People 'n' Issues

Machine-learning takes on ecommerce

September 7th, 2016
While machine learning is a relatively new concept for many, there are an increasing number of platforms and services that are adopting this construction of algorithms. ROBERT SCOTT, SafariNow CCO, talks about the relevance of supervised and unsupervised machine learning.
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Given the massive increase in the amount of data that companies – of all sizes and across sectors – are generating, it is no surprise that data analytics and machine learning are fast becoming key components of every innovative company’s toolkit. For the uninitiated, machine learning refers to the way in which companies can now leverage computing power to find important patterns within their data – and then use these patterns to improve their service or product offering.

Because of the sheer volume and complexity of the data being created today, it is often far beyond the capacity of any human – no matter how analytically gifted – to find any relevant trends or insights within what has been tagged ‘Big Data’.

Notably, one of the big differences between machine learning and computer-assisted analysis (where humans are involved) is that the recent breakthroughs in machine learning enable computers to teach themselves how to solve problems. So previously, when humans were directing computers, they were limited to very direct questions and answers (for example, “what is my top selling item?”) and required the person using the machine to dictate which method to use to the solve the problem. Now, machine learning enables computers to find answers in ways that are unguided by human intervention.

Although it is a relatively new and novel concept for many, the technology has already been applied to platforms and services that we use daily. Take Google Search, for example. When we enter a search term, Google uses elements of machine learning to analyse our behaviour once the first results have been served up (i.e. did we need to type in the same search term again, or did we follow some of the top links provided?) and then refines and improves its service according to the data. Other examples include Google’s self-driving car, how Netflix suggests which movies you should try next, and how a dating site suggests which people are most likely to be a suitable match for you…

Unexpected Insights

As with most technological tools today, almost any company or sector can leverage machine learning to better serve their customers. The challenge for companies is to recognise where – and how – certain insights and trends can improve their product or service offering.

Within the travel sector, we have identified various areas in which machine learning can be applied in order to fine tune our offering and help travelers locate their dream destinations. One of the great benefits of this tool is that it often finds relationships between factors that are completely unexpected and unplanned.

Machine learning has led us to the insight, for example, that some accommodation providers have a preference for prioritising requests from customers who would like to stay with them in the next few days – whereas other providers would much rather prioritise requests far in advance (for the school holidays, for example). Often, it is these unexpected – or unplanned – insights that can be the most beneficial for customers.

Continual Improvement

As an online travel aggregator, there are in fact infinite possible use cases for machine learning – and we are at the tip of the iceberg in terms of harnessing its potential to improve our offering to consumers looking for the next adventure.

Looking ahead, machine learning will perhaps become a standard application within the travel and e-commerce environment. Companies that are open to innovative ways of finding insights in their data can ultimately serve their customers more efficiently – and even develop closer relationships with them in the long-term. The key for companies is to keep an open mind as to whether or not their long-held beliefs about what customers want is actually supported by the data.

By always remaining alert to new patterns and insights, companies can make adjustments – both big and small – to enhance their offering.

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