What exactly does the customer want?
Recall when was the last time that you used Google Search, Facebook, Twitter, Youtube or Instagram. Probably you landed on this page from any of those websites only.
Now, try to recall when was the last time you paid for any those services or for that matter any of the thousand such services which are practically free. The answer would be never.
This dilemma spawned the business of online ads that crowd up our browser windows.
With the deluge of content reducing our attention span to seconds this revenue model is also facing immense pressure.
The reason: Online ads work earn money when someone sees them or clicks on them. With attention being scarce both of them are getting tougher to get.
Ads are a major portion of this commission/royalty business, with personalised mails and suggestions on e-commerce websites making up for the latter.
Therefore,Similarly Using this algorithm can help banks and non-banking finance companies to monitor the risk emanating from present borrowers and gauge the repaying ability of thin- and no-file customers, who have come to the bank for their first loans.
Although this sounds tailored for the lending segment, the algorithm in its foundation is a classification process that is able to tell the good from the bad, the better from the best and so on.
Therefore, it can deployed in other sectors also such as insurance and telecom.
In case of the former, by analysing consumer data and policy features for an insurance aggregator we can help them recommend the best policies personalised according to the needs of the consumer. This will help reduce cost of lead generation and significantly improve the probability of customer acquisition.
In the telecom sector, classification can be used to detect frauds on the network. Our algorithm can be used to learn from call detail records where and why there are network glitches. This can help telecom companies detect, localise and isolate problems on their networks.
Therefore, despite the primary goal being the sale of a product there is a lot of money to be made before that happens in case you exactly what is worthy of a click.
This will be implemented using a combination of methods that can segregate pictures, analyse text and classify products.
In case of images this will be accomplished by using well known clustering algorithms. We successfully manage to separate pictures without having to tell the computer what they are. Consider the pictures below.
The segregation of the three panels happens as given in the panel below. Observe how the similar coloured sots cluster together. This process was executed 20 times due to lack of computing power, with more iterations the separations become clearer.
In order to analyse and classify text we deploy a method called text fingerprinting. This will analyse and categorise similar words together on the basis on which they have been used and not just their obvious meaning.
Our model has learned to fingerprint text on the basis of a large number of documents uploaded on Wikipedia. Similar words are termed to be close to each other in terms of distance.
The table below gives a snapshot of the optimised results. As can be seen former US president Barack Obama is predicted closest to his vice-president Joe Biden. You can also see Joe the Plumber, Obama’s conversation with whom turned out to become a very popular story.
The clustering of words can also be seen through the relation between the radius of query and the number of documents, the number of queries and the distance between the words from the following plots.
The last level of classification, that for the products, will happen on the basis of the metatags that describe that describe these products and their sales history.
The final model will combine all these three algorithms to come up with an integrated solution.