7MK521 – Consumer Data Analysis Assignment CW1 – Sample Draft

Description

This assignment is worth 50% of the marks for this module. It is a business report of no more than 2000 words written as if you were a market research agency presenting information to a client in the Sustainable Beauty and Personal care sector in UK.

50% of the marks for the module

Deadline: 12.00pm on Monday 28th November 2022

Feedback will be available by 21 December 2022

Section 1: background and context

Use secondary sources of information, such as Mintel, company reports, academic and trade journals, Government statistics and news stories to research the UK market for the sustainable beauty and Personal care sector and provide an overall situation analysis from the point of view of the client company, as well as addressing the why this report matters.

Among others, you will provide information such as:

• What is the value of the market? (covered in lecture 1)
• Who are the major players and what is their market share? (covered in lecture 1)
• What are the key factors that affect the market (PESTEL analysis) and how marketing is conducted in this industry? How are competitors reacting to these factors? (covered in lecture 1)

Section 2: customer analysis

• Extract data from the provided dataset of sustainable beauty and personal care consumers (available on course resources in the assessment folder), to summarise the data in particular highlighting views, perceptions, opinions, attitudes, and behaviour of the overall population of sustainable beauty and personal care consumers.  (Covered in lecture 2/3/4)
• Conduct in depth analysing, using Segmentation/Targeting/Positioning (S-T-P) theory, to identify the characteristics of different segments in this sector, particularly suited to the client you are consulting. (Covered in lecture 3/4)

Section 3: Recommendations

• Using the results of your research make conclusions about the ‘typical’ consumer in the target segments and suggest how best to target the different segments. You need to justify your recommendations with your own data and wider reading. (covered in lecture 5)