How web mining is helping e-commerce to be more successful

Web mining is the process within you are extracting insightful information from World Wide Web. It is possible at different scales and for different purposes. In general, web mining is the term used for data mining application within online collected information. Such technical achievement gives businesses pretty interesting opportunities for the e-commerce businesses and online entrepreneurs.

It is not a secret that there is a staggering amount of information located on the Internet. The annual internet traffic is rated as a 1 zettabyte per year. If you ever have wondered how much data is that, just imagine a cup of coffee on your desk is equal to 1 gigabyte. A zettabyte is the volume of the Great Wall of China.

web mining

Carefully targeted data crawling is capable of extracting a lot of insightful information. Unfortunately, the data source is quite far of homogeneity. This is where web mining comes to help in order to turn available data into useful information.

Sure, it wouldn’t be really wise if online businesses do not analyze thoroughly the data generated by their own websites and domains.

Here comes the reasonable question of how data analytics might help e-commerce business to be more successful?

Segmentation of group of users for marketing purposes

Dividing users into groups is a key tool for identification of target groups segments. This empowers marketers to be more productive and successful in terms of ROI. Data analytics and web mining are capable of doing a great job right here. Including predictive work with potential conversion rates in order to minimize guessing and to create stable and reasonable planning.

Segmentation also is a step for reducing the analytical problem dimension and for fighting with the high level of data uncertainty. Instead of managing a large number of (potential) users the analytical problem can be reduced to a reasonable number of segments. Moreover, appropriate segmentations leads to a reduction of the random data variations and at the same time to keep the important data patterns. This step can be applied without human intervention, which makes it attractive for automated decision making.

Clients’ classification

Through classification of existing customers, online businesses are capable of identifying the most valuable customers. They can be targeted with tailor made offerings and promotions. Classification is another step in the decision-making process and fortunately, this step is also appropriate performing without manual actions.

Enhanced customer support

Web mining is a priceless tool for extracting information regarding customers’ preferences to particular products and services. This is enabling e-commerce entrepreneurs to apply custom approach when serving the particular customer as well as to increase the customer lifetime value, retention rate and to generate more sales. In this area is increasing the number of automated decision-making systems, which optimize the interactions between businesses and their customers both on the segment and on the individual level.

Correlations between events and products/services

Data analytics & web mining are capable of discovering correlations which may or may not lead to causations. For instance, Customer Basket Analysis might discover products that look unrelated but are purchased together relatively often. Web sessions information might be a source for correlations in customers’ behavior which gives information on how the customer journey might be improved.

Marketing campaigns analysis

The success is a measurable value within the online business. So are the marketing campaigns. Good data analytics is capable of recommending a campaign timing, target, messages, best converting landing pages, investment, placement, etc. When the most efficient combination is mastered, the campaign model might be scaled to new products, markets, budgets and target groups.

Market basket analysis

Planning new products/services and evaluating the existing ones is a key tool for further improvement. The Boston Matrix is among the possible approaches in this direction. Even though it is not a rocket science, Boston Matrix is capable of clearly emphasize which of the offerings are the most valuable asset of a particular product portfolio. It also hints which of the offerings should be discontinued as well as the products with unleashed potential.

Fraud prevention

Finding unusual customers` behavior and highlighting extremely rare scenarios help to get even more insights into the online business performance. By finding exclusions, fraud detection is possible through calculation of various parameters such as averages, quantities, probability distributions and so on. Time-series analysis and time-dependent data are also pretty helpful. In the past, fraud detection systems were built up as expert systems – based on preliminarily defined rules. But now-a-days the presence of a large number of historical data gives the opportunity to train fraud models which estimate the probability a given customer’s behavior to be malicious.

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