For aspiring artists the world of music marketing can almost always be linked to an online audience.
Ken Dardis, President of Audio Graphics Inc., spoke with us about what artists should be doing and the mistakes many new artists are making when distributing music online. It’s small websites like these that the majority of artists are giving small attention to.
Dardis also encourages artists to fully understand social media websites and other sites before self-marketing their music. When it comes to online distribution it is essential for an artist to conduct heavy research. Each day, local business owners and managers juggle the time-consuming yet critical task of managing promotional offers.
The GroundLevel service is an online and mobile service that allows local merchants to quickly and easily link their media accounts to a single source.
GroundLevel Technologies is a premier source for real-time, location-enabled data collection and management of local merchant promotional offers. First off, the X with the line over it is the symbol for Xbar which is the sample mean (average) of our data set and can be quickly calculated in MS Excel: = average(data set).
The s is our sample standard deviation which can be easily calculated in MS Excel: =stdev(data set).
Once we get to the t distribution table we will also see “df” which stands for degrees of freedom.
This study offers to explain the change in lenders' views and the implications for California's agricultural sector, and to provide information about the kinds of calculations farmers need to make in assessing market opportunities. In California, this shift has led to emphasis on the business risks faced by agricultural producers, including (1) production and yield risks, (2) market and price risks, and (3) income risks. In particular, lenders are paying more attention to the volatility in incomes of certain crops, rather than expected farm income levels. Lenders will (and borrowers must) consider both absolute risks and relative risks in specific enterprises. The value of k is usually made zero, but it can be made some other critical level of income. The calculated z score is looked up in a statistical table for the relevant distribution of incomes to get the PL estimate. As noted, the PL is an absolute measure of income risk for an enterprise in a specific market.
Production and price data are combined with average cost data for each product to generate estimates of income. In table 2 and table 3, price and net income data are summarized for a cross-section of field crops and tree and vine crops, respectively, to illustrate the absolute risk in producing each enterprise. Interpretation of PL results in the last columns of table 2 and table 3 is straightforward.
Another aspect of the results, which comes from using historical data and a statistical table to find the PL, is that PL values will range between 0 and 100%, but will never be either 0 or 100%. On the other end of the distribution, a grower should not consider any crop with a PL of 50% or greater unless the grower expects better-than-average results. Lenders are diversified across products and locations, so they are concerned with relative risks, as well as absolute risks, in lending to a particular grower. Ranking enterprises within a county relative to their PL is a means of rating the riskiness of the grower's chosen enterprise versus the alternatives available. Although no lenders have completely withdrawn from the agricultural sector, large diversified lenders have tightened loan requirements, causing some borrowers to be dropped as customers. When making investment decisions, both lenders and borrowers have always studied income levels, adjusted by risk. Develop a basic understanding of the properties of a sampling distribution based on the properties of the population. In this simulation, 100 numbers are either sampled from a normal distribution or a uniform distribution. This simulation samples 100 values from a normal or uniform distribution and calulates the the Chi Square value. The Internet has become the launchpad for new independent artists pushing their way to the ears of new listeners all across the world. Dardis has a rich history in multiple avenues of music including decades of experience in the radio industry and as a musician .
If you are going to hire someone to do that there are many companies that claim to have an inside track to nightclubs and radio stations,”Dardis explains. RRadio Music gives independent artists the opportunity to submit their music and potentially get played not only on the site but on one of the radio subscriber channels as well for free. It costs millions of man-hours for bands trying things and getting absolutely no success,” explains Dardis. An artist needs to fully understand how to utilize sites and take advantage of the smaller sites that can prove to be the most beneficial. The benefit is that the merchant is assured the promotional offer is as impactful as possible by reaching all visitors, followers and friends simultaneously and in real-time.


Promotional offers can also be simultaneously published on SpecialsAgent, the consumer portal for local merchant offers. Specifically, we talked about the difference between population and sample parameters and how they play a major role in understanding what a confidence interval is. Oscar and Ron discussed TWI, with a particular emphasis on the JR, or Job Relations component. To assist those looking for new market opportunities, a new study offers ways of calculating the kinds of financial risks that concern the lenders who read today's credit applications.
Estimates of income variability for a cross-section of crops are presented in an index farmers can use when deciding on what to produce.
The farm financial crisis of the mid-1980s and the Savings and Loan crisis have shown lenders the risks of holding predominantly real estate loans.
Traditional measures of risk are based upon the standard deviation (SD) of historical price and net income data for individual crops. For example, most of the time it is reasonable to assume that incomes are normally distributed; therefore, table 1 can be used. However, this measure facilitates a comparison of relative risks between crops and locations that is shown below with empirical examples from California markets. For most crops, data from two counties are presented to demonstrate the variability in results across locations. The values presented are the probabilities of suffering a loss for specific enterprises listed. Although some crops have low PL values, such as the 0.5% for carrots in Monterey County, none will have a zero value.
A PL value of 50% indicates that average income over the data period was zero; thus, on average, growers in that county made no money over the period. Table 3 indicates that Stanislaus County peach growers face greater risks than do Fresno County peach growers. Some have not been able to borrow the amounts they want, and interest rates have become higher for some growers than for others. Lenders, however, have traditionally weighted risk much higher in the decision process than have agricultural borrowers. Clearly, also, individual producers need to respond by incorporating the use of a risk analysis tool, such as the PL measure, in choosing enterprises. Compare the observed frequencies from the "From a Uniform Distribution" section to the expected frequencies. Simulate several experiments and see if the significance for the test of a uniform distribution is always significant.
As can be seen from the image below, the simulation begins by displaying a table with expected frequencies.
An artist is in a constant battle to stay on top of the ever changing technological advances that move the way music is distributed. Small websites, local blogs, and smaller local radio stations can help an artist build relationships with people who will help push their music to new listeners and their career to new levels.
They have to know things like email distribution, email subject lines, search engine optimization, and how to assemble data banks for their user banks. This has led to an increase in artist attempting to market their music but failing to use the tools available in the appropriate way. Artists need to be prepared for changes that come with new technology and how to respond to them.
Coordinating distribution to their website, blog and social media resources makes the task even more challenging.
Based in Evanston, Illinois, GLT delivers an online and mobile service via a team experienced in technology solutions and the promotion marketing industry. In this formula we take the square root of n, or our sample size, which can be done in MS Excel: =sqrt(sample size).
The result has been a shift from the common practice of lending on equity to lending on income.
This illustrates that credit analysis has shifted from the borrower's balance sheet to the income and cash-flow statements.
Combining SD with mean (average) net income data over a time period enables a new absolute measure, probability of loss (PL), to be calculated for a product's market. The data were collected by county Cooperative Extension staff of the University of California. Costs per acre (C), as reported in Extension budgets published for each crop by county, include total fixed and variable costs of production.
For example, Fresno County alfalfa hay producers have a 33.4% (one out of three) chance of losing money in any particular year, according to historical data.
This is a characteristic of statistical tables for distributions, but in this application it reminds us that there is always some chance of suffering a loss in agricultural production. Even though above-average growers were making money, such a high PL indicates that risks incurred may not be justified by income level. In general, the method is to rank a product in two ways: (1) A product's risk is ranked according to its PL relative to the entire list of crops grown in the county, and (2) all production regions for a single enterprise are ranked according to their PLs.


For example, table 2 and table 3 list a number of crops grown in Fresno County with peaches ranked best in terms of PL. Even though interest rates have trended downward for some time, rates have not fallen equally for all enterprises due to perceived differences in risk among products. This means that those producers that large diversified lenders consider risky will have to seek capital elsewhere with their best prospects being small, local lenders. Lenders have a shorter investment horizon than borrowers and, hence, are less willing to risk loss (loan default). The expected frequencies based on both a normal distribution (on the left) or a uniform distribution (on the right) are shown just to the left of the observed frequencies. In today’s music world it is now more important than ever for artists to fully understand the multitude of options available for online music distribution. Dardis has taken note of the changes in the music industry, paying close attention to how music is being distributed and what artists are not fully aware of. From his experience and research Dardis finds it more effective for up and coming artists to reach out to smaller online or local sites which can eventually spark a buzz that will lead them to mainstream outlets.
There are new sites and technology created every day that can change the way an artist is currently marketing themselves.“Once something has change you have to go back to what you learned to see how it affects it,” says Dardis.
For this reason Dardis insists, after an artist grasps hold of how to take advantage of a site, they have to ask, “How much time should an artist devote to properly use a site so that it is the most efficient when they sit down to spend time with it?
While online marketing is difficult, learning how to take advantage of small and local sites is a great starting point for artists to begin distributing their music online.
In most cases, we are interested in 95% confidence intervals which means our alpha is 0.05 (5%). Lenders no longer want to foreclose on property and take their chances selling in real estate markets that may decline rather than rise as they did in the past. This measure indicates the chance (in percentage terms) that an average producer in a particular county will generate a negative annual net income from a specific product.
If some other value is used for k, such as the amount of income needed to cover the payments on a new loan under consideration, the PL found represents the probability of earning insufficient income to cover k; in other words, the PL would indicate the chance of defaulting on the loan.
If the year being forecast is expected to be typical of years in the data set, PL results are good measures of income risk. This means that lenders concerned only with the risk of default will favor peach producers over other potential borrowers in the county.
This also helps explain differences in credit availability and interest rates across locations.
The drought and the freeze have caused many lenders to re-assess the risks involved in production across products and locations. This situation creates the danger that over time small rural lenders will accumulate much riskier loan portfolios than large lenders, making the rural banks more likely to fail if they suffer loan defaults. Thus, Fresno growers with land suitable for lettuce could increase their profits and lower their risk of loss by shifting from hay and corn into lettuce. Compare the observed values in the "From a Normal Distribution" section to the expected values. Unusual circumstances, like a drought, may raise the level of income risk (the PL), but the absolute amount of increase is not predictable. This could mean producers of other crops will have more difficulty in gaining loans or they may have to pay a higher interest rate than peach producers to compensate the lenders for accepting the higher risk. To avoid such risk, rural lenders may have to turn borrowers away, and some agricultural producers will be without sufficient capital to operate effectively. For the same reasons, lettuce growers in Monterey County may be better off shifting from lettuce to carrots. This shift in credit requirements increases competition among producers across crops and locations when a diversified lender is deciding to whom it will extend credit. If so, then the null hypothesis that the numbers were sampled from a uniform distribution could be rejected.
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They continue to market the way they began doing things even though certain internet changes have altered that marketing scheme.
The index used is that reported in the Economic Report of the President, 1988, adjusted so that 1986 = 100.
The fact that pistachio production was being established during the historical data period may have biased results downward.
Although such competition improves efficiency in statewide resource allocations over time, it also creates some dislocation in cropping patterns and in the structure of resource allocation among market participants.
Of course, in this simulation, you know where the numbers were sampled so you know the null hypothesis is false. But our regualar clients who can sign up for the service on an ongoing basis - the monthly charges are 309 USD only.



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