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Challenges of click-stream data

Hello Everyone, are you excited about the new week's recipe? I think you are but with a little twist, we will also learn about Clickstream Analysis. So, to cook-offs, we need an ingredient to make a perfect platter for this evening. Based on a set theme, this involved using all imagination, and creativity to transform something sometimes questionable ingredients into the ultimate meal.

In this food blog series, we have collected the data to use all knowledge and creativity to compete in extracting a given data s’s most useful flavors via. reductions, measures, KPI’s. Delicious!

Ingredient Theme, with a dataset which works as data about the web sessions extracted from the original web blog which contains users ID, timestamp, visited web pages and clicks. With the set of user’s data, the file contains birthdate, gender, and all details that are in IDs. The third file is a map of web pages and their associated metadata, for example, home page, customer reviews or comments, video review, celebrity recommendation, and other recipe pages.

So, how clickstream analysis works and summarizes the data from users’ patterns and relationships?

Clickstreams is a sequence or stream of events that represent users’ actions (clicks) on a website or mobile application. However, in practice, the scope of clickstream extended beyond clicks. It includes product searches, views, and events that might be relevant to your business.

To be the cherry on the cake, its challenging to make a cake with the ingredients. In the same way in clickstream analysis, there are some challenges

As we make the cake we come to raw ingredients, blending, measuring ingredients, a method to bake by the pressure cooker, microwave or oven, and if we need to pre-heat or not, and if yes, what temperature.

 


In the same way, clickstream analysis has challenges:

  • Cleaning a time-based variable
  • Classifying online behavior
  • Predicting an online purchase.

 

The Data – Our challenge is to use statistical methods and machine learning to discover patterns in the behavior of customers.

Technologies - The major challenge is the awareness of the size of the data and statistical models which need to expand our new technologies and updates

Data Exploration and Cleaning – We expected the length of time spent on web pages to be a strong indicator of the intent to make an online purchase, i.e. a conversion.

Prediction – Our goal is to model the browsing journey conversation and use it as clickstream data. Which shows the number of pages of visits, URL, device type, and conversion rates.

To sum up, these are the major challenges that come under the interest of browsing the internet and processing it. Once this has been done, the statistical proposal will be identifying consumer behavior which leads to personalization and higher quality targeted advertising.





Bibliography

https://sloanreview.mit.edu/projects/using-analytics-to-improve-customer-engagement/

https://www.businesswire.com/news/home/20190917005452/en/Clickstream-Data-Analysis-Businesses-Identify-New-Up-selling

 

Comments

  1. Sidharth Naresh RajpalJune 5, 2020 at 4:03 PM

    Wonderful Prachi, Very informative :)

    ReplyDelete
  2. Arun ChaudharyJune 7, 2020 at 3:18 AM

    Good to read and Images look delicious.

    ReplyDelete
  3. Shravan DasJune 7, 2020 at 11:07 AM

    Very informative article.

    ReplyDelete
  4. Vibhor SharmaJune 11, 2020 at 4:58 PM

    The way you write the blog is amazing, its kind of keeping your audience attract towards the blog. keep it up Prachi Good going

    ReplyDelete

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