Gartner defines clickstream analysis as tracking the visits to a website, most typically by using web server log files to get raw data regarding the visitor. As most business marketing departments know, clickstream data can be a useful tool in determining several metrics, including visitor location and how long the customer spends on a particular page.
Enterprise-level clickstream data usually falls under the purvey of Big Data, since each entry comes from a unique visitor, and some sites may see traffic of several million individuals per day. But how does a business use clickstream data?
The Uses of Clickstream Data
Business implementation of clickstream analytics leads to a company discovering things about their customers that can help them to adapt their product or marketing to appeal to a broader demographic. Among the most common uses of clickstream analytics in business are:
Granular Customer Segmentation:
Company websites cover different areas, and a granular view of customer clickstream data offers insight into which area of the site the customer spends the most time on. These insights can help to inform the company about what sort of copy attracts and keeps customers, as well as whether their sales page is performing as expected with their target demographic compared to other segments of the market.
Resource Allocation for Websites:
Clickstream data can be used to help companies determine what paths in their website have the most traffic during a given period. By analyzing this data, IT departments can redirect website resources to the most trafficked locations, allowing for a streamlined user experience without the worry of bottlenecking.
Next-Best-Product (NBP) Insights:
By keeping track of which products users visit in sequence, a business can keep a running record of which products are usually sold together. The result is that the company has a better probability of upselling new customers on products that were bought together. In some industries, this can provide quite a competitive edge.
Individual Path Optimization:
Clickstream data allows businesses to see where customers visited on their site and in what order. As Entrepreneur notes, user experience is a significant part of a user enjoying their interaction with a company. Analysis of clickstream data allows businesses to refine their navigation to make it easier for consumers to get from one page to another.
Clickstream analysis has become one of the most dependable methods of improving a business’s operations. However, the traditional way of using collected web logs to figure out these metrics is outdated. It’s time companies replaced their traditional clickstream analysis with something that suits business in the twenty-first century, as explained by this link.
Taking a Novel Approach to Clickstream Analysis
The current method of dealing with clickstream data has improved a lot since the early days where web log data was dumped into a file and pored over by webmasters trying to make sense of the raw figures they were getting. Handy tools like Google Analytics went one step further, providing preformatted reports that could help a business understand its customers’ browsing habits. However, as the volume and velocity of data burgeoned, it meant that these methods of garnering insights usually gave outdated information.
The obvious response would be to implement real-time data analytics to offer faster, more agile insights that could help a business catch and keep more customers. Proactive decision-making starts with having access to data as fast as possible. However, real-time data analysis seems like it might be both prohibitive in cost, and reliant on skills that not many professionals in the field possess. Luckily, there’s a trifecta of open-source software that can be used together to provide real-time insights of data streams.
A Scalable Stack for Modern Clickstream Analysis
Three open-source software solutions come to our rescue by providing a scalable, cost-effective method of performing real-time clickstream analysis. These three software suites are Divolte, Apache Kafka, and Apache Druid referred to as the DKD clickstream stack. Each of these open-source tools has its own responsibility in collecting and processing data within the clickstream stack.
Divolte can send the data it collects directly to Kafka, with only a minor change in the file configuration. Kafka performs the task of the data broker – collecting the data from sources, and performing pre-processing, analytics (including machine learning and patter-matching) and then forwarding it to reporting tools.
Druid receives the processed data from Kafka and can be used to build a dashboard or visualizations of the data to make it easier for non-specialists to understand. The display allows for a unique approach to data exploration and analysis. Real-time data exploration will enable marketers and executives alike to spot trends in the data far more quickly than if they were collecting and batch-processing data from web logs.
A More Efficient Approach to Clickstream Analysis
Time is always of the essence in data analysis. As Ger Koole notes in his book Introduction to Business Analytics, the relevancy of data is inversely proportional to its age. Real-time data ensures that the business has only the most current data to generate its insights. The collected data can be stored to later analysis to create business intelligence for one or more departments. However, having access to those critical insights early on can be crucial to a company’s agility in being able to respond to immediate changes in the market.