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Machine learning: The path to intelligent analysis of big data

Dealing with huge data volumes is a challenge that has a significant impact on various sectors of the economy, industry and science. The processing of this constantly growing data volume and the acquisition of relevant insights are among the central questions of our society.

One key to processing and evaluating big data is machine learning. The use of machine intelligence is the current trending topic in the area of digitalization, with research and applications in this field booming. It will continue to shape the future of our everyday and working worlds. Current applications of so-called social or chat bots among the online market leaders are flourishing. Amazon’s Alexa, Google’s Alpha Go and Microsoft’s Cortana suggest what machine learning is going to bring in the future.

But what does machine learning mean?

With machine learning, an artificial system can generate complex knowledge from past experience. To this end, it learns from specific examples and can then generalise what is learned afterwards. It is important that it does not just memorise these examples, but instead identifies patterns in the data, which are then transferred to unmapped data.

Application areas of machine learning in online retail

The applications of machine learning seem virtually limitless. The above-mentioned chat and service bots are also to be mentioned in online retail. These would be queried, for example, in live chats in the online shop regarding products, shipping or returns. Additionally, intelligent forecasting systems and personalisation play an important role for online retailers.

“The data volumes are growing at a rapid rate and this doubly fuels the technology: On the one hand, they form the starting point for machine learning, and, on the other, make an intelligent data selection essential. Nobody, after all, wants to check hundreds of documents tomorrow when only 2 or 3 are relevant for a decision. This development is also not about to stop in retail[..] “ – Dr. Dominique Ziegelmayer, Director, Trusted Enterprise

When using machine learning, the focus is often upon applications for end users. However, machine learning is of particular importance in eCommerce for product classification. There is a high number of products here, all of which have different characteristics. If you seek to assign products to a class or find similar products in this mountain of data, problems often arise, since product information about an identical product can appear very differently. Names and product descriptions often do not match and an EAN is not always available. It is particularly difficult to compare articles from different sources in different languages. As a result, products are frequently misassigned by conventional solutions.

Winning formula of big data analysis

A manual cleanup of the data is time and cost intensive. In the era of big data, information is also so varied that it can no longer be reviewed manually.  At this point, machine learning steps in, which can reliably analyse huge amounts of data – but in a complex way for the human mind to comprehend. With the help of intelligent algorithms, product data can be reliably classified and a manual allocation avoided.

Sophisticated algorithms are necessary to analyse and understand big data. Herein also lies the secret formula of our Business Intelligence software. The unique matching algorithm of blackbee can determine all the relevant characteristics in a huge data volume and thus determine the likeness or variability of the products. Manufacturers and retailers can use blackbee to monitor competitors with identical or similar products on the Internet. In doing so, they can make profitable decisions regarding their product ranges and pricing strategies.

The self-learning matching algorithm from blackbee

How does it actually work? Each product has special characteristics and features that are stored as attributes. These attributes allow us to search for products based on specific parameters, such as colour, material or size. The matching of product data is therefore based on its attributes. Success of the matching, however, is dependent on a combination of the correct benchmarks for different attribute values. In the majority of cases, one single parameter does not lead to an optimal result. Due to the large number of existing similarity indicators, however, determining an effective matching strategy is an enormously complex task.

Machine learning methods, as used by our matching algorithm, reduce the manual tuning effort by semi-automatically determining an optimised matching strategy. Examples of matches and non-matches are required for this. blackbee supports the generation of such examples and also provides a feedback mechanism. Classification errors arising can thus be corrected by feedback. The system subsequently learns from these corrections – by standardising the examples. It will thus receive new information upon each run for improving accuracy, which will gradually increase by up to 98.2 percent.

Using machine learning for intelligent business processes

By employing a self-learning algorithm, blackbee finds automated solutions for matching product data without having to follow rigidly pre-programmed rules. For this reason, the software achieves a considerably higher degree of efficiency. Where other solutions fail to meet the challenges of big data, blackbee recognises even the most complex of products from the most varied of vendors. The data is then processed in such a way that, for retailers and manufacturers, important information about their position amidst the competition becomes evident. With this advantage, they can accelerate and optimise their processes, as well as extend their competitive edge.

Profit from the advantages of our self-learning, automated software solution. Test blackbee now!