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65. Analysing The Raw Data, All Of The Categories Had The Same Average Sale ...

65. Analysing the raw data, all of the categories had the same average sale amount of 0.95 when it was at its normal price and increased it to 0.97 when it was on offer (assumes an average of 1.5 units per sale during the special offer).
FRUITOTSAL = 21.35083333*OFFERS + 240.9247917*BEST + 134.9797917*FREQUENT + 146.2372917*SPENDER + 68.37479167*UNCERTAIN
Total sales as a result of there being a special offer is 21.35. The lowest of all the products analysed. However, the way each category contributes to total sales differs from the products before. Here Spenders have a larger impact than Frequent buyers, with 146.2 of total sales vs 135 for Frequent. The Best category always has the largest impact on total sales with 240.9 and the Uncertain buyers the least with 68.4.
The regression results below show that all of the estimated parameters are statistically significant at all levels, except for Offers, which is statistically significant at the 5% level:
Dependent Variable: FRUITOTSAL

Method: Least Squares

Sample: 1 16

Included observations: 16

Variable
Coefficient
Std. Error
t-Statistic
Prob.

OFFERS
21.35083
8.399803
2.541826
0.0274

BEST
240.9248
7.571480
31.82004
0.0000

FREQUENT
134.9798
7.571480
17.82740
0.0000

SPENDER
146.2373
7.571480
19.31423
0.0000

UNCERTAIN
68.37479
7.571480
9.030572
0.0000

R-squared
0.963781
 Mean dependent var
152.9669

Adjusted R-squared
0.950610
 S.D. dependent var
65.46502

S.E. of regression
14.54889
 Akaike info criterion
8.443212

Sum squared resid
2328.371
 Schwarz criterion
8.684646

Log likelihood
-62.54570
 F-statistic
73.17599

Durbin-Watson stat
2.575843
 Prob(F-statistic)
0.000000

We have been able to analyse the effect of having a special offer on three specific products in the supermarket and seen the expected increase in sales as a result of the offer and affecting all the categories. Chicken and yogurt had increases in total sales as predicted by the definition of the four client categories, but the fruit cocktail had a higher effect from the Spenders than the Frequent buyers. It would be interesting to determine other products where this was the case.

In this second analysis it was difficult to determine the effectiveness of the loyalty scheme during special offers as there was no data on how many extra points the clients earned for purchasing the products that were on offer. As a next step, I would suggest to collect this information in order to understand not only the amount of total sales that result from offers or no offers but any additional points that the client may obtain from them. This way, offers could be used to continue incrementing client total points (and thus spending) so as to get the incremental benefits from each category analysed in the first part of this document.

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