Pardon the extensive quotes of Marx here, but I need to establish a theoretical basis for the argument that will follow:
Perhaps, I am misinterpreting what Marx is saying here, but it seems to me he is stating what serves as money has significance to consideration of the price-form itself. If an ounce of gold has 15 times the labor contained in it as is contained in an ounce of silver, the price of a commodity denominated in units tied to an ounce of gold will be 1/15th that of a price tied to an ounce of silver. On the other hand, a given price using the gold standard, will have 15 times the value of that same price using the silver standard.
In both cases, the price of the commodity may be one dollar, but one dollar using the gold standard contains 15 times the value of one dollar using the silver standard. In both cases, one dollar may be the “money name” of an ounce of gold or an ounce of silver, but the value differs significantly depending on which metal serves as the standard of price. The term “one dollar” will represent more or less labor time depending on the commodity serving as the price standard. This relation, of course, is entirely independent of the commodity to which the price $1.00 is attached, although it has significance for it as well.
If it takes 15 hours of labor to produce one ounce of gold, 1 hour to produce one ounce of silver and ten minutes to produce one ounce of copper; the same price $1.00 will express far different labor times when an ounce of gold, silver or copper is the standard for one dollar. The same “money name”, $1.00, will alternately represent 15 hours, 1 hour, or ten minutes of labor. We cannot know what $1.00 signifies in terms of labor time, unless we know the standard to which this price refers.
So, the Marxist theoretician Fred Moseley is almost entirely wrong when he states non-commodity money can express the value of a commodity. He is right insofar as the price of the commodity expresses the value of the commodity but he is wrong to assume this expression is equal generally to the socially necessary labor time contained in the commodity itself. Non-commodity money does indeed always express the value contained in the commodity, but it always express this quantity as zero hours of labor time. No matter the quantity of non-commodity money we are dealing with — the price of a pair of shoes, a house, an aircraft carrier, or the US trade deficit with the People’s Republic of China — the quantity of non-commodity money to which we refer expresses the value of any of these as zero hours of labor.
This does not mean the commodities have no socially necessary labor time, nor that their values have ceased to exist; it simply means, this value was detached from the price of the commodity, when token money was detached from its commodity.
Value, socially necessary labor time, is dark matter in our economy — we cannot see it, but can only infer its existence. We infer its existence from the countless acts of exchange of unlike use values in the market. Since these use values are themselves not commensurable, we infer there is some hidden thing allowing us to compare them as trade-able goods. Money relations make this hidden other thing, socially necessary labor time, visible to us, but in a way this hidden other thing appears as a quality of money.
Moseley has absorbed this much of Marx’s theory, but then he stumbles, and, facing the puzzle of non-commodity money — he seems to forget the function of money in expressing socially necessary labor time, depends entirely on the thing serving as money. The circulation of commodity money is a reflex of the circulation of commodities themselves and dependent on this latter. In Marx theory, when the circulation of commodities increase, all things being equal, the amount of commodity money pulled into circulation must increase, when the circulation of commodities decreases, the quantity of money in circulation decreases.
There are, of course, a number of qualifiers to this which do not concern me right now — I am trying to get the general drift.
Generally speaking, just as the value of the commodity is expressed in a definite quantity of gold, so the total value of commodities in circulation, determined the total quantity of gold in circulation. This rule, however, DOES NOT apply to non-commodity money — and Moseley should know this. He should know it, because in chapter three of Capital Marx states:
Note here, Marx does not state the quantity of tokens of money in circulation is determined by the sum of values in circulation; rather, he says a given quantity of tokens will represent a greater or lesser quantity of value depending on fluctuations in the circulation of commodities. Token money, therefore, does not behave at all like commodity money, because it has no value of its own and cannot reflect in itself the value of the commodities for which it is exchanged.
But, surprisingly, Moseley takes this very section of Capital and attempts to erect a theory for how non-commodity money can serve as its own measure of value, and standard of price. And, he does it by turning Marx completely on his head, by proposing something he and other Marxist academics call, The Monetary Expression of Labor Time (MELT). This argument says Marx held to the idea that money had to be a commodity, but this is not a necessary feature of his theory of money. They are, I admit, trying to “salvage” Marx’s theory in an age where non-commodity money is the rule, and to defend Marx against charges his theory is irrelevant or anachronistic.
But, in so doing, they neglect Marx’s warning, issued in the words of Marie Anne de Vichy-Chamrond, marquise du Deffand. Madame du Deffand, upon hearing of the miracle of St Denis, who, having had his head chopped off, picked up his severed head and walked six miles, preaching the Gospel, observed:
Marx’s warning in this regard is apt: the use of non-commodity money is not the least difficult to explain, rather we have to explain how it came to exist in the first place.
Is Your Database In Great Shape?(Brief Article)
Direct Marketing September 1, 1999 | Oglesby, Wm. Ellis With the advent of the Internet, data cleaning and enhancement services are becoming available to companies of all sizes. Here’s how an online service can get your database in the best shape possible.
Real world consequences of bad data:
* A contractor in Newbury Park, California, recently ripped the roof off the wrong house;
* The Austin, Texas school system mailed 35,000 report cards to the wrong parents;
* IRS tax refund checks fail to reach 100,000 recipients each year;
* Repair workers went to the wrong address after the company was notified of a gas leak. No repair was made, and the leak caused an explosion that destroyed a house, killing both occupants;
* Last May, The United States inadvertently destroyed the Chinese embassy in Kosovo;
* Ten to 20 percent of all advertising mail in this country is undeliverable, and returned to sender.
“The state of data quality in today’s businesses is abysmal and its getting worse,” says leading international data quality expert Larry P. English, author of Improving Data Warehouse and Business Information Quality (John Wiley & Sons, New York, 1999) and CEO of Information Impact International. According to English, the average marketing database contains 15 percent bad data, and it’s costing businesses about 10 percent of their revenue. This is particularly costly for direct marketers who depend on their databases for their livelihood.
Fortunately, the problems of data quality are solvable. By building a mature data quality environment, businesses can dramatically increase the ROI on their marketing activities and boost the bottom line.
For years, data quality initiatives were the sole domain of large organizations with millions of records in their data warehouses, because high startup costs kept many tools and services out of the reach of small to mid-size database users. Recently, the Internet and other technological improvements are making data quality available to all types of direct marketers.
Here are 10 steps you can take to improve the quality of your direct marketing database. The following data quality program is an example based on procedures implemented within HotData, the Internet-based data cleaning and enhancement service that integrates directly into sales and marketing software applications.
Once “HotData-Enabled,” these applications are able to access data from HotData’s online warehouse, which stores data from Dun & Bradstreet, Polk, Experian, U.S. Postal Service and other leading data providers. HotData uses this information to complete, correct, update, and enhance mission-critical customer and contact data over the Internet, using secured connections, and protected by state-of-the-art RSA cryptography.
Whether you use HotData, another data enhancement service, or implement these techniques yourself, you can improve your data quality, decrease needless frustration and boost the ROI of your direct marketing activities.
Step 1: Put Someone in Charge Next to human resources, information is arguably a business most strategic asset. Isn’t it odd that businesses so rarely have professionals responsible for it?
Change doesn’t happen by itself. Before you embark on a data enhancement project, put someone in charge, and make sure that person has the authority, resources and education to do the job right.
Maintaining data quality is process, and no database is ever perfect. Not for long, anyway. Do not ignore the data quality problem because you can’t fix it completely. Any improvement can have measurable results. As always, you should enhance your data in the order of greatest marginal return, i.e. if you’re about to launch a massive direct mail campaign, clean the addresses before worrying about the phone numbers.
Step 2: Back Up Your Data Making a secure backup of critical data is one of the most important precautions a business can make, yet it is astounding how many companies fail to follow this simple procedure. As a rule you should back up your files at least once a week and immediately before any enhancement operations.
Step 3: Determining What You Need to Know All companies don’t have the same data needs. For example, stockbrokers need to know more about their customers than paper towel manufacturers do. Companies often have too much or too little data (or too much of the wrong data, which is even worse). The list of data items you need to support your business is called the “Data Definition” or sometimes “Metadata.” Entire books have been written on data definition as it relates to model completeness, support for operational processes, database design, entity relationships, ad infinitum. Here is a simple way to understand your data needs.
Make a chart showing the data elements you wish to maintain in one column and the uses of that data in another. Try to anticipate all the uses for the information, not just the immediate ones. For example, a photo lab has an immediate need for a customer’s phone number, but not for a customer’s address, because when the prints are ready, they call the customer. They don’t mail a notification. However, the company may later require the address for a direct mail campaign or to determine where to build a second franchise. Non-immediate data uses are called “downstream” uses.
After you have made this chart, rethink each item for relevance. Do you really need to store your customer’s middle initial?
Step 4: Standardize Once you know what data you need, you should also make some decisions on what it should look like. Remember, although you may know that the data “50K” and “50,000″ represent the same piece of information, your computer sees them differently. Maintaining a standard greatly simplifies querying and sorting, which makes your data more usable and more valuable.
Here are some suggested standards for various common fields:
Names Nothing irritates a customer more than seeing their name butchered. Barbra Streisand once closed her bank account because they spelled her name “Barbara.” Marketers should pay special attention to getting names right. When defining your standards, consider how the information will appear after a mail merge. Would your customer prefer “Dear SMITH, JOHNATHAN” or “Dear Johnny?” a. Use standard capitalization, not upper case.
b. Only use middle initial and suffixes when necessary.
c. Select titles (Mr., Mrs., Father, Monsignor) from a dropdown.
Company Names If you do not maintain standard company names, you can not have an accurate picture of your penetration into that company. ATT may be one of your largest customers, but you may not know it if the company name is written AT&T, ATT, A T T, and A T and T in various records. You may omit them from a mailing targeting your best accounts, or if you’re conducting test mailings, you could easily send them multiple mailers with conflicting offers.
a. Use company names consistently. Sorting your database alphabetically by company name simplifies the process of standardization.
b. Do not include suffixes like Inc., Co., and Ltd.
c. Include descriptors that are part of names, e.g. Dell Computer instead of Dell and Subway Sandwiches, not Subway.
Addresses According to the U.S. Postal Service, between 20 percent to 40 percent of addresses are in error if not been verified by an address matching program. This isn’t surprising, because there are so many ways to write an address and such strict postal standards. What is surprising is that so few businesses use address updating software. Non-standard addresses can needlessly delay mail delivery and increase postage costs.
a. Use U.S. postal standards. Always put the street address in Address 1 and Suite in Address 2.
b. Use directionals and locators (N.W., Ste. 7).
c. User standard two character state abbreviations.
d. Use correct suffix abbreviations (Aye, Blvd., St.).
e. Use ZIP+4.
Phone/Fax Numbers a. Include area codes on all phone/fax numbers including local ones.
b. Include country codes for international numbers.
c. Let your software enforce formatting, e.g. (###) ###-####, but don’t forget to check formatting of international numbers.
Numbers a. Store as integers. here irs tax refund
b. Let your software enforce formatting.
c. Use codes to denote ranges.
Step 5: Update Changed Addresses and Area Codes Since 1985, the North American Numbering Plan Administration (NANPA) has added over 100 new area codes, affecting over 120 million phone numbers. NANPA will be adding around two new area codes per month for the next 10 years. Outdated area codes can be worse than a daily annoyance. They can be disastrous.
A small company in Corvallis, Oregon, used broadcast faxing to recruit participants for focus groups in San Francisco and San Jose. They didn’t realize that the 408 area code had split, so less than half of the faxes went through. Of course, the broadcast fax software redialed each wrong number three times, dragging out the procedure all night.
Addresses change constantly, too. According to the U.S. Census Bureau, 16.5 percent of Americans relocate each year.
Most mail houses can run your list through the United States Postal Service’s change of address database. However, one problem that you might run into is that they rarely return the corrected list to you. Your database remains inaccurate.
Using an Internet-based data cleaning and enhancement service which integrates directly into your sales and marketing software, the new addresses can be automatically updated in your database without the trouble of exporting and importing your list.
Step 6: Compare Your List to a Reputable Source On average, 15 percent of customer data is wrong. The question is: which 15 percent? Determining which records are accurate and which are not is the most difficult task of data enhancement. The most efficient method is to compare your list to a reputable source.
Not only can third parties cleanse your data, they can also add high value customer intelligence in the process. For marketers who primarily target businesses, HotData, for example, appends a company’s annual sales, number of employees, year founded, SIC code, line of business description, CEO name and title, and more.
For those who market directly to consumer, HotData adds demographics such as age, marital status, and education level, home and car values, and buying tendencies such as mail order or credit card user. In addition, HotData adds psychographic “dimensions” that measure interest in athletics, fitness, culture, do-it-yourself, outdoors, technology, etc.
Pyramid Financial, a small financial services firm in Boston, Massachusetts, uses this data to market to insurance sales representatives and financial planners. Says President Steve Champagne, “This data really helps to target market. Knowing a prospect’s age group, family status, hobbies and interests, I can slant my pitch. For a group with a high athletic dimension, I might begin a letter with a quote from a sports legend. When marketing to a group with a high culture quotient, I’ll change the quote to Shakespeare.” Not all data sources are equal, so be careful whose data you use. Trusting your data to unreliable sources can have catastrophic consequences, as one Oklahoma City-based insurance company found out.
This company paid $400,000 dollars to market life insurance to the National Baptist Conventions USA’s 8.5 million African American members. In actuality, the only list available contained fewer than 15,000 names, many of whom were dead.
To solve the problem, the National Baptist Convention USA compiled a list from a CD containing 100 million American households that an administrative assistant bought at a computer store for $90. They tried to choose “black-sounding names” from cities where they believed the convention had many members.
The insurance company did six direct mailings to the bogus list at a cost of over $1 million. Recipients included the chairman of the board of the insurance company and a grand dragon of the Ku Klux Klan.
“It was the quickest and most negative response that I have ever received in my 27 years of direct marketing,” said the vice president of marketing of the insurance company.
When purchasing third-party data, stick to companies with a long history of commercial success. Here are five of the vendors who stock HotData’s virtual data warehouse:
Dun & Bradstreet Dun & Bradstreet (D&B), a company of The Dun & Bradstreet Corporation, is the world’s leading provider of business-to-business marketing, credit and purchasing information, and receiv-ables management services. The Dun & Bradstreet Corp-oration, which also includes Moody’s Investors Service, is headquartered in Murray Hill, New Jersey, and employs 13,000 people in 39 countries. Dun & Bradstreet is providing profiles on 11 million U.S. businesses.
Experian Information Solutions, Inc.
Experian is a quality supplier of consumer and business credit, direct marketing, automotive and real estate information services. Founded in 1996, Experian acquired Direct Marketing Technology Inc. (Direct Tech) in 1997 and Metromail in 1998, firmly establishing itself as a major source of list compilation and processing, computer services, database management, direct marketing, data mining, reference services, consulting and analytic services. With worldwide headquarters in Nottingham, U.K, the company emp-loys more than 11,000 people in the United States, United Kingdom, Continental Europe, Africa and Asia Pacific. Annual sales exceed $1.6 billion. Experian supplies access to a database of over 13 million U.S. business listings.
MAILER’S Software MAILER’S Software is dedicated to meeting the needs of direct marketers and bulk mailers to streamline list management, qualify mailings for maximum postal discounts, and increase response revenue through accurate, focused, target marketing. They specialize in simple, cost-effective software, databases, and list enhancement services — with a priority of customer service and support. Mailers provides HotData with a comprehensive list of changed area codes.
The Polk Company Founded in Detroit in 1870, The Polk Company is the oldest consumer marketing information company in America. The firm compiles lifestyle and other demographic data on more than 100 million consumer households across the United States and Canada. The Polk Company employs over 4,100 people in the United States, Canada, the United Kingdom, Germany and Costa Rica. Polk provides demographic and psychographic data covering more than 12 million addressed street segments in the United States as well as nationwide census and current postal boundary products.
Westminster International Westminster International is a leading direct marketing supplier providing data cleansing services to large volume mailers across North America. Their Address Redirection service provides the new mailing address for individuals and organizations who have moved since 1995. Using data licensed directly from Canada Post, this information can be invaluable to organizations wishing to maintain contact with clients/donors/members whose mail has been returned or finance departments trying to collect outstanding amounts from creditors who have moved.
Westminster will be providing HotData users with Canadian address correction, address standardization and address redirection services.
Problems Using Traditional Data Services First, exporting and importing lists can be an extremely complicated procedure, especially if the database has a complex structure. Many marketing departments don’t have access to IS technicians who are qualified to perform these operations.
This can even happen in extremely technical companies such as the software tools company discussed in the following example.
“The quality of our data was abysmal,” said their direct marketing specialist. “I would have loved to export my 60,000 names out of our fancy salesforce automation application. Unfortunately, it stored related information in dozens of different tables. Maybe, just maybe, I could have gotten the information out, but I never could have gotten the information back in again.” His solution: he paid a fortune to hire a small army of temps to verify each record by hand.
The second problem is matching every contact record to exactly one corresponding record in the information provider’s data warehouse. If the database search returns several Bill Smiths in Austin, Texas, for example, the data provider’s automated software can’t choose between them. Therefore it does not return a result, even though the correct information is in the system. For traditional list processing services, finding two matches is as useless as finding none. see here irs tax refund
Through its integration directly into applications, HotData overcomes this problem by prompting users to select the correct record from multiple matching records. This type of feature can boost match rates by 10 to 30 percent.
Step 7: Ask As usual, the best method is the simplest. Unfortunately, it’s the most expensive and time consuming. If you need to confirm information about a customer, just ask.
Some business people think of information gathering as a tug of war. The seller is struggling to get information and the consumer is resisting. The successful intelligence gather uses techniques more akin to judo than brute force.
You can also embark on a separate project solely for data collection. This tends to be expensive and time consuming. Don’t assume customers will return a fax survey or even speak with you simply because you call. Consider offering some incentive such as a T-shirt or a free upgrade.
In any case, you should make it clear to the customer about what you are going to do or not do with the data. A person is much less likely to give his address if he suspects you will share it with third parties. Fair use should always be outlined in an easily accessible privacy policy.
* Step 8: Preventing Decay Cleaning your database is a large accomplishment, but it will be short-lived if you fail to implement procedures for keeping it clean at the source, According to the second law of thermodynamics, ordered systems tend to disorder, and a database is a very ordered system. Contacts move. Companies grow. Knowledge workers enter new customer information incorrectly.
Without constant attention to quality, your information quality will disintegrate. This phenomenon is called information quality decay — the fact that quality of some data goes down when facts about real world objects changes over time, but those facts are not updated in the database.
This section discusses three methods of keeping your data clean: preventive maintenance, improving data acquisition events, and leveraging the information.
To keep your information current, you must update it on a regular basis. Specifically:
a. Update area codes at least once per month.
b. Check for changed addresses at least once per month and before any direct mail activity.
c. Confirm business profile information (revenue, number of employees, etc.) at least once per year.
* Step 9: Improving Data Acquisition Processes Some information simply starts out wrong, result of data input errors such as typos, transpositions, omissions and other mistakes. These are often easy to avoid.
In his new book, Improving Data Warehouse and Business Information Quality (New York, Wiley Computer Publishing, 1999), Larry English offers many suggestions for improving the data acquisition processes. Here is a paraphrasing of five of his best tips:
In the second case, the users registers directly on the manufacturers Web site. Which do you think has better information?
2. If you have data entry personnel, compensate them on accuracy as well as speed. It is not uncommon for an administrative assistant to hear something like this: “Why don’t you input the information from these business response cards into the database and then take the rest of the day off?” If speed is the goal, accuracy will suffer.
3. Post a list of standards near every data input terminal. It is impossible to maintain standards if you can’t remember what they are supposed to be.
* Step 10: Use it The best way for a direct marketer to keep valuable customer data pristine is to use it on a regular basis. The more you use it, the more value you will derive from it and the more attention you’re willing to give it. The data quality promotes itself. In addition, the more you use it, the more opportunities you will have to enhance it.
* Conclusion None of the information sources described here are new. Some have been around for over a century, but because of cost and barriers to access, they have been unavailable to a large segment of the business community. Now with the advent of the Internet, data cleaning and enhancing services are starting to be available not just to large corporations, but small and midsize companies as well.
While everyone is moving into the Information Age at their own pace, direct marketers who leverage new information technology can gain a strategic advantage in the marketplace over competitors who do not.
Finding ways to successfully implement these new technologies into a comprehensive data quality program not only increases the quality of your customer information, but saves time, reduces frustration, improves customer relations, and ultimately increases revenue.
Wm. Ellis Oglesby is the Marketing Director of HotData, Inc. He lives in Austin, Texas, with a Blue and Gold Macaw named Shakespeare.
Oglesby, Wm. Ellis
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