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## Summary Expenditure on mobile phones per month

Discuss about the Response to Analysis of Mobile Phone Data.

In response to the overview of the monthly expenditure on mobile phones, the behavior particularly from the monthly bill of the mobile users was examined. In the evaluation and analysis of the monthly bill for the mobile users sampled, their mean monthly phone bill was 67.65 dollars. This estimated how much Australian phone users were most likely to spend in any given month as from the cases provided. Furthermore, descriptions of the monthly phone bills also revealed amount that was frequently spent by the phone users in a month. The modal (most frequent) amount per month from the responses of the participants was 50 dollars. Additionally, the interest was in the amount that would be least used in a month by the customers which in this case, the least amount spent monthly by the phone users was found to be 11 dollars and the highest amount that could ever be spent by the customers in a month was as much as 216 dollars. Telcos can use these values in the estimation of the range of their monthly income. In regards to that, the range was given by the difference between the highest amount spent on phones and the least amount spent on phones (i.e. highest amount – least amount). The measure of the spread of monthly bill from the estimated monthly bill mean revealed that they would be dispersed from that mean value by 31.48 dollars (i.e. the amount that can be expected by the phone company can either be lower than the estimated mean amount by 31.48 dollars or greater than the mean value by that same amount). Taking the characteristics of monthly phone bills into consideration, the company can be expecting the monthly income from the customers’ monthly bill to be about \$67.65. In most of the time across the months, the company’s sales from the customers’ monthly bill on phones will be above the estimated mean monthly bill (the data was right skewed).

Following monthly bill and the life factors (Achievers, Independents and Suburban Splendour), the test was carried out to check for the difference that could be existing among the three geoTribe categories out of which the results showed that there was no mean difference among the three factors i.e. Achievers, Independents and Suburban Splendour on their monthly bills. The means seemed relatively the same across all the life factors, for instance, the amount that was estimated to be used by the achievers categories had some insignificant difference with the mean of monthly bill for the independents. The same was observed from the interaction between achievers and suburban splendor. consequently, small negligible difference was observed between independents and suburban splendor thus, it could not be concluded that life factors was the factor to go by when it came to monthly expenses on phone. The amount that was spent by different life factors particularly the three mentioned among others showed that by planning for the market for the phone products, geotribe was not a factor to go by or considered. Although, all the geotribe categories were as important for the marketing of phone products as shown from the sampled data.

## Monthly bills against life factors

The purchase magnitude is important when it comes to marketing of products. Competitive prices of the products is what draw customers (Payaud, 2014). The phone products are supposed to be made to have affordable prices for the customers. From the sample, the estimated mean monthly bill for the customers who used phones was found to be 67.65 dollars. This was the estimated mean from the collected sample for which it could be assumed that the phone users’ population was properly represented (Mandel, 2012). Customers will only find the products affordable if the prices are within their financial efforts (Shende, 2014; He, Liu, Xia and Zhou, 2014; Mulhall and Bryson, 2014). As a result, the population monthly bill mean for smart phone owners would be within the brackets (65.1 – 70.2) dollars. the mean monthly bill for all the phone users in Australia would be either at the lower end 65.1 dollars or in the upper end 70.2 dollars.

Technology grows so fast and is transforming the ways activities are being undertaken across the world (McClellan III, and Dorn, 2015). People are now days changing from the use of cash to the cashless mode of payment (Tee and Ong, 2016). The smart phone users were selected at random and thereby had to respond whether they used their phones as the payment devices. In response to that, the percentage representation of the smart phone owners who used their phones for payment was estimated to be 76% of all the smart phone owners in Australia. The remaining fraction of the smart phone owners in Australia were using their phones for different purposes but not for making payments in their daily activities.

Smart phone owners comprised of both genders (male and female) of varied ages from teenagers to those above thirties. The claim that there was a difference in the proportion of male and female phone owners who used their phones for payment was tested out of which the test showed that there was no difference between male and female when it came to using phones for payment. Out of all the male smart phone owners, 80.3% of them were using their smart phones as payment devices for their shopping activities. This confirmed that most of the male smart phone owners in Australia were despite of using their phones for other purposes, they were also using them as payment devices with only few of them represented by 19.7% who were not using their smart phones as payment devices. On the other hand, female smart phone owners who used their phones as payment devices were represented by 72.2% against those who owned smart phones and were not using them as payment device. As a result, this confirmed that larger proportion of the female smart phone owners were despite of using them for other activities, they were as well using them for payment. Generally, the percentage of all the smart phone owners (male and female) who used them as payment device were represented by 76% against 24% of the Australians who owned smart phones but were not using them as payment devices.

## Mobile phone affordability

Mobile phones are one of the communication devices that have gained popularity around the world very fast (Latonero, Musto, Boyd, Boyle, Bissel, Gibson and Kim, 2012). Australia is believed to be among the highest smart phone users (Poushter, 2016). Different reports are produced at different times almost annually testing for the smart phone usage in the world (Ragsdale and Hoover, 2016). Mobile phones are put to different use that add value to the lives of the users such as keeping track of health records and also helping in messaging and keeping in touch with people in different parts of the world (Haddon, 2017). As a result, surveys are conducted and results from the survey printed out and even published on how users embrace the use of their smart phones and many others. In response to the previous report that stated that the percentage of the smart phone users who used their phones for work related activities was at most 75%, other people were in doubt of the report and therefore data was collected from the smart phone users which were then used to test if the claims raised by the doubters that the percentage of smart phone owners who used their phones for work related activities was greater than 75% was conducted. Consequently, the results from the test disproved that report published by the business Insider. The test from the smart phone users’ data that were collected indicated that the smart phone owners in Australia who used their phones for work related activities was indeed greater than 75%. This confirmed that over three quarters of the smart phone owners in Australia were meaning good use of their smart phones as only less than a quarter of the smart phone owners were not using their phones for work related activities. The widespread of using smart phones for work related activities might be resulted to by other factors which were not revealed in the test.

Smart phones and phones in general are useful devices for communication. Despite the fact that smart phones were subjected to other work related activities, they still remained to be the communication medium. The error of using telephone booths is long gone and taken over by portable smart phones and various companies are taking that as an opportunity to widen their sales in the market. Phones enable people to communicate through various ways including through calls, SMS and MMS. Calls seem to have the attention of phone producers and therefore were monitored and the average number of calls made in a month recorded. In response to that, a competitor business stated from their research that the number of calls that were made in the previous month was at least 27. Usually not at all times the results for the findings can receive a unison node but are in some cases if not most received with groans. From the collected data, Chief data analyst’s doubt of the report was tested that the number of calls in a month was possibly less than the reported average. From the test of chief data analyst’s claim, the results stated that indeed, the average number of calls that were made by smart phone users in Australia last month was less than that which was reported by the rival business insider. The chief data analyst’s claim was confirmed right by the test. This showed that other communication alternatives were as well being used for communication and not just depending on phone calls.

## Mobile phone usage

The success of businesses lie upon so many factors which can either be internal or external. Factors can be related and resulting different effects to the business performance. In relation to that, monthly bills on phones varies from one phone user to another depending on what they rely on the most, for those who make more calls find themselves spending more on calls, other spend more on SMS’s and others on data allowances. In regards to elucidating and bringing facts to tables, the relationship test was conducted out of which the relationship between smart phone monthly bill and the number of calls revealed that there was a relatively strong relationship between the two. Further test on the monthly bill and the number of SMS’s showed that relationship existed between the two but the strength of the relationship was lower compared to that of number of calls and monthly bills. Lastly, the relationship tested between data allowance and smart phone monthly bill showed to have the strongest relationship of all the three. In terms of percentage relationship, (monthly bill vs call was 17.36%, monthly bill vs SMS was 10.37% and monthly bill vs data was 51.58%). As a result, the most important factor that was having the greatest effect was data allowance meaning that most of the customers were spending more on data allowance as compared to all those other factors then followed by number of calls and lastly the SMS’s.

In research, the size of the sample always play important role in reducing the sampling error as the sample grows (Marshall, Cardon, Poddar and Fontenot, 2013). Though it is always encountered with challenges such as incurring much cost in the data collection process and also time consuming (Button et al, 2013; O’reilly and Parker, 2013). Small sample size are always opted for since they save the researcher of the cost and time but with the disadvantage of not giving the true representation of what is really happening (Button et al, 2013). As a result, in the next year a proportion of 942 individuals was calculated for use in order to increase the coverage and have relatively true picture of what is on the ground. The average monthly bill was found to be in the range of (64.007 – 68.939) dollars.

References

Button, K.S., Ioannidis, J.P., Mokrysz, C., Nosek, B.A., Flint, J., Robinson, E.S. and Munafò, M.R., 2013. Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), p.365.

Haddon, L., 2017. Domestication and mobile telephony. In Machines that become us (pp. 43-55). Routledge.

He, Y.X., Liu, Y.Y., Xia, T. and Zhou, B., 2014. Estimation of demand response to energy price signals in energy consumption behaviour in Beijing, China. Energy Conversion and Management, 80, pp.429-435.

Latonero, M., Musto, J., Boyd, Z., Boyle, E., Bissel, A., Gibson, K. and Kim, J., 2012. The rise of mobile and the diffusion of technology-facilitated trafficking (p. 43). Los Angeles, CA: University of Southern California, Center on Communication Leadership & Policy.

Mandel, J., 2012. The statistical analysis of experimental data. Courier Corporation.

Marshall, B., Cardon, P., Poddar, A. and Fontenot, R., 2013. Does sample size matter in qualitative research?: A review of qualitative interviews in IS research. Journal of Computer Information Systems, 54(1), pp.11-22.

McClellan III, J.E. and Dorn, H., 2015. Science and technology in world history: An introduction. JHU Press.

Mulhall, R.A. and Bryson, J.R., 2014. Energy price risk and the sustainability of demand side supply chains. Applied Energy, 123, pp.327-334.

O’reilly, M. and Parker, N., 2013. ‘Unsatisfactory Saturation’: a critical exploration of the notion of saturated sample sizes in qualitative research. Qualitative research, 13(2), pp.190-197.

Payaud, M.A., 2014. Marketing strategies at the bottom of the pyramid: Examples from Nestle, Danone, and Procter & Gamble. Global Business and Organizational Excellence, 33(2), pp.51-63.

Poushter, J., 2016. Smartphone ownership and internet usage continues to climb in emerging economies. Pew Research Center, 22.

Ragsdale, J.M. and Hoover, C.S., 2016. Cell phones during nonwork time: A source of job demands and resources. Computers in Human Behavior, 57, pp.54-60.

Shende, V., 2014. Analysis of research in consumer behavior of automobile passenger car customer. International Journal of Scientific and Research Publications, 4(2), p.1.

Tee, H.H. and Ong, H.B., 2016. Cashless payment and economic growth. Financial Innovation, 2(1), p.4.

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