Predictive Analytics: Do They Work?

Data mining and predictive analytics have been used to complete many tough tasks, such as cross-selling, direct marketing and fraud detection, in different types of businesses. In fact, where there is any type of data warehousing there should be implementation of a business intelligence program that includes predictive analytics to some degree. However, predictive analytics are still not commonly employed in web analytics. With this article, I’m going to explore the possibilities of predictive analytics in web analytics.

About Predictive Analytics

Basically, the main idea of predictive analysis is to use past data to predict future events. The statistics models is used to capture relationships among many factors to allow assessment of risk, determine market patterns, or predict potential opportunities for growth.

Why Is It Difficult to Include in Web Analytics?

One of the most important factors responsible for reliable predictive analysis is data quality. Because all predictive events are based on the data we have, the information can only be as effective as the abundance and accuracy of data available. Due to the mechanism of data collection, unfortunately, web data for the most part is completely anonymous, usually incomplete and unstructured.  For example, cookies and JavaScript could be blocked by some proportion of users or it could be loaded and activated improperly due to slow network speed or other technical reasons.  And it is important to know that these are quite normal for data collection in web analytics today. So it is really hard to do predictive analysis when the core things you are relying to capture data are anonymous cookies and sensitive JavaScript tags.

Available Predictive Web Analytics

Many web analytics solutions providers have started to research to offer predictive analytics solutions along with web analytics. One example is The Intelligent Offer, which is provided by Coremetrics and generates personalized cross-sell and up-sell recommendations based on web data. It uses cookies to build individual visitor profiles based on historical and current session data. The profile is then fed into the personalisation algorithm, which determines if and how recommendations are tailored to the individual.

Traffic prediction also is a feasible possibility for implementing predictive analysis in web analytics. Because visitor traffic is time series data and relatively cleaner than the other metrics we get from the web analytics tool, many statistical models could be used to analyze it and forecast future traffic based on past data.  Here is a mobile traffic prediction example we did for a client. We employed a polynomial regression model to forecast future mobile traffic.

Web analytics Needed for Multichannel CRM

Many companies now have realized that basic click stream analytics don’t tell the whole story. They are looking for a more intelligent way to find hidden customer behaviors. In order to implement predictive data analysis for web data, they need to expand their web analytics strategies and integrate web analytics with CMS (customer relationship management) systems.

With multichannel CRM, aggregated web data could be integrated into offline channels and provide great insight, even from simple analysis. It would enable online analytical processing (OLAP) to produce historical perspectives on customer acquisition cost, cost per conversion, etc. And combining web analytics with predictive analytics, such as data mining, could provide both historical and predictive insights.