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May 10, 2024
Step by step Sales Analysis Use Case to implement effective sales strategies to maximize revenue & market share?
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The global Beverage market size reached USD 207 billion in 2023 and is expected to grow to USD 459 billion by 2027, with an average annual growth rate of 14.22%. Beverages include both soft drinks like sodas and juices and alcoholic drinks like beer and wine.
This growth shows that more and more people are buying drinks, influenced by their changing tastes and lifestyles. Soft drinks are for those who don't drink alcohol, while alcoholic drinks include beer, wine, and spirits. As people look for drinks that are easy to buy and healthier, companies are coming up with new flavours and packaging. This growth means there are good opportunities for companies to make and sell drinks that people want.
Alright, let's take a closer look at our Dataset: Beverage Sales Dataset
So, we've got this awesome Beverage Sales Data. We can see what drinks are selling where, whether it's online or in vending machines. You've got everything from transaction dates to retailer details to product specifics. It covers all sorts of beverages and sales channels, like online or vending machines.
And the best part?
This data is super helpful for spotting trends, figuring out what customers like, and seeing how well new drinks are doing and best suitable for sales analysis.
Each row in the dataset corresponds to an order made by a customer. We have the following features:
When initiating the sales analysis to boost beverage sales, it's crucial to adhere to the 5 essential factors of Data Analysis:
1. Determine Stakeholders: Start by identifying the key users or stakeholders who will utilize the dashboard.
2. Create Empathy Map: Develop an empathy map to understand users' goals, challenges, and pain points effectively.
3. Identify Vital Metrics: Pinpoint the most important metrics or Key Performance Indicators (KPIs) crucial for measuring success.
4. Define Objectives: Gain a clear understanding of the objectives and goals that the analysis aims to achieve.
5. Pose Business Questions: Ask pertinent business questions that will guide the analysis and provide valuable insights for decision-making.
Define the User or stakeholder who will use the Beverages dataset . In our case, We’ll use a Sales Manager. Recognizing their diverse needs, challenges, and priorities becomes the cornerstone for tailoring an effective data story.
This individual is responsible for steering the sales team towards meeting revenue targets and expanding market share. Understanding the background, roles, responsibilities, needs, and pain points of this persona allows us to tailor the sales dashboard to meet their specific requirements effectively.
Understanding the Strategic Analyst is important, and that's where an empathy map comes in handy. This tool helps us grasp their feelings, goals, and challenges, giving us insights into their work beyond the usual tasks.
By putting ourselves in their shoes, we can create a data story that not only works well but also connects with them on a personal level. It's about finding the right balance and designing our analysis with their perspective in mind.
Key Performance Indicators (KPIs) serve as the compass guiding our persona's journey towards revenue growth and market expansion. These metrics provide tangible benchmarks for measuring success and identifying areas for improvement. Identify KPIs such as
By tracking these KPIs through the sales dashboard, our persona gains real-time visibility into the health and performance of the sales organization.
Now, based on the insights from the Empathy Map and KPIs, we need to define the goals and objectives of the Strategic Analyst user persona. This ensures that our data story aligns with their needs for effective decision-making.
Based on the defined goals & objectives, your next step will be to provide actionable insights to make a mark on Business Decisions.
Finally, to conclude your analysis, you need to ask right questions. These questions should be directly tied to the objectives and goals identified earlier and should guide our exploration of the data. By asking insightful questions, we can uncover hidden patterns, identify areas for improvement, and generate actionable insights that drive informed decision-making. Here are following Business Questions to ask:
Python Code: Sales over the Years
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#Set date column to datetime format and extract year
df['TransactionDate'] = pd.to_datetime(df['TransactionDate'])
df['Year'] = df['TransactionDate'].dt.year
#Aggregate Sales by Year
sales_by_year = df.groupby('Year')['SalesValue'].sum().reset_index()
#Set the style and figure
sns.set_style("whitegrid")
plt.figure(figsize = (10,6))
#Create the chart
sns.barplot(x='Year',y='SalesValue', data = sales_by_year)
plt.title("Beverage Sales by Year")
plt.xlabel('Year')
plt.ylabel('Sales')
plt.show()
Visualization:
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Python Code: Top 5 Country by Sales & Profit
#Aggregating the data by Country
country_agg = df.groupby('Country').agg({
'SalesValue': 'sum',
'Profit': 'sum'
}).reset_index()
#Top countries by Sales
top_countries = country_agg.nlargest(5, 'SalesValue')
#Setting the style and figure
sns.set_style("whitegrid")
plt.figure(figsize=(14, 6))
# First subplot for Sales
plt.subplot(1, 2, 1) # 1 row, 2 columns, 1st subplot
sns.barplot(x='Country', y='SalesValue', data=top_countries)
plt.title('Top 5 Countries by Sales')
plt.xlabel('Country')
plt.ylabel('Total Sales')
plt.xticks(rotation=45)
# Second subplot for Profit
plt.subplot(1, 2, 2) # 1 row, 2 columns, 2nd subplot
sns.barplot(x='Country', y='Profit', data=top_countries)
plt.title('Top 5 Countries by Profit')
plt.xlabel('Country')
plt.ylabel('Total Profit')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
Visualization :
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Python Code: Packaging Impact on Sales in India
#Aggregating Sales in INDIA
india_sales = df[df['Country'].str.upper()== 'INDIA']
india_sales_packaging = india_sales.groupby('PackagingType')['SalesValue'].sum().reset_index()
# Setting the style
sns.set_style("whitegrid")
# Create the pie chart
plt.figure(figsize=(8, 8))
plt.pie(india_sales_packaging['SalesValue'], labels=india_sales_packaging['PackagingType'],
autopct='%1.1f%%', startangle=140)
plt.title('Sales by Packaging Type in India')
plt.show()
Visualization :
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Python Code: Compare the products by Sales and Cost per unit
#Aggregating the Sales and Cost per Unit by Brand
brand_data = df.groupby('Brand').agg({
'SalesValue':'mean',
'CostPerUnit':'mean'
})
#Set the style and figure
sns.set_style("whitegrid")
plt.figure(figsize = (10,6))
#Create a scatterplot
sns.scatterplot(x="SalesValue", y= "CostPerUnit", data = brand_data,
hue = "Brand", style = 'Brand', palette ='viridis',s=200)
#Create Quandrant Axis
median_sales = brand_data['SalesValue'].median()
median_cost = brand_data['CostPerUnit'].median()
plt.axhline(y=median_cost, color ='red', linestyle = '--')
plt.axvline(x=median_sales, color ='red', linestyle = '--')
#Set the title,labels and legends
plt.title("Sales and Cost per Unit by Brands")
plt.xlabel("Sales")
plt.ylabel("Cost per Unit")
plt.legend(title='Brands', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
Visualization :
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Explore Sales Analytics Data Story Here👇
By framing this data story in alignment with business goals, organizations can get deeper insights that drive impactful decisions.
Creating a strong strategy for the beverages market is a team effort.
We need to understand what the strategic analyst cares about and ask the right questions. This helps us make a strategy that not only meets but also exceeds the strategic analyst's expectations. This is how we stay ahead in the competitive beverage market.
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