Develop real-time predictions through Deep Learning techniques

CNN

Convolutional Neural Networks are used to precisely filter the significant information from unstructured texts..

Artificial Intelligence

Artificial Intelligence techniques enable real-time prediction of customer-specific product affinities.

LSTM

Long-Short-Term-Memory networks interpret the click-behaviour at your online store and provide the customer with made-to-measure recommendations.

Deep Learning and Artificial Intelligence


Just imagine a highly talented person with a significantly above-average IQ. In the wrong learning environment, that person will fail – for example in an environment designed for an average learning aptitude. He or she will only develop their potential if they are supported in an adequate way. This applies in a similar way to artificial intelligence, which can only deliver exceptional results through Deep Learning. And Deep Learning consistently improves intelligent applications.

  • Real-time predictions
  • Recognising Customer Lifetime Value
  • Dynamic buying recommendations
  • Raising performance through re-targeting

Highly effective applications through Deep Learning


Neural networks (or classic artificial intelligence techniques) are currently experiencing a noticeable renaissance. Thanks to our comprehensive portfolio of newly developed Deep Learning algorithms, Dastani Consulting is able to provide state-of-the-art solutions to the most important sales and marketing questions. Our models have also proved entirely convincing for the financial sector.

Highly effective applications through Deep Learning


Neural networks (or classic artificial intelligence techniques) are currently experiencing a noticeable renaissance. Thanks to our comprehensive portfolio of newly developed Deep Learning algorithms, Dastani Consulting is able to provide state-of-the-art solutions to the most important sales and marketing questions. Our models have also proved entirely convincing for the financial sector.

Long-Short-Term-Memory-Netzwerke


Long-Short-Term-Memory Networks (LSTM) are able to interpret the specifics of click-behaviour at online stores. With these inputs, the networks predict customers’ future buying behaviour. Our speciality is that our Deep Learning systems determine buying intention and also customer-specific product affinity in real time. LSTM networks learn autonomously, so they can ensure that customers are consistently offered the most relevant products for their needs. Furthermore, we use our Deep Learning technique to formulate the correct conclusions out of billions of transactions. This enables us to determine future Customer Lifetime Value.

Convolutional-Neural-Networks


In addition to LSTM Networks, we also rely on Convolutional Neural Networks (CNN) for many predictions. The underlying concept of overlapping neurons contributes significantly to the ongoing development of image recognition. This enables us to filter the relevant details from the torrent of information with which we are confronted. For example, unstructured texts can be interpreted, so that networks based on CNN and LSTM can learn to classify and increasingly understand the content of emails, websites or PDF documents.

Convolutional-Neural-Networks


In addition to LSTM Networks, we also rely on Convolutional Neural Networks (CNN) for many predictions. The underlying concept of overlapping neurons contributes significantly to the ongoing development of image recognition. This enables us to filter the relevant details from the torrent of information with which we are confronted. For example, unstructured texts can be interpreted, so that networks based on CNN and LSTM can learn to classify and increasingly understand the content of emails, websites or PDF documents.

Better performance through Deep Learning


Time series in particular are excellently suited to being integrated into the Short- and Long- Term network concept. This applies especially to the use of Deep Learning techniques in the financial sector, or to the prediction of market prices. The newest tools coupled with many years of experience allow us to set up the most complex predictions.

Additionally, our latest experiments with image recognition techniques have led to very promising results. Using CNN methods based on artificial intelligence, we have been able to transpose customers’ buying behaviour into images for subsequent interpretation. This has shown that our Deep Learning techniques are significantly superior to classic methodology, especially in the area of re-targeting (Behaviour-Based Offering) and Next Best Offer processes.

"The grand theme of the 21st century is the rise of artificial intelligence"

Prof. Dr. Jürgen SchmidhuberIDSIA