Using statistical models to predict future sales

Statistical Analysis and Modeling Demo


Modeling techniques include nonlinear multiple regression, binary or multinomial logistic regression, and canonical analysis.


Exploratory statistical modeling is used to discover which variables are associated with sales


Simplifying the Picture using a Regression Model
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Weather, Sales Trend demand forecasting
Step 1. Combining product sales and historic weather data.
By determining how weather has predictably influenced sales in the past (and to what extent), we can remove weather-related factors from the overall picture. We're one step closer towards establishing a direct link between Marketing and Sales. The next step is to further simplify the picture by identifying how (and to what extent) general trends, seasonal cycles, and holidays influence sales. By determining how weather has predictably influenced sales in the past (and to what extent), we can remove weather-related factors from the overall picture.  Now, instead of the containing the total number of sales made, the "Sales, Revenue, and Profit" box in the diagram to the right only contains how many sales could not be explained by fluctuation in weather

An (overly) simplified example (see the table below for clarification): If you sold 71, 77, 59, 62, 69, and 97 units on days where the daily temperatures were 60F, 65F, 70F, 75F, 80F, and 85F respectively, the daily number of sales that could not be explained by fluctuation in temperature would be approximately +11, +12, -11, -13, -11, and +12 units. Please note that this is an extremely overly-simplified example and is neither accurate nor theoretically correct. (The purpose of the example is to convey an intuitive understanding of the process).

(You lost me, just show me the next diagram...)

Sales:  71  77  59  62  69  97
Temperature (Degrees F):  60  65  70  75  80  85
Residual sales that could not be
explained by Temperature:
 +11  +12  -11  -13  -11  +12

We're one step closer towards establishing a direct link between Marketing and Sales. The next step is to further simplify the picture by identifying how (and to what extent) general trends, seasonal cycles, and holidays influence sales.


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