Analyzing Ecommerce Customer Data For Insights

We will be using the following Statistical & ML Models to accomplish our objectives

Analyze online shoppers' purchase intentions using Logistic Regression, K-means clustering & A/B Testing

• Logistic Regression
• K-Means Clustering
• A/B Testing
• Vizualization

Outlining the Approach

From online shopping dataset, we will be trying to draw insights from a customer's behavior on the site. We will analyze a number of factors, such as conversion rate and total revenue generated. We will also perform univariate and bivariate analysis while taking various dataset features into consideration, such as pageview duration, types of visitors, types of traffic, and browsers used. Then, we will implement the K-means algorithm and elbow method to find the optimum value of clusters, and visualized scatterplots based on this value. These plots, in turn, provide us with useful information so that we can plan our next course of action.

Summary of Insights

At the very outset lets check if there is statistical difference between visits with transaction and visits without.

Last column with ttest pval contains the signifance value, and we can see that except Region, Traffic Type and Operating system all other features are significantly different between Visit with Transaction and Visits with no Transaction. Its a good place to start analyzing our dataset.

Correlation Between Bounce Rate vs Exit Rate

As you can see, there is a positive correlation between the bounce rate and the exit rate. With the increase in bounce rate, the exit rate of the page increases.

Page Value Versus Bounce Rate

As we can see in the preceding plot, there is a negative correlation between page value and bounce rate. As the page value increases, the bounce rate decreases. To increase the probability of a customer purchasing with us, we need to improve the page value—perhaps by making the content more engaging or restructuring the content.

Modeling the Relationship Via Logistic Regression

Logistic Regression is excellent way of estimating the relationship between m different features when the target variable is binary - Transaction and No Transaction.

We use Statsmodel to run Logistic regression after converting categorical features with into dummy variable and dropping the first to prevent multi collinearity.

We get the following output

1. Coefficients for each of the month indicate that this retail store does the most business in November(0.54), followed by July and then January(0.08) Dropped month)
2. Store does better on weekend than weekdays
3. Very intrestingly data shows us that First time visitor(Dropped variable which is 1 and the compared is -0.32) are more likely to purchase. Which explains why While the number of returning customers to the website is high, the conversion rate is low compared to that of new customers.
4. We can see that Exit Rates have the single biggest negative impact on likelihood of purchase(-15.5). Using K-means clustering we will arrive at what healthy Exit Rate should we be aiming for.

Insights from K-Means Clustering

Given that we have identified High Exit Rate to be a big factor through Logistic Regression, it's important to understand from Business perspective relationship of Exit rate to informational duration and product related duration.

Visitors with information Duration of 480+ Seconds are highly likely to result in transaction. With majority of customers taking 480 to 1000 Seconds before making a purchase decision.

What about Page Value and Bounce Rate?

We can see that Bounce rate is a problem, but not so much for Pages that have have Page value of 25+, despite bounce rate page values of 25+ have a very solid chance of resulting in transactions.