Part-1.pdf

SALES
ANALYSIS
PART-1
Wai Lin – Learn Business Intelligence

COURSE OUTLINE
2
Introductio
n
1
Data Collection
and
Management
2
Key
Performance
Indicators
(KPIs) for Sales
Analysis
3
SalesTrend
Analysis
4
Customer
Segmentation
and Analysis
5
Product
Performance
Analysis
6
Sales Channel
Analysis
7
Pricing and
Promotion
Analysis
8
Competitor
Analysis
9
Sales
Forecasting and
Planning
1
0

3
PART-1 INTRODUCTION TO SALES ANALYSIS
Sales Analysis is the process of examining and understanding the sales data of a business to gain insights into its performance and
trends. It involves collecting, organizing, and evaluating information related to sales, such as revenue, quantities sold, customer
demographics, product performance, and sales channels.
The goal of sales analysis is to identify patterns, strengths, weaknesses, and opportunities for improvement, which can help businesses
make informed decisions to boost their sales and overall success. In a nutshell, sales analysis is like a magnifying glass that helps
businesses see how well they are doing in selling their products or services and where they can make changes to achieve better results.
• What was the best Year for sales?
• What was the best month for sales?
• How much was earned that month?
• What City had the highest number of sales?
• What time should we display advertisement to maximize likelihood of customer’s buying product?
• What products are most often sold together?
• What product sold the most? Why do you think it sold the most?

4
DATA > INFORMATION
Data Information
• 2022 Total Revenue: USD 10.50M
• Number of products sold: 109K
• Highest average sales month: May (USD 190.35M) Lowest
average revenue month: September (USD 180.51M)
• Best revenue month: December (USD 4.6M)
• Best-selling location: Yangon (Total revenue: USD 8.3M)
• Top product combinations: iPhone and Lighting Charging
Cable, Google phone and USB-C Charging Cable
• Peak sales time: 12 PM and 7.20 PM

5
THE MERCHANT'S WISDOM: A TALE OF SALES ANALYSIS
What can we learn from this study ?
• Importance of Seeking Advice
• Value of Data-Driven Decision-Making
• Focus on Customer Satisfaction
• Adapting to Market Changes
• Continuous Improvement

6
TRADITIONAL SALES VS ANALYST SALES
Traditional Sales
¡ Traditional salesmen often rely on interpersonal skills,
charisma, and persuasive communication to make sales. They
might focus on building relationships, understanding customer
needs through conversation, and tailoring their pitch based on
intuition and experience.
¡ Traditional salesmen might make decisions based on their gut
feeling, experience, and personal observations. They rely on
their ability to read customers and adjust their tactics
accordingly.
¡ Traditional salesmen may personalize their interactions based
on their understanding of the customer's personality,
preferences, and needs. This personalization often comes
from direct conversations and relationship-building.
Analyst Sales
¡ Analyst salesmen, on the other hand, heavily rely on data and
analytics to drive their sales efforts. They use customer data,
market trends, and performance metrics to make informed
decisions about which products or services to recommend to a
specific customer.
¡ Analyst salesmen make decisions based on data-driven
insights and analysis. They might use algorithms, predictive
modeling, and historical data to identify patterns, forecast
customer behavior, and suggest the most relevant offers or
solutions.
¡ Analyst salesmen personalize their approach based on data-
driven insights. They can tailor their recommendations by
analyzing past purchasing behavior, browsing history, and
demographic information.

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TRADITIONAL SALES VS ANALYST SALES
Traditional Sales
¡ Traditional sales strategies often involve building rapport,
establishing trust, and addressing customer objections
through effective communication. These strategies focus on
the emotional and interpersonal aspects of the sale.
¡ Traditional salesmen excel in interpersonal skills, negotiation,
persuasion, and relationship-building. They are good at
reading body language and adapting their communication
style to different individuals.
¡ While technology might be used to some extent, traditional
salesmen rely more on personal interactions and direct
communication..
Analyst Sales
¡ Analyst sales strategies are driven by the analysis of market
trends, customer behavior, and performance metrics. They
might use techniques like cross-selling, upselling, and
targeted promotions based on data patterns.
¡ Analyst salesmen require strong analytical skills, data
interpretation abilities, and a solid understanding of market
dynamics. They might also have programming and data
analysis skills to work with the tools and technologies required
for data-driven sales.
¡ Analyst salesmen heavily depend on technology for data
collection, analysis, and automation. They may use customer
relationship management (CRM) systems, data analytics
platforms, and AI-driven tools to enhance their sales efforts.

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BEFORE & AFTER OF SALES ANALYSIS
Before Sales Analysis
¡ The small clothing store has been running for a year, but the
owner has limited information about the business's
performance. They have no clear idea about which clothing
items are the bestsellers, which customer segments are most
profitable, and which marketing efforts are driving sales. The
owner is making decisions based on intuition and guesswork,
which can lead to inefficiencies and missed opportunities.
After Sales Analysis
¡ The owner decides to conduct a sales analysis to understand the
business better. They gather sales data from the past year,
including sales figures, customer information, and marketing
expenses. Here are some key findings from the sales analysis:
1. Bestselling Products: The analysis reveals that t-shirts and jeans
are the most popular items, contributing to the majority of sales.
2. Peak Sales Period: The store experiences a surge in sales during
seasonal promotions and holiday seasons.
3. Customer Segments: The analysis shows that young adults
between the ages of 18 and 30 are the primary customers,
followed by teenagers and middle-aged individuals.
4. Marketing Effectiveness: The owner discovers that social media
advertising generates more sales compared to traditional print ads.
let's take an example of a small clothing store to illustrate the before and after effects of sales analysis:

9
After-effects of Sales Analysis: With these insights, the owner can now take several informed actions to improve the store's
performance:
1. Inventory Management: They can focus on stocking more t-shirts and jeans, while reducing less popular items, optimizing
inventory and avoiding overstocking.
2. Targeted Promotions: During peak sales periods, the owner can plan special promotions or discounts on popular items to attract
more customers.
3. Customer Segmentation: By understanding the primary customer segments, the store can tailor marketing messages and
promotions to better appeal to each group.
4. Marketing Strategy: The owner can allocate more budget to social media advertising and reduce spending on less effective print
ads, maximizing the return on marketing investments.
5. Sales Training: Armed with insights about peak sales times and popular items, the owner can provide targeted sales training to
staff to upsell during high-demand periods.
BEFORE & AFTER OF SALES ANALYSIS

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UNDERSTANDING THE IMPORTANCE OF SALES ANALYSIS
1. Performance Evaluation: Sales analysis allows businesses to assess their overall performance and track progress towards sales
targets and goals. By analyzing sales data over different periods, companies can identify patterns and assess whether they are on
track to achieve their objectives.
2. Decision-Making: Sales analysis provides valuable insights that aid in making informed decisions. For example, identifying high-
performing products or customer segments can help allocate resources effectively and focus on areas with the highest potential for
growth.
3. Identifying Trends: Analyzing sales data helps in spotting market trends and changes in customer behavior. This enables businesses
to adapt their strategies proactively, ensuring they stay competitive and relevant in the ever-changing market landscape.
4. Forecasting: Sales analysis is essential for accurate sales forecasting. Businesses can project future sales based on historical data,
allowing for better resource planning and inventory management.
5. Improving Efficiency: By understanding the factors that drive sales, businesses can streamline their operations, optimize marketing
efforts, and improve the efficiency of their sales processes.
6. Performance Measurement: Sales analysis provides a basis for measuring the effectiveness of sales and marketing initiatives. It
helps evaluate the return on investment (ROI) of different sales strategies and promotional campaigns.

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KEY CHALLENGES AND OPPORTUNITIES IN DISTRIBUTION AND RETAIL SALES
1. Data Quality and Integration: One of the main challenges in sales analysis is ensuring the accuracy and integrity of the data.
Businesses may collect data from various sources, and consolidating it into a coherent and reliable dataset can be complex.
2. Data Volume: Distribution and retail businesses often deal with large volumes of sales data. Analyzing this vast amount of information
can be overwhelming without the right tools and techniques.
3. Seasonality and Variability: Sales in retail and distribution can be heavily influenced by seasonal factors, consumer trends, and
economic fluctuations. Understanding and accounting for these variations is crucial for meaningful analysis.
4. Customer Behavior: Customers' preferences and behaviors can be diverse and ever-changing. Analyzing sales data to uncover
customer insights helps in tailoring marketing strategies and improving customer satisfaction.
5. Multi-Channel Sales: Many distribution and retail businesses operate across multiple channels, such as physical stores, e-commerce
platforms, and wholesale distribution. Analyzing sales data across these channels and coordinating strategies can be challenging but
presents opportunities for increased reach and market penetration.
6. Competitive Landscape: Understanding the competitive landscape is vital for success in distribution and retail. Analyzing competitor
sales data and strategies can reveal opportunities for differentiation and improvement.
7. Pricing and Promotion Strategies: Determining the most effective pricing and promotion strategies requires thorough analysis of
sales data and understanding customer responses to pricing changes and promotions.

12
EXAMPLE (1) A CASE STUDY OF A DISTRIBUTION BUSINESS
¡ Introduction:
This case study examines the journey of a sub-distributor business operating in the consumer goods sector. The company, named
"Brightway Distributors," is a sub-distributor that sources products from a larger distributor and supplies them to retailers and small
businesses in a specific region. Over the years, Brightway Distributors has faced various challenges and opportunities, and this case
study highlights how they leveraged sales analysis and strategic decisions to achieve success.
¡ Background:
Brightway Distributors started its operations five years ago as a small-scale sub-distributor in a growing urban area. Their initial product
portfolio included fast-moving consumer goods (FMCG) such as snacks, beverages, and toiletries. The company's goal was to fill the gap
in product availability and timely delivery within the region, targeting small retailers and businesses that were underserved by larger
distributors.
¡ Challenges Faced:
1. Market Penetration: At the beginning, Brightway Distributors faced challenges in penetrating the market due to the presence of
established competitors. The team needed to devise a unique selling proposition to stand out and gain the trust of potential retailers.
2. Inventory Management: Managing inventory efficiently was critical for meeting customer demands while avoiding overstocking and
wastage. The company needed to strike a balance to ensure optimal inventory levels.
3. Sales Forecasting: Accurate sales forecasting was essential to plan inventory procurement and avoid stockouts. However, without
historical data, forecasting proved to be a significant challenge.

13
¡ Opportunities Identified:
Customer Segmentation: Through sales analysis, Brightway Distributors identified different customer segments, including small retailers,
cafes, and convenience stores. This insight allowed them to tailor their product offerings and delivery schedules for each segment.
Leveraging Technology: The company saw an opportunity to use technology to streamline operations. They invested in an order
management system and mobile app to facilitate efficient order processing and real-time inventory tracking.
Diversifying Product Portfolio: By analyzing customer preferences and market trends, Brightway Distributors identified opportunities to
expand their product portfolio. They added health and wellness products to cater to the growing demand for healthy snacks and
beverages.
¡ Strategic Decisions and Outcomes:
Market Expansion: After analyzing sales data and demand patterns, Brightway Distributors strategically expanded its distribution network
to neighboring regions. This move resulted in increased sales and a broader customer base.
Performance Metrics: The company implemented key performance indicators (KPIs) to monitor sales performance, delivery efficiency, and
customer satisfaction. This data-driven approach helped them identify areas for improvement and set ambitious targets.
Tailored Promotions: Through customer segmentation, Brightway Distributors conducted targeted promotions and loyalty programs. This
led to increased customer retention and boosted sales among specific customer segments.
Collaboration with Suppliers: By collaborating closely with suppliers and sharing sales data, Brightway Distributors negotiated better
terms, including volume discounts, which contributed to higher profit margins.

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¡ In this case study of a sub-distributor business, several calculation methods are essential to gain valuable insights and make informed
decisions. The calculations will help the company analyze its sales performance, inventory management, and profitability. Below are
some key calculation methods that would be relevant to this case:
Sales Performance Analysis:
a) Sales Revenue: Calculate the total sales revenue by multiplying the quantity sold by the selling price for each product.
b) b) Sales Growth Rate: Determine the sales growth rate by comparing current period sales revenue with previous period sales
revenue.
Inventory Management:
a) Inventory Turnover: Calculate the inventory turnover by dividing the cost of goods sold (COGS) by the average inventory value.
b) b) Days Inventory Outstanding (DIO): Calculate DIO by dividing the average inventory value by the cost of goods sold per day. DIO
measures how long inventory is held before being sold.
Sales Forecasting:
a) Moving Average: Use the moving average method to forecast future sales by averaging sales data over a specific period.
b) Time-Series Analysis: Implement time-series analysis techniques, such as exponential smoothing or ARIMA (AutoRegressive Integrated
Moving Average), to predict future sales based on historical data.

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Key Performance Indicators (KPIs):
a) Customer Retention Rate: Calculate the CRR by dividing the number of retained customers by the total number of customers.
b) b) Customer Acquisition Cost (CAC): Calculate CAC by dividing the total marketing and sales expenses by the number of new customers
Profitability Analysis:
a) Gross Profit Margin: Calculate the gross profit margin by dividing gross profit (sales revenue minus the cost of goods sold) by sales
revenue.
b) b) Net Profit Margin: Determine the net profit margin by dividing net profit (total revenue minus total expenses) by sales revenue.
Product Performance Analysis:
a) Product Contribution Margin: Calculate the contribution margin for each product by subtracting variable costs (e.g., production cost,
shipping) from the selling price.
b) Product Sales Growth Rate: Determine the sales growth rate for each product by comparing current period sales with previous period sales.

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EXAMPLE (2) XYZ ELECTRONIC – NAVIGATING SALES ANALYSIS
XYZ Electronics - Navigating Sales Analysis in a Retail Business
¡ Introduction: XYZ Electronics is a chain of electronic retail stores with locations across the country. The company sells a wide range
of electronic products, including smartphones, laptops, home appliances, and accessories. The management at XYZ Electronics is
interested in enhancing their sales strategies through data-driven insights.
¡ Importance of Sales Analysis: The management team at XYZ Electronics understands that analyzing sales data is crucial for
making informed decisions. By examining sales patterns, they can identify which products are performing well, understand customer
preferences, and optimize inventory management.
¡ Challenges and Opportunities:
Challenges:
1. Seasonality: XYZ Electronics experiences fluctuations in sales due to seasonal trends. For instance, there might be a surge in
smartphone sales during the holiday season, while home appliances might see higher demand during the back-to-school period.
2. Product Cannibalization: The introduction of new products could lead to cannibalization, where sales of existing products
decline as customers switch to newer options.
3. Competition: The electronics retail sector is highly competitive, with online and brick-and-mortar competitors. XYZ Electronics
needs to understand their market positioning and how competitors' strategies impact their sales.

17
EXAMPLE (2) RETAIL STORE SALES ANALYSIS FOR ELECTRONIC
GADGETS
Opportunities:
1. Cross-Selling and Upselling: Analyzing customer purchase patterns might reveal opportunities for cross-selling (offering
complementary products) and upselling (encouraging customers to buy higher-end products).
2. Customer Segmentation: Identifying different customer segments based on demographics or buying behavior can help tailor
marketing efforts and product offerings.
3. Promotion Effectiveness: Through sales analysis, XYZ Electronics can assess the impact of different promotions and
discounts on customer behavior.
¡ Outcome: By embracing sales analysis, XYZ Electronics gained valuable insights. They found that smartphone sales spiked during
the holiday season and were able to stock up accordingly. They also identified a particular customer segment interested in gaming
laptops and started offering specialized bundles, leading to increased sales in that category. Additionally, by analyzing data on
promotion effectiveness, they optimized their promotional calendar for maximum impact.
¡ Conclusion: This case study demonstrates how sales analysis plays a pivotal role in addressing challenges and capitalizing on
opportunities in a retail business. Retailers like XYZ Electronics can harness the power of data to refine their sales strategies, enhance
customer experiences, and ultimately drive growth.

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HOME WORK
¡ Imagine you are a manager in a retail store or a distribution center. Describe a specific situation where conducting a thorough sales
analysis would be highly beneficial for your business. Explain the potential insights you could gain and how they could positively
impact decision-making and overall performance.
¡ Research and list five common challenges that distribution and retail businesses typically face regarding sales. Explain each challenge
in detail and provide potential strategies or solutions to address them effectively.
¡ Identify and elaborate on three potential opportunities that distribution and retail businesses can leverage to improve their sales
performance. These opportunities could involve emerging market trends, technological advancements, or changes in consumer
behavior.

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