STEP 4 -  FORECAST THE SALES PERCENTAGES FOR EACH PRODUCT IN YOUR PRODUCT LINE:

The next step required in creating your weighed average selling price and weighted average product cost is to assign sales percentages for each product within your product line. These percentages are equally important since they estimate the percentage at which customers will purchase each product, model, or brand-name. To further explain this process, lets continue with our retail clothing store example.

Recall in Step 3, John had estimated 40% of all his customers would purchase Denim Jeans. The 40% appearing under the middle column represents that forecast. Also, recall from Step 2, the Denim Jeans (product line) John plans to sell are Levi, Edwin, Guess, and Ikeda (brand names of actual products). The sales percentages at which each brand name is anticipated to sell is represented by the third column. Furthermore, John and his family analyzed their research data and felt that 50% of customers purchasing Denim Jeans will actually purchase Levi jeans, 25% of customers purchasing Denim Jeans will purchase Edwin jeans, 15% of customers purchasing Denim Jeans will buy Guess jeans, while 10 % of customers purchasing Denim Jeans will purchased Ikeda jeans.

It is extremely important to understand the meaning of these forecasted percentages. Lets assume again that John estimates 5000 customers will purchase something from his store within his first year of operation. Using this assumption as well as John's product line and brand name selling percentage forecasts, we can conclude the following;

DENIM JEANS

• 2000 customers will purchase Denim Jeans (40% x 5000 customers)
• Of the 2000 customers, 1000 will purchase Levi jeans ( 50% x 2000 customers)
• Of the 2000 customers, 500 will purchase Edwin Jeans ( 25% x 2000 customers)
• Of the 2000 customers, 300 will purchase Guess Jeans ( 15% x 2000 customers)
• Of the 2000 customers, 200 will purchase Ikeda Jeans (10% x 2000 customers)

As mentioned above, John estimated 40% of customer purchasing something from his clothing store will purchase Denim Jeans. Therefore, of the 5000 forecasted customers, 2000 of them will purchase Denim Jeans ( 40% x 5000 customers). John forecasts that 50% of customers purchasing Denim Jeans will actually purchase Levi jeans, thus 1000 customers are estimated to purchase Levi jeans (50% of 2,000 customers = 1,000 Levi jean customers). Since John estimated 25 % of his customer that buy Denim Jeans will select Edwin jeans, we can assume that 500 pair of Edwin jeans will be sold within the year. And so on...

If you were to total 1000 Levi customers, 500 Edwin customers, 300 Guess customers, and 200 Ikeda customers, you would receive a total of 2000 customers - which is the total number of forecasted customers John feels will purchase Denim Jeans (40% x 5000 customers during the year = 2000 Denim Jean customers). Notice the sales percentages allocated to each brand name product total 100%. (50% for Levis, 25% for Edwins, 15% for Guess, and 10% for Ikeda = 100%)

Now John and his family must allocate percentages to the products within the second product line (IE Casual Pants), namely; Docker Pants and Creme Pants. Recall in Step 3, from above, John estimated that 15% of his customer will purchase Casual Pants during the year. This percentage is represented under the "Sales Percentage of Product Line" column presented below. Lets assume that John, after analyzing his marketing research, estimates 60% of customers purchasing Casual Pants will actually purchase Docker Pants and 40% will purchase Cream Pants (not a color). The sum of these percentages add to 100% (60% + 40% = 100 %). John would then develop the following chart.

If John forecasted 5000 total customers for his first year of operation, then 750 of them should purchase Casual Pants (5000 customers x 15%). Of the 750 customers purchasing Casual Pants, 450 pair of Docker Pants are expected be sold (60% x 750 customers) and 300 pairs of Cream Pants are expected to be sold during the year (40% x 750 customers). 450 pairs of Docker Casual Pants plus 300 pairs of Cream pants result in a total of 750 pair of Casual Pants (15% x 5000 customers).

Now John must take a look at his Sweater line and decide on which sweaters will be the most popular and which will be the least popular. John and his family once again reviewed their market information and interviews with clothing supplier etc, and estimated that 62% of customers purchasing sweaters will purchase a London Fog sweater , and 38% will purchase a Guess sweater. The following chart can be developed for John's "Sweater" product line.

If John's first year forecasted sales in units (customers) is 5000 and 5% of John's total customers are estimated to purchase a sweater, then 250 customers throughout the year will purchase a sweater. 155 customers will buy a London Fog sweater (62% x 250 customers = 155) and 95 customers will choose a Guess sweater (38% x 250 sweater customers = 95).

Below illustrates and briefly explains John's sales percentage forecasts for each of his remaining brand name items. Remember the total percentages within each category must total 100%.

EXPLANATION:
John feels that 25% of his first year customers will purchase a long sleeve shirt. Of these customers, John feels 60 % will purchase a Polo long sleeve shirt while 40% will buy a gasoline long sleeve shirt. The next product line item to consider is T-shirts. Below illustrates John's sales percentage forecast for each brand name T-shirt he plans to sell.

EXPLANATION:
John feels that 10% of his first year customers will purchase a T- shirt. Of these customers, he feels 50 % will purchase a Hollywood T-shirt while 50% of them will buy a Manager T-shirt. Below charts the sales percentage forecasts for John's final product line item; namely, Belts.

EXPLANATION:
John feels that 5% of his first year customers will purchase a belt. Since John plans to carry only one brand-name belt (Razzy), he knows that 100% of the customers purchasing a belt will actually purchase a Razzy belt.

This concludes John's planned product line and brand name sales percentage forecasts. Below neatly summarizes our work to date.