Females Are the Best Marketing Agents than Their Male Counterparts - Essay Example

Executive Summary It has been argued that females are the best marketing agents than their male counterparts. To conform whether the above assertionsare true, a study involving two star marketing agents (Mr. Jim and Ms. Jenny) selling homes was conducted. Jim sold about 103 homes while Jenny sold 105 homes. However, Jim managed to sell home with the highest price and the mean difference was significant implying that, Jim was more effective than Jenny in terms of sales. Further, it is clear that, the material used in the building of homes determines a lot whether customers will buy them or not. If a home has a pool is another factor in buying homes which should be well known to the selling agents. Introduction Nowadays, how aggressive one is while marketing the wares of the company he/she works for determines how the management of the company will view him/her. Marketing opportunities are there but only the go-getters are better placed to notice them. Various companies have come up with marketing strategies on how they will market their wares to the public in this world full of competitions and new innovations from time to time. In this regard, companies have resulted into employing what to them are the best personnel people who can take the company a notch higher and are either males of females. However, many companies employ females in their sales departments. This is so because, according to Learned, A (2006), females have a different communications approach than their male counterparts. Females are regarded to have a soft approach which convinces many to buy wares and recommend them to other people. In buying homes, many factors come into play. The number of bed rooms offers a challenge to any selling agent since customer need houses according to sizes which is determined by the number of bedrooms. This also is occasioned by the fact that, many families have more than one family member and are in dire need of more than 1 bedroom. According to Breen, R. & Goldthorpe, J.H. (1999), the aspect of diving people according to classes in one of the causes of the above phenomenon. Further, people are interested in what was used to construct the house. Fibros, single brick and double brick are some of the materials used in the constructions of houses. To Breen, R. & Goldthorpe, J.H. (1999), many people value double bricked house as according to them the aspect of security comes up. People feel secure when they live in a place which they perceive to be secure and where there is privacy. According to Carchedi, G. (1987), a research done on social aspects of housing shows that, having extra bits in any building affects the price that building is likely to fetch. To him, such things like swimming pools and an area besides the real house are some of the additional which lead to higher prices as well as the willingness to buy a home. According to Erikson, R. & Goldthorpe, J. (1992), many buyers in developed countries cannot accept to take a house without a pool. Many value this is a necessity which should form part of their housing requirements and thus to have a house minus a pool is unacceptable to them. Many say that it is better for one to continue staying wherever he/she is staying rather than changing a home to another without a pool. To Erikson, R. & Goldthorpe, J. (1992) further, the year a house was built says a lot about the life span of the house. To many home buyers, the year when a home was build well tells the year it will expire as well as the time it will need repairs. According to Argyrous, G. (2000), with a good sales agent, a home even without all the perceived necessities named above can get a buyer and fetch some good price. To Argyrous, G. (2000), males are the best in selling homes since customer view them as able in determining what a good home should have. To try to answer all the above queries, two sales agents were selected and given the task of selling homes. Their sales information as well as the information of the homes was collected. The year the home was build, the materials used in the construction, the price of the home, extras, number of bedrooms as well as the area of the home was collected. The data was analyzed and the following are the results of analysis. Analysis Results and Discussion Table 1a: Descriptive statistics SELLER Mean N Jim 408.50 103 Jenny 372.77 105 Total 390.47 208 Table 1b. Descriptive statistics Seller Lower Bound Upper Bound Jim 386.7 430.2 Jenny 361.6 383.9 From table 1 above it is evident that, Jim sold 103 homes while Jenny sold 105 homes. The prices of the homes the two sold was varying with Jim selling homes at a mean price of $408,500 with a standard deviation of 111 and lying between $386,700 and $430,200. Jenny sold homes at an average price of $372, 770 with a standard deviation of 57 lying between $361,600 and $383,900. Table 2: Material used in the construction Frequency Percent Cumulative Percent Valid Fibro 71 34.1 34.1 Single Brick 71 34.1 68.3 Double Brick 66 31.7 100.0 Total 208 100.0 Of the 208 homes sold by both, those constructed using Fibro and single brick were 71 in number respectively. The double bricked house, which had the support of Breen, R. & Goldthorpe, J.H. (1999), was 66 in number. The representation of both Fibros and single bricked house was 68.3% of all the homes. Table 3: Number of Home Bedrooms Frequency Percent Cumulative Percent Valid 2 60 28.8 28.8 3 85 40.9 69.7 4 49 23.6 93.3 5 14 6.7 100.0 Total 208 100.0 Homes with 2 bedrooms were 60 which were 28.8% of the total homes, 3 bed roomed homes were 85 representing 40.9%. 4 bed roomed homes were 49 representing 23.6% while 5 bed roomed homes were 14 making 6.7% of the homes. Table 4: If the Home has a Pool Frequency Percent Cumulative Percent Valid No 189 90.9 90.9 Yes 19 9.1 100.0 Total 208 100.0 Most of the homes in this study had no pools. 189 homes had no pools which were 90.9% of the homes in the study while only 19 had representing 9.1% Table 5: Independent Samples T-Test (sellers) t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper PRICE Equal variances assumed 2.915 206 .004 35.73 11.562 59.905 Equal variances not assumed 2.898 152.455 .004 35.73 11.376 60.091 From the t-test table above, it shows that Jim had the highest income from selling homes than Jenny with a mean difference in the earnings of $35,730. This mean difference in earnings is significant at 95% level of significance. The mean difference lies between $11,562 and $59,562. Table 6: Multiple Comparisons Price and Material used to build the home (LSD) (I) MATERIAL (J) MATERIAL Mean Difference (I-J) Sig. 95% Confidence Interval Lower Bound Upper Bound Fibro Single Brick -16.42 .237 -43.75 10.90 Double Brick -85.49(*) .000 -113.33 -57.66 Single Brick Fibro 16.42 .237 -10.90 43.75 Double Brick -69.07(*) .000 -96.91 -41.24 Double Brick Fibro 85.49(*) .000 57.66 113.33 Single Brick 69.07(*) .000 41.24 96.91 Table 6 above shows that homes build using Fibros and those build using a single brick yield the same price which is far different than those that are build using double brick. Double bricked homes are expensive and cost an average of $443,230. The Fibro build homes cost $357,730 on average while single bricked homes cost $374,150 on average. The above information is also available from the table below (table 7) Table 7: Descriptive statistics MATERIAL Mean N Std. Deviation Fibro 357.73 71 54.323 Single Brick 374.15 71 81.653 Double Brick 443.23 66 105.573 Total 390.47 208 89.993 Table 8: Price per bedroom BEDROOMS Mean N Std. Deviation 2 322.65 60 33.316 3 371.72 85 37.854 4 447.10 49 36.940 5 596.71 14 167.917 Total 390.47 208 89.993 Table 8 above confirms that, the more the bedrooms a home has, the more its price. A 2 bed roomed home cost $322,650 on average, a 3 bed roomed home cost $371,720, a 4 bed roomed home cost $447,100 while a 5 bed roomed home cost $596,710. Further, the differences in the average cost of the homes are significant. All the homes prices are significantly different in terms of bed rooms and the price (appendix) Table 8: ANOVA-Price an material Model Sum of Squares df Mean Square F Sig. 1 Regression 246299.938 1 246299.938 35.478 .000(a) Residual 1430131.826 206 6942.388 Total 1676431.764 207 The ANOVA table above shows that, the price of a home is affected by the material used to build it. The material has a contribution of $42,419 toward the price of the house with a confidence interval of $28,378 and $56,460. Table 10: ANOVA (Bed rooms) Model Sum of Squares df Mean Square F Sig. 1 Regression 973292.705 1 973292.705 285.147 .000(a) Residual 703139.059 206 3413.296 Total 1676431.764 207 Table 10 shows that, the number of bedrooms have an impact to the price of a home at 95% level of significance. The number of rooms has a contribution of $77,129 with a confidence lying between $68,124 and $86,134. Also the area is significant in determining the price of the house. However, its contribution is only $290 dollars to the price of the home. In addition, the year it was build has a contribution of $3,449 which is significant to the price of the home (appendix). Further, extras have a significant contribution of about $12,953 towards the price of the home. Table 11: ANOVA (Pool) Model Sum of Squares df Mean Square F Sig. 1 Regression 715.458 1 715.458 .088 .767(a) Residual 1675716.306 206 8134.545 Total 1676431.764 207 If a home has a pool its price is a bit higher than a home with out a pool. A pool contributes a bout $6,437 towards the price of that home. Its confidence interval is -$36,358 and $49,233 on average. Conclusion From the above analysis, it is evident that, males are aggressive in selling homes than their counterparts. Argyrous, G. (2000) may be true in this as males concern themselves with home matters more than females and people may see them as able sellers since they have information. Further, it is evident the material used in the construction of a home is determinant in price. To constructors is they would like what they are constructing to get good price, then double brick is the best. Further, they should aim at getting as many bed rooms as possible since forms the basis in buying. To a seller, a point to consider before placing the price tag is the year a home was built. With all the above factors considered and with the right selling agent, then good price is a must. Reference Breen, R. & Goldthorpe, J.H. (1999). Class Inequality and Meritocracy: A Critique of Saunders and an Alternative Analysis British Journal of Sociology Vol. 50, No. 1, pp. 1-27. Carchedi, G. (1987). Class Analysis and Social Research Oxford, Basil Blackwell Erikson, R. & Goldthorpe, J. (1992). The Constant Flux: A Study of Class Mobility in Industrial Societies Oxford: Clarenden Press Learned, A. Marketing to Women Online. 2006. 15 May 2008. http://marketingtowomenonline.typepad.com/blog/2006/02/male_vs_female_.html Argyrous, G. (2000). Statistics for Social & Health Research. With a Guide to Excel. London: Sage Publications Appendix 1. Multiple Comparisons Dependent Variable: PRICE LSD (I) MATERIAL (J) MATERIAL Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound Fibro Single Brick -16.42 13.859 .237 -43.75 10.90 Double Brick -85.49(*) 14.119 .000 -113.33 -57.66 Single Brick Fibro 16.42 13.859 .237 -10.90 43.75 Double Brick -69.07(*) 14.119 .000 -96.91 -41.24 Double Brick Fibro 85.49(*) 14.119 .000 57.66 113.33 Single Brick 69.07(*) 14.119 .000 41.24 96.91 * The mean difference is significant at the .05 level. 2. Report PRICE MATERIAL Mean N Std. Deviation Fibro 357.73 71 54.323 Single Brick 374.15 71 81.653 Double Brick 443.23 66 105.573 Total 390.47 208 89.993 3. Multiple Comparisons Dependent Variable: PRICE LSD (I) BEDROOMS (J) BEDROOMS Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound 2 3 -49.07(*) 9.280 .000 -67.36 -30.77 4 -124.45(*) 10.597 .000 -145.35 -103.56 5 -274.06(*) 16.335 .000 -306.27 -241.86 3 2 49.07(*) 9.280 .000 30.77 67.36 4 -75.38(*) 9.872 .000 -94.85 -55.92 5 -225.00(*) 15.874 .000 -256.29 -193.70 4 2 124.45(*) 10.597 .000 103.56 145.35 3 75.38(*) 9.872 .000 55.92 94.85 5 -149.61(*) 16.678 .000 -182.50 -116.73 5 2 274.06(*) 16.335 .000 241.86 306.27 3 225.00(*) 15.874 .000 193.70 256.29 4 149.61(*) 16.678 .000 116.73 182.50 * The mean difference is significant at the .05 level. 4. Coefficients (a) Model Unstandardized Coefficients Standardized Coefficients t Sig. 95% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 306.648 15.212 20.158 .000 276.656 336.639 MATERIAL 42.419 7.122 .383 5.956 .000 28.378 56.460 a Dependent Variable: PRICE Coefficients (a) Model Unstandardized Coefficients Standardized Coefficients t Sig. 95% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 152.776 14.647 10.430 .000 123.899 181.654 BEDROOMS 77.129 4.568 .762 16.886 .000 68.124 86.134 a Dependent Variable: PRICE Coefficients (a) Model Unstandardized Coefficients Standardized Coefficients t Sig. 95% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 219.229 13.803 15.883 .000 192.016 246.442 AREA .290 .022 .676 13.162 .000 .247 .334 a Dependent Variable: PRICE Coefficients (a) Model Unstandardized Coefficients Standardized Coefficients t Sig. 95% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) -6435.569 1104.723 -5.826 .000 -8613.581 -4257.556 YRBUILT 3.449 .558 .395 6.179 .000 2.348 4.549 a Dependent Variable: PRICE Coefficients (a) Model Unstandardized Coefficients Standardized Coefficients t Sig. 95% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 342.703 12.531 27.348 .000 317.998 367.409 EXTRAS 12.953 2.985 .289 4.339 .000 7.067 18.838 a Dependent Variable: PRICE Coefficients (a) Model Unstandardized Coefficients Standardized Coefficients t Sig. 95% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 383.441 24.501 15.650 .000 335.136 431.745 POOL 6.437 21.707 .021 .297 .767 -36.358 49.233 a Dependent Variable: PRICE Read More