Understanding the value of Predictive Analytics on Web Data

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In this blogpost, I will be talking briefly about Predictive Analytics and why it holds value from a web analytics perspective. Broadly speaking, Predictive Analytics is a set of methodologies that assist us in anticipating customer behavior. The customer behavior of interest could be anything ranging from spend, buying habits, page views, response to a certain trigger or something else. From a business perspective, there could be a variety of reasons why you would opt for Predictive Analytics strategies. Some of them are:

  • Traditional Web Analytics tools generate tons of clickstream data. Predictive Analytics helps filter out the noise and go beyond aggregate level metrics.
  • It helps you understand the complex patterns between metrics and these patterns now form the basis of your decision making process.
  • It helps you allocate investments wisely since your decisions are now based on your data rather than gut.

I should point out here that Predictive Analytics does not mean that you need to predict customer behavior with a very high probability and be very accurate in your findings. Let me illustrate this with an example: Every time a new customer lands on your website, you know that he has a 50% probability of converting. Now, if you build a predictive model which indicates that he has 52% chance of converting, it holds great value for your business since it suggests that your customer is more likely to convert. You can now segment all your customers who have a higher than 50% chance of converting and channel your marketing efforts towards these customers.

Now, in order to perform Predictive Analytics, you will require the following :

  1. Clear Objective: The business problem that you want to model
  2. Data: Having the right data is absolutely imperative. If you have a user centric business model, where you can get rich data regarding your customers behavior, that’s a big plus.
  3. Methodology: Once you have the data and a clear objective, you can start thinking about the statistical method you will use to build the prediction model
  4. Tool: There are a variety of predictive analytics tools available. Selecting the right tool for your business depends on your in house analytics talent pool and allocated budget.

If you are interested in knowing more about deploying Predictive Analytics techniques at your organization, join us in our webinar where we will show you how to leverage Predictive Analytics on your clickstream data using the R language. R is the lingua franca for data analysis. Savvy Web companies, like Facebook, have successfully used R in predictive analytics to answer questions like “Which data points predict whether a user will stay? And if they stay, which data points predict how active they’ll be after three months?” We will also be covering data visualization, since the use of good visualization leads to better understanding of the nuances between your variables.

See you at the webinar !

PS: You might want to warm up and read some additional posts on Predictive Analytics. Find them here.

Would you like to understand the value of predictive analysis when applied on web analytics data to help improve your understanding relationship between different variables? We think you may like to watch our Webinar – How to perform predictive analysis on your web analytics tool data. Watch the Replay now!

Kushan Shah

Kushan Shah

Kushan is a Web Analyst at Tatvic. His interests lie in getting the maximum insights out of raw data using R and Python.

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