View source for ANLY482 AY2017-18 T2 Group15 Project Overview

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[[ANLY482_AY2017-18_T2_Group 15 |<font face ="Helvetica" color="#3f3d3d"><strong>HOME</strong></font>]] 

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[[ANLY482_AY2017-18_T2_Group15_Project_Overview|<font face ="Helvetica" color="#3f3d3d"><strong> PROJECT OVERVIEW</strong></font>]]

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[[ANLY482_AY2017-18_T2_Group15_Data_Analysis|<font face ="Helvetica" color="#3f3d3d"><strong> DATA ANALYSIS</strong></font>]]

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[[ANLY482_AY2017-18_T2_Group15_Documentation|<font  face ="Helvetica" color="#3f3d3d"><strong> DOCUMENTATION</strong></font>]]

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[[ANLY482_AY2017-18_T2_Group15_Reflections|<font  face ="Helvetica" color="#3f3d3d"><strong> REFLECTION</strong></font>]]

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[[ANLY482_AY2017-18_T2_Group15_About_group|<font  face ="Helvetica" color="#3f3d3d"><strong>ABOUT US </strong></font>]]

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[[ANLY482_AY2017-18_Term_2|<font  face ="Helvetica" color="#3f3d3d"><strong>BACK TO MAIN ANLY82 </strong></font>]]

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::To address the company’s concerns as stated by their representatives, our group aims to add value to the company by helping them to discover the determinants of a stronger brand – the extent to which advertising impacts sales. This would give the company’s marketing team a clear view of the concrete steps to take so their brands achieve increased market share.

::In addition, we aim to create a comprehensive information dashboard displaying statistics and analytics which will allow users to obtain a clear overview of sales trends, business outcomes from various marketing decisions, and other details to facilitate management’s decision-making process.
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::Our project methodology will consist of, but is not limited to, the following:

:::1.	'''Correlation Analysis''' 

::::Correlation Analysis will be done between Marketing Expenditure and Brand Equity Scores. This will show how a particular marketing action affects the sales of the various beverage brands.

:::2.	'''Market Basket Analysis '''

::::This is an analysis between how the sales of different brands correlate to each other. One of the main deliverables of interest will be the analytics of cannibalization effects among the brands. 

:::3.	'''Seasonality Trends''' 

::::We will analyze how sales fluctuate on a broader scale, such as during holiday months and seasons, as well as how marketing campaigns affect sales trends.

::The main tool to be utilised will be JMP Pro 13. This tool is selected for its combination of statistical analyses capabilities and visualization features provided. The end deliverable will be an interactive, updatable (through synchronisation) HTML dashboard that is accessible through mobile devices. 


::Some examples of JMP functionalities that we may use is shown below.
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<p><center>'''Time Series''' </center></p>
[[File:time_series.png|center|300px]]
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This is a simple and easily understood visualization to display sales trends. Individual graphs could be used to visualize the sales trends for each product, or for each sales channel, etc.
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<p><center>'''Actual by Predicted''' </center></p>
[[File:actual_by_predicted.png|center|300px]]
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This plot provides analysis of model fit, taking into account variations due to random effects. After establishing the impact of marketing spending on sales, this visualization can help users discern the accuracy of sales predictions based on marketing spending by comparing the predictions against actual historical sales. 

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::We will be working with data of all beverage products sold by the company in Singapore. In terms of data, we will be working mainly with Marketing and Sales data as our primary focus is to link these two segments meaningfully.
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::The data we obtain from the company will include historical sales figures, marketing expenditure, market share in comparison with competitors, distribution split across the company’s various channels, and brand equity scores.


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