¶ … Pizza Shop Demand Forecasting Pizza Company Entry: Consumer Demand Forecast

There are a number of issues a company needs to think about before entering any particular market. In an industry such as the food service industry, there are even more factors to consider. Entering into any market can be a gamble, yet food service is often even more vulnerable in regards to the already strong presence of successful competitors and the income levels of the consumer markets being served. Within this particular analysis, the data presented in the course shell will be used as a way to determine if the Pizza Company should enter this particular market, and if so, which conditions will be most desirable in order to facilitate the most successful entry into the market.

Regression Analysis

One of the best forecasting tools companies can use is regression forecasting in order to determine the potential for consumer demand to work in the company's favor. Such statistical testing can help calculate elements and market factors in a computable way, where certain factors can be adjusted based on changing market conditions as a way to calculate potential changes in consumer demand. The data set was provided within the course shell. This data set was then put into excel in order to run regression statistical testing to better explore the coefficient determinate and thus understand hw various factors of price, competitor price, advertisement budgets, and the average income levels of the consumers who dominate that market environment. Manipulating these factors to then mimic market conditions as they stand can help provide information for forecasting the future consumer demand, if market conditions do not change dramatically.

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.912678

R Square

0.832981

Adjusted R. Square

0.806258

Standard Error

14875.95

Observations

30

ANOVA

df

SS

MS

F

Significance F

Regression

4

2.76E+01

31.1708

0

6.90E+09

1

2.20E-09

5.53E+00

2.21E+00

Residual

25

9

8

3.31E+01

Total

29

0

Coefficient ts

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

69974.8

1.84112

0.0775

272948

272948

Intercept

128832.2

2

3

1

-15283.6

1

15283.6

1

5.54E

Price (P)

-19876

6

4.84678

5

-28321.8

11430.1

28321.8

11430.1

Competitor

4.47143

0.00014

22592.4

22592.4

Price...

Based on the regression testing, the estimated regression line equation is as follows:
Q= 128832.2-19876*Price + 15467.94*Competitor Price + 0.260701*Advertisement + 8.780403*Income

With this equation, various entries can be used to thus forecast future consumer environments. The coefficient of determinate is essentially the R. Squared value as computed in Excel by the regression analysis. Within the cope of this analysis, the coefficient determinate represents the largest factor in determining the variation for the consumer demand for the product, pizza. Within this particular analysis the coefficient determinate is as follows:

R=0.832981

As such, this impacts the level that these factors, price, competitor price, advertising funding, and consumer income levels, affects the consumer demand at a specific rate. Within the scope of this analysis, 83.2981% of the demand variation is thus directly related to the price, competitor price, advertisement and income combined by the methods of the equation presented above that represents the true line of regression. Therefore, it is clear that within the next four periods, there would need to be optimum conditions for the Pizza Company to succeed in the given market place. For demand to remain in their favor, they would have to have a lower price than their competitors, spend more in advertising to keep the brand relevant within the consumer mind frame, and try to work in areas where consumer income was relatively higher than the average.

Discussion

It would be best for this particular pizza company to enter into this community marketplace in three most desirable situations, first being a high competitor price already present in the market. It would be wise to enter into the marketplace if the cost of the competitor's product was rated at a high value. If this was the case, the pizza company could come into the market with a lower price point and take over a greater portion of the consumer demand for pizza in that area. Therefore, there is a high level of price elasticity attached to the competitor pricing model within this particular consumer marketplace. As such, within the competitor pricing model "a slight increase will have a major affect on the demand for the product," especially when a brand new company enters into the marketplace at a lower price for the same quality product (Symes, 2013, p 1). With a greater competitor price, the Pizza Company will benefit dramatically over…