Statistics Analysis of Mobile Calling Plan
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Statistics Analysis of Mobile Calling Plan
Unless proper managed, call rates and their plans especially for post paid customers may be tricky, challenging, and eventually result in a raw deal. It would therefore be worth to come up with a statistical analysis of your current mobile bill plan so that you can be able to monitor and reduce unnecessary costs in the plan. This would be an intelligent business decision. A number of drivers led to the concerns over the perceived high costs of the present mobile plan. Having spoken with the company accountant, it was noted that the mobile phone bill had hit the highest mark ever; therefore, it was very necessary that a study be carried out to see some of the underlying factors that might have led to such increase in bills.
The other driving factor to such concerns was the existence of some general perception that the family or company might have been in a position to put the necessary measures which would work the right way in the reduction of such bills. This was something that could be achieved through basing the current bill with the previous bills and carrying out a complex statistical analysis of the billing patterns. Finally, the other driving factor for such a research was to help reduce expenses as the economy continued to roll down, and in that, way the research would help in reducing all possible expenditures to the lowest limit as possible. Apparently, such has been a common concern with all today’s business people and institutions’ managers (Jimmy, 2008).
Statement of the Problem
Over the past few years, different organizations have been trying to come up with measures which would be applicable in ensuring that the business and the running of their organization was done with the least amount of expenses as possible. This would be necessary in improving the overall gains of such organizations (Jimmy, 2008). The main cause for such plans has been the declining global economies, which have been affected by the ongoing economic recessions. This has hence been forcing institutions and organizations to come up with workable measures in ensuring that all their business operations are run at the minimum expenses. Some of these moves have included retrenchments, sacking, and incorporation of technology to reduce human participation, and so on. This has also seen a number of companies trying to carry out studies in order to take note of the overall expenses and try to limit such expenditures (Gideon, 2007).
One of the most effective ways of ensuring that the least money is spent in the company or organization will be through the adoption of mechanisms, which would reduce the amount of mobile phone bills. It will be observed that the only sure way of ensuring smooth business and industrial operations is by engaging in constant communication with the clients. This means that it would be necessary to have an intelligent mobile phone-service provider who will give quality services, be it post paid or pre-paid. This has been the way forward for most of these businesses (Jimmy, 2008). Over the last years, concerns have been raised that the provision mobile phone services can be one of the greatest reasons behind the collapsing of a number of organizations and even business operations. Mobile phone calls being a service tends to go not carefully noted and therefore the bills come being so high such that it may not be easy for the company to deal with. This is so because most of the mobile-calls providers tend to enjoy monopoly hence exploiting their customers. It would hence be necessary for such companies to be monitoring their operations and call rates so that no much expense are incurred during the entire period (Gideon, 2007).
There are ways in which these mobile phone expenses can be minimised. For instance, it would be workable for any given company or organization to save a lot of money by moving to another network provider offering different mobile phone charge-plans (Gideon, 2007). Such plans may be flexible, workable and cheaper than the existing ones. This move of changing to a cheaper mobile phone service provider would be one of the right ways of ensuring that the company spends the least amount of cash on such calls. At the long run, the company would realise that it has incurred the least possible expenses. Majority of business experts have argued that the best way to improve business performances would be through limiting expenditures and especially which can be easily avoided without interfering with the business or organization’s operations.
The other workable strategy of reducing mobile phone expenses would be the identification of the commonly dialled phone numbers from the database, which may be outside the calling plans (Jimmy, 2008). This would be necessary because some of the numbers, which result in bill increment, may not be significant in improving the business or company’s income and therefore doing away with them can be something worthwhile, as a result led to lesser expenses in the business operations. This way the business will be able to realise maximum profits or gains. In addition, the adoption of such a strategy by non-profit organizations can also help them reduce their expenses, and instead improving their operations to the clients. Therefore, these issues led us to this research project.
This research was mainly aimed in coming up with a comparison of the present mobile phone bills with the previous bills. This came as a result after realising that the current mobile phone bills had been rising. After getting such comparisons, the nest thing was to carry out statistical data analysis in order to come up with the necessary measures, which would be workable in minimising the total expenses on mobile phone bills. Such a move would be required as many business people were highly involved in looking for measures that would be applicable in minimising the general expenditures of their companies, institutions and even firms (Jimmy, 2008).
The hypotheses for the research as quite complex and would be necessary in leading me to my findings. The first hypothesis was that: One can be able to save a lot of money by moving to anew mobile phone plan, which is a little bit cheaper as compared with the current mobile phone plan, and thereby be able to reduce the number of expenses incurred by the organization or company. In order to get the best results from the study, it would be necessary to analyse the all the data from the previous months and see the differences in the bills and how shifting to a new provider would be useful to the company.
The other hypothesis was: it would be possible to lower the total number of minutes used in calls further by making sure that I identify some of the ten most commonly dialed phone numbers, and that happen to lie outside the calling plan list. The assumption here is that such numbers, once done away with, would be necessary in lowering the bills of the company. After finding answers to the above hypotheses, it would be also important to see whether there would be some other questions that may have been answered by this research study.
The major objective behind this study was to look for ways through my company could reduce its amount of bills that are incurred from mobile phone services. This would be necessary in ensuring that the business would run without incurring many expenses. This would in the end ensure that such a business was realizing the maximum profits every time of financial auditing. In addition, the research would also come up with knowledge on how any other given company can reduce its expenses incurred in phone making and calling. This is so because most of the calls made in such business tend to have lesser significant in the major business operations which improve the income.
The research would also be useful in looking for major discussion and recommendations which be applied by different companies and organizations in reducing their overall expenses. That way such businesses will be in a position of improving their gains. It would be necessary to note that most of the expenses incurred in business operations today can be easily avoided by the application of simple techniques, therefore this research findings, discussion and recommendations would be necessary if future families and business organizations were to be operated within the least expenses possible (Jimmy, 2008).
The sampling methodology involved the gathering of information and data for the last six months. This was to be obtained from the call plan records that are automatically saved within the company’s databases with the Information Management Systems. All the stored data and information was very necessary for the success of this study. This would then incorporate the analysis and comparison of the recorded data to see whether there were any major variations and differences in the call rates and the number of minutes spent on every particular phone call. Once such data for the previous six months was gathered, the next thing to be done was to analyze the very data and thereby determining the mean amount of minutes used. This would therefore be useful in the determination whether I could safely move to a cheaper mobile call plan, which would result in lesser expenses as compared with the present.
Statistical Tools Used
The first statistical tool that I used was the employment of a frequency distribution analysis. It was necessary that I were to find out the mean number of minutes that had been spent of phone calls used over the sample period, as already described in the study. The other very important statistical tool that I was to use was the application of the Tests of Hypothesis. With this tool, I used the major five steps method used in working out the hypotheses as I had outlined previously. The third tool for the study final Hypotheses testing which would be useful in determining the accuracy and agreement with our already stated hypotheses. This would be necessary into leading us to the final discussions, conclusions and recommendations.
Data Analysis and Summary
In the June mobile plan, the average minutes for all calls made was recorded as 4.66%, the standard deviation was 12.121 minutes while the variance was 146.912 minutes. Based on this calling plan, an average call lasted a minimum of 1 minute while the longest lasting call lasted 166 minutes. The analysis revealed that there were 349 off-peak calls representing a percentage rate of 36.8%, as compared to 596 during peak hours, which represented 62.9%. The sum totals of both off-peak and peak frequencies of the calling plan were 948. In terms of the usage type, M2MALLOW had the highest frequency of 447 representing 47.2 % of the total usage. PlanAllow had a 26.7% of the total usage whereas N&W was third placed with 19.0% of the total usage. Others includes PlanAllow at 4% ,CallVM at 3.5%, N&W,CallVM at 2.1%, PlanAllow,CallWait at 0.6% and both N&W,CallVM and N&W, CallWait at 0.1%.
Statistical analysis of origination revealed that Slippery RPA reported the highest number of calls at 65.8%. The remaining 34.2% was shared by other origins with each recording below 5%. In terms of minutes, 474 calls lasted over 474 minutes representing 505 of the total calls. However, 209 calls lasted f0r at least 2 minutes representing 22% of the total calls. Analysis of the frequency statistics also indicate that 28.2% of the calls lasted over 2 minutes. The pie chart below indicates that during the month of June, there were more peak calls representing more than 50% of the originating calls as compared to off-peak calls, which represented over 455 of the calls. In the same month, Incoming CL was the destination that recorded the highest number of calls followed closely by Butler PA. Other destinations recorded less than 12% of the total calls made during the month. In terms of minutes, calls that dominated the months were one-minute calls that represented a percentage of at least 50% of the total calls during the month.
During the month of June a statistical analysis Allow Only variables indicated a mean of 7.96%, a standard deviation of 69.724 and a variance of 4861.417. When frequencies were run, the variable frequency table indicated a frequency of 306 for peak calls, which represented 99.4% of the calls that were made under the Plan Allow Only. This plan was mainly dominated by peak calls as compared to the mixed plan already discussed above. Other none peak calls only accounted for a paltry 0.6% of the total calls under this plan. A run of the frequency on the usage type variable indicated that plan allow recorded the highest frequency of 277 out of a total of 308. On the other hand, Plan Allow, CallVM had a frequency of 26 representing 8.4 %. In terms of origination, Slippery R PA recorded the highest frequency of 146 representing 47.4 % under plan allow only. Pulaski PA recorded a frequency of 23 representing 7.5 %. Similarly, Plan allow only had the highest number of calls destined to Incoming CL. This destination had the highest frequency of 84 representing 27.3% followed by Butler PA, which recorded a frequency of 63 that corresponded to 20.5%. In terms of accumulated minutes, plan allows only recorded a frequency of 130 with a corresponding 42.2% of the calls that lasted 1 minute. Under this plan, calls lasting 2 minutes constituted 18.8% of the calls made during the month of June.
In the month of July, the average call under the mobile mixed plan lasted 7.96 minutes, while the median was 2.00 minutes. Other statistics includes mode = 1, standard deviation = 69.724and a variance = 4861.417. The minimum minutes for a single call were 1 minute while the maximum recorded was 1222 minutes. The total sum of the minutes used during the month totaled to 2444. The calls that dominated this month were made during peak hours. Under usage type, PlanAllow dominated with over 80% of the total calls made followed by PlanAllow, CallVM which constituted 15% of the calls. PlanAllow, CallWait constituted the least number of calls. In terms of originating calls, Slippery RPA constituted the highest number of calls. The statistics also revealed that Incoming CL, received the highest number of calls during the month of July compared to other destinations. Other destinations that received a high volume of calls during the month include Portersvyl PA and Butler PA both of which almost received a similar volume of calls during the month. Just like the month of June, a bulk of the calls made in July either lasted 1 or 2 minutes.
The month of August reported the following statistics: n=222, missing values = 0; mean = 4.10, the standard error of mean = 0.397; median = 2.00; mode = 1; standard deviation = 5.96; variance = 35.537; range = 33; minimum = 1, maximum = 43. The frequencies indicated that more calls were made during the peak hours. The peak recorded a frequency of 211 out of the total 226 representing 93.4% of the calls. Off-peak calls reported a frequency of 15, representing 6.65% of the mobile traffic during the month. The analysis of usage type variable suggested that M2MALLOW had the highest frequency of 94, which corresponded to 41.6%, closely followed by PlanAllow which had a frequency of 91, corresponding to 40.3%. PlanAllow, CallVM recorded 11.5 % while the rest reported below 10% usage. This statistics were recorded under Plan Allow Only, for the month of August. Incoming CL still registered the highest number of calls compared to other destinations with 31.9%. Out of the total 226 calls, 105 calls representing 46.5% were one-minute calls whereas calls lasting 2 minutes representing 19% were 43 in total. Calls representing 8.4% of the total calls were 3-minute calls. Therefore this statistics indicate that a bulk of the calls made during this month were calls which lasted for 1 minute.
During the month of September a statistical analysis Allow Only variables indicated a mean of 6.93%, a standard deviation of 59.567 and a variance of 3361.21. When frequencies were run, the variable frequency table indicated a frequency of 276 for peak calls, which represented 99.7% of the calls that were made under the Plan Allow Only. This plan was seen to be dominated by peak-time calls. All other calls only accounted for a paltry 0.3% of the total calls during this time plan. A run of the frequency on the usage type variable indicated that plan allow recorded the highest frequency of 299 out of 307. On the other hand, Plan Allow, CallVM had a frequency of 24 representing 6.4 %. Looking at the originality, Slippery R PA recorded the highest frequency of 141 representing 43.5 % under plan allow only. In addition, Pulaski PA recorded a frequency of 21, which represented about 7.1 %. On the other hand, Plan allow only had the highest number of calls destined to Incoming CL. Such a destination had seen highest frequency of about 88 representing about 26.5%. This was followed by Butler PA which recorded a frequency of 61 that corresponded to 19.7%. Looking at the accumulated minutes during this month of September, plan allows only recorded a frequency of 127 with a corresponding 44.2%, which were seen to last for a minute. Under this plan, calls lasting 2 minutes constituted 21.8%.
Coming at the month of October, the average call in the mobile mixed plan lasted 6.96 minutes, and a median of 2.450 minutes. Other statistics includes mode = 1, standard deviation = 68.724 and a variance of 4988.417. The minimum minutes for a single call were 1 minute while the maximum recorded was roughly 1317 minutes. The total sum of the minutes used during the month totaled to 2987. As usual, the calls that dominated this month were made during peak hours. Under usage type, PlanAllow dominated with over 82% of the total calls made followed by PlanAllow, CallVM that constituted 13% of the calls. PlanAllow, CallWait constituted the least number of calls. In terms of originating calls, Slippery RPA constituted the highest number of calls. The statistics also revealed that Incoming CL, received the highest number of calls during the month of October compared to other destinations. Just like the month of September, a bulk of the calls made in July either lasted between 1 and 2 minutes.
The month of November reported these statistics: n=211, missing values = 0.01; mean = 4.07, the standard error of mean = 0.427; median = 2.04; mode = 1; standard deviation = 5.34; variance = 33.242; range = 32; minimum = 1, maximum = 48. The researched frequencies indicated that more calls were made during the peak hours just like all the other moths. The peak recorded a frequency of 204 out of the total 237 representing 95.4% of the calls. Off-peak calls reported a frequency of 16, representing 3.65% of the mobile traffic in the entire month. The analysis of usage type variable suggested that M2MALLOW had the highest frequency of 95 which corresponded to 42.6%, closely followed by PlanAllow which had a frequency of 89, corresponding to 38.36%. PlanAllow, CallVM recorded 12.4 %, and others reported data of less that 10% for all their usage. Looking at the incoming CL, it once again registered highest number of calls as compared with all the other available destinations with 33.4%. Of all the 267 calls made, 107 of the calls represented 47.3% with one minute time period while the ones lasting for about 2 minutes representing 21 %. Calls representing 9.1% of the total calls were 3 minute long. Therefore this statistics indicate that a bulk of the calls made during this month were calls which lasted for 1 and 2 minutes. It will be noted that for this month the call minutes were very high and above 2100 minutes in the month. This was the major reason why the presented bill was very high. It would therefore be necessary to come up with measures which can help reduce these minutes so that the least amount of bills are paid by the family, organization or business firm.
Presentation of Visual Aids and Pie-Charts
Mixed Plan Pie Chart, June 2009
Plan Allow Only, June 2009
Plan Allow Only, June 2009
Plan Allow Only, June 2009
From the research and data analysis, it would be necessary to conclude that it would be appropriate for any organization or family to cut down its phone call duration if the necessary reductions are to be realised in the call time and billing (Gideon, 2007). Looking at the last month, we see that the number of minutes surpassed the 2100-minute mark, which was highly charged. In that condition, it would be possible to cut some of the long lasting calls so that it can be possible to arrive at the 1400-minute mark plan per month. This would be useful in ensuring that the lowest bills were paid for such calls. Once that has been met, it would still be better decisions, which would reduce the minutes to a lower level. This is through doing away with some long lasting calls which may not be important or significant in the business operations or for the family, in turn resulting in inappropriate expenses. If it would be unavoidable to come up with such strategies, the other better option would be look for a cheaper service provider so that the costs can remain as low as possible. Looking at our hypotheses, we will agree that we can reduce the costs of the bills by terminating some numbers whose calling result in high minute counts. We will also agree that it would be necessary to migrate in case such costs remain high, and this would be appropriate n reducing the bills.
For further researches, it would be necessary that we incorporate different sources of data and information from different organizations, families and even industries to be able to get a better picture of such shifts in mobile phone billing. In addition, it would be better if a research would focus on a number of mobile service providers since this would lessen biasness in the study. This would bring about homogenous data, which would be workable in ensuring that better and relevant decisions are made about the study. Time has also been noted to be a hindrance if such a study would come out with goods results. This means that it would be necessary if such a study would comprise of a year and not a pried of six months as with our case here. This would result in better results and better decisions. The other recommendation is that a similar study can be done on all other services provided to homes and companies such as internet provisions and connections so that we can be able to reduce expenses hence increase the final income.
Gideon, K. (2007). Organizational management and decision making in the 21st Century: Oxford: Oxford University Press.
Jimmy, H. (2008). Business operations: minimizing expenses: New Jersey: Prentice Hall.
Survey on consumers, cell phones and the economy. (2009, March 15, p. 4). Retrieved November 21, 2009, from Opinion Research Corporation: