Data analytics

The new information economy is increasingly digital and intelligent. This data defines the terms of reference for retail industry today and within this millennium mastering data has become a cornerstone for success.

 

Mastering the four dimensions of data, i.e., social, customer, market, and product for actionable and monetized insight requires newfound technical capabilities, robust process, and decision management frameworks to handle the challenges of data analytics, which is also known as Big data analytics.

 

Social Network Data Analysis

Social data is one of the four dimensions of Big Data in retail. Customer data sources, market data sources, and supply data sources round out the retail insights typology. For example, insight from loyalty and shopper profile data can improve customer segmentation and promotional targeting.


Recommendation System Analysis

One approach to the design of recommendation system analysis is that it has seen wide use of Collaborative Filtering. Collaborative Filtering methods are based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending by manually understand the system.