Fewer technologies are hotter than artificial intelligence and machine learning, which mimic the behavior of the human mind. And for companies embarking on digital transformations, AI and ML are being viewed as pivotal technologies for engaging customers in a better manner.
The U.S. Bank had collected a wealth of customer data. And like most banks, the U.S. Bank has struggled to derive actionable insights from this data. After adapting to Machine Learning, the bank has been using Machine Learning technology to increase personalization across the bank’s small business, wholesale, commercial wealth and commercial banking units.
Post adaptation, if a customer searched on the U.S. Bank’s website for information about mortgage loans, a customer service agent can follow up with that customer the next time they visit a branch. It also helps U.S. Bank find patterns humans might not see.
A simple change that was observed was that the software can recommend agents to call a prospective client in a particular industry on Thursday between 10 a.m. and 12 p.m. because they are more likely to pick up the phone. It can also put a calendar invite into the agent’s calendar to remind them to call the candidate the following Thursday.
Such capabilities get to the core of what many financial services organizations are trying to do; cultivate a 360-degree view of customers to recommend relevant services in the moment.
The industry is transforming from a world that was describing what happened or what is happening to a world that is more about what will or should happen. It is all about staying a step ahead, anticipating customer needs and a suitable channel to communicate with them.
Facebook uses Machine Learning in quite a few ways! The People You May Know feature is an implementation of ML. If you browse for a certain product in E-Commerce websites, Facebook will show an ad related to that product on your news feed. That is implemented using Machine Learning as well. The list of Suggested Friends that you see when you join Facebook is based on your current workplace or school or college. That uses Machine Learning as well.
A vivid example of Facebook using Machine Learning is mentioned below:
Consider all the web pages in the world as nodes of a graph. All the hyperlinks i.e mentions of a website on another website as the edges.
Now, Google Page Rank basically ranks the web pages . So, a generic score needs to be assigned to every web page upon which they’ll be ranked. This score is dependent on the web pages that point it with a factor of alpha and a constant term with a factor of beta. The computations are made and the scores are computed till no further change in scores can be obtained.
PageRank is a ranking system designed to find the best pages on the web. A webpage is good if it is endorsed by other good webpages. The more webpages link to it, and the more authoritative they are, the higher the page’s PageRank score.
If one webpage links to a lot of webpages, each of its endorsements count less than if it had only linked to one webpage. That is, when calculating PageRank, the strength of a website’s endorsement gets divided by the number of endorsements it makes.
Note that this ranking is recursive, to put it more simply, the PageRank score of one webpage depends only on the structure of the network and the PageRank scores of other webpages.
Page Rank algorithm gives weight to every incoming link a web page gets, every incoming link increases Page rank, while links from pages with high page rank have high weight and matter more, links from pages with just a few outgoing links matter more.
Page Rank does not include relevance information, so incoming links from pages that have nothing to do with the page will increase page rank. However, Page Rank is only a very small portion of what determines search results. Trust rank algorithm influences search results more, since it takes into consideration how likely the site is to be trustworthy and not give irrelevant outgoing links.
In layman’s terms, all this enabled through Machine Learning, where in Google utilises its capabilities and build up a database and ranks it accordingly which enables users to access the better websites rather than the mediocre ones.
Thinking about the future, machine learning will make its biggest mark in helping workers and businesses to more efficiently use time and gain a deeper understanding of their data. There is so much industry knowledge locked away in PDFs, medical files ,and even cookbooks. Tapping into this data, being able to organize, process and assimilate years of unstructured data points will accelerate the acquisition of knowledge, reducing the time to innovation and unearthing of new ideas.
Adapt Machine Learning In Your Business Or Fall Behind.