What Is Data Mining?

child with binoculars

What is Data Mining? And what is Predictive Behavioral Targeting again? With all the technical terms in the field of onsite user analysis you can easily lose track of what’s going on. That’s why, we tried to define the terms for you and set the record straight.

How can you define Data Mining?

Data Mining is a generic term for different types of data analysis. Data Mining experts discover hidden relationships in large amounts of data that are useful for companies or research institutions. The main difference in comparison to other data analysis techniques is that Data Mining doesn’t presuppose an assumption beforehand. The assumptions will be developed during the analysis process, known as KDD (Knowledge Discovery in Databases). Data Mining is only a small part of the whole analysis process.

Data Mining in eCommerce

Why is Data Mining important for online shops?

Data Mining is often applied to shopping cart analysis in eCommerce. Retail companies are using a special Data Mining technique called Pattern Mining to find relations within the customer’s shopping habits like: 50% of our customers, who bought a scarf, also bought gloves. These recurring sequences are called frequent patterns and are used for product recommendations („Customers Who Bought This Item Also Bought“).

If you combine the Pattern Mining technique with statistical data like surveys or demographic data and use your findings to predict future behavior, you call this Predictive Behavioral Targeting. Akanoo applies Predictive Behavioral Targeting to avoid abandoned carts. We are looking for behavior sequences that are occurring again and again. This could be a specific order of product sites or patterns in date and time of the visits. As soon as we have discovered frequent patterns, we use them in combination with available demographic data to predict the probability of intentions such as “will not convert”, “will not add second product to basket”, etc.

To cut a long story short: Data Mining is an important data analysis technique to extract valuable, hidden information from large data sets. It is particularly help in digital marketing and assists transaction-based websites in maximizing their profits.

Do you like to maximize your profit with Predictive Behavioral Targeting? Contact us now!

Business Opportunities in Click Stream Data Mining – from Cart Abandonment Prevention to Upselling

When we started Akanoo 2½ years ago, none of us would have believed that the click-stream of online shop visitors reveals so many applications of statistical analysis. Our goal was simple: use JavaScript to display a voucher to people on the first pages if you are certain they otherwise won’t buy. A logistic regression on the target variable “will buy” and a web service written in Groovy for real-time prediction did the job.

This led to roughly 10-20% of incremental revenue compared to control groups. But only on a small group of people: those that we knew for sure won’t buy.

2015-09-29-potential-by-trigger-moment
Starting with cart abandonment prevention, we have identified different strategies to make additional revenue with statistical models. What will be the next strategy? We’re working on it.

So, we took a deeper look at the behavior of the millions of online shop visitors that we had tracked so far. And we quickly realized: there is a business opportunity way bigger than that. We saw visitors responding negatively to vouchers and some of them becoming less profitable in the long run compared to the control group. So we had to rethink our initial solution.

Let’s draw an analogy here: most online retailers offer the same assistance as a supermarket – none. However, there are millions of visitors out there that rather like the shopping experience of a fashion outlet or shoe retailer (they know from the offline world). There is staff around that guides you through the offering. How can we deliver both at the same time?

The solution is: create algorithms that have a lot more freedom. We started working on algorithms that are able to predict the intention and next steps of visitors and pick the right way of interacting with these visitors from an array of different incentives and pieces of information. With the ultimate goal: converting visitors into happy customers.

Now we’ve expanded the audience of our JavaScript targeting to a wide array of scenarios besides cart abandonment prevention: up-selling, inspiration, return reduction, loyalty. (And we started using Spark, Hadoop, Docker, Redis and Akka to handle the data…)

What’s next? The data we analyze and track on a daily basis is growing steadily. Just for the up-selling models, we look at the data of 11M users every night. Other kinds of transaction-based websites come into play and modeling of more complex decision making processes.

Who makes this possible? The data science team behind Akanoo.
Yours, Fabian