Data Mining -Sales and Marketing - Research Proposal Example

Data Mining Proposal-Sales and Marketing (Customer Loyalty) by (Name) Foundation Course – Data Analytics (Data Mining) Tutor: University of ………… Department of ……………. 20th August 2016 Table of Contents Table of Contents 2 1.0 Introduction 3 2.1 Aim of the Study 4 2.2 Study objective 4 2.2.1 Specific objectives 5 2.3 Possible Outcome of the study 5 3.0 Background of the Study 6 3.1 Understanding of the Customer Loyalty 7 3.1.1 Customer Interviews 7 3.1.2 Observation of the Customer Behaviour 8 4.0 Data analytics scenario and methodology 10 4.1 Customer Satisfaction 10 5.0 Plan and Timetable 11 6.0 Conclusion 12 1.0 Introduction With economic changes which are continuously taking across the globe, company’s relationship with their customers have changed. In the past, companies were only focusing on selling their product and services to customers without bothered concerning the customers who normally buy the products continuously (Deng, Wei and Zhang 2013). Having detailed knowledge concerning the customers who buys company product is very important. With increase of competitors, it has become more difficult to appeal to customers, such that companies had to intensify efforts to keep their current customers. With changes in social economic which has come with changes in lifestyles, this has resulted into consumers who are less inclined to absorbing all the information which they receive from the company (Deng, Wei and Zhang 2013). This has resulted to companies to also evolve from product service centered strategies to customer centered strategies. One of the main strategies goals is the loyalty relationship with the customers where companies have deployed customer loyalty cards and points wishing that the customers would remain at the leading edge and are improving the service levels in order to ensure a good business relationship with customers. Most of the company have built a data base that are in a position to collect huge sales data. For every customer, there information are collected including the number of sales, frequency of purchase and the most preferred commodities. These information alone does not translate to any useful information and any business decision if not well utilized. The data must be transformed to a manner which it can be consumed for business decision hence the concept of data mining. 2.0 Aims, Objectives and Possible Outcomes 2.1 Aim of the Study According to Reinartz (2012), the most important aspect of any given business entity is to realize increased sales volume in every business cycle. Therefore, in order for this to be a reality the market trend need to be clearly understood through putting properly mechanisms in place to facilitate in predicting the said trend hence the significance of this particular study. This study intends to outline some of the best criteria that can be useful in predicting the market trend based on the customer loyalty thus enabling business entities to continue enjoying increasing sales output (Chen and Popovich 2013). Based on the sales and marketing experts, business entities that have inadequate knowledge on the market trend to enable them understand different market segments may never sustain their customers hence chances of survival in their respective industries will remain to be elusive. As a result, with the usage of data mining technique, it will be much easier to collect data based on the customers’ purchase trends in different seasons and analyze the same so as to have a clear prediction of the future market. This will eventually guide companies as well as other business entities to prepare adequately in terms of stock management so as to realize the eventual demand of customers in the near future (Thakur 2016). 2.2 Study objective The main objective of this study is to establish the trends in customer loyalty through studying of the customers’ loyalty card scheme, that is, having an adequate understanding of the customer loyalty will enable the company to retain the customer through continuously improving on their sales and services as per the expectations of the customer. 2.2.1 Specific objectives The study will try to achieve the following specific objectives 1. To establish factors that leads to customer loyalty 2. To establish the trends in customer loyalty 3. To establish ways of maintaining customer loyalty 4. To establish the psychology of consumers that how he thinks, feel, reasons and select between different alternatives. 2.3 Possible Outcome of the study The expected outcome of this study is to practically enable forward-thinking companies and business entities in realizing that without proper knowledge of their customers, their dream of increasing market share will be hampered even before it kicks off. Therefore the best way to avoid such scenarios is to rely on their customers’ loyalties and find a way of improving even better to retain such a clientele base (Park and Yoo 2014). This study will as well enable a company to have an agreement on the appropriate business strategy to employ in their sales and marketing. Based on the fact that marketing involves constant seeking of new sources of leads in addition to crafting of messages that is often expected to result in interest in the companies’ products and services from as many customers as possible, the proper understanding of the customers’ loyalty will make this a possibility while attracting even new customers who will be interested in trying their hand in the company’s product and services (Park and Yoo 2014). As stated by Liao, Chu, and Hsiao (2012), the outcome of this study is expected to be of great importance especially to business entities, there would however a limiting factor in the sense that some costs will have to be incurred in the process. As stated earlier, this study intends to study the trend of the customer’s behaviour through the loyalty program cards that normally tracks customers’ visits to the business outlet, the products often purchased during such visits as well as the amount of money spent in such visits. Therefore, to have a comprehensive data for this study, some costs may have to be incurred in the following areas:- To visit different customers and set up interviews with them on one-on-one basis or giving them questionnaires to file. To visit different business outlets so as to find out which products they tend to realize high sales volume and in which seasons are the sales volumes high or low. In addition, rewards points will have to be scrutinized so as to understand in which periods were the points rewarded. Among other issues that will involve spending of money, this study is expected to be costly and thus adequate budget should be drawn to support the entire process, that is, the success of the study will depend on budget that will be eventually allocated for the study. 3.0 Background of the Study Customer loyalty and purchase trends can be analyzed in a systematic way. Goods purchased at different periods by the same customers can be grouped into sequences. Methods of sequential pattern mining can then be used to investigate changes in customer consumption or loyalty, and suggest adjustments on the pricing and variety of goods in order to help retain customers and attract new ones (Braha 2013). According to Kharya (2012), customer loyalty can be described as a positive belief which is generated over the course of several interactions, in the value that the products and services of a company will continue bringing this interaction and purchases over time. It is a customer sustained commitment to a company as demonstrated by repeat purchases increased wallet share and positive word of mouth referrals. In circumstances where the company is able to command such loyalty, the benefits is much beyond revenue increment. Normally loyal customers go out of the way to use company services. With increased product usage resulting to more sales revenue. 3.1 Understanding of the Customer Loyalty 3.1.1 Customer Interviews According to Shmueli, Patel and Bruce (2016), understanding of the customer loyalty seems to be the new marketing tool. Currently, customers have an access to an endless amount of information that practically enables them to choose among the many available products. The research shows that customers are willing to make an end to dating around and stick with specific companies who are willing to go above and beyond to create wide and fantastic customer experience, that is, whenever customers have a feeling that they have been taken good care of they are often more inclined to continue buying from the seller again and again. As a matter of fact, these studies have shown that it can cost to as much as 6 to 8 times more for a business entity or a seller to acquire a new customer than to continue keeping an old customer, outdoing a business competitor will depend upon having a loyal group of ever happy customers Fan and Bifet (2013). This study intends to look at how must companies or business entities have maintained beloved brands that have enabled them to instill the kind of legendary loyalty that have continued keeping them way beyond their rivals in terms of maintain their clientele base. In reference to different research that have been reported, customers tend to place priority on where they receive great service. As outline in the report that was published by the American research in 2011, out of the five customers that were sampled for the study, three out of them were willing to forgo their former favorite product so as to experience better service somewhere else. The same was reported by Right Now Customer Experience Impact in 2010 which revealed that nine out of the ten customers interviewed were willing to spend even more with those business entities they believed provided excellent customer service. As outline in those published reports, almost eight percent of the customer interviewed shared the belief that smaller business outlets place greater emphasis on excellent customer service as compared to larger business entities (Braha 2013). 3.1.2 Observation of the Customer Behaviour As stated by the White House Office of Consumer Affairs, News of Bad customer services often reaches more than twice as many ears as compared to a praise for a good customer experience. According different consumer report surveys, close to ninety one percent of observed customers may never purchase from a business entity for the second time if the first encounter with the business outlet is botched. It was even reported that from the many observations, two-third of loyal customers had walked out of a business outlet where they had intended to make some purchases after having the feeling that the service that was been offered by the business entity’s representatives was subpar. Despite all these, the business owners may not know to what extend the impact of their poor quality of service may be having to their business prospects until when it is too late to have control of the situation (Deng, Wei and Zhang 2013). Thus, it is essential for business entities to have in mind that understanding and building of customer loyalty is more important than never before. Just like in personal relationship, when a misunderstanding occur in interactions between business entities and customers, the mindsets of the individuals, the strength of the relationship that existed before the misunderstanding and how the situation is subsequently managed may have a strong influence on the outcome of the situation. A complaint is normally crucial in the customer relationship and whenever the business entity or the company gets it right, there is often potential of eventually improving customer loyalty and thus maintaining clientele base, that is, the human touch is critical in this since customers often want to feel that they are valued (Deng, Wei and Zhang 2013). Other aspect that business entities ought to take into consideration is the employer relationship with the customers. The company’s employees often play a key role in the customer relationship. Although links between employee attitude and customer satisfaction have not been consistently proven, employees to some extend matter a lot, their poor morale may not only damage business operations but also have a negative impact on the customer experience since in addition to delivering the customer service, they also personalize the relationship between the customers and the business owners (Thakur 2016). 4.0 Data analytics scenario and methodology 4.1 Customer Satisfaction Most consumers don’t always know what is good for them, what they want is somebody to tell them what is good for them, therefore they would wish to be buying a product that they believe they can trust to be the best, therefore it is the duty of the business enterprise to build customer loyalty through being relentlessly customer-focused Fan and Bifet (2013). In order to build this loyalty, there are a number of data elements that will have to be combined together through data mining process so as to achieve this objective. The most crucial data that will be helpful in this situation will be based on the analysis of the customers’ loyalty cards. This information can be achieved from the business outlets where the customers make their purchases as well as one-on-one interviews from the sampled customers. Likewise, observations from the business point of sales can also provide some information Fan and Bifet (2013). The data analytic tool that will be appropriate for this study is the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology. Once these information has been collected and documented in some paper work, they should be feed in a computer and have properly labelled columns and rows, and this is known as data preparation. The table below shows an extract of how the data prepared in the excel sheet should look like Name Age Location Beauty Product Expenditure Loyalty Rewards Points Highest Purchase Lowest Purchase Eddie 33 Brisbane O&M Surf Bomb $500.00 50 June February Chelsei 24 Canberra Mr. Smith Shampoo $6,000.00 600 August February Jane 62 Melbourne O&M Seven Day Miracle $5,200.00 520 December March Rashida 31 Sydney Grown Alchemist Lip Balm $3,200.00 320 December April Paul 22 Adelaide Grown Alchemist Lip Balm $900.00 90 June July Britney 45 Hobart Mr. Smith Shampoo $5,320.00 532 November February Eve 29 Perth O&M Surf Bomb $3,390.00 339 June September Sharoy 42 Sydney O&M Seven Day Miracle $3,560.00 356 December June Semone 32 Darwin Grown Alchemist Lip Balm $3,210.00 321 January September Karlie 21 Canberra Mr. Smith Shampoo $4,400.00 440 January July Mimi 27 Perth O&M Seven Day Miracle $1,670.00 167 December January Erica 31 Melbourne Mr. Smith Shampoo $2,760.00 276 November February Once the data has been feed into the system and analyzed using SPSS, the output should be able to communicate to the business entities. Among the output information that might be realized after the analysis would be that:- The beauty products are preference of younger generation of age group (21-45) thus manufactures should take this into consideration Customers tends to purchase more during the months of January, June, August, November and December as compared to February, March, April, July and September Beauty products are preferred mostly be women as compared to men Therefore, such information would be useful to business entities in terms of knowing when to increase stock of beauty products as well as reward points so as to attract high sales volume In addition, they would business entities would know favorable discount rates to offer to their clients while still make adequate amount of profits Fan and Bifet (2013). 5.0 Plan and Timetable In this study, the project plan will be expected to run as shown in the table Activities Duration   No. of Days Data Collection   Interviews of Respondents 3 Questionnaires 3 Observation 3     Data Preparation   Selection of the Data 2 Clean the Data 2 Input the Data in the computer 1 Integrate the Data 1 Format the Data 1     Modeling   Choose the suitable modeling technique 2 Generate test design and build model 2     Evaluation of the Data   Evaluation and review of the output 1     Deployment   Generate final report and review the project success 2 6.0 Conclusion This project is anticipated to give a proper view of how companies and business entities can utilize their knowledge on the customers’ loyalties to maintain their relevance in the changing business environment. Despite the fundamental differences, understanding of their customers’ loyalties will provide businesses with useful frameworks for thinking through customer relationship hence improving on their service delivery as much as possible. Bibliography Deng, Z., Lu, Y., Wei, K.K. and Zhang, J., 2013. Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China. International journal of information management, 30(4), pp.289-300. Reinartz, W.J., 2012. Understanding customer loyalty programs. In Retailing in the 21st Century (pp. 409-427). Springer Berlin Heidelberg. Chen, I.J. and Popovich, K., 2013. Understanding customer relationship management (CRM) People, process and technology. Business process management journal, 9(5), pp.672-688. Thakur, R., 2016. Understanding Customer Engagement and Loyalty: A Case of Mobile Devices for Shopping. Journal of Retailing and Consumer Services, 32, pp.151-163. Park, D.Y. and Yoo, S., 2014. The Redemption Behavior of Loyalty Points and Customer Lifetime Value. Journal of the Korean Operations Research and Management Science Society, 39(3), pp.63-82. Liao, S.H., Chu, P.H. and Hsiao, P.Y., 2012. Data mining techniques and applications–A decade review from 2000 to 2011. Expert Systems with Applications, 39(12), pp.11303-11311. Braha, D. ed., 2013. Data mining for design and manufacturing: methods and applications (Vol. 3). Springer Science & Business Media. Kharya, S., 2012. Using data mining techniques for diagnosis and prognosis of cancer disease. arXiv preprint arXiv:1205.1923. Shmueli, G., Patel, N.R. and Bruce, P.C., 2016. Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner. John Wiley & Sons. Fan, W. and Bifet, A., 2013. Mining big data: current status, and forecast to the future. ACM sIGKDD Explorations Newsletter, 14(2), pp.1-5. Read More