Customer Retention Decision
Introduction
Data analysis reveals hidden patterns, relationships, and give insights to make appropriate company decisions. Organizations have currently discovered the need to evolve from knowing to a learning organization. Businesses desire to be more objective and data-driven by using technology to analyze data(Lee et al., 2019). Therefore, businesses lessen the analytics time for quick decision-making. Data will be derived from business literature, businesses that constitute a reference for the advertising industry, and other relevant projects. Because data is essential in decision-making, marketers should collect and analyze data to solve customer retention dilemmas.
Part One
Steps 1: Data can enhance advertising decisions. In advertising, the situation that requires data in decision-making is customers’ retention decisions. Customer retention measures the company’s success in acquiring new customers and satisfying existing ones. The parameters used to retain customers include affordability, Return on Investment(ROI), loyalty and referrals (Nasır,2019). Acquiring new clients is more expensive than keeping existing one. In addition only a five percent raise in customer retention can boost the returns of an organization by twenty five to ninety five percent (Nasır,2017). Besides, loyal retained clients spend more because they know the value of the product. These customers can refer others bringing in new clients at no expense. Therefore, embracing data analysis through observation of online activity and identifying active adjustments in customers’ trends is vital.
Through this approach, stakeholders like marketers and customer success teams, can pull off focused and targeted campaigns and work with other teams directly to boost and maintain retention rates (Nasır,2017). These campaigns save money because they target high prospective clients with the right products. Data will help illuminate customer retention decisions by uncovering trends with a specific section of clients that can help in the detailed investigation (Verhoef, 2016). Besides, data gives insight into customers’ behaviors that compel particular patterns, determine the next best action, and offer an analysis in customer retention.
Step 2: Customer retention KPIs measure the ability to keep customers and generate recurring returns from existing customers. These indicators will help one understand this situation and assess better advertising strategies. Since all advertising variables are experimental, measuring marketing KPIs ensures that digital marketing thrives (Saura et al., 2017). Solving customer retention problems requires a solid return on investment. Therefore, it is necessary to concentrate on KPIs that identify areas of most significant impacts. These KPIs include Customer Acquisition Cost, which measures product, research, and marketing costs. A well-designed CAC can highlight areas where one is making wise and poor investments. Customer Lifetime Value (LTV) is another indicator used to forecast the returns generated from relationships with customers (Cavalcanti, 2019). LTV helps determine customers’ buying history information to determine whether to invest in customer acquisition or customer retention. This project will consider cases defined by the capacity to reveal the organization’s communication style with clients and the outcomes attained for customers. These metrics matter to customers relating to the project’s goal and methods of producing tremendous experiences around positives and accomplishments (Lee et al., 2019). The indicators mentioned above will measure variables such as the number of customers acquired, attrition rate, customers retained, and their budgets, which will be considered in this project. Each variable is quantitative because it is presented as a percentage, helps measure retention rates, and provides insightful information to make an informed strategic decision on ways of improving customer loyalty and retention.
Step 3: The success of projects depends on the quality of the data. Data will be derived from case studies, business literature, businesses that constitute a reference for the advertising industry, and other relevant projects. Data collection methods, like online surveys and experimental methods, will be employed to assess the project’s meaning. These collection methods will offer varied and unbiased data (Summers, 2019). Purposive, simple random, and stratified sampling methods will help get a sample size of fifty and ensure that the population is represented well (Etikan & Bala, 2017). This study will use three graduate students with previous experience in case study analysis as codes to analyze several examples and discuss their questions to minimize biasness. The coders will then concurrently code a randomly selected sample of all case studies included in the study.
Part 2
The best statistical summaries and visualization that will help make marketing and advertising decisions include the histogram and statistics that are more sensitive to outliers like mean because it can be used for continuous and discrete numerical data. A histogram is the most frequently used graph to illustrate frequency distribution (Prodromou, 2017). In making an advertising decision, a histogram can help analyze whether various methods meet customers’ requirements, and develop the most efficient pricing plans and advertising campaigns. The histogram will summarise information concerning the cost of acquiring customers over lifetime value, and returns per consumer over one month.
Part 3
The Lifetime Value Model is a great tool that can help set campaign investment levels and exhibit future value which marketing operations can generate. A vital aspect of using LVT is that the value put in by customers over time is computed in terms of its present value. Therefore, organizations can set practical investment levels and marketing financial plans for customer acquisition initiatives at the average order value for the initial sales ( Feiz et al., 2016). However, this does not take future orders or revenues into consideration and restricts the budget. Organizations can also set suitable Customer Acquisition (CPA) at a future level, which considers the future value of a customer based on the proportion of new clients who make repeated sales and optionally propose the services to others. Organizations estimate data by creating spreadsheet representation based on the proportion of customers who repeatedly order or renew every year( Óskarsdóttir et al., 2018). Other factors determining the creation of spreadsheets include the amount spent per year by each customer, direct and indirect costs per year, and a discount factor, which considers future revenues in present value given inflation.
KPIs like customer retention rate, acquisition rate, attrition rate, and customer wallet share will help assess clients’ allegiance. The customer acquisition rate determines the percentage of new clients gained in a given period. The customer attrition rate determines the number of customers lost as a fraction of the whole customer bases over a specified period. On the other hand, the customer retention rate determines the ability to retain customers as a percentage of the total number of the customer over a period (Jasek et al., 2019). Lastly, customer wallet share measures the percentage of customer budget for a product category over a given period, usually yearly. The customer behavior indicator looks at brand perception and behavior towards the business.
However, the LTV model fails to determine customer returns and expenditures over time and assumes that retention is constant, making it unsuitable for an organization that is practicing share of client marketing objectives to follow increased loyalty marketing objectives (Jasek et al., 2019). Lastly, the model does not apply the discount rate to future customer’s returns and expenditures, thus overstating customer lifetime value (Jasek et al., 2019). It is vital to track the acquisition cost and average value of orders before adding customer acquisition cost to the dashboard (Jasek et al., 2019). This model is useful in making customer retention decisions because it determines how existing clients invest in and the summary of customers who generate high LVT.
Conclusion
In summary, data helps make customer retention and loyalty decisions by revealing hidden trends and giving suitable insights to the organization. This project considers customer retention KPIs such as cost of client acquisition, client loss rate, retention rate, and customer wallet share. These KPIs measure experimental variables such as the number of customers acquired, those lost, customers retained, and their budgets. This data can be collected using surveys and experimental methods from numerous cases and sample size using purposive, stratified, and simple random sampling techniques. Data will be summarised using statistics sensitive to outliers like the mean and presented in a histogram.