Data science for e-commerce
E-commerce is one of the fastest growing industries. The following shows an overview of concrete case examples where data science can support significantly and improve your e-commerce.
Deployment of Dashboard Apps
To get insight into your e-commerce, such as customer behavior and product analysis, it may be valuable to produce dashboards that can be consulted by each business member of the team, at anytime, any place. Such dashboards allow to visualize and interact with different aspects of the data, and can be fully customized to your business needs.
Customer segmentation analyses
Businesses need the best insight using customer segmentation analyses.
These analyses concern demographic metrics, – age, gender, education – , geographic metrics, – where do customers come from – , as well as behavioral characteristics, – which day and at what hour do customers buy their products online. These parameters altogether can be used to personalize marketing campaigning and optimize supply.
In addition to these metrics, there are also unknown dimensions that can be tapped and nevertheless segment customers in a useful way. These can be detected using unsupervised learning methods such as k-means clustering.
These analyses concern demographic metrics, – age, gender, education – , geographic metrics, – where do customers come from – , as well as behavioral characteristics, – which day and at what hour do customers buy their products online. These parameters altogether can be used to personalize marketing campaigning and optimize supply.
In addition to these metrics, there are also unknown dimensions that can be tapped and nevertheless segment customers in a useful way. These can be detected using unsupervised learning methods such as k-means clustering.
Barplot of behavioral characteristics
Treemap of geographic characteristics
Worldmap of geographic characteristics
Tracking product sales using time-series
Product sales, expressed by volume and price, can be tracked using time-series analyses. Sales may differ across time of the day, as well as day of the week. By tracking the sales, the dynamics of total sales volume and price can be followed for each product. This is crucial to see sales evolution over time, and for example to decide if the stock supply should be adapted. Statistical metrics can be used to detect those products that show increasing trends versus those that show a decreasing trend across a certain time window.
Seasonality effects
Seasonality effects may show up by differences in sales volume, unit price, number of customers, over different periods.
22933 - Baking mould Easter egg milk choc.
85049A - Traditional Christmas ribbons
22910 - Paper chain kit vintage Christmas
85049A - Traditional Christmas ribbons
22910 - Paper chain kit vintage Christmas
Relation between sales price and volume
In order to optimize profit and regulate supply, the relationship between product price and sales volume need to be established. This relationship can change across time, products, and customer segments.
Returning versus churning customers
It is well known that acquiring new customers is harder than retaining customers.
It is vital for businesses that customers are satisfied and return. For this reason it is essential to forecast if and when customers return, and how much time after a first purchase customers will make a second purchase. Marketing strategies can benefit from these data by designing personalized email campaigns, so customers receive an email just at the moment they are about to make their next purchase.
It is vital for businesses that customers are satisfied and return. For this reason it is essential to forecast if and when customers return, and how much time after a first purchase customers will make a second purchase. Marketing strategies can benefit from these data by designing personalized email campaigns, so customers receive an email just at the moment they are about to make their next purchase.
Product affinity analyses
Product affinity analyses uses the purchase history to predict what customers are going to buy next. For example, if customers buy a certain product, there may be a high probability that they are also buying other products. This is for ex. the case for Art 85099B and Art 23203 in the heatmap underneath.
Customer lifetime value
Customer lifetime value is an indicator how much a customer is expected to spend during the whole customer relationship. It can help to segment customers in different categories and design marketing strategies.
About the data and visualizations
The online retail dataset was used for visualizations with Python Matplotlib and Plotly.
