📊 Project Title: Global Retail Store - RFM Analysis

🤵 Author: Tri Nguyen
📆 Date: Jan. 10, 2025
💻 Tools Used: Python

📑 Table of Contents

📌 Background & Overview
📂 Dataset Description & Data Structure
🧠 Design Thinking Process
📊 Key Insights & Visualizations
🔎 Final Conclusion & Recommendations

📌 Background & Overview

Objective:

📖 What is this project about?

Understanding customer behavior through RFM (Recency, Frequency, Monetary) analysis helps businesses make informed decisions and enhance customer loyalty. The Marketing department needs to segment customers for holiday campaigns, and the Marketing Director suggests using RFM analysis.

RFM Segmentation is a method to analyze customer behavior based on three key metrics:

Recency (R): Time since the last purchase. Recent buyers are more likely to purchase again.
Frequency (F): Number of purchases within a period. Frequent buyers are more loyal.
Monetary Value (M): Total money spent. High spenders are more valuable to the business.

👤 Who is this project for?

The Marketing Department who need to understand customer behavior and thier values for new marketing statergy.

❓Business Questions:

Customer Segmentation for Marketing Campaigns: How can the Marketing department classify customer segments effectively to deploy tailored marketing campaigns for Christmas and New Year, appreciating loyal customers and attracting potential ones?
Implementing RFM Model: How can the RFM (Recency, Frequency, Monetary) model be utilized to analyze and segment customers to enhance the effectiveness of marketing campaigns?

🎯Project Outcome:

Summarize key findings and insights/ trends/ themes in a concise, bullet-point format.

Example:

✔️ Sales Trends: The top X% of products generate Y% of revenue.
✔️ Inventory Optimization: Certain products are frequently out-of-stock, causing revenue loss.
✔️ Customer Behavior: Returning customers spend Z% more per transaction than new customers.

📂 Dataset Description & Data Structure

📌 Data Source

Source: (Mention where the dataset is obtained from—Kaggle, company database, government sources, etc.)
Size: (Mention the number of rows & columns)
Format: (.csv, .sql, .xlsx, etc.)

📊 Data Structure & Relationships

1️⃣ Tables Used:

Mention how many tables are in the dataset.

2️⃣ Table Schema & Data Snapshot

Table 1: ecommerce_retail

Fields	DataType	Description
InvoiceNo	String	Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'C', it indicates a cancellation.
StockCode	String	Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.
Description	String	Product (item) name. Nominal.
Quantity	Integer	The quantities of each product (item) per transaction. Numeric.
InvoiceDate	DateTime	Invoice Date and time. Numeric, the day and time when each transaction was generated.
UnitPrice	Decimal	Unit price. Numeric, Product price per unit in sterling.
CustomerID	Integer	Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer.
Country	String	Country name. Nominal, the name of the country where each customer resides.

Table 2: segmentation

Segment	RFM Score
Champions	555, 554, 544, 545, 454, 455, 445
Loyal	543, 444, 435, 355, 354, 345, 344, 335
Potential Loyalist	553, 551, 552, 541, 542, 533, 532, 531, 452, 451, 442, 441, 431, 453, 433, 432, 423, 353, 352, 351, 342, 341, 333, 323
New Customers	512, 511, 422, 421, 412, 411, 311
Promising	525, 524, 523, 522, 521, 515, 514, 513, 425, 424, 413, 414, 415, 315, 314, 313
Need Attention	535, 534, 443, 434, 343, 334, 325, 324
About To Sleep	331, 321, 312, 221, 213, 231, 241, 251
At Risk	255, 254, 245, 244, 253, 252, 243, 242, 235, 234, 225, 224, 153, 152, 145, 143, 142, 135, 134, 133, 125, 124
Cannot Lose Them	155, 154, 144, 214, 215, 115, 114, 113
Hibernating customers	332, 322, 233, 232, 223, 222, 132, 123, 122, 212, 211
Lost customers	111, 112, 121, 131, 141, 151

⚒️ Main Process

1️⃣ Load Dataset

ecm = pd.read_excel('ecommerce_retail.xlsx', sheet_name='ecommerce_retail')
ecm.head(10)

2️⃣ Exploratory Data Analysis (EDA)

# @title Get infor about data type & data value
print(ecm.info())

print('---')

# detect data value (min, max, count,...)
ecm.describe()

Using ProfileReport

# @title Using ProfileReport to Understand more about Category Data Type
profile = ProfileReport(ecm)
profile

Detect why "Quantity" and "Price" have negative values (<0)

# subset dataframe để xem qua các rơw có giá trị âm 
print('Dòng có Quantity âm (<0)')
ecm[ecm['Quantity'] < 0]

# được biết ký hiệu C ở "InvoiceN0" nghĩa là đơn bị cancel, check xem Quantity âm thì có phải đơn cancel không? 
# tạo cột check tình trạng cancel của data
# .astype(str) de khong bi loi TypeError: 'int' object is not subscriptable
ecm['InvoiceNo'] = ecm['InvoiceNo'].astype(str)
ecm['check_cancel'] = ecm['InvoiceNo'].apply(lambda x: True if x[0] == 'C' else False)

# check lý do cột Quantity < 0 có phải do đơn bị cancel hay không
ecm[(ecm['check_cancel'] == True) & (ecm['Quantity'] < 0)].sort_values('Quantity')
ecm[(ecm['check_cancel'] == True) & (ecm['Quantity'] <= 0)].sort_values('Quantity')

# có những đơn nào không bị cancel "C" mà vẫn có quantity âm không 
ecm[(ecm['check_cancel'] == False) & (ecm['Quantity'] < 0)].sort_values('Quantity')
ecm[(ecm['check_cancel'] == False) & (ecm['Quantity'] <= 0)].sort_values('Quantity')

# check lý do 
ecm[(ecm['check_cancel'] == False) & (ecm['Quantity'] < 0)]['Description'].value_counts()

Handling abnormal data values

# drop abnormal values 
# UnitPrice < 0 , unitprice = 0 có thể là hàng tặng 
ecm = ecm[ecm['UnitPrice'] >= 0]

# xử lý loại bỏ data của hàng không thuộc nhóm đơn cancel "C" nhưng có quantity âm 
ecm_cancel = ecm[ecm['check_cancel'] == True]
ecm_p_qty = ecm[ecm['Quantity'] >= 0]
ecm = pd.concat([ecm_cancel, ecm_p_qty], copy= False)
ecm.sort_values('CustomerID')

Checking, Detecting and Handling missing values and duplicates

# Thống kê những cột có missing values 
print('Thống kê số lượng và tỉ lệ missing data')
missing_dict = {'volume':ecm.isnull().sum(),
                'percent':ecm.isnull().sum() / (ecm.shape[0])}
missing_df = pd.DataFrame.from_dict(missing_dict)
missing_df

# detect why missing values were very high 
ecm[ecm['CustomerID'].isnull()].head()

ecm[ecm['CustomerID'].isnull()].tail()

# tạo cột yyyy-mm-dd sau đó dùng apply để bỏ 3 giá trị cuối của cột này, chỉ lấy yyyy-mm 
ecm['Date'] = pd.to_datetime(ecm['InvoiceDate']).dt.date
ecm['Month'] = ecm['Date'].apply(lambda x: str(x)[:-3])

# tính tổng đơn hàng bị missing customerID theo tháng 
ecm_group_month = ecm[ecm['CustomerID'].isnull()][['Month','InvoiceNo']].groupby(['Month']).count().reset_index().sort_values('Month')
ecm_group_month

# mỗi tháng đều có một số lượng missing customerID, không bị tập trung vào một số tháng nhất định nào 
# các đơn missing customerID vừa có thể là đơn bị cancel, vừa có thể là đơn bình thường 

# do yêu cầu là chia nhóm khách hàng, nên những đơn bị mất customerID sẽ không tính được -> nên bỏ đi

# xử lý missing values -> bỏ đi 
ecm = ecm[ecm['CustomerID'].notnull()]
ecm.sort_values('CustomerID')

# check duplicated values trong dataset
# thế nào là duplicate data (điều kiện duplicate đưa ra những cột nào)
ecm_dups = ecm.duplicated(subset=["InvoiceNo", "StockCode","InvoiceDate","CustomerID"])
print(ecm[ecm_dups].shape)
print('')
print(ecm.shape)

# dat gia thuyet de kiem tra loi

# vi du de kiem tra 
ecm[(ecm['InvoiceNo'] == '581538') & (ecm['StockCode'] == 23343)]

# vi du de kiem tra 
ecm[(ecm['InvoiceNo'] == '581538') & (ecm['StockCode'] == 21194)]

# vi du de kiem tra 
ecm[(ecm['InvoiceNo'] == '538341') & (ecm['StockCode'] == 22988)]

# khi kiểm tra thử 1 đơn cancel, không tìm thấy đơn mua tương tự
# -> thay đổi phương án, bỏ luôn những đơn cancel

# đối với duplicates, sẽ chỉ giữ lại dòng đầu tiên 
ecm = ecm.drop_duplicates(subset=["InvoiceNo", "StockCode","InvoiceDate","CustomerID"], keep= 'first')

# convert những columns sang về đúng format string -> tránh mất dữ liệu vì số dễ bị làm tròn 
ecm.columns

# col_list to be converted to 'str' format 
str_col_list = ['InvoiceNo', 'StockCode', 'Description', 'CustomerID', 'Country']
for c in str_col_list: 
    ecm[c] = ecm[c].astype(str)

ecm.info()

Data Processing - RFM calculating

# create new column "cost" 
ecm['Cost'] = ecm['Quantity'] * ecm['UnitPrice']
last_day = ecm['Date'].max() 

RFM_ecm = ecm.groupby('CustomerID').agg(
    Recency = ('Date', lambda x: last_day - x.max()), 
    Frequency = ('CustomerID', 'count'), 
    Monetary = ('Cost', 'sum') 
).reset_index()

# astype column "recency" 
RFM_ecm['Recency'] = RFM_ecm['Recency'].dt.days.astype('int16')
RFM_ecm.dtypes

# use qcut to divide the dataset into 5 ranges 
RFM_ecm['R'] = pd.qcut(RFM_ecm['Recency'], 5, labels= range(1,6)).astype(str)
RFM_ecm['F'] = pd.qcut(RFM_ecm['Frequency'], 5, labels= range(1,6)).astype(str)
RFM_ecm['M'] = pd.qcut(RFM_ecm['Monetary'], 5, labels= range(1,6)).astype(str)
RFM_ecm['RFM'] = RFM_ecm.apply(lambda x: x['R'] + x['F'] + x['M'], axis= 1)

📊 Key Insights & Visualizations

🔍 Dashboard Preview

1️⃣ Dashboard 1 Preview

%matplotlib inline

order_ = ['Champions', 'Loyal', 'Potential Loyalist', 'New Customers', 'Promising', 'Need Attention', 'About To Sleep', 'At Risk', 'Cannot Lose Them', 'Hibernating customers', 'Lost customers']
pivot_ecm = pivot_ecm[order_]

plt.figure(figsize=(18,9))
r = sns.lineplot(data= ecm_distribution_table, x= 'Segment', y= 'avg_recc')
f = sns.lineplot(data= ecm_distribution_table, x= 'Segment', y= 'avg_freq')
plt.xticks([])
plt.ylabel('avg_recc and avg_freq')
plt.xlabel('')
plt.title('Avg Recency and Frequency by Segmentation (days)')

rcolors = np.full(len(pivot_ecm.index), 'linen')
ccolors = np.full(len(pivot_ecm.columns), 'lavender')

table = plt.table(cellText=pivot_ecm.values, 
          rowLabels= pivot_ecm.index, 
          colLabels= pivot_ecm.columns,
          bbox=(0, -.5, 1, .5), rowLoc= 'center', colLoc= 'center', loc= 'center', 
          rowColours= rcolors, 
          colColours= ccolors)
table.scale(1, 1)
table.auto_set_font_size(False)
table.set_fontsize(9)
table.set_label('Customer Segmentation Detail')

Avg Recency and Frequency by Segmentation

📌 Analysis 1:

Highly Engaged: Segments like "Cannot Lose Them" and "Champions" are highly engaged with recent interactions. Keep nurturing these relationships.
At Risk & Inactive: "At Risk" and "Lost customers" show signs of declining interest. Prioritize re-engagement and understand reasons for inactivity.
New Customer Focus: "New Customers" have high volume but low engagement. Encourage repeat purchases and build brand loyalty.

2️⃣ Dashboard 2 Preview

order_ = ['Champions', 'Loyal', 'Potential Loyalist', 'New Customers', 'Promising', 'Need Attention', 'About To Sleep', 'At Risk', 'Cannot Lose Them', 'Hibernating customers', 'Lost customers']
pivot_ecm = pivot_ecm[order_]

plt.figure(figsize=(18,9))
hue_colors = sns.color_palette("Paired")
hue_colors2 = sns.color_palette("flare")

p1 = sns.barplot(data= ecm_distribution, x='Segment', y='distribution_percent', 
                 hue='type', palette= hue_colors, 
                 order= order_)
for container in p1.containers : 
    p1.bar_label(container)
p2 = sns.lineplot(data= ecm_distribution, x='Segment', y='distribution_percent', 
                 hue='type', palette= hue_colors2, solid_capstyle= 'round')
plt.ylabel('')
plt.xlabel('')
plt.xticks([])
plt.title('Customer Segmentation Contribution (%)')

rcolors = np.full(len(pivot_ecm.index), 'linen')
ccolors = np.full(len(pivot_ecm.columns), 'lavender')

table = plt.table(cellText=pivot_ecm.values, 
          rowLabels= pivot_ecm.index, 
          colLabels= pivot_ecm.columns,
          bbox=(0, -.5, 1, .5), rowLoc= 'center', colLoc= 'center', loc= 'center', 
          rowColours= rcolors, 
          colColours= ccolors)
table.scale(1, 1)
table.auto_set_font_size(False)
table.set_fontsize(9)
table.set_label('Customer Segmentation Detail')

plt.show()

Customer Segmentation Contribution using Seaborn

📌 Analysis 2:

High-Value Customers: Keep engaged with loyalty programs and strong relationships.
At Risk & Growth Potential: Re-engage, target with personalized offers, and convert to loyalty.
New & Inactive Customers: Encourage repeat purchases, build brand loyalty, and re-engage.

3️⃣ Dashboard 3 Preview

# total value for recency 
f_total = len(RFM_ecm['Frequency'])


# Create the plot for Distribution of Frequency
binsF = [0, 2, 5, 20, np.inf]
labelsF = ['1-2', '2-5', '5-20', '20+']
RFM_ecm['FrequencyGroup'] = pd.cut(RFM_ecm['Frequency'], bins=binsF, labels=labelsF)
fig, ax = plt.subplots(figsize=(8, 3))
sns.countplot(x='FrequencyGroup', data=RFM_ecm, ax=ax)

ax.set_title('Distribution of Frequency')
ax.yaxis.set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Add percentage label
for container in ax.containers:
    labels = [f'{int(height)}\n({height/r_total:.1%})' for height in container.datavalues]
    ax.bar_label(container, labels=labels, label_type='edge', padding=2)
plt.show()

Distribution of Frequency

📌 Analysis 3:

Observation: The first bin (0-50 days) shows the highest frequency with 1884 customers (43,4%). This suggests a strong and recent engagement with a significant portion of the customer base actively making purchases.
Recommendation:
- Continue to nurture and engage the segment that has recently made purchases through personalized offers, loyalty programs, and exceptional customer service.
- Regularly analyze customer purchasing behavior to identify trends and adjust strategies accordingly. Use predictive analytics to forecast potential churn and proactively address it.

4️⃣ Dashboard 4 Preview

# total value for recency 
r_total = len(RFM_ecm['Recency'])

# Create the plot for Distribution of Recency
fig, ax = plt.subplots(figsize=(12, 3))
sns.histplot(data=RFM_ecm, x='Recency', bins =10, ax=ax)

ax.set_title('Distribution of Recency')
ax.set_xlim(left=0)
ax.yaxis.set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Add percentage label
for container in ax.containers:
    labels = [f'{int(height)}\n({height/r_total:.1%})' for height in container.datavalues]
    ax.bar_label(container, labels=labels, label_type='edge', padding=2)
plt.show()

📌 Analysis 4:

Observation:
- The "20+ days" group has the highest frequency with 3044 occurrences, making up 70.2% of the total. The majority of customers fall into the "20+ days" frequency group, indicating that a significant portion of your customer base takes longer intervals between purchases. This segment may need special attention to encourage more frequent buying.
- The "1-2 days" and "2-5 days" groups have the fewest occurrences, with 125 (2.9%) and 175 (4.0%) occurrences respectively. The "1-2 days" and "2-5 days" groups have the fewest occurrences, indicating very frequent buyers. These customers are highly engaged and should be rewarded for their loyalty.
Recommendation:
- "20+ days": Implement targeted marketing campaigns to encourage more frequent purchases. Offer personalized incentives or discounts to reduce the time between purchases.
- "1-2 days" and "2-5 days": Recognize and reward their loyalty through VIP programs or special deals. Maintain their engagement with personalized communication and exclusive offers.

5️⃣ Dashboard 5 Preview

# total value for recency 
m_total = len(RFM_ecm['Monetary'])

#Distribution of Monetary
binsM = [0, 100, 1000, 10000, np.inf]
labelsM = ['0-100', '100-1k', '1k-10k', '10k+']
RFM_ecm['MonetaryGroup'] = pd.cut(RFM_ecm['Monetary'], bins=binsM, labels=labelsM)
fig, ax = plt.subplots(figsize=(8, 3))
sns.countplot(x='MonetaryGroup', data=RFM_ecm, ax=ax)

ax.set_title('Distribution of Monetary', pad=20)
ax.yaxis.set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

for container in ax.containers:
    labels = [f'{int(height)}\n({height/m_total:.1%})' for height in container.datavalues]
    ax.bar_label(container, labels=labels, label_type='edge', padding=2)
plt.show()

📌 Analysis 5:

Observation:
- The majority of customers fall into the "100-1k" spending group, indicating that most of your customer base spends within this range. This segment contributes significantly to your overall revenue and should be nurtured and engaged effectively.
- The "1k-10k" group is also a significant segment, representing 35.8% of the total occurrences. These customers are moderate spenders who have the potential to be moved into higher spending categories with proper engagement strategies.
- The "10k+" group also has a relatively low frequency, representing 2.4% of the total. These are your high-end spenders who make substantial purchases. Special attention and personalized offers can help retain these valuable customers.
Recommendation:
- "100-1k" Group: Continue engaging these customers with personalized offers, loyalty programs, and exceptional customer service. Recognize their value and provide incentives for repeat purchases.
- "1k-10k" Group: Implement targeted marketing campaigns to encourage higher spending. Offer exclusive discounts and promotions to move them into higher spending categories.
- "10k+" Group: Recognize and reward their loyalty through VIP programs or special deals. Maintain their engagement with personalized communication and exclusive offers.

6️⃣ Dashboard 6 Preview

#Assign color
segment_colors = {
    'Champions': '#a7a6dd',
    'Loyal': '#e2a5de',
    'Potential Loyalist': '#ffa6c5',
    'At Risk': '#ffb49e',
    'Hibernating customers': '#ffd379',
    'Lost customers': '#f9f871',
    'Need Attention': '#aba9bc',
    'About To Sleep': '#efedff',
    'New Customers': '#ffefca',
    'Promising': '#2c8f8c',
    'Cannot Lose Them': '#e29896'
}

# % and Monetary values by Segment
segment_monetary = ecm_f.groupby('Segment')['Monetary'].sum().reset_index()
total_monetary = segment_monetary['Monetary'].sum()
segment_monetary['Percentage'] = segment_monetary['Monetary'] / total_monetary * 100

segment_monetary = segment_monetary.sort_values('Monetary', ascending=False)

# Create the treemap

fig, ax = plt.subplots(1, figsize=(15,10))
squarify.plot(sizes=segment_monetary['Monetary'],
              label=[f"{s}\n${int(m):,}\n{int(p)}%"
                     for s, m, p in zip(segment_monetary['Segment'],
                                        segment_monetary['Monetary'],
                                        segment_monetary['Percentage'])],
              color=[segment_colors[segment] for segment in segment_monetary['Segment']],
              alpha=0.8,
              bar_kwargs=dict(linewidth=1.5, edgecolor="white"))

plt.title('Monetary contribution by RFM segment - Treemap', fontsize=16)
plt.axis('off')

plt.show()

📌 Analysis 6:

Observation:
- Cannot Lose Them: Contribute 45% of total revenue. High spenders with frequent purchases.
- At risk: Contribute 24% of total revenue. Significant spenders but showing signs of potential churn.
Recommendation:
- Cannot Lose Them: Provide exclusive offers and VIP treatment to ensure exceptional customer service and regular engagement.
- At risk: Gather feedback to understand their concerns. Implement targeted win-back campaigns with personalized offers.

🔎 Final Conclusion & Recommendations

Segment	Description	Recommendation
Champions	high purchase frequency and value	Reward them, ask for feedback, and promote referrals -> keep them always be loyal customers -> their feedback will help improving products and services
Loyal	consistently buy from you but aren't your highest spenders	Offer exclusive deals and keep them engaged with loyalty programs -> encourage them to buy more products and services
Potential Loyalist	recently purchased and show potential to become loyal	Encourage repeat purchases with personalized offers and excellent service -> encourage them to buy more products and services
New Customers	customers who have made their first purchase recently	Exceptional service and follow-up communication -> Make a great first impression to customer and encourage to keep considering using our product and services
Promising	who have shown interest but haven't yet made significant	Nurture them with targeted marketing campaigns and special promotions -> Encourage them to make the first purchase
Need Attention	who used to purchase frequently but have slowed down	Reach out with re-engagement campaigns and personalized offers, Communication to understand reasons why they haven't purchase frequently
About To Sleep	who are at risk of becoming inactive	Send reminders, special promotions, and reactivation incentives -> Try encouraging them repurchase with any product or service
At Risk	who haven't purchased in a while and are close to becoming inactive	Actively re-engage them with strong offers and personal outreach -> Try encouraging them repurchase with any product or service
Cannot Lose Them	High-paying customer About to leave	Take immediate action with special offers, personalized communication, and excellent service -> To understand their problems, solve their problems and keep them using service
Hibernating customers	who haven't purchased for a long time	Send win-back campaigns and try to understand their reasons for inactivity -> Try encouraging them repurchase with any product or service
Lost customers	who have not engaged or purchased for a very long time	Decide whether to invest in re-engagement or focus on new customer acquisition -> Send reminder messages to encourage customers to reuse the service.

Name		Name	Last commit message	Last commit date
Dataset		Dataset
README.md		README.md
RFM_analysis.ipynb		RFM_analysis.ipynb
ecommerce_retail.xlsx		ecommerce_retail.xlsx

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📊 Project Title: Global Retail Store - RFM Analysis

📑 Table of Contents

📌 Background & Overview

Objective:

📖 What is this project about?

👤 Who is this project for?

❓Business Questions:

🎯Project Outcome:

📂 Dataset Description & Data Structure

📌 Data Source

📊 Data Structure & Relationships

1️⃣ Tables Used:

2️⃣ Table Schema & Data Snapshot

⚒️ Main Process

📊 Key Insights & Visualizations

🔍 Dashboard Preview

1️⃣ Dashboard 1 Preview

2️⃣ Dashboard 2 Preview

3️⃣ Dashboard 3 Preview

4️⃣ Dashboard 4 Preview

5️⃣ Dashboard 5 Preview

6️⃣ Dashboard 6 Preview

🔎 Final Conclusion & Recommendations

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📊 Project Title: Global Retail Store - RFM Analysis

📑 Table of Contents

📌 Background & Overview

Objective:

📖 What is this project about?

👤 Who is this project for?

❓Business Questions:

🎯Project Outcome:

📂 Dataset Description & Data Structure

📌 Data Source

📊 Data Structure & Relationships

1️⃣ Tables Used:

2️⃣ Table Schema & Data Snapshot

⚒️ Main Process

📊 Key Insights & Visualizations

🔍 Dashboard Preview

1️⃣ Dashboard 1 Preview

2️⃣ Dashboard 2 Preview

3️⃣ Dashboard 3 Preview

4️⃣ Dashboard 4 Preview

5️⃣ Dashboard 5 Preview

6️⃣ Dashboard 6 Preview

🔎 Final Conclusion & Recommendations

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