ANLY FPX 5510 Assessment 3 Comparison of Analytic Methods

ANLY FPX 5510 Assessment 3 Comparison of Analytic Methods

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Capella university

ANLY-FPX5510 Advanced Business Analytics

Prof. Name

Date

Comparison of Analytic Methods

The marketing department of a large financial services organization recently faced a budget reduction and sought the expertise of the analytics team to optimize the utilization of their remaining budget. The analytics team was tasked with analyzing the company’s communication strategies aimed at different customer groups. The main objective was to prevent contacting potential customers who are unlikely to open accounts with the company, thereby minimizing wasted marketing resources.

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The analytics team proposed two possible solutions. The first solution involved using cluster analysis to assess whether customers were being advertised to through various channels, such as telephone, email, Facebook, Twitter, Instagram, Pinterest, Yahoo, Google search, and US Postal Service mail. This analysis sought to identify groups of customers with similar behaviors to help reduce the number of communication channels used by the company. The results indicated that customers preferred specific communication methods. Based on these findings, the team recommended continuing advertisements via phone, mail, and Google, while discontinuing other forms of communication.

The second solution involved decision tree analysis. This approach was similar to cluster analysis in that it also examined whether customers were being advertised through the same channels mentioned above. However, the decision tree analysis also incorporated demographic information and data on the total deposits customers made in the previous year. The goal was to identify characteristics that would allow the marketing department to focus on high-deposit customers. Based on the analysis, the team recommended continuing Instagram advertising for customers under 43 years old. For customers aged between 56 and 80, it was advised to discontinue advertising, as this group showed a lower percentage of high deposit amounts in the previous year.

Appropriateness of Analytic Methods

Cluster analysis is a set of techniques used to group objects or cases into clusters. This method is frequently applied in marketing to identify buyer groups with similar characteristics. The process typically involves several steps, such as defining the problem, selecting a distance measure, choosing a clustering procedure, determining the number of clusters, understanding cluster profiles, and assessing the validity of the clusters. In this business scenario, cluster analysis was an appropriate method because it effectively grouped customers based on their behavior, which in turn informed the company’s advertising and marketing strategies (Statistics Solutions, 2019).

On the other hand, decision tree analysis represents various alternative solutions to a problem in a graphical format and is often used in organizational decision-making. When faced with a decision, management can predict different solutions, each of which is graphically displayed as part of a decision tree to simplify the decision-making process (Mulder, 2017). In this business case, decision tree analysis was a relevant choice for analyzing data based on particular criteria. The method was utilized to help the marketing team focus on customers with higher deposit levels, resulting in more targeted advertising efforts.

Recommendations

The recommended solution for addressing the business problem is the cluster analysis approach. This method focuses on identifying the most effective communication channels instead of excluding specific customer groups. By analyzing the effectiveness of different advertising channels, such as telephone, email, Facebook, Twitter, Instagram, Pinterest, Yahoo, Google search, and US Postal Service mail, cluster analysis helps determine which methods are most efficient for reaching customers. The decision tree analysis, while useful, mainly concentrated on customer demographics and suggested excluding specific age groups from receiving advertisements. Additionally, cluster analysis offers several advantages, such as being more cost-effective. Observing clusters in a population is typically less expensive than randomly selecting units scattered across a large area. Furthermore, cluster analysis allows the company to accumulate larger samples, facilitating the collection of data from multiple regions (Money Matters, 2019).

Implementation

Incorporating analytics into an organization is not a one-time process but a shift toward a data-driven business approach. As innovations become more personalized to customer needs, products, and operational goals, analytics-driven solutions will become essential in decision-making. To implement the cluster analysis results, a centralized strategy should be used. This strategy would involve managing all relevant assets, such as software, hardware, skilled resources, metadata, business relationships, and project management capabilities, under a single organization.

A centralized approach provides benefits like knowledge sharing, consistent implementation, better support, visibility, and clear communication lines. It also helps reduce costs by sharing software and hardware across multiple projects. The first step in this implementation is introducing the data and process of the cluster analysis. Following this, the team would be trained in the necessary methods and data handling. Once this foundation is in place, a project management and development methodology will be established before moving forward with real projects (Sheikh, 2013). Monitoring and maintaining the cluster analysis will be essential to ensure the company continues to apply its findings effectively.

References

Mulder, P. (2017). Decision tree analysis. Retrieved from https://www.toolshero.com/decision-making/decision-tree-analysis/

Sheikh, N. (2013). Implementing analytics: A blueprint for design, development, and adoption. Waltham, MA: Morgan Kaufmann.

Statistics Solutions. (2019). Conduct and interpret a cluster analysis. Retrieved from https://www.statisticssolutions.com/cluster-analysis-2/

ANLY FPX 5510 Assessment 3 Comparison of Analytic Methods

Money Matters. (2019). Cluster sampling | Definition | Advantages & Disadvantages. Retrieved from https://accountlearning.com/cluster-sampling-definition-advantages-disadvantages/