Customer-Segmentation

Use Case

• Use Case Summary

• Objective Statement:

  • Get business insight about how many product sold every month.
  • Get business insight about how much customer spend their money every month.
  • To reduce risk in deciding where, when, how, and to whom a product, service, or brand will be marketed.
  • To increase marketing efficiency by directing effort specifically toward the designated segment in a manner consistent with that segment’s characteristics.

• Challenges:

  • Large size of data, can not maintain by excel spreadsheet.
  • Need several coordination from each department.
  • Demography data have a lot missing values and typo.

• Methodology / Analytic Technique:

  • Descriptive analysis
  • Graph analysis
  • Segment Analysis

• Business Benefit:

  • Helping Business Development Team to create product differentiation based on the characteristic for each customer.
  • Know how to treat customer with specific criteria.

• Expected Outcome:

  • Know how many product sold every month.
  • Know how much customer spend their money every month.
  • Customer segmentation analysis.
  • Recommendation based on customer segmentation.

Business Understanding

• Retail is the process of selling consumer goods or services to customers through multiple channels of distribution to earn a profit.- This case has some business question using the data:
• How many product sold every month?
• How much customer spend their money every month?
• How about Customer segmentation analysis?
• How about recommendation based on customer segmentation?

Data Understanding

• Data of Retail Transaction from 01 December 2010 to 09 December 2011
• Source Data: Online retail dataset by UCI Machine Learning Library. https://archive.ics.uci.edu/ml/datasets/Online+Retail
• The dataset has 8 columns and 541909 rows.
• Data Dictionary:
• InvoiceNo: Invoice number uniquely assigned to each transaction.
• StockCode: Product (item) code.
• Description: Product (item) name.
• Quantity: The quantities of each product (item) per transaction.
• InvoiceDate: The day and time when each transaction was generated.
• UnitPrice: Product price per unit in sterling.
• CustomerID: Customer number uniquely assigned to each customer.
• Country: The name of the country where each customer resides.

Data preparation

• Code Used:
• Python Version: 3.7.6
• Packages: Pandas, Numpy, Matplotlib, Seaborn, Sklearn, and Feature Engine

Data Cleansing

• There are about 25% of Null CustomerID in the data. We need to remove them as there is no way we can get the number of CustomerID.
• There are few records with UnitPrice<0 and Quantity<0. We need to remove them from the analysis. This could represent cancelled or returned orders.
• There is more than 90% of 'United Kingdom' customers, therefore we will restrict the data to only United Kingdom customers.

Exploratory Data Analysis

• How many product sold every month? Product sold in November has highest quantity that has around 13,41% product sold from all transaction along 1 year. Therefore the business team can increase sales in this month such as promoting new products to customers in this month.

• How much customer spend their money every month? Revenue in November has highest amount that has 13,41% revenue from total revenue along 1 year. Therefore the business team can replicate the success of sales strategies in November to be implemented in other months

RFM Analysis

• Recency Frequency Monetary (RFM)
• RFM analysis allows you to segment customers by the frequency and value of purchases and identify those customers who spend the most money.
• Recency — how long it’s been since a customer bought something from us.
• Frequency — how often a customer buys from us.
• Monetary value — the total value of purchases a customer has made.

Modeling Data: RFM Quantiles

• Now we split the metrics into segments using quantiles.
• We will assign a score from 1 to 4 to each Recency, Frequency and Monetary respectively.
• 1 is the highest value, and 4 is the lowest value.
• A final RFM score (Overall Value) is calculated simply by combining individual RFM score numbers.

Modeling Data: K-Means Clustering

• K-Means clustering algorithm is an unsupervised machine learning algorithm that uses multiple iterations to segment the unlabeled data points into K different clusters in a way such that each data point belongs to only a single group that has similar properties.
• K-means gives the best result under the following conditions:
• Data’s distribution is not skewed.
• Data is standardised.
• The data is highly skewed, therefore I will perform log transformations to reduce the skewness of each variable and I standardised the data.
• Finding the optimal number of clusters

Evaluating Model: K-Means Clustering

• Davies Bouldin Score is a metric for evaluating clustering algorithms.
• The smaller Davies Bouldin Score is The more optimal the cluster. K-Means 4 clusters has lowest Davies Bouldin Score than other cluster. Therefore the optimum cluster is 4.

Interpretation of the clusters formed using K-Means

• "Cluster 0" has 29% customers. It belongs to the “Loyal Customer" segment as they Haven’t purchased for some time, but used to purchase frequently (F=2) and spent a lot.
• "Cluster 1" has 20% customers. It can be interpreted as “Almost Lost". They purchase recently (R=2). However they do not purchase frequently and do not spent a lot.
• "Cluster 2“ has 30% customers. It can be interpreted as "Lost Cheap Customers". Their last purchase is long ago (R=4), purchased very few (F=4) and spent little (M=4).
• "Cluster 3“has 21% customers. It belongs to the "Best Customers" segment which we saw earlier as they purchase recently (R=1), frequent buyers (F=1), and spent the most (M=1).

Recommendation

• Recommendation for “Best Customers" segment: Focus on increasing customer purchases therefore it is necessary to form a cross/Up Selling Strategy.
• Recommendation for “Loyal Customers" segment: The business team must optimize the budget campaign and the time campaign for this customer segment in order to maintain their loyalty and increase their value.
• Recommendation for “Almost Lost" segment: This customer segment is very at risk for churn, so focus on activating customers and making repurchases by forming a Reactivation Strategy, Retention Strategy.
• Recommendation for “Lost Cheap Customers" segment: This customer segment has churned, so the focus of the campaign is to reactivate the customer by forming a Reactivation strategy.