Be Customer Wise or Otherwise - Project Overview

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PROJECT OVERVIEW

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PROJECT MANAGEMENT

About the Project

Our sponsor, GLC, is an international postal and logistics company that has a global network spanning more than 220 countries and territories. Its product offerings include global freight forwarding, international express deliveries, warehousing solutions, and other customised logistic services.

International freight and logistics services have been expanding rapidly as a result of rising incomes across the globe especially in emerging economies like China and Vietnam, driving increased consumption levels. Coupled with the ubiquity of the Internet, changes in consumption habits such as the rise of e-commerce have also further pushed up the demand for freight services. Furthermore, with increasingly integrated global networks, supply chains have gone international with goods often needing to be shipped across continents from manufacturers to distribution centres to customers. Therefore, being able to do this efficiently is one main key to success in this industry.

Motivation

Having identified the potential of Asia-Pacific, GLC has been expanding its operations in the region. However, it has faced fierce competition from other players in the industry. In addition, GLC also has to contend with the 'new, globalised customer' (Cister, Ebecken, 2002) who is extremely demanding and has become accustomed to quick satisfaction with quality.

Objectives

GLC, like many companies, have been collecting and have stored a copious amount of data about customers, suppliers, business partners, etc. However, the inability to draw out valuable findings from the data prevents this information to be used in any meaningful way (Berson, et al., 2000). Through this project, the team aim to assess the relevance of the current data collected, discover insights on improving data collection and utilisation and also suggest how GLC can better utilise this to shape its business strategies.

In order to stay competitive in this market, the company believes that besides producing cutting-edge products, it also needs to understand the needs of its customers. GLC is thus seeking to profile their customer base so as to give them an edge in better tailoring their service offerings to meet market demands to boost revenue and increase market share.

With the motivation to maximise sales revenue and market share through devising appropriate product strategies and distribution channel policies, the team will try to uncover any information in the available data that may be useful in meeting the business objectives. The team will also assess the relevance of the data provided and suggest how GLC can make better use of the historical sales data to shape this aspect of its business strategy and operations, before proposing the recommendations to the management that follow from this.

Methodology

Customer Relationship Management

This direction is indicative of a larger shift towards Customer Relationship Management (CRM), which refers to the shift in focus from products to customers. This has been enabled by technological developments that allow companies to use capture, process, analyse and distribute data in order to understand and anticipate current customers’ needs. The impact of this is an increased ability for the company to fulfil these needs more efficiently as well as improve the retention rate of customers.

According to Rogers and Peppers, there are four basic strategies with regard to CRM: identify customers, differentiate customers, interact with customers and personalise the relationship. Through this improved understanding of customers, companies are able to adjust their business strategies and customise their marketing approaches for different customers, thus improving “customer acquisition, customer retention, customer loyalty, and customer profitability” (Swift, 2001).

Recency, Frequency and Monetary Index

The purpose of this project is to apply descriptive analytics techniques to the sales and marketing data made available by GLC to yield knowledge discovery. From the data, we aim to create clusters of customers that can be profiled uniquely across different metrics, including a customised Recency, Frequency, and Monetary (RFM) measure.

The RFM index has three components, mainly:

• Recency - refers to date of the customer’s last purchase
• Frequency - refers to the number of purchases made within a given time period
• Monetary - refers to the total dollar amount spent by the customer within a certain time frame

Using these three measures, RFM analysis then teases out the most valuable customers. In doing so, we aim to address the following questions:

1. Who are the high-value customers?
2. Which customers have a high potential for growth?
3. What common behavioural patterns do customers of different profiles exhibit?
4. What are the needs of different types of customers?
5. How can current data collection be improved to aid analysis?

Tackling these issues above will pave the way for the management to devise actionable marketing strategies to improve revenues and gain market share which is the initial intent.

Limitations

Although simple and easy to implement, the RFM model has its limitations as well.

Due to the limitations of the RFM model, we will proceed on with clustering techniques to analysis the calculated RFM input to cluster customers into profiles based on their similarities. From the outcome, GLC can then apply different marketing strategies to different target groups based on their clustered RFM behaviour.

Moving Forward

Cluster Analysis