Methodology
100 members were considered in this result for various sectors such as education, transport, telecommunication, health, and financial services, 21, 22, 27, 20, and 10 respectively. They were of sectors with different needs. This data was collected for this study on the optimum pricing strategy as shown by Excel’s statistical report. All the data was estimated to the nearest 2 decimal points while the measurements of membership were done in groups with an estimation to the nearest 1.
The data has been analyzed using SSP version 15.0 for personal Computers there were testing of data for normality using statistical tests, quantitative and qualitative variables for data were also used. They were not paired in terms of details but differences in means standard deviation have been subjected to the confidence level.
Interpretation
The result has been obtained and analyzed from the data of 100 members of five sectors. The graphs from SSP attached have all the results. The optimum profits, price, and contributions which are measured at 5% confidence are represented in the tables attached. The contribution for the education sector is 5,203,125 for 22.5members and this one can be used to measure. The price obtained using contribution is 250,000 per member in the education sector. Therefore the education sector should target over 23 members to break-even
This report on pricing, profitability, positioning and targeting of five sectors has been analyzed in contribution, raw materials costs, membership ship fees, and profits. The results show that there is accuracy in the findings and the difference is minimal. The findings of this research are most important although it does not give a proper and accurate estimate of all sectors under consideration the education sector only. The differences between optimal pricing strategy and pricing using other contributions show fewer profits.
Recommendation
There is needed to make an effort to carry out results in the future in the market using adequate statistical methodologies to obtain a good result on optimal pricing strategy. This is because a great deviancy shows that the result was not adequate and it needs further research in the five sectors to determine the best pricing strategy for membership fees in the sectors. Proper formulas should be used in order to incorporate all variables such as fixed costs, variable costs, fees, and the sectors to enable one to come up with a proper pricing strategy for good profitability. The methods that have been developed that have been used are useful in estimating this pricing strategy that gives profitability.
Model critique
The essence of this approach is, first problems must be expressed in quantity, and second, those symbolic modes of expression and reasoning one to be preferred. To the extent possible, problems are examined with a systems orientation and in practice. In the domain of the descriptive model, the focus of the study is how people behave and make decisions not on how they ought to behave. The purpose is to describe the process by which managers in full go about making decisions. Normative decision models which are the main focus of economies and statistics deal with how decisions should be made. These models prescribe for the manager the most courses of action. The model involves decision making under certainty
Sensitivity analysis
Sensitivity analysis involves analyzing various factors and variables of the pricing strategy. It involves identifying and estimating various. Then the analysis is carried out to estimate the optimal price of the membership fees. It’s a methodology framework for designing problem-solving interventions using cognitive mapping. Cognitive mapping represents a problem space by a series of interconnected causal maps. They structure the problem through agreement regarding the action plan. The methodology includes a sequence of strategy workshops that enhances the managing process of solving problems in a workshop that is the management of group dynamics and the decision-making process.
Appendix #1
Methodology
The data has been clusterized into two groups: – cluster 1 and cluster 2. This data has been compiled using a regression model and the data has been combined into 19 stages. Each stage RSQ has been calculated to find the best segmenting method. In each stage, there is a combination of a number of units of each cluster. Then the next stage is predicted and what appears in each cluster is shown. All the data was estimated to the nearest 1 while the coefficients to the nearest 3 decimal points but RSQ was done with estimation to the nearest 13 decimal points.
For cluster requirements, combination and coefficient the whole market was analyzed for the membership, then this was subjected to complex mathematics formulas to produce accurate results. It was processed using nonparametric tests to give the outcome of the result that was required
Interpretation
In segmenting the market reaches a climax when you reach stage 15 of combining cluster and cluster 2. In this cluster, a combination that gives 1 will mean a low score is poor positioning and segmentation, but a combination that gives 10 or more will mean a high score for the company to have a better standing it should have a higher score to reduce the cost. At stage 15 the coefficient is 3.232 and the appearance of 9 and 12 at cluster 1 will be 9 and cluster 2 – 12 meaning that the company has a good standing and can compete easily in the market. In stage 17 cluster 1 gives a rating of 6 while cluster two gives a rating of 9 after combing the two what appears is rating 12 for cluster one and 16 for cluster two. This means for membership to be successive stage 17 considered.
Recommendation
The best stage is stage 17 which gives a maximum RSQ for the data and a higher rating for both clusters. This stage will give a good rating as positioning segmentation and market share. This stage gives proper market targeting and shows good cluster preferences. In positioning shows that the company will have a competitive advantage at this level. At this level, the company will be able to collective action to reduce and positioning or confusion positioning.
Proper formulas should be used in order to incorporate all variables in coming up with the best market rating for clusters and proper statistical data should be used. The equations that have been developed by various researchers on the same should be used in carrying out the analysis.
Model critique
The regression model used shows the downward trend from stage 1 onward. This model needs to be improved by incorporating other models; otherwise, this single model will not give a proper and good result. The model is widely used by most marketing executives to analyze the company’s competitors’ rating in the market.
Appendix #2
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