Data Collection and Management for FMCG Sales Analysis

Introduction

This section covers the practical implementation of data collection and management for analyzing sales data from FMCG (Fast Moving Consumer Goods) stores across the east coast of Australia. The goal is to set up a system to gather, store, and preprocess data to derive meaningful insights that can improve store performance and strategy.

Setting Up the Data Collection Process

Data Sources

1. Point of Sale (POS) Systems: Collect transaction-level data from POS systems installed at each store.
2. Inventory Management Systems: Gather inventory data showing stock levels, reorder points, and replenishments.
3. Customer Feedback: Collect qualitative data through surveys and reviews.
4. External Data: Include weather data, economic indicators, and local events information.

Data Collection Workflow

1. POS Data Extraction:

   • Identify the data fields required: transaction ID, product ID, quantity sold, price, discount applied, timestamp, etc.
   • Schedule regular data extraction using ETL (Extract, Transform, Load) tools.

2. Inventory Management Data:

   • Extract stock levels, reorder points, purchase orders, and supplier data.
   • Establish API connections or scheduled CSV downloads.

3. Customer Feedback:

   • Design surveys and feedback forms.
   • Use feedback aggregation tools.

4. External Data:

   • Integrate APIs for weather data, economic indices, and event schedules.

Example Workflow (Pseudocode)

// Initialize data storage (e.g., a database)
initializeDatabase();

// POS Data Extraction
posData = extractDataFromPOS();
storeDataToDatabase(posData);

// Inventory Management Data Extraction
inventoryData = extractDataFromInventorySystem();
storeDataToDatabase(inventoryData);

// Customer Feedback Collection
feedbackData = collectCustomerFeedback();
storeDataToDatabase(feedbackData);

// External Data Integration
weatherData = getWeatherData();
economicData = getEconomicData();
eventData = getEventData();
storeDataToDatabase(weatherData);
storeDataToDatabase(economicData);
storeDataToDatabase(eventData);

Data Storage

• Choose a relational database (e.g., MySQL, PostgreSQL) or NoSQL database (e.g., MongoDB) suitable for handling large-scale data.
• Define tables and schemas based on collected data types.

Example Schema Design

CREATE TABLE Transactions (
    transaction_id SERIAL PRIMARY KEY,
    product_id VARCHAR(50) NOT NULL,
    quantity_sold INT NOT NULL,
    price DECIMAL(10, 2) NOT NULL,
    discount DECIMAL(5, 2),
    timestamp TIMESTAMP NOT NULL
);

CREATE TABLE Inventory (
    product_id VARCHAR(50) PRIMARY KEY,
    stock_level INT NOT NULL,
    reorder_point INT NOT NULL,
    last_restock TIMESTAMP
);

CREATE TABLE CustomerFeedback (
    feedback_id SERIAL PRIMARY KEY,
    customer_id VARCHAR(50),
    feedback_text TEXT,
    timestamp TIMESTAMP NOT NULL
);

CREATE TABLE ExternalData (
    data_id SERIAL PRIMARY KEY,
    data_type VARCHAR(50),
    data_value TEXT,
    timestamp TIMESTAMP NOT NULL
);

Data Management and Preprocessing

Data Cleaning

1. Handle missing values.
2. Normalize data formats.
3. Identify and remove duplicate records.

Example Pseudocode for Data Cleaning

// Function to clean POS data
function cleanPosData(data):
    cleanedData = []
    for record in data:
        if all(requiredFieldsPresent(record)):
            cleanedData.append(standardizeRecord(record))
    return cleanedData

// Function to remove duplicates
function removeDuplicates(data):
    uniqueData = set(data)
    return list(uniqueData)

Data Integration

• Merge data from different sources based on common keys (e.g., product_id).

Example Pseudocode for Data Integration

// Merge POS and Inventory Data
integratedData = mergeDataOnKey(posData, inventoryData, 'product_id');

Data Storage (Post-Cleansing)

• Store cleaned and integrated data back into the database for analysis.

Conclusion

With the above steps, the data collection and management system for analyzing sales data from FMCG stores is set up. These steps ensure that the data is collected accurately, stored securely, and preprocessed for deriving meaningful insights in further stages of the project.

Data Cleaning and Preprocessing: FMCG Sales Data Analysis

Objective

The goal of this section is to demonstrate how to clean and preprocess sales data from FMCG stores to make it ready for analysis.

Assumptions

• The sales data is in a tabular format (e.g., CSV file)
• Columns might include: Date, Store_ID, Product_ID, Sales_Amount, Units_Sold, Promotion_Active, etc.

Steps

1. Load Data

General Approach:

1. Load the data from a given file format (e.g., CSV).

data = LoadData("sales_data.csv")

2. Remove Missing or Incomplete Data

General Approach:

1. Identify and handle missing values - either by removing rows with missing data or imputing them with appropriate values.

# Identify missing values
missing_values = data.IdentifyMissingValues()

# Drop rows with missing values
cleaned_data = data.DropRowsWithMissingValues()

3. Handle Duplicates

General Approach:

1. Check for duplicated rows and decide whether to keep or remove them.

# Identify and remove duplicate rows
duplicates = data.IdentifyDuplicates()
cleaned_data = data.RemoveDuplicates()

4. Convert Data Types

General Approach:

1. Ensure all columns have appropriate data types, such as converting dates to datetime format and numerical columns to integer/float.

# Convert data types
cleaned_data['Date'] = ConvertToDateTime(cleaned_data['Date'])
cleaned_data['Sales_Amount'] = ConvertToFloat(cleaned_data['Sales_Amount'])
cleaned_data['Units_Sold'] = ConvertToInteger(cleaned_data['Units_Sold'])

5. Handle Outliers

General Approach:

1. Remove or cap outliers in numerical data to avoid skewing the analysis.

# Identify outliers
outliers = IdentifyOutliers(cleaned_data['Sales_Amount'])

# Remove outliers
trimmed_data = cleaned_data.RemoveOutliers(outliers)

6. Standardize and Normalize Data

General Approach:

1. Standardize or normalize numerical data to ensure consistency.

# Standardize/Normalize data
normalized_data= NormalizeData(cleaned_data['Sales_Amount'])
standardized_data = StandardizeData(cleaned_data['Units_Sold'])

7. Encode Categorical Variables

General Approach:

1. Convert categorical variables into numerical format using techniques like one-hot encoding.

# Encode categorical variables
encoded_data = OneHotEncode(cleaned_data['Promotion_Active'])

8. Aggregate Data (if necessary)

General Approach:

1. Aggregate or summarize data (e.g., total sales per store per month).

# Aggregate data
aggregated_data = AggregateData(by=['Store_ID', 'Month'], metrics=['sum(Sales_Amount)', 'sum(Units_Sold)'])

Conclusion

The above steps outline a practical implementation approach to clean and preprocess sales data from FMCG stores to ensure it is ready for insightful analysis. This will facilitate more accurate and meaningful business insights.

Sales Data Exploration

1. Load Cleaned Data

Assuming the data has been collected, cleaned, and is available as a CSV file or DataFrame.

# Pseudocode for loading the cleaned data
sales_data <- load_data("cleaned_sales_data.csv")

# Display first few rows to verify
print(head(sales_data))

2. Summary Statistics

Generate summary statistics to understand the distribution of sales data.

# Calculate summary statistics
summary_statistics <- summary(sales_data)

# Display summary statistics
print(summary_statistics)

3. Sales Trends Over Time

Analyze sales trends over different periods (e.g., daily, weekly, monthly).

# Convert dates if needed
sales_data$Date <- to_date(sales_data$Date, "YYYY-MM-DD")

# Aggregate sales by month
monthly_sales <- aggregate(sales_data$Sales, by=month(sales_data$Date), sum)

# Plot monthly sales
plot(monthly_sales$Date, monthly_sales$Sales, type="line", title="Monthly Sales Trend", x_label="Month", y_label="Sales")

4. Product Performance

Identify best and worst-performing products.

# Aggregate sales by product
product_sales <- aggregate(sales_data$Sales, by=sales_data$Product, sum)

# Sort to find best and worst performing products
sorted_product_sales <- sort(product_sales$Sales, decreasing=TRUE)

# Top 5 and Bottom 5 products
top_5_products <- head(sorted_product_sales, 5)
bottom_5_products <- tail(sorted_product_sales, 5)

# Display top and bottom products
print("Top 5 Products:")
print(top_5_products)
print("Bottom 5 Products:")
print(bottom_5_products)

5. Sales by Region

Analyze sales data by region to understand geographic performance.

# Aggregate sales by region
region_sales <- aggregate(sales_data$Sales, by=sales_data$Region, sum)

# Plot sales by region
plot(region_sales$Region, region_sales$Sales, type="bar", title="Sales by Region", x_label="Region", y_label="Sales")

6. Seasonal Analysis

Determine if there are any seasonal trends in the sales data.

# Extract month from date
sales_data$Month <- month(sales_data$Date)

# Aggregate sales by month across all years
seasonal_sales <- aggregate(sales_data$Sales, by=sales_data$Month, mean)

# Plot seasonal sales trends
plot(seasonal_sales$Month, seasonal_sales$Sales, type="line", title="Seasonal Sales Trends", x_label="Month", y_label="Average Sales")

7. Correlation Analysis

Analyze correlations between different columns, such as promotions and sales.

# Calculate correlations
correlation_matrix <- cor(sales_data)

# Display correlation matrix
print(correlation_matrix)

8. Sales Distribution

Visualize the distribution of sales.

# Plot sales distribution
histogram(sales_data$Sales, bins=30, title="Distribution of Sales", x_label="Sales", y_label="Frequency")

By following the steps above, you can obtain meaningful insights from your sales data that can help in improving performance and strategy.

Customer Segmentation Analysis

Step 1: Load Preprocessed Sales Data

Assume the sales data is preprocessed and stored in sales_data containing necessary features like customer ID, transaction amount, frequency, and recency.

Step 2: Feature Engineering for RFM Model

RFM (Recency, Frequency, Monetary) Analysis is a marketing technique used to quantitatively determine which customers are the best ones through the recency, frequency, and monetary value of their purchases.

# Assume 'sales_data' contains columns: 'customer_id', 'transaction_date', 'transaction_amount'
import DateUtils

# Create an empty dictionary to store RFM metrics for each customer
rfm_metrics = {}

# Iterate over each transaction in the sales_data
FOR transaction IN sales_data:
    customer_id = transaction['customer_id']
    transaction_date = transaction['transaction_date']
    transaction_amount = transaction['transaction_amount']
    
    # Initialize customer entry if it doesn't exist
    IF customer_id NOT IN rfm_metrics:
        rfm_metrics[customer_id] = {
            'recency': None,
            'frequency': 0,
            'monetary': 0
        }
    
    # Update frequency and monetary values
    rfm_metrics[customer_id]['frequency'] += 1
    rfm_metrics[customer_id]['monetary'] += transaction_amount
    
END FOR

latest_date = DateUtils.get_latest_date(sales_data)

# Calculate recency for each customer
FOR customer_id, metrics IN rfm_metrics.items():
    transaction_dates = sales_data[sales_data['customer_id'] == customer_id]['transaction_date']
    recency = DateUtils.calculate_days_difference(latest_date, max(transaction_dates))
    metrics['recency'] = recency
    
END FOR

Step 3: Assign RFM Scores

Segment the metrics into categories on a scale. Simplified example using quintiles:

# Convert RFM metrics dictionary to a list for easier processing
rfm_list = [ (customer_id, metrics['recency'], metrics['frequency'], metrics['monetary']) 
             FOR customer_id, metrics IN rfm_metrics.items() ]

# Sort the list by each metric to assign scores on a scale of 1-5
rfm_list.sort(key=lambda x: x[1])  # Sort by recency
FOR i, entry IN enumerate(rfm_list):
    entry.append((i // len(rfm_list) * 5) + 1)  # Recency score

rfm_list.sort(key=lambda x: x[2], reverse=True)  # Sort by frequency
FOR i, entry IN enumerate(rfm_list):
    entry.append((i // len(rfm_list) * 5) + 1)  # Frequency score

rfm_list.sort(key=lambda x: x[3], reverse=True)  # Sort by monetary
FOR i, entry IN enumerate(rfm_list):
    entry.append((i // len(rfm_list) * 5) + 1)  # Monetary score

Step 4: Customer Segmentation using K-Means Clustering

Using the RFM scores for clustering:

# Convert rfm_list to a DataFrame or array for clustering
rfm_array = [ [entry[-3], entry[-2], entry[-1]] FOR entry IN rfm_list ]  # Extract R, F, M scores

# Implement K-Means Clustering (using a clustering function/library)
clusters = KMeans(n_clusters=4).fit_predict(rfm_array)  # Example for 4 clusters

# Assign cluster labels to each customer
clustered_customers = [
    { 'customer_id': entry[0], 'cluster': clusters[i], 'rfm_scores': entry[1:-3] } 
    FOR i, entry IN enumerate(rfm_list)
]

Step 5: Analyze and Interpret the Clusters

# Analyze each cluster
cluster_analysis = {}
FOR cluster IN set(clusters):
    cluster_members = [
        entry 
        FOR entry IN clustered_customers 
        IF entry['cluster'] == cluster 
    ]
    
    # Calculate average RFM scores for this cluster
    average_recency = mean([member['rfm_scores'][0] FOR member IN cluster_members])
    average_frequency = mean([member['rfm_scores'][1] FOR member IN cluster_members])
    average_monetary = mean([member['rfm_scores'][2] FOR member IN cluster_members])
    
    # Store analysis data
    cluster_analysis[cluster] = {
        'count': len(cluster_members),
        'average_recency': average_recency,
        'average_frequency': average_frequency,
        'average_monetary': average_monetary
    }

# Display/Use analysis results
DISPLAY(cluster_analysis)

This pseudocode provides the real implementation of Customer Segmentation Analysis based on RFM model scores and K-Means clustering. Adjust the specifics based on your data and requirements.

Seasonal Trends and Patterns Analysis

To analyze seasonal trends and patterns, we will follow these steps:

Step 1: Load the Processed Data

The data should have already been collected, cleaned, and preprocessed. We will directly load the processed data.

# Pseudocode for loading data from a processed file
data = load_processed_data("processed_sales_data.csv")

Step 2: Extract Time Features

Add time-based features to your dataset to facilitate seasonal analysis:

# Add month and year features
data['Month'] = extract_month(data['Date'])
data['Year'] = extract_year(data['Date'])

Step 3: Aggregate Sales Data

Aggregate the sales data to reveal trends per month and year.

# Aggregating sales
monthly_sales = aggregate(data, by=['Year', 'Month'], sum, 'Sales')

Step 4: Identify and Visualize Seasonality

To visualize seasonal patterns effectively, use techniques such as time series decomposition or plotting sales over time using line plots.

# Pseudocode for visualizing sales
plot_line_chart(monthly_sales, x='Date', y='Sales', title='Monthly Sales Over Time')

# Alternatively, using decomposition
decompose = time_series_decompose(monthly_sales['Sales'], model='multiplicative')
plot_decomposition(decompose)

Time Series Decomposition

Break down the sales data into seasonal, trend, and residual components to better understand the components driving the patterns.

# Decomposition
result = time_series_decomposition(monthly_sales['Sales'], model="multiplicative")

# Plotting the decomposition
plot_decomposition(result)

Step 5: Calculate Year-over-Year Growth

Compute the year-over-year (YoY) growth to highlight significant trends and changes:

# Year-over-year growth calculation
def calculate_yoy_growth(data):
    yoy_growth = {}
    previous_year_sales = {}

    for year in unique(data['Year']):
        current_yearly_sales = filter(data, year=year)['Sales'].sum()
        
        if year-1 in previous_year_sales:
            growth = ((current_yearly_sales - previous_year_sales[year-1]) /
                       previous_year_sales[year-1]) * 100
            yoy_growth[year] = growth
        
        previous_year_sales[year] = current_yearly_sales
        
    return yoy_growth

yoy_growth = calculate_yoy_growth(monthly_sales)
plot_bar_chart(yoy_growth, x='Year', y='Growth', title='Year-over-Year Sales Growth')

Step 6: Heatmaps for Seasonal Patterns

Create heatmaps to emphasize the seasonal patterns in the data:

# Create a pivot table
pivot_table = create_pivot_table(data, values='Sales', index='Month', columns='Year')

# Plot heatmap
plot_heatmap(pivot_table, title='Seasonal Sales Heatmap')

Step 7: Forecasting

If needed, create forecasts using time series forecasting methods like ARIMA, SARIMA, or Exponential Smoothing to predict future trends.

# Time series forecasting
forecast_model = train_forecasting_model(monthly_sales['Sales'], model='SARIMA')

# Predict future trends
forecast = forecast_model.predict(steps=12)
plot_forecast(forecast, title='12-Month Sales Forecast')

Conclusion

With these steps implemented, you can derive and visualize seasonal trends and patterns in sales data, leading to actionable insights.

Sales Performance Metrics

Step 6: Implementation of Sales Performance Metrics

1. Metric: Total Sales

Calculate the total sales for each store.

SELECT 
    StoreID, 
    SUM(SalesAmount) AS TotalSales 
FROM 
    SalesData 
GROUP BY 
    StoreID;

2. Metric: Average Sales Per Transaction

Calculate the average sales per transaction to understand the average spending per customer.

SELECT 
    StoreID, 
    AVG(SalesAmount) AS AvgSalesPerTransaction 
FROM 
    SalesData 
GROUP BY 
    StoreID;

3. Metric: Sales Growth Rate

Calculate the month-over-month sales growth rate.

WITH MonthlySales AS (
    SELECT 
        StoreID, 
        DATE_TRUNC('month', SaleDate) AS Month, 
        SUM(SalesAmount) AS TotalSales
    FROM 
        SalesData 
    GROUP BY 
        StoreID, DATE_TRUNC('month', SaleDate)
),
SalesGrowthRate AS (
    SELECT 
        StoreID, 
        Month, 
        TotalSales, 
        LAG(TotalSales) OVER (PARTITION BY StoreID ORDER BY Month) AS PreviousMonthSales,
        (TotalSales - LAG(TotalSales) OVER (PARTITION BY StoreID ORDER BY Month)) * 1.0 / LAG(TotalSales) OVER (PARTITION BY StoreID ORDER BY Month) AS GrowthRate
    FROM 
        MonthlySales
)
SELECT 
    StoreID, 
    Month, 
    GrowthRate 
FROM 
    SalesGrowthRate;

4. Metric: Sales by Category

Calculate the total sales for each product category.

SELECT 
    StoreID, 
    ProductCategory, 
    SUM(SalesAmount) AS TotalSales 
FROM 
    SalesData 
GROUP BY 
    StoreID, ProductCategory;

5. Metric: Customer Retention Rate

Calculate the retention rate by comparing customers who made purchases in consecutive periods.

WITH CustomersMonthly AS (
    SELECT 
        StoreID, 
        CustomerID, 
        DATE_TRUNC('month', SaleDate) AS Month
    FROM 
        SalesData
    GROUP BY 
        StoreID, CustomerID, DATE_TRUNC('month', SaleDate)
),
Retention AS (
    SELECT 
        StoreID, 
        Month, 
        COUNT(DISTINCT CustomerID) AS TotalCustomers,
        LAG(COUNT(DISTINCT CustomerID)) OVER (PARTITION BY StoreID ORDER BY Month) AS PreviousMonthCustomers,
        COUNT(DISTINCT CASE WHEN CustomerID IN (SELECT CustomerID FROM CustomersMonthly cm2 WHERE cm2.Month = DATE_TRUNC('month', DATEADD('month', -1, cm.Month)) AND cm2.StoreID = cm.StoreID) THEN CustomerID END) AS RetainedCustomers
    FROM 
        CustomersMonthly cm
    GROUP BY 
        StoreID, Month
)
SELECT 
    StoreID, 
    Month, 
    (RetainedCustomers * 1.0 / PreviousMonthCustomers) AS RetentionRate 
FROM 
    Retention;

This allows you to apply the performance metrics to the provided sales data efficiently. Ensure your data is already cleaned and preprocessed to get accurate results from these calculations.

7. Geospatial Sales Analysis

Objective

To identify geographic patterns in sales data that can help improve territorial strategies and allocation of resources for FMCG stores across the east coast of Australia.

---

Steps and Implementation

1. Import Required Libraries

   # Import necessary libraries
   library(ggplot2)
   library(sf)
   library(dplyr)
   library(leaflet)

2. Load Geospatial and Sales Data

   # Load geospatial data (assuming a shapefile for store locations)
   stores_locations <- st_read("path/to/store_locations.shp")
   
   # Load sales data (assuming it to be in a CSV file)
   sales_data <- read.csv("path/to/sales_data.csv")

3. Merge Geospatial Data with Sales Data

   # Assuming both data sets have a common 'store_id' key
   geospatial_sales_data <- merge(stores_locations, sales_data, by="store_id")

4. Geospatial Visualization using ggplot2

   # Basic geospatial sales plot
   ggplot(data = geospatial_sales_data) +
       geom_sf(aes(fill = sales_amount)) +
       theme_minimal() +
       labs(title = "Sales Distribution Across Stores",
            fill = "Sales ($)") +
       scale_fill_continuous(type = "viridis")

5. Interactive Geospatial Visualization Using Leaflet

   # Create an interactive map with Leaflet
   leaflet(data = geospatial_sales_data) %>%
       addProviderTiles("CartoDB.Positron") %>%
       addCircleMarkers(~longitude, 
                        ~latitude, 
                        radius = ~sqrt(sales_amount) * 0.1,
                        color = ~ifelse(sales_amount > median(sales_amount), "blue", "red"),
                        popup = ~paste("Store ID:", store_id, "
   ",
                                       "Sales Amount: $", sales_amount)) %>%
       addLegend("bottomright", 
                 pal = colorNumeric("viridis", domain = geospatial_sales_data$sales_amount),
                 values = ~sales_amount, 
                 title = "Sales ($)",
                 opacity = 1)

6. Clustering Analysis on Sales Data

   # Let's assume you have already fitted a clustering algorithm such as k-means and have cluster labels
   sales_clusters <- kmeans(geospatial_sales_data$sales_amount, centers=3)
   geospatial_sales_data$cluster <- sales_clusters$cluster
   
   # Visualize clusters
   ggplot(data = geospatial_sales_data) +
       geom_sf(aes(fill = factor(cluster))) +
       theme_minimal() +
       labs(title = "Store Clusters Based on Sales",
            fill = "Cluster") +
       scale_fill_viridis_d()

7. Geospatial Hotspot Analysis

   # Load additional required libraries
   library(spdep)
   
   # Convert to spatial coordinates for spatial dependency analysis
   coordinates(geospatial_sales_data) <- ~longitude + latitude
   
   # Create neighbor list and weights
   neighbors <- knearneigh(coordinates(geospatial_sales_data), k = 4)
   nb <- knn2nb(neighbors)
   listw <- nb2listw(nb, style = "W")
   
   # Moran's I test for spatial autocorrelation
   moran_test <- moran.test(geospatial_sales_data$sales_amount, listw)
   print(moran_test)
   
   # Local Indicators of Spatial Association (LISA)
   lisa <- localmoran(geospatial_sales_data$sales_amount, listw)
   geospatial_sales_data$lisa <- lisa[,1] # Extract the LISA statistic
   
   # Visualize LISA
   ggplot(data = geospatial_sales_data) +
       geom_sf(aes(fill = lisa)) +
       theme_minimal() +
       labs(title = "Local Indicators of Spatial Association (LISA)",
            fill = "LISA Statistic") +
       scale_fill_viridis_c()

---

This implementation provides a basic but comprehensive approach to analyzing geospatial data in the context of FMCG sales. You can extend this analysis by incorporating more sophisticated tools and techniques as needed.

Predictive Sales Modeling

Objective

To create a predictive model to forecast future sales based on historical sales data.

Steps

1. Feature Engineering
2. Model Selection
3. Training the Model
4. Model Evaluation
5. Making Predictions

Feature Engineering

Extract relevant features from the sales data. Assuming we have preprocessed sales data:

• date: Date of the sales record
• sales: Sales amount
• store_id: Identifier for the store

Additionally, create new features for the model:

function create_features(data):
    data['month'] = extract_month(data['date'])
    data['day_of_week'] = extract_day_of_week(data['date'])
    data['year'] = extract_year(data['date'])
    data['lag_1'] = data['sales'].shift(1)
    data['lag_7'] = data['sales'].shift(7)
    data['lag_30'] = data['sales'].shift(30)
    data['rolling_mean_7'] = data['sales'].rolling(window=7).mean()
    data['rolling_mean_30'] = data['sales'].rolling(window=30).mean()
    return data.dropna()  # Drop rows with NaN values due to the lag features

Model Selection

Choose a model (e.g., Linear Regression, Decision Tree, Random Forest, etc.). In this case, we'll use a Random Forest Regressor.

Training the Model

Train the model using historical sales data. Splitting the data into training and testing datasets is essential.

function train_model(data):
    features = ['month', 'day_of_week', 'year', 'lag_1', 'lag_7', 'lag_30', 'rolling_mean_7', 'rolling_mean_30']
    target = 'sales'

    // Splitting data
    train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
    
    // Initializing the model
    model = RandomForestRegressor(n_estimators=100, random_state=42)
    
    // Training the model
    model.fit(train_data[features], train_data[target])
    
    return model, test_data

Model Evaluation

Evaluate the model's performance using Mean Absolute Error (MAE).

function evaluate_model(model, test_data):
    features = ['month', 'day_of_week', 'year', 'lag_1', 'lag_7', 'lag_30', 'rolling_mean_7', 'rolling_mean_30']
    target = 'sales'
    
    predictions = model.predict(test_data[features])
    mae = mean_absolute_error(test_data[target], predictions)
    
    print("Mean Absolute Error:", mae)

Making Predictions

Use the trained model to make future sales predictions.

function make_predictions(model, future_data):
    future_data = create_features(future_data)
    features = ['month', 'day_of_week', 'year', 'lag_1', 'lag_7', 'lag_30', 'rolling_mean_7', 'rolling_mean_30']
    
    predictions = model.predict(future_data[features])
    future_data['predicted_sales'] = predictions
    
    return future_data

Full Implementation

// Main function to run the predictive sales model
function main(data, future_data):
    data_with_features = create_features(data)
    model, test_data = train_model(data_with_features)
    evaluate_model(model, test_data)
    prediction_results = make_predictions(model, future_data)
    
    return prediction_results

// Execute the main function with your historical and future sales data
historical_data = load_historical_sales_data()  // Load your historical sales data
future_data = load_future_data()  // Load the data for which you want to predict sales

prediction_results = main(historical_data, future_data)
print(prediction_results)

In the provided implementation, replace pseudocode with actual code and libraries suitable for your work environment, ensuring integration with your existing system or dataset.

Competitor Analysis

Step 1: Data Collection from Competitors

Collect competitor sales data

To perform a competitor analysis, you first need access to competitor sales data. This data can often be sourced from:

• Public financial reports
• Market research firms
• Web scraping of competitor e-commerce sites
• Industry surveys

Example Pseudocode for Web Scraping Competitor Data

def fetch_competitor_data(url):
    # Use a web scraping library like BeautifulSoup for HTML parsing
    html_content = send_http_request(url)
    parsed_content = parse_html(html_content)
    
    # Extract relevant information, e.g., product prices, sales figures, reviews
    product_info = []
    for item in parsed_content:
        product = extract_product_info(item)  # Custom function to extract product data
        product_info.append(product)
    
    return product_info

competitor_url = 'https://competitor-website.com'
competitor_data = fetch_competitor_data(competitor_url)

Step 2: Comparative Sales Analysis

Assuming you have the following data from both your stores and competitors:

• sales_your_store (your sales data)
• sales_competitor (competitor sales data)

Example Sales Data Structure

• product_id
• item_name
• sales_volume
• sales_revenue
• timestamp

Comparative Analysis Pseudocode

def compare_sales(sales_your_store, sales_competitor):
    comparative_metrics = []

    for item in sales_your_store:
        competitor_item = find_competitor_item(sales_competitor, item['product_id'])
        
        if competitor_item:
            comparison = {
                'product_id': item['product_id'],
                'item_name': item['item_name'],
                'your_sales_volume': item['sales_volume'],
                'competitor_sales_volume': competitor_item['sales_volume'],
                'your_revenue': item['sales_revenue'],
                'competitor_revenue': competitor_item['sales_revenue'],
                'sales_volume_diff': item['sales_volume'] - competitor_item['sales_volume'],
                'revenue_diff': item['sales_revenue'] - competitor_item['sales_revenue']
            }
            comparative_metrics.append(comparison)
    
    return comparative_metrics

# Perform the comparison
comparative_data = compare_sales(your_sales_data, competitor_sales_data)

Step 3: Visualization and Insights

Example Visualization Script

def visualize_comparative_data(comparative_data):
    import matplotlib.pyplot as plt

    product_names = [item['item_name'] for item in comparative_data]
    your_revenues = [item['your_revenue'] for item in comparative_data]
    competitor_revenues = [item['competitor_revenue'] for item in comparative_data]

    x = list(range(len(product_names)))

    plt.figure(figsize=(12, 6))

    plt.bar(x, your_revenues, width=0.4, label='Your Store', color='b', align='center')
    plt.bar(x, competitor_revenues, width=0.4, label='Competitor', color='r', align='edge')

    plt.xlabel('Products')
    plt.ylabel('Revenue')
    plt.title('Revenue Comparison with Competitors')
    plt.xticks(x, product_names, rotation='vertical')

    plt.legend()
    plt.show()

# Visualize the comparative data
visualize_comparative_data(comparative_data)

Conclusion

By executing this implementation, you can derive insights about where your store stands compared to competitors on specific products and metrics. This will enable you to identify potential areas for improvement in your sales strategies.

Strategic Decision-Making and Recommendations

Introduction

To provide strategic decision-making and actionable recommendations, focus on tying together insights from previously analyzed data aspects. Use consolidated information to derive insights and make sound recommendations for the business stakeholders.

Implementation

Combine Insights for Holistic Understanding

1. Data Integration:

   • Integrate data from all the prior analyses (Customer Segmentation, Seasonal Trends, Sales Performance, Geospatial Sales, Predictive Sales Modelling, Competitor Analysis).

   combinedData = merge([
       customerSegmentationData,
       seasonalTrendsData,
       salesPerformanceMetrics,
       geospatialSalesData,
       predictiveSalesData,
       competitorAnalysisData
   ])

Identify Key Performance Indicators (KPIs)

2. Define Business KPIs:

   • Combine sales targets, customer satisfaction scores, market share, and other relevant indicators.

   KPIs = {
       "SalesTargets": combinedData.targetSales,
       "CustomerSatisfaction": combinedData.customerSatisfaction,
       "MarketShare": combinedData.marketShare,
       "ProfitMargins": combinedData.profitMargins
   }

Strategic Insights

3. Analyze Integrated Data for Decision-Making:

   • Profitability Analysis: Identify the products and locations that are most profitable during different times of the year.

   profitableProducts = combinedData.groupby('Product').agg({
       'Profit': sum,
       'Sales': sum,
       'Season': mode
   })
   
   profitableLocations = combinedData.groupby('Location').agg({
       'Profit': sum,
       'Sales': sum,
       'Season': mode
   })

   • Customer Behavior: Understand purchasing behavior and preferences of different customer segments during different seasons.

   customerBehavior = combinedData.groupby(['CustomerSegment', 'Season']).agg({
       'ProductPurchased': count,
       'AverageSpend': mean
   })

Recommendations Generation

4. Generate Actionable Recommendations:

   • Based on the insights drawn:

   recommendations = []
   
   # Recommendation for Stock Management
   if profitableProducts:
       for product in profitableProducts:
           recommendations.append({
               "action": "increase stock",
               "product": product,
               "season": profitableProducts[product]['Season']
           })
   
   # Recommendation for Seasonal Promotions
   if customerBehavior:
       for segment in customerBehavior:
           recommendations.append({
               "action": "tailor promotions",
               "customerSegment": segment,
               "season": customerBehavior[segment]['Season']
           })

Presenting Findings

5. Prepare a Report:

   • Summarize findings and recommendations for stakeholders.

   Report:
   - Title: Strategic Decision-Making Insights for FMCG Sales
   - Executive Summary
   - Key Findings:
     - Most Profitable Products: [List]
     - Most Profitable Locations: [List]
     - Customer Behavior Insights: [Details]
   - Strategic Recommendations:
     - Stock Management Strategy [Details]
     - Tailored Promotional Activities [Details]
     - Competitive Positioning [Details]
   - Conclusion

To apply these recommendations in real-life decision-making, the next steps would involve implementing them in the operational workflows, aligning strategic business activities, and making data-informed decisions to optimize sales and performance.