Product Data Management
Today, there is so much of data over the internet. Harnessing of accurate data can give you detailed insights that boost your E Commerce business by many folds. Data mining in simple terms is a set of automated techniques that can be used to understand various patterns shown by visitors on your website.
There is obviously extensive data relevant to each E Commerce business but more often than not lacks clarity. To get the best mileage from such invaluable data, you need the experts. This is where Vision Global with its 20 years of experience will fit your requirement like a glove. So, what can we do?
With the most up to date technology in Ecommerce Data mining, some of the most prominent insights and actionable data that you can get access to are
- Affinity (or) Basket Analysis
- Customer Behavior
- Market Segmentation
- Competitor Analysis
- Sales Forecasting
- Product Performances
- Demand Trends
- Fraud Activity Patterns
There are very specific E Commerce Data Mining techniques used by Vision Global to provide extensive intelligence about your Ecommerce website
Is the most commonly used mining technique to analyze trends and patterns from webpage and provide amazing insights to customer behaviors.
Used to identify the navigational trend or patterns and flow of the user from web server logs and clicks data. This data is very useful in marketing, allowing you to target visitors with advertisements
Classification and Clustering
While both are similar and used in ML (Machine Learning), Classification uses predefined classes to label each data object and Clustering is about grouping the similar objects.
Is used to analyze the correlation of various data points. It is most commonly used to project and forecast based on factors such as product availability in the market, demand for it and competitor sales of the product.
Typically a technique used to identify outliers or anomalies in the data. This technique is primarily used to identify technical issues such as network failures or online frauds.
Last but not the least, this is a combination of various techniques from above such as clustering and classification to analyze past trends and predict future ones.