Good Example Of Cellphones Brand Switching Model Report

Type of paper: Report

Topic: Brand, Telephone, Market, Business, Customers, Information, Mobile Phones, Products

Pages: 6

Words: 1650

Published: 2021/01/08

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Introduction

Brand switching is the process of making choice of a same product or brand other than from the one that was previously used in routine. It is common that customers switch from one product or brand to another similar kind of other product; however, when it happens, the likely behavior of brand switching picture is built. If the propensity of customers to switch to other brands is known then it is easier to model the future relative positioning and market shares of competing brands. Customers evaluate any product or brand based on its attributes and compare them with the other product or brand of the same category. Product attributes include various features that builds up the image of the product or the brand. These are branding, packaging, design, labelling, quality, coloring, price, quality, servicing, and warranty, and so on. Consumers expect to have a utility function for each of these product attributes. It enables the product consumers to study variation in the product satisfaction with the attributes’ alternative levels.
This paper is based on the study of smartphones brand switching and proposing a model for this to forecast the future trends. In case of smartphones, the common characteristics or features that the customers look for are the quality of touchscreen, processer speed, memory space, camera quality, weight, size, and other software related features like applications, operating system, and GPS, and so on. The highest level of functions and services that are offered by any specific brand or smartphone builds an image for the customers and make it an ideal smartphone for them; however, in addition to brand or product features, there are number of other reasons that are responsible for the product or brand switching. These are as follows,

Unpredictable brand positioning of the brand or product

Unavailability of the product
Rapid price increase in comparison to the market trends
Variety and uniqueness in other brands
Customers switching to different brands in excitement of trying new things, and possession of collecting different brand products
Definition of Terms
Brand – brand is a name, symbol, sign, or design that is primarily used to distinguish it from other brands or businesses, and to define its value or meaning.
Brand Loyalty – brand loyalty refers to the repeated behavior of the customers to purchase the same specific product or brand for over a particular period.
Brand Image – brand image refers to the reputation or symbolic construct created in the customers’ mind regarding the specific product or brand. It consists of all the expectations and information that the customers have from the product or brand.
Global Brand – global brand offers the same set of values and features across the world. The brand excels in its origin and builds up strong relationship with the customers across different cultures and countries.

Problem Definition

In current times, there have been number of smartphone brands been introduced in the global market of telecommunication. The most famous brands in the category of smartphones are iPhone, Samsung, NOKIA, and Sony Ericsson. These consumer products have different features and services. Customers purchase specific brand smartphones based on their needs and demands; however, the market share varies in different countries for each brand name or product due to differing attitudes and culture. The consumer behavior needs, and demands varies from area to area and therefore, the products should be designed as according to the customers’ needs and demands. This study is based on determining the consumer behavior in the United Kingdom market of smartphones. The new entrants in the smartphone market can take help from the study as to how the product should be designed for the specific market of smartphones .

Description of Data Collection Process

After performing the data collection through different sources, transition probability matrix will be constructed and thus, by using the Discrete Time Markov Chains, market share of each brand that is NOKIA, Samsung, iPhone, and Sony Ericsson will be determined. Descriptive methods of data analysis will be used by the using the Markov Switching model to analyze the generated data through the questionnaires and data collected through the previous records. The descriptive method will be used to represent and determine the frequencies and probabilities of the brand switching behavior of consumers from one product to other. Graphs and tables will be drawn to represent the data in a better way. Several statistical programs can be used to test the records and forecast the future trends in the smartphones market; however, SPSS 16.0 will be used to draw the graphs and determine frequencies .

Markov Chains or Transition Probability Matrix

Discrete Time Markov Chains is a series of stochastic events or states that are more based on probabilities rather than certainties. In this process, the current state of a system or variable is independent of all past events, except the present state. The different examples of Markov Chains include rise or fall in the organization’s market share, or variation in the stock or share prices. During the process of Markov Chains, the system behavior in each state is recorded and then these readings are used to predict the future value like Tn represents the present state and Tn+1 is the prediction of the future state based on the current state value .

Markovian model is represented as follows,

Pij= S1S2..SnS1P11P21..Pn1S2P12P22..Pn2SnP1nP2n..Pnn
In the above model, let Xn be the random variable, and S as a discrete space. The sequence X is called as the stochastic process. Let P be the probability measure of X. In this case, X would be called as the Markov chain on S. Here, Pij refers to the probability movement of variable from state ‘i’ to ‘j’ over a specific period of time. It is also called as the transition matrix. This model will be later used for the data generated from the questionnaires. It will help in forecasting the future market trends in smartphone industry .

Past Trends in the UK industry of Smartphones

The UK market of smartphones is one of the most advanced and systematically watched market of Europe. In the past five years, the smartphone market in the UK has rapidly grown. As per the estimates, it is expected that about three users of every four mobile users would be smartphone users by the year 2016 in UK. In the year 2012, there were 83 million mobile subscribers in the UK. Of this number, 36 million users had smartphones, and 47 million had non-smartphones making up 130% of market penetration of smartphones in the UK market. Similarly, in 2013, of the 83 million users, 44 million were smartphone users and 39 million non-smartphone users. As per forecast based on the past trends of increasing smartphone sales, of 84 million users, 63 million would be smartphone users, and 21 million non-smartphone users .

As according to the global smartphone sales statistics from 2013-2014,

The market share growth percentage will later be used as the starting steady state in Markovian chains for predicting future market share of the smartphone brands.

Data Analysis and Results

The questionnaires were distributed among the smartphone users to analyze their behavior towards smartphone brand switching. The data was collected randomly from the users of different ages. The descriptive data consisting of the number of consumers and their percentages with respect to total number of users have been calculated and given in the following table;

Brand Preference

Brand Switching Rates
Brand switching rates have been computed for each brand and their respective probabilities have been given in the following table;
Difficulties Encountered in the Process
Several difficulties were encountered during the process of determining future forecasted market share of the smartphone brands. First, during the data collection phase, the data had to be collected through the past records of the organizations. The study required the market share growth percentages of each of the brands in their area of smartphones. The market share percentages were available in quarters or the total company market share; however, the individual smartphone market share was not easily available. Like in case of Samsung that deals in large number of electronic products and services, so the overall market share of the organization was given, but it was difficult to find for smartphones. Second, during the survey data collection phase, it was difficult to focus on any one of the customers’ age group; therefore, data was collected randomly. In addition, the customers had different views about the smartphone brands; since, other than iPhone, other smartphone brands offer large variety, so the customers do not remain loyal to any specific brand, but keep on trying new smartphones of different brands. The random data collection from different group customers and diverse views of people make the predicted data based on the market share and market survey somewhat insignificant. Moreover, the survey data was collected in UK; however, the market share considered is global. It was not possible to find the market share of the specific country or area for all brands; therefore, overall market share was considered.

Future Work

The systematic process will start from the data collection of market share through request from the organization. After secondary data collection, the team will move towards the plan for primary data collection through surveys. For this purpose, first the sample size will be determined. Second, design the survey and then check for its significance. After initial testing, start collecting data through distribution of surveys among the smartphone users. After data collection, the data will be inputted in the Excel or any other statistical software program. The market share values and survey data will be computed to determine the future trends of market share of each brand. The research will complete in about 2 months.
Different teams will be involved in carrying out this research. Two teams will be assigned to carry out the data collection procedure; one for surveys and the other for the secondary data collection. Similarly, data analysis will be conducted by different team and so that for carrying out the statistical tests in the software program.
Gantt chart

References

Adeyeye, A., & Smart, O. (2012). Modeling Brand Switching in Consumers' Products. Journal of Economics and Sustainable Development.
Afzal, S., Chandio, A., & Shaikh, S. (2013). Factors Behind Brand Switching in Cellular Networks. International Journal of Asian Social Science, 299-307.
Colombo, R., & Sabavala, D. (n.d.). Approaches to Analyzing Brand Switching Matrices. Journal of Empirical Generalizations in Marketing Science.
Delotte. (2014). Mobile Consumer 2014: The UK cut Revolution and Evolution. UK: Delotte.
eMarketer. (2013, Jan 18). Three out of Four UK Mobile Users to Own Smartphones by 2016. Retrieved from eMarkter: http://www.emarketer.com/Article/Three-of-Four-UK-Mobile-Users-Own-Smartphones-by-2016/1009614
Hassan, R., & Nath, B. (2005). Stock Market Forecasting Using Hidden Markov Model. Proceedings of the 2005 5th International Conference on Intelligent Systems Design and Applications. IEEE.
Lehe, L. (2014, 07 26). Markov Chains. Retrieved from setosa: http://setosa.io/blog/2014/07/26/markov-chains/
Sathish, M., & Kumar, S. (2011). A study on consumer switching behavior in cellular service provider: A study with reference to Chennai. Far East Journal of Psychology and Business.
Statista. (2015). Apple's Revenue from 2004-2014. Retrieved from Statista: http://www.statista.com/statistics/265125/total-net-sales-of-apple-since-2004/
Statista. (2015). Global market share held by leading smartphone vendors from 4th quarter 2009 to 4th quarter 2014 . Retrieved from statista: http://www.statista.com/statistics/271496/global-market-share-held-by-smartphone-vendors-since-4th-quarter-2009/

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