Big Data Analytics
Big Data Analytics platforms such as cloud-based Hadoop have become powerful tools for businesses looking to leverage vast sets of customer data for competitive advantage. But with so much rich data streaming in from multiple sources, the analytics challenge for many businesses is determining what types of data will yield the highest amounts of useful information and insights in a cost-effective manner.
What is Clickstream Data?
Clickstream data—the trail of digital breadcrumbs left by users as they click their way through a website—is loaded with valuable customer information for businesses. Captured on weblogs, clickstream data was one of the first types of data analyzed during Hadoop’s early days. Since that time, clickstream data analysis has emerged as a powerful and cost-effective tool that can benefit businesses in the following ways:
1. Click-path optimization
Using clickstream analysis, businesses can collect and analyze data to see which pages web visitors are visiting and in what order.
Through traffic analysis, which relates to the path the user takes when navigating through the site, web marketers can look at key metrics that affect the user experience, such as the number of pages served to the user, how fast or slow the pages load, the amount of data transmitted before a user moves on, and how often a visitor hits the back or stop button on their browser.
Using e-commerce-based clickstream analysis, marketers can quantify a user’s behavior while on the website to get an idea of how effective the site is at producing sales. Clickstream data shows what pages users linger on, what items are placed into or removed from a shopping cart, and what items are purchased.
Armed with information from clickstream analytics and traditional market evaluation resources, marketers can optimize the click-path by making changes to the site to reduce bounce rates and increase conversions.
2. Market basket analysis
The benefit of basket analysis for marketers is that it can give them a better understanding of aggregate customer purchasing behavior. Just as drivers can take different roads to arrive at the same destination, customers take different paths online and end up buying the same product. Basket analysis helps marketers discover what interests customers have in common, and the common paths they took to arrive at a specific purchase. That’s valuable information for determining the most efficient path a site visitor can take for researching and buying a product.
3. Next Best Product analysis
Clickstream analytics gives marketers a predictive edge through Next Best Product analysis (NBP). Related to basket analysis, NBP analysis helps marketers see what products customers tend to buy together. A basic example would be that customers who buy nuts typically buy bolts to go with them. As these purchasing correlations are recognized, marketers can look at what a customer purchases and send them real-time offers for the products that they will most likely buy next, thus increasing the chances of making another sale, either during the same online visit or in the future.
4. Website resource allocation
A major challenge for marketers is determining how best to allocate website resources for optimal results. Clickstream data analysis tells marketers which paths on the site are hot and which ones are not. This information enables companies to provision the bulk of website resources where they are needed most in order to optimize the user experience on the site.
5. Customer segmentation on a granular level
Analysis of clickstream and other user data gives marketers a granular look at how individual customer segments are using the website. As a result, marketers can gain actionable insights to help personalize the user experience and convert more web visitors from browsers to buyers.
Thanks to big data analytics, clickstream analysis is a valuable tool companies can use to drive sales by optimizing every aspect of the user experience on their websites from the first mouse click to the last.