Sales Forecasting Tools by epn46924


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									Sales Forecasting Models

  I.    All businesses make some kind of sales forecast.

        a. Implicit forecast

               i. Owners develop of notion of expected sales from past experience

               ii. Most likely in small businesses

        b. Explicit forecast

               i. Forecasts are made and distributed to managers

               ii. Follows a defined process

              iii. An survey on sales forecasting from a fairly diverse population of
                   companies reveled

                   49% companies used simple tools like Excel, Access for demand

                   9% did not use any software

                   42% used mathematical models

                   93.5% of companies that used some forecasting model achieved
                    significant benefits

                   The percentage of companies having forecasting error less than
                    10% increased from 25 to 67 on use of forecasting models.

  II.   Types of explicit forecasts

        a. Executive Forecasts

               i. Team of company experts meets to formulate forecast

               ii. May utilize outside consultant to direct process

              iii. Desirable to draw from all segments of the business

        b. Delphi Method

               i. Utilizes experts as in Executive Forecast
      ii. May use external experts

     iii. Each member makes individual forecasts

     iv. Then is shown forecasts of others anonymously

      v.   Revise forecasts in light of feedback from others

     vi. Keep this up until process converges on some result

c. Time Series Analysis

      i. Use time series model to historical trend data

      ii. Example:

     iii. Look sophisticated and involve sophisticated math techniques

     iv. Work very well with data that continues to follow past trends
         regardless of how complex

      v. Shortcoming is not real information about what drives the sales
         trends or cycles

d. Regression techniques

      i. A dependent variable, usually sales, is forecast using a set of
         explanatory variables each of which contributes to sales

      ii. Choice of explanatory variables is critical

             1. Variables should meet the logic test and not rely on
                correlation to determine which variables are included

             2. Data mining

                    a. Process of testing dependent variable against
                       hundreds to thousands of possible explanatory

                    b. Build model using those are have the strongest

                    c. Need to be careful using this technique
              d. Example

                     i. Suppose you want to forecast weekly banana
                        sales by county.

                     ii. You discover, by data mining, that in countries
                         where people own a lot of French Poodles they
                         also consume a lot of bananas

                    iii. Should you include French Poodles per capital
                         in model as an explanatory variable for banana

iii. Linear regression often used when using aggregate data such as
     zipcode demographics

iv. Logistic regression often used when using individual data

       1. Gives probability a consumer with a set of characteristics will
          buy or not buy a product based on past sales and
          characteristics data

       2. If people with personal characteristics A have a 5%
          probability of buying the product then, if you approach 1000
          type A people you will make 50 sales.

       3. Example targeted direct mailings

       4. http://www.extenza-


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