial”;
  • When products are sold in the marketplace, they are bundled together with various other services; differences in the values consumers place on the bundled characteristics contribute to price dispersion.
  • Sellers employ a variety of different sales strategies; differences in sales strategies across sellers contribute to price dispersion.
  • While e-commerce has grown faster than any other retail sales category over the past two decades, it continues to constitute only a small portion of total US retail sales.

Now I’m prepared to answer the questions I initially proposed:  To what extent will on-the-spot price comparisons cause either/or

  • retailers to lose sales?
  • in-store prices to converge to the lowest price point?

Based on my analysis, I would conclude that the retailers who are most likely to lose sales from consumers who use smartphones to do in-store price comparisons are those retailers who purport to be the lowest price sellers at the point in time when shoppers are doing price comparisons.  In contrast, sellers who bundle products with services that consumers (continue to) value will be less likely to lose sales resulting from in-store price comparisons.   As such, I would not expect prices to converge to the lowest price point.

What characteristics would be conducive to higher prices in stores than on-line and/or to general price dispersion of products across retailers?

  • Unique items sold exclusively by certain retailers;
  • Immediate availability of items purchased in-store;
  • No shipping charges for items purchased in-store (where shipping charges exceed sales tax);
  • The provision of in-store sales services valued by consumers (product information, demos, easy returns, availability of complementary products/services);
  • Ability to touch and demo products;
  • Items sold in stores that are not conducive to shipping (i.e., the cost of shipping would be greater than the value of the item), such as items that are large, heavy, bulky, perishable, etc.;
  • High search costs (in this case, high gas prices). 

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