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Estimating retail breakeven using markup pricing.

An inexperienced entrepreneur planning the start of a small retail business often must rely on extraordinarily broad, imprecise assumptions and estimates when forecasting what it will take for the business to become profitable. There are many unknowns in this situation that are potentially dangerous. Yet every year, thousands of people launch into the deep waters of starting a retail business, perhaps blissfully unaware of some of the hazards.

Cost-volume-profit (CVP) analysis can be a good starting point in forecasting if the decision maker does not rely on faulty assumptions. In retail operations, the cost of goods sold (COGS) is one traditional way to estimate the breakeven point. But for the first-time entrepreneur who expects to sell scores or hundreds of different products, each one with a different cost of goods sold, it is impractical to try such analysis before operations begin. When the COGS is unknown in advance, only large, round numbers can be used for estimating. Many new owners of retail businesses have neither the luxury nor the knowledge to achieve precise estimates before they rent space and order inventory.

In these situations, markup pricing may be useful. Markup pricing involves adding a percentage to the cost of a product to arrive at a selling price. Also known as costplus pricing, markup pricing is a straightforward way to determine product prices. It could appear to make breakeven analysis unnecessary, for example, by assuming that competitors are marking up a certain product category by a particular percentage and still can be in business. Alternately, dividing products into 10 to 12 different categories, each with a related markup percent, can provide a better estimate of breakeven.

A CVP formula based on markup pricing can provide a practical solution for first-time retail entrepreneurs if information is available on the markup percentage for each product category and the estimated proportion of sales per category. Although it has drawbacks, the biggest advantage of markup pricing based on cost is that it simplifies decision making. For example, a 40% markup for a particular product line, even if there are many different products in the line, can be applied quickly without the need to consider the price on an item-by-item basis. Using markup pricing also may be seen as a way to prevent a price war if the entrepreneur believes that competitors use the same markup percentage. Another benefit of markup pricing based on cost is that it overcomes the inherent uncertainty that a first-timer would face in setting prices because the retailer may not be able to accurately predict future changes in costs or future sources of costs.

Though pricing based on cost is a handy, time-saving shortcut, it also has a downside. It takes an accounting point of view of profit, ignoring the opportunity costs associated with selecting one product line over another or selecting one type of business over another. Applying a markup percent may also lead to a false sense of confidence that the prices will lead to profitability: If that percent is chosen arbitrarily without the user thoroughly understanding the breakeven dynamics of the business, it can have the opposite effect and undermine profitability. The profitability of an entire industry can be impacted if retailers follow each other down this path. Finally, markup is a gross estimate, and gross estimates introduce uncertainty. When used in forecasting where uncertainty already exists, adding more uncertainty is far from ideal.


Pricing decisions typically are more complicated than simply marking up the wholesale cost of a product or product line. Retailers must think in terms of the overall perceived value being offered (value pricing) instead of individual prices or even product line markups or margins. That requires considering more than one piece of information, including competitor pricing on products that are anchors for customers' perception of overall pricing, consumer perception of value, and an understanding of which products most likely will drive customer traffic and which products are vital to the perception of assortment and quality.

In addition, the presence of suitable substitutes--such as the availability of online stores--affects price elasticity of demand. Yet very little information, if any, usually is available on price elasticity, leaving a firsttime entrepreneur to depend on assumptions. For the first-time retail entrepreneur, these and other factors can combine to turn the whole idea of margins into a mystery with which the entrepreneur must grapple.

Many of these dynamics of business are hidden to the first-time retailer. Lacking verifiable facts about reality, the entrepreneur must make assumptions about many things, such as:

* The nature of demand and how to stimulate it;

* Differences in elasticity on a product-by-product basis;

* Future costs if the business grows;

* Competitive responses to pricing, advertising, distribution, and product mix;

* Access to credit;

* The optimal inventory mix;

* Inventory turnover;

* Future cash flow;

* The impact of the sale of complementary products; and

* How much to discount prices in the future in order to stimulate demand if initial attempts fail to achieve expectations.

All these and more are "black boxes"--things that are unknown to the first-time retailer, who may have nothing more to go on except personal purchasing experiences, the intuition gained from general marketplace experiences, or the advice of friends and family.

Understanding the markup of each product line, as well as how all product lines collectively affect the chances of breaking even, can help the entrepreneur think through the pricing decisions.


Despite first-time entrepreneurs having all the unknowns just discussed, they still must calculate breakeven. But the approach I present in this article builds on the knowledge of the COGS method for calculating breakeven. So we must review that method before discussing how to use my formula for CVP analysis based on markup pricing.

The COGS method can be used to calculate breakeven with a known contribution margin (CM%), which in this article refers to the contribution margin ratio or equivalent percentage. The breakeven (BE$) point is calculated using the formula:

BE$ = FC / CM%


BE$ = breakeven point in dollars

FC = fixed costs

CM% = contribution margin

But what if the contribution margin is unknown, which is a likely possibility if the entrepreneur is planning to open a retail store for the first time? Can the markup be used instead? To find out, first consider the relationship between the contribution margin and markup.

The contribution margin is the proportion of the selling price that contributes to fixed costs. In this discussion, the markup is considered a portion of the cost of the product rather than the selling price. For example, looking at the last line of Table 1, a markup (MU%) of 25% on a particular product line does not generate a contribution margin (CM%) of 25% for that product line; it results in a contribution margin of 20%. The contribution margin of a product marked up is always less than the markup percentage.

If the markup is known and the full cost of the product is known or carefully estimated, it is possible to cal culate the selling price for the product using the formula:

SP = (C * MU%) + C


MU% = markup (based on cost)

SP = the selling pric

C = cost

The contribution margin (CM%) is CM% = (SP--VC) / SP, where VC stands for variable costs. The contribution margin for a particular product assumes that all variable costs are included in the contribution margin. When scaling up to include all products, this assumption also must be true.

The issue of what constitutes variable costs in a retailer is of concern when calculating breakeven. First, there is a difference when comparing some retailers with others. Second, the major caveat about the formula I present in this article is that the formula assumes that all variable costs are included in the contribution margin.

In a small retail store operation, the costs of the product from suppliers may be very close to the unit variable costs. But if the retail operation pays its sales clerks on or partly on commission, part or all of the labor costs for these staff members becomes a variable cost and needs to be factored into the calculation. For retail stores that sell high-volume, low-cost products and whose sales clerks are standing at the cash register waiting for customers to come to them with their purchases, a sales bonus does not make a lot of sense. In a boutique clothing store, however, management may want to offer sales clerks a sales bonus for helping customers decide which clothing best meets their desires. The labor cost of a stock clerk who prepares the goods for sale may also be a component in the cost of goods sold. The costs of managing inventory such as warehousing may be a variable cost, a fixed cost, or both.

If it is possible to estimate that the variable costs are essentially the cost of the product from the supplier, then use the formula:

CM% = (SP - C) / SP

Set the cost of the product equal to $1. Thus for every $1 in variable cost, where all variable costs are included, the following is true about markup percentage and contribution margin:

MU% = (SP - C) / C

Here C equals the cost of purchasing the product from the supplier plus all other variable costs, and

MU% = ($2 - $1) / 1 = 1, or 100%

The product markup percentage is 100% given the selling price ($2) and cost ($1). The contribution margin is different:

CM% = (SP - C) / SP

CM% = (2 - 1) / 2 = 1 / 2 = 0.5, or 50%

Notice that the selling price minus the cost of the product (SP - C) appears in both the markup percentage formula and the contribution margin formula as the numerator. Only the denominator is different with the markup ratio, which is based on the product's cost, while the contribution margin is based on the product's selling price.

Because the assumption is that the selling price will normally be higher than the cost of the product (except for unusual discount or sale situations, such as when the company is selling a product for less than cost to clear it out of inventory), the denominator of the contribution margin normally will be higher than the denominator of the markup ratio.

If one could use a known markup percentage as the starting point, it is possible to derive a mathematical relationship between the markup percentage and the contribution margin:

CM% = (SP - C) / SP

MU% = (SP - C) / C

(SP - C) = CM% * SP

(SP - C) = MU% * C

Since in this particular case of an entrepreneur starting a small retail business,

(SP - C) = (SP - C)

if the markup percentage is known, the contribution margin can be found using a standard, constant value for cost ($1) and deriving from this the selling price at the markup percentage:

CM% * SP = MU% * C


CM% = (MU% * C) / SP

Table 1 compares the markup percentage with the contribution margin. Holding the product's cost constant at $1 in order to compare the markup percentage with the contribution margin, the algebraic relationship between the two is used to populate fields in the table. The CM% / MU% column in Table 1 compares the values of the CM% column with the values of the MU% column. This gives us CM% as a proportion of MU%, illustrating that CM% is always less than MU%.

To visually illustrate the difference between MU% and CM%, the MU% and CM% columns can be graphed (see Figure 1). Both Table 1 and Figure 1 illustrate that:

* At lower markup values, CM% is closer to MU%; but for all practical purposes, CM% is always less than MU%.

* The difference between CM% and MU% increases as MU% increases. The greater the markup, the greater the difference between them.

* The slope (rate of change) of the CM% curve is always less than or equal to the slope of the MU% curve.


Instead of calculating breakeven, first-time entrepreneurs can use markup pricing to estimate breakeven with my CVP analysis formula. As I have noted, this is especially useful in situations where calculating the traditional cost of goods sold is impractical. In fact, it really does not matter what the actual cost of the product is for the purpose of estimating breakeven using my CVP analysis formula as long as one assumes that all variable costs are included in cost (C) for calculating CM%. I cannot overemphasize how important it is to ensure the validity of this within the retailer's particular situation. One can simply set C = $1. Additionally, one can replace SP with (C * MU%) + C since SP = (C * MU%) + C.

With this refinement, the following is the result:

CM% = (MU% * C) / SP

For every dollar value of variable cost (C), the contribution margin of that product can be found when the markup percentage is known. If SP = (C * MU%) + C and if C = $1, then CM% = (MU% * 1) / [(1 * MU%) + $1] and CM% = MU% / (MU% + 1).

This formula is useful in that it is not dependent upon knowing actual total variable cost (C) or selling price (SP). Instead, it shows the direct relationship between MU% and CM% when CM% includes all variable costs.

Now one can complete the construction of the allinclusive practical breakeven formula that uses markup percentage instead of CM%. Substituting MU% / (MU% + 1) for CM% in the traditional COGS formula produces the CVP analysis formula:

BE$ = FC * (MU% +1) / MU%


To see how this method of weighted average markup percentage works in an actual situation, I obtained data from a suburban retail store. The store has been in business for several decades, but management has changed hands several times since the store opened. The store's products include greeting cards, clothing, snacks, books, office supplies, software, gifts, and the like. One product line that is popular with customers is a private label line carrying the store's brand. In all, 12 product lines or "departments" are tracked using a point of sale (POS) system tied to a popular small business accounting software package. The store has a small mail-order service, and shipping costs are passed to customers for mail-order products sold.

According to the store's accountant, annual fixed costs for fiscal year 2015 were $478,944. These costs include salary, wages and benefits of employees, rent, utilities, travel, depreciation, and general administrative and advertising expenses. The POS software database contained information on the wholesale cost of products, the retail prices of products, the number of transactions, unsold inventory, and so forth, categorized by product line. To preserve anonymity for the store and its products, I used generic terms to describe the product line report generated by the POS software while still revealing the actual dollar amounts (see Table 2).

More than 90% of store revenue is accounted for by just three product lines (G, J, and L in Table 2). Over the years, the store's success has gradually been built on these high-demand products. Some of the store's products, including two of the three most popular product lines, now face vigorous competition from online retailers, and customers have become more price-sensitive when buying them. The store manager works hard to stay current on which specific products in the high-demand product lines customers want the most. Thus, while the markup percentage on some of these products is comparatively lower than for some other products that do not face the same level of competition, the specific mix of products carried at various times of the year is crucial to counteract what is assumed to be unfavorable price elasticity caused by competition, such as online retailers offering the same product at attractive prices.

While there are variances in demand based on holiday consumer behaviors, some products experience seasonal differences in demand that are not tied to holiday shopping patterns. Thus, the store manager makes it a high priority to select and stock inventory for these additional seasonal demand peaks.

The store offers low-cost, impulse-purchase products sold in small quantities, such as snacks. Customers also find it more geographically convenient to buy some products, including high-demand ones, from the store instead of from competitors.

While this is not a first-time retail store situation, the data illustrates the impact of using the formula for CVP analysis presented in this article in a real situation. When applying the weighted average markup percentage (35.86%) from Table 2, the formula generates the following result:

BE$ = FC * (MU% +1) / MU%

BE$ = $478,944 * (0.3586 + 1) / 0.3586

BE$ = $478,944 * 1.3586 / 0.3586

BE$ = $1,814,383

(The $1,814,383 figure is correct; it differs from the result of doing the math on the previous line ($478,944 * 1.3586 / 0.3586) due to rounding of MU%.)

Using POS data reported for just a few weeks of transactions in the store rather than for a full year, I calculated the weighted average markup to be 66.42%. Using this weighted average markup percentage to calculate the breakeven point for a full year of operations would have resulted in a dramatically underestimated result of BE$ = $1,200,028, which is 33% less than the BE$ estimate when using a full year of historical data. This shows the importance of considering the impact from errors in estimating markup percentage for a first-time entrepreneur.


Making a forecasting error can be potentially dangerous. Following the principle of being conservative when estimating for breakeven analysis, the most damaging error is to overstate the markup percentage. What happens to the breakeven point if that occurs?

Consider a hypothetical example involving the weighted average markup percentage for a very small suburban health food store.

A retail store may have several product lines that each use a different markup percentage. With multiple product lines, the retailer can estimate a weighted average markup percentage that will improve the precision of the breakeven analysis. This is done by multiplying the markup percentage for one product line by the estimated proportion of sales that product line is expected to generate. Repeat the process for the other product lines, and sum the results.

The first step is to estimate the fixed costs of operations. For this example, assume annual fixed costs of $250,000.

The second step is to estimate a weighted average markup percentage based on cost for each product line. For example, a natural foods store entrepreneur may decide to sell three product lines: vitamins and supplements, packaged natural foods, and bulk foods (nuts, grains, and the like). The retailer can get this information from distributors and other suppliers who work with similar businesses.

The third step is to estimate the relative proportion of sales (weight) for each product line. Estimates for this will depend on a host of assumptions, each of which is subject to an overoptimistic bias of judgment. If specific information is lacking, such as historical data in a particular local market, the entrepreneur can ask suppliers and other trusted sources for conservative estimates of the typical markup percentage. See Table 3 for the expected markup percentages and weights for sales of the three product categories.

The streamlined way to estimate breakeven is to use the formula: BE$ = FC * (MU% +1) / MU%. That results in the following calculation:

BE$ = $250,000 * (0.48 + 1) / 0.48 = $250,000 * 1.48 / 0.48 = $250,000 * 3.08333 = $770,833

Then the breakeven amount can be adjusted for the variable costs of sales taxes and bank card fees, each with a known percentage.

The weighted average markup can be calculated for traditional product lines and for other methods of grouping or bundling products. Products that the entrepreneur knows or believes are tied to consumer perception of value can be their own markup percent group or groups. Products that the retailer believes will drive customer traffic to the store can be treated as a separate product line. This method can accommodate groupings that are important to consumer decision making and a store's reputation. They can be separate product lines in the calculation.

Now look at what happens to the breakeven point if the first-time entrepreneur overstates the markup percentage. Assume that the markup percentage for three product lines is as follows: vitamins and supplements, 60%; natural foods, 40%; and bulk foods, 40%. Holding the proportion of sales by product line constant for the moment, if one overstates by 10% the markup for the three product lines, then the weighted average MU% is 52.8%. Using this and the fixed costs of $250,000, the breakeven point is $723,485. This is a difference of -$47,348 (or -6.14%) from the original calculation of $770,833. In other words, overstating the markup by 10% makes the breakeven point 6.14% less. Furthermore, an even larger error of overstatement by 20% results in a breakeven point of $684,028, which is $86,805 less (a -11.26% difference), making the breakeven point appear even more achievable than it truly is.

The usual human tendency is to be overly optimistic, but similar estimation errors can happen in the opposite direction, too. Thus, if markup is understated by 10%, the breakeven point becomes $822,917. That represents a difference of $52,084 (6.76%) from the original calculation. And a 20% understatement error for the markup percentage, holding other variables constant, results in a breakeven estimate of $875,000, which is $104,167 more (a 13.51% difference). Understating the markup makes the breakeven point appear to be more difficult to achieve. These results are summarized in Table 4.


A decision maker might also estimate the weight (proportion of sales) for a particular product line incorrectly. There is a general bias to be overly optimistic about sales of the more profitable products. Thus, the most damaging error on the weighting of product line sales affected by an overoptimistic bias is to overestimate the sale of high-margin products and underestimate the sale of low-margin products. How does this affect the breakeven calculation using the weighted mean markup percentage as the basis for CM% calculation?

For example, if the proportion of the high-margin product line sales is overestimated by 20%, the breakeven point will be 2.18% less than what it was in our original calculation. Conversely, if there is an understatement of the proportion of high-margin product line sales by 20%, the breakeven point will be overstated by 1.91%.

In this example, the impact of a [+ or -] 20% error on proportion of sales or weight (-2.18%, 1.91%) is less than the impact of a [+ or -] 20% error on markup percentage (-11.26, 13.51%). An error in markup percentage has roughly five to six times greater impact on the breakeven estimate compared with the impact of a [+ or -] 20% error in estimating the proportion of sales of the high-margin product line. From this I can offer the following general observations:

* Holding the markup percentage constant, if the weight of high-margin products is overstated, the breakeven point will be understated.

* As might be expected from the foregoing, if both the markup percentage and the weight of highmargin products are overstated, this compounds the error and results in dramatically understating the breakeven point.

* Errors in estimating the proportion of sales to any one product line has less of an impact on the overall error compared with making an error in the markup percentage.

Because errors in estimating the markup percentage can easily result in making a bad business decision, the entrepreneur who uses this breakeven method to forecast must do due diligence to conservatively estimate markup percentage based on cost--taking into consideration seasonal event sales, special discounts, and other decisions that have an impact on the actual markup.

In addition to these errors that the first-time entrepreneur might make, another insidious error may come into the forecasting--the value of the average customer transaction. The entrepreneur must also guard against overoptimism at this point.


Potential users must realize that my CVP analysis formula has both benefits and limitations, like all other methods of CVP analysis.


The CVP analysis method presented here offers a number of benefits to the first-time retail entrepreneur.

This method can contribute to the entrepreneur thinking more specifically about the reality of the market and the reality of the costs of doing business. Further, if the wholesale cost of retail products is essentially all of the variable costs, this relieves the first-time entrepreneur of the task of identifying all variable costs. But the corresponding downside of this is that if additional variable costs are incurred as the store operation becomes more sophisticated or as customer service requires, then the entrepreneur must determine whether or not this approach to breakeven analysis is still valid.

Note that some people may naturally think in terms of markup. Thus, for some retailers, markup percentage may be the most psychologically accessible information on which to forecast breakeven. Bear in mind also that if the retailer can forecast the weight of each product line, this may be as useful as the traditional COGS method.

Dividing the products into product lines results in a weighted average markup, which means my CVP analysis method may be incrementally more accurate than the traditional COGS method. Of course, the same degree of precision could be gained if the retailer knows or assumes the contribution margin of each product line.

Another benefit of my CVP analysis method is that adjusting the breakeven point for charge card and debit card bank fees includes this variable cost with the COGS method. In addition, understanding the magnitude of potential error from underestimating or overestimating markup and weights may act as a brake on the natural human tendency to be overly optimistic.


There also are some limitations that one needs to acknowledge. First, no consideration is given for returns, chargebacks, and other factors that can affect cost and price in the short run, such as giving discounts to customers or receiving discounts from suppliers. These may not be known in advance when forecasting the operations of a future organization.

Another limitation is that the first-time entrepreneur may fail to take into consideration all relevant costs. Grasping the significance of some variable costs that are present may elude the first-time entrepreneur even after encountering variable costs. If the variable costs are not identified accurately, using the CVP analysis formula derived here can produce an unreliable result. If the retailer does not consider all of the variable costs in the cost of the product, he or she will underestimate the breakeven point.

This CVP analysis method assumes that all variable costs are included in the contribution margin calculation. In reality, this may not be the case in some situations, or it might be the case for a while but not for long if the business grows and becomes more sophisticated. For many small retail stores, however, this may reflect reality accurately while the business is small.

Also note that this method for CVP analysis does not apply broadly across all types of businesses. Yet in some business situations, such as small retail stores and some small businesses that offer a few services where the costs can be predicted accurately, practically all variable costs are tied up with the wholesale cost of goods or a known labor cost that can be marked up. For some retailers, installation costs, alteration costs, delivery costs, and advertising costs may be nonexistent, relatively fixed, or variable. The cost of processing returns and refunds may be embedded in the owner's labor cost or whatever "salary" the owner is able to take after meeting all expenses. Additionally, there may be some variable costs that are unknown to the first-time retailer until they are encountered, such as dealing with bulk orders or gift wrapping some orders but not others. Given this limitation, there is no known method of breakeven analysis that overcomes these types of challenges faced by the first-time small business retail entrepreneur.

Bear in mind also that the weighted average markup percentage can vary month to month and season to season as demand varies. If the forecast is based on assumptions that are relatively true for one particular month or one season, the error in the overall forecast may be dramatically unfavorable. Additionally, it may be difficult to predict variation in demand for each anticipated product line.

The precision of this method is impacted directly by the precision of the entrepreneur's assumptions regarding markup percentage and the weight of each product line. Forecasting the breakeven point before opening a business must be accomplished at a point in time with one particular set of assumptions. But business is dynamic. A point-in-time forecast ignores the dynamic nature of pricing. It glosses over the fact that business model assumptions change over time as the entrepreneur learns the truth about the market. For the first-time retailer who desires both brick-and-mortar and online retail sales, the rate of change in pricing for online retail will likely outpace the rate of change for in-store pricing. Sophisticated online retailers may change pricing daily (or more often) in response to the outcomes of pricing and demand analytics. This weakness applies to other methods of CVP analysis as well.

Markup percentage by itself does not take into consideration elasticity of demand (even if it is known). A supplier might tell the retailer that the usual markup is 60% to make a product line look more promising than others. Overly optimistic thinking here will likely be deadly! Be aware, for example, that information coming from distributors and wholesalers may be filtered through their overly optimistic bias in judgment. Discounting an expected overly optimistic report from a supplier will contribute to the safety of the breakeven estimate.

Of course, suppliers are not the only ones who fall prey to excessive optimism. The entrepreneur who uses this or any other method to forecast may truly have difficulties managing the natural human tendency toward overly optimistic bias of judgment. Overly optimistic bias will lead the decision maker to discount information to the contrary. Furthermore, the overoptimistic decision maker will not actively seek information to the contrary. The decision maker who uses this method will need to establish the level of error that he or she can tolerate. Of course, the risk-averse decision maker will tend to tolerate fewer errors.

Also recognize that the disadvantages of markup as a pricing method are not avoided. Additionally, the disadvantages of the COGS method for estimating breakeven are not overcome with this method.


The markup method of breakeven analysis presented here, and the thinking required of the entrepreneur to walk through the analysis, can help pull the entrepreneur away from an overly optimistic biased judgment and help the person move toward the world of reality. But although I wrote this article to assist the first-time entrepreneur who has limited information available to forecast a breakeven point, I think that my CVP analysis method also may become useful for evaluating operational factors--such as a staffing plan and the number of transactions per week or month. For example, once a breakeven point is forecasted, the number of transactions per day or per week and the average value per transaction can be estimated--while thinking about the levels of staffing required to provide the level of service needed to meet that estimate.

Michael E. Cafferky, DBA, is the Ruth McKee Chair for Entrepreneurship and Business Ethics at Southern Adventist University. You can reach him at (423) 236-2658 or

Caption: Figure 1: Visual Illustration of the Difference Between MU% and CM%
Table 1: A Comparison of Markup and Contribution Margin

COST (C)    SP     MU%     CM%     CM%/ MU%

1.00       1.05   5.0%    4.76%     .9524
1.00       1.06   6.0%    5.66%     .9434
1.00       1.07    70%    6.54%     .9346
1.00       1.08   8.0%    7.41%     .9259
1.00       1.09   9.0%    8.26%     .9174
1.00       1.10   10.0%   9.09%     .9091
1.00       1.11   11.0%   9.91%     .9009
1.00       1.12   12.0%   10.71%    .8929
1.00       1.13   13.0%   11.50%    .8850
1.00       1.14   14.0%   12.28%    .8772
1.00       1.15   15.0%   13.04%    .8696
1.00       1.16   16.0%   13.79%    .8621
1.00       1.17   170%    14.53%    .8547
1.00       1.18   18.0%   15.25%    .8475
1.00       1.19   19.0%   15.97%    .8403
1.00       1.20   20.0%   16.67%    .8333
1.00       1.21   21.0%   17.36%    .8264
1.00       1.22   22.0%   18.03%    .8197
1.00       1.23   23.0%   18.70%    .8130
1.00       1.24   24.0%   19.35%    .8065
1.00       1.25   25.0%   20.00%    .8000

Table 2: Product Line Report Data: A Real-Life Example

PRODUCT    Wholesale    Net Retail    % Total
LINES       Cost of       Sales        Retail
Fiscal     Goods Sold   Revenue ($)     Sales
Year       (COGS) ($)

LINE A           697          $967      0.04%
LINE B        40,687        67,028      2.68%
LINE C         4,889         5,273      0.21%
LINE D        11,588        21,221      0.85%
LINE E         3,116         6,249      0.25%
LINE F         1,619         2,304      0.09%
LINE G     1,379,259     1,746,993     69.74%
LINE H        69,677        83,784      3.34%
LINE I        25,029        33,069      1.32%
LINE J        89,127       146,684      5.86%
LINE K         2,606         4,499      0.18%
LINE L       239,581       386,922     15.45%
TOTAL      $1,867,875   $2,504,993       100%

Weighted Average Markup Percent

PRODUCT    Markup on   MU% on    Weighted
LINES       Cost ($)    Cost       MU%

LINE A          271    38.8%       0.01%
LINE B       26,341    64.7%       1.73%
LINE C          384     7.9%       0.02%
LINE D        9,633    83.1%       0.70%
LINE E        3,133    100.5%      0.25%
LINE F          685    42.3%       0.04%
LINE G      367,734    26.7%      18.59%
LINE H       14,107    20.2%       0.68%
LINE I        8,040    32.1%       0.42%
LINE J       57,557    64.6%       3.78%
LINE K        1,892    72.6%       0.13%
LINE L      147,342    61.5%       9.50%

Weighted Average Markup Percent   35.86%

Table 3: Health Food Store Expected Markup Percentages and Weights

Product Line                Estimated Markup    Weight
                            Percentages (MU%)

Vitamins and supplements           60%          40.0%
Natural foods                      40%          45.0%
Bulk foods                         40%          15.0%
Weighted average MU%                            48.0%

Table 4: Results of Overstating or Understating Markup

10% Error            Breakeven    Difference   Percentage
                       Point         ($)       Difference

Theoretical Real      $770,833
10% High Estimate     $723,485     -$47,348      -6.14%
10% Low Estimate      $822,917      $52,084       6.76%

20% Error            Breakeven   Difference   Percentage
                       Point        ($)       Difference

Theoretical Real      $770,833
10% High Estimate     $684,028    -$86,805     -11.26%
10% Low Estimate      $875,000    $104,167      13.51%
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Author:Cafferky, Michael E.
Publication:Management Accounting Quarterly
Geographic Code:1USA
Date:Jan 1, 2017
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