BI - Analytics in E-Commerce

In the current scenario, with the market contribution from the present generation getting saturated, it is the older generation getting more accustomed with the technology that’s causing the growth in the e-commerce market.

New technologies have increased our ability to analyse so called ‘Unstructured Data’. Basically, unstructured data is the data, we can’t easily store and index in traditional formats or databases and includes email conversations, social media posts, video content, photos, voice recordings, sounds, etc.


Combining this messy and complex data with other more traditional data is where a lot of the value lies. Many companies are starting to use Big Data analytics to complement their traditional data analysis in order to get richer and improved insights and make smarter decisions and get more help to increase e-commerce based business.

“Data Analytics” is a mid-way bridge between the business and technology and it can be segmented into four major verticals:

1. Data capture - Data capture essentially focuses on the key business aspects to decide upon the elements to be captured.
2. Data warehousing - Data warehousing deals majorly with data modelling. The structured forms of the data are in general stored in SQL while the unstructured data such as the data from web or social groups are stored majorly with the help of Hadoop or Mongodb.
3. Data analysis - Data analysis does the augmentation and analysis based on the data collected. This is the stage wherein the regression models are applied. This particular domain requires the knowledge of various data analysis tools like SAS, R2, SPSS.
4. Data visualization - Data visualization comes at a later stage of decision making where the data needs to be visualized in a presentable format.

Digital marketing (increasing trends of analytics in social media through loyalty programs, Google analytics etc.) plays a very significant role in the current era to increase your business and introduce it to the world.

It is now very common for people to share their experiences as they happen in almost real time, photos, tweets and videos about the disaster were being posted on social media.

For example, profiling based on transaction details of the customer helps in analyzing their interests based on various segments like location, age group etc. which might act as the key points of analysis.

Of course, as with most big data technologies, privacy is the most pressing concern for many. As technology becomes cheaper, easier to use, and more ubiquitous, the possibility that we will be video recorded almost everywhere we go becomes more and more realistic.