Machine Learning applications in Tertiary Sector-part 1

Machine Learning applications in Tertiary Sector-part 1

Tertiary Sector is the service industry in an economy of a nation. It the largest sector in the western world and is growing at a rapid pace. It involves people interacting with other people and serving customers not transforming physical goods.This blog includes machine learning applications in Tertiary Sector.

Described below are some of the examples of machine learning applications in tertiary sector.


Machine learning techniques, for instance, probabilistic graphic model help in obtaining patterns and models from data by describing multivariate continuous densities. Real-time data model traffic patterns to support inferences and support large-scale traffic simulations. An example is the use of algorithms to discover real-time hot spots for congestion in Sydney CDB, Australia . Also, it predicts near future traffic jams due to an incident on the road. As efficient performance and management process depends on estimating the total duration from response activation to clearance time, the ideal route for emergency vehicles avoids congestion to reach a destination. Traffic watch microscopic simulation save 40 percent of travel time during peak hours or if applied to whole motorway system in Sydney there is a potential for savings of more than Australian $ 500 million per annum.

Maritime transport is the backbone of international trade as three-quarters of global trade by volume is carried by sea. Due to an enormous increase in the data being collected by the electronic tracking system, automatic analysis improves situational awareness in the maritime domain. Lack of knowledge is made up by learning from data. Also, machine learning helps in improving detection of anomalies.  A perceived situation is normal if it is predictable and the rest is anomalies.


Every aspect of the entertainment industry uses machine learning. It is used in online advertisements, measurement of audience accurately, media pricing, product distribution and content delivery. Social graphs help in analytics and predicting user behaviour with relevant message yielding three times increase in users’ engagement. Also, automated categorization generates classifications for digital media say news articles in Reddit. Sites use recommendation to users such as Pinterest, which uses multi- class image categorisation to suggest board. Likewise, market research analysis improves application’s notifications sent to the users. In video games, designed bots stand in as opponents where humans are not available or desired. Alpha Go uses machine learning to master the ancient game of Go.  It is a testing ground for researchers to invent smart, adaptable algorithms to tackle problems. Social network service and algorithms are used to forecast box office takings of a motion picture.

Other machine learning applications in Tertiary Sector include:


In retail sector machine learning techniques select the product mix, dynamic pricing, promotions and placement on sites, apps or a store window. It increases sales by 10 to 20 percent and offers better shopping experience . It helps to predict the stock for each season, hence eliminating waste across the supply chain, saving time and resources. For example, Walmart analysed weather data and social media to stock more pop tarts than usual in hurricane-affected stores.   Apple, Amazon, Nordstrom with smart mirrors and beauty e-retailer, Birchbox, opened brick and mortar store to provide an experience to their customers while collecting data to get insight and feedback to improve their products and services online. Exploration of trackable digital data in real-time helps in providing recommendations, matching styles according to preference and persuades customers to purchase according to their taste even before consumer searches for an item. Retailers such as Stitch Fix offer services such as personal shopper by matching people to products, shoppers to stylists to optimize the lifetime value of consumers and recommend apparel to shoppers.


At present most of the restaurant recommendation sites are review based. A customer searches a search engine for local choice with countless results to sift through. Restaurant recommendation sites such as Luka and Nara mine internet for information, do trend’s analysis, learn user’s restaurant preferences and scans online reviews to recommend a place and not a list. Predictive analysis is also used to forecast annual sales of restaurants based on given objective measurements.

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