Difference between revisions of "ANLY482 AY2016-17 T2 Group10 Analysis & Findings"
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| − | ==<div style="background: #ffffff; padding: 17px;padding:0.3em; letter-spacing:0.1em; line-height: 0.1em; text-indent: 10px; font-size:17px; text-transform:uppercase; font-weight: light; font-family: 'Century Gothic'; border-left:8px solid #1b96fe"><font color= #000000><strong>Data | + | ==<div style="background: #ffffff; padding: 17px;padding:0.3em; letter-spacing:0.1em; line-height: 0.1em; text-indent: 10px; font-size:17px; text-transform:uppercase; font-weight: light; font-family: 'Century Gothic'; border-left:8px solid #1b96fe"><font color= #000000><strong>Initial Data Exploration and Analysis</strong></font></div>== |
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| − | Exploratory Data Analysis is the | + | We conducted initial data analysis using Exploratory Data Analysis (EDA) to gain general insights on key determinants that affect the relationship between interaction counts and |
| + | sales revenue. We hypothesized that such factors could be “channel”, “therapy area” (sales team). | ||
| + | <br/><br/> | ||
| + | “Channel” is the classification for different types of clinics, such as General Practitioners, Restructured & Private Hospitals, Specialists. From our basic understanding, each channel has | ||
| + | its own protocols and practices that are likely to affect receptiveness of interactions. For instance, interactions with hospital doctors may not be that impactful as that with GP doctors | ||
| + | because hospital doctors get their drugs from a centralized system, while GP doctors have the power to make decisions for their own clinics. | ||
| + | <br/><br/> | ||
| + | “Therapy area” defines the name of sales teams, such as Uro CNS (Urology), Respi (Respiratory), Paed Vx (Pediatrics Vaccines), Allergy, Al Derm (Dermatology), Ad Vx (Adult Vaccines), | ||
| + | and it decides the corresponding product brands to promote. We postulate that different drugs have different demands and established drugs may need small number of interactions to achieve good sales results whereas new types of drugs may need more interactions to achieve the same level of sales results. | ||
| + | <br/><br/> | ||
| + | To give us a better understanding of the natures of different channels and therapy areas, in this initial data analysis, we will explore 1) how sales revenue differs for different channels | ||
| + | across different quarters and 2) which is the most valued channel for each sales team. | ||
| + | <br/><br/> | ||
| + | Exploring sales revenue by channels and quarters allow us to understand significant demand patterns that arise from practices or secondary consumers (patients). | ||
| + | <br/><br/> | ||
| + | For instance, we will plot a line graph of total sales amount (response) against different quarters (explanatory) by different channels. | ||
| + | <br/><br/> | ||
| + | A first look at the visualization gives us an understanding that there are indeed intrinsic | ||
| + | differences across different channels and quarters. | ||
| + | <br/><br/> | ||
| + | An observation of trend across quarters is that the highest sales for most channels were made in the first quarter. This is especially prominent for pharmacy, which made more than half of | ||
| + | the second quarter sales. To rationalize such trend, we propose two reasons, 1) higher demand from secondary consumers and 2) practice of stockpiling at the start of the year. | ||
| + | <br/><br/> | ||
| + | Exploring which channel is most valued for each sales team allows us to identify channel-therapy area pair that generates the most sales. To visualize, we will plot a mosaic plot of sales revenue by therapy area and channel. | ||
| + | Examining the mosaic plot, we can see that each sales team has a most valued channel to focus their efforts on. For Urology, it is Restructured Hospital; for Respiratory, they are GP and Restructured Hospitals; for Paediatric Vaccines, it is Polyclinics; for Allergy, it is Pharmacy; | ||
| + | for Dermatology, it is GP; for Adult Vaccines, it is GP. This illustrates the behaviour of secondary consumers (patients), who has preference over certain channels when it comes to treat certain illnesses. This could be important in the future, after completing ANOVA, to understand the effects of multi-factor-group impacts on sales interaction vs sales revenue. | ||
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Revision as of 13:22, 15 April 2017
Initial Data Exploration and Analysis
We conducted initial data analysis using Exploratory Data Analysis (EDA) to gain general insights on key determinants that affect the relationship between interaction counts and
sales revenue. We hypothesized that such factors could be “channel”, “therapy area” (sales team).
“Channel” is the classification for different types of clinics, such as General Practitioners, Restructured & Private Hospitals, Specialists. From our basic understanding, each channel has
its own protocols and practices that are likely to affect receptiveness of interactions. For instance, interactions with hospital doctors may not be that impactful as that with GP doctors
because hospital doctors get their drugs from a centralized system, while GP doctors have the power to make decisions for their own clinics.
“Therapy area” defines the name of sales teams, such as Uro CNS (Urology), Respi (Respiratory), Paed Vx (Pediatrics Vaccines), Allergy, Al Derm (Dermatology), Ad Vx (Adult Vaccines),
and it decides the corresponding product brands to promote. We postulate that different drugs have different demands and established drugs may need small number of interactions to achieve good sales results whereas new types of drugs may need more interactions to achieve the same level of sales results.
To give us a better understanding of the natures of different channels and therapy areas, in this initial data analysis, we will explore 1) how sales revenue differs for different channels
across different quarters and 2) which is the most valued channel for each sales team.
Exploring sales revenue by channels and quarters allow us to understand significant demand patterns that arise from practices or secondary consumers (patients).
For instance, we will plot a line graph of total sales amount (response) against different quarters (explanatory) by different channels.
A first look at the visualization gives us an understanding that there are indeed intrinsic
differences across different channels and quarters.
An observation of trend across quarters is that the highest sales for most channels were made in the first quarter. This is especially prominent for pharmacy, which made more than half of
the second quarter sales. To rationalize such trend, we propose two reasons, 1) higher demand from secondary consumers and 2) practice of stockpiling at the start of the year.
Exploring which channel is most valued for each sales team allows us to identify channel-therapy area pair that generates the most sales. To visualize, we will plot a mosaic plot of sales revenue by therapy area and channel.
Examining the mosaic plot, we can see that each sales team has a most valued channel to focus their efforts on. For Urology, it is Restructured Hospital; for Respiratory, they are GP and Restructured Hospitals; for Paediatric Vaccines, it is Polyclinics; for Allergy, it is Pharmacy;
for Dermatology, it is GP; for Adult Vaccines, it is GP. This illustrates the behaviour of secondary consumers (patients), who has preference over certain channels when it comes to treat certain illnesses. This could be important in the future, after completing ANOVA, to understand the effects of multi-factor-group impacts on sales interaction vs sales revenue.
