Buying Intentions of Customers, Research Paper Example

Research on the buying intentions on various commodities

Introduction

There are numerous factors that determine the actual purchases that the clients make. The intentions range from personal decision to influence from other people. The correlation that exist between each factor that the purchases that clients make should be understood for good marketing strategy. It has been argued that the correlation that there exists a correlation amid the purchases that various customers make in the market and their intention purchases though it has been brought out clearly. The paper examines the correlations that exist between the buying intentions and the actual purchases. To examine the correlations, data was collected from one supermarket in US through interview. Many customers were interview and the responses noted.

Hypothesis

• The aggregate buying intentions and aggregate purchases are positively related.
• Relation exists between the surprises and purchases net of buying intentions. Purchase net depends on surprises.
• There is a relationship between buying intentions and purchases of household durables and of automobiles.

Methodology of the research

The basic objective of this research is analysis and evaluation of consumer intentions to buy durable goods. For each of the 6 consumer durable (ranging from new automobiles to garbage disposal units) the subsequent purchases rates of households who reported buying intentions (intenders) and those who do not (non-intenders) are summarized. Purchase rates are calculated for both six and twelve months subsequent to the intention survey, and for seven alternatives intender-non-intender classification based on the differences in the type of intentions question asked. The following are the questions.

A1 (which among the following products do you intend to buy in next 12 months) B1 (product that x will buy in 12 months) B1 (product x plans to buy in next y6 months) B2 (products x plans to buy later) C(products x plans to buy in the next12 months) C2)(products x plans to buy in the next 12 months if income increases) C3( products to be bought in the next 12 months if the income reduces) D1 (products x plans to buy in 12 months time) E1 (products x plans to buy before next five months) E1(products x plans to buy within next three months and a year from now).

The first eight columns contain the raw data on purchases and buying intentions. Columns 1 and 2 show the number of purchases, per hundred respondents, for the sample as a whole during the six months and twelve months, respectively, following the intentions survey. Column 3 shows the total number of the intenders, again per hundred respondents, while columns 4 and 5 show the number of intenders who purchased within six and twelve months, respectively. Columns 6, 7 and 8 contain comparable data for non-intenders- total number per hundred, and of these, the number purchasing within six and twelve months.

The remaining 8 columns contain statistics based on these data. Columns 9 and 10 show purchase rates among intenders and non-intenders, respectively, for the six-month period; columns 11 and 12 list the same statistic for the twelve –month period. Column 13 and 14 show the proportion of the total accounted for by intenders during the six-month and the twelve-month periods, and the last two columns show the simple correlation between intentions and purchases for the respective purchase periods.

In making the latter calculation, household were assigned values of unity or zero, depending on whether they reported intentions or purchases (=unity) or did not do so (=zero). Column 1 is necessarily equal to the sum of columns 7 and 4, since total purchases must comprise purchases by intenders and non-intenders; similarly column 2 is the sum of columns 5 and 8. And columns 3 and 6 add up to 100. This is since they simply constitute different ways of breaking up the respective samples into intenders and non-intenders. The numbers of intenders and non-intenders who did not purchase within either six or twelve months after the survey (data shown) are the respective differences (columns 3 and 4) and (columns 6 minus 7 or 8).

The last eight columns are obtainable from the others. Column 9 is simply column 4 divided by column 3, column 10 is 7 divided by 6, column 11 is 5 divided by 3, column 12 is 8 divided by 6. Purchase rates for intenders and non-intenders during the period from seven to twelve months after the survey can readily be calculated from the differences between columns 9 and 11 (intenders) or between 10 and 12 (non-intenders); the same figure can be also be obtained by dividing column 3 into the difference between 7 and 8 (non-intenders). Column 13 is simply column 4 by 1, while column 14 is 5 divided by 2.

Data analysis

The data was used to obtain and check the correlations. All the values are obtained from excel. It is believed that there is a correlation that exists between the aggregate buying intentions and aggregate purchases. The individual-commodity buying intentions and purchases reported by each household have been combined into crudely weighted aggregates designed to measure intended and the actual dollar magnitudes for each household.

Aggregate buying intentions and aggregate purchases

The prices that are paid for different commodities vary as per the quantity and quality. This is why it was important to give them assigned weights. Second, it is clear that the probabilities associated with responses to an intention questions are not similar for all the items. The average levels of purchases by the households are calculated and correlation denoted. The following results are shown for the intention questions. The correlation that exists between buying intentions and aggregate purchases are also shown. Let the buying intention be p and the aggregate purchase by (P). The two are closely similar only that the aggregate the p is bracketed. The following table shows the buying intentions compared with weighted mean purchases. The values are obtained from the data (excel).

A1 (plans within twelve months), B1 (plans within six months), C1 (plans within 12 months if income is expected) D1 (plans within twelve months) and they Indicate average purchase of households for intentions Questions (a).

The regression statistics (P) is given by P = [a + b (P)] ^b.

The table above, one is immediately impressed by the closeness of buying intentions-purchases relationship, particularly when the data are grouped in order to decrease the random variation inherent in individual behavior. The average value of (aggregate) purchases rises steadily with the level of (aggregate) intentions for all the 4 questions, although average purchases drop off somewhat at very high levels of intentions. The correlation data also indicates a very powerful relation between aggregate intentions and purchases, with intentions explaining from 9 and 12 percent of the variance.

Buying intentions and the purchases of household durables

Relationship between buying intentions and purchases of household durables and of automobiles is shown in the following table.

A (Intend to buy auto) b (Do not intend to buy auto) c (Intend to buy some household durable) d(Do not intend to buy any household durable) E(intend to buy house) F (do not intended to buy house). The respondents are those that are in group B, intentions question B1. All regression coefficients are more than three times their standard errors. The regression questions can be calculated each in turn. This is in s,t and u and dd.

If the durables of very large unit costs have a dominant influence on the aggregate P(P)  relationship, the equations 5.1 and 5.4 ought to have significant lower correlation than 6.2 and 6.5; 7.4 ought to have a significant lower correlation than either 7.1, 7.2, or 7.5; and 8.1, a significant lower correlation than 8.2.

r(5.1), r (5.4) < r(6.2), r(6.5)

r(7.4) < r(7.1), r(7.2), r (7.5)

r(8.1) < r(8.2). The above table represents the correlation and regression coefficients. The correlation amid Pa and (P)a is just as strong as that between Pa and (P)h and it makes no appreciable differences to either correlation whether the household intenders to purchase only household goods, only an automobile, or both. The P(P) relationship seems however very strong for both household durables and autos if the household also reported plans to buy a house, but the differences are well within the limits of sampling variability.

Surprises and purchases net of buying intentions

There was no evident relation that exists between the surprises and purchases net of buying intentions. When an experiment with more stringent definition of favorable and unfavorable surprise is cried out it will suggest the possibility that favorable surprises, in interaction with contingent buying intentions, might be weakly positively related to purchases; Unfavorable surprises in interaction with the standard buying intentions might be negatively related to purchases. While the data generally yield regression coefficients with the appropriate signs, many of the coefficients are to significantly different from zero; the contribution to explained variance is generally very small; and in some cases, the data yield coefficient with inappropriate data.

References

Achol, P. (2001). Research methods. Page 45-56. Rotor publishers.

Dolo, L. (1999). Research methods and Data analysis. 2^nd edition, volume 4 pg 12-34. Pager publishers.

Welo, O. (2005). Economics: customer relations and choices. Pg 109-132. bolo publishers.

Xola, H. (1999). Data analysis. The methods of analyzing research data and interpretations. Pg 67-76. Toronto publishers.