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How Machines Make Decisions with Less Data

Sep 26 2019 | by Loop54

The term artificial intelligence (AI) describes the process by which a machine simulates the human mind to make decisions. They do this by following a pre-defined set of rules that enable them to 'think' in a way recognisable to us.

In the world of e-commerce, the benefits of this technology are clear. Deciding what to show individual visitors in real-time is difficult without the help of a computer - parsing through reams of data to reach actionable conclusions would simply take too long. Perform a basic search on the majority of e-commerce sites and, chances are, you'll see almost identical results appear for every user. To overcome this, e-commerce businesses establish different segments and rules to categorise visitors into separate compartments - but even this has its limitations.

To improve the accuracy and reliability of this technology, the rules that govern AI must be refined. And, to do that, we need machine learning.

Design a frictionless shopping experience for your users. Download our 'Search  and Navigation UX Design Guide'.

Data Utilisation and the Limitations of Artificial Intelligence

One of the biggest challenges to the wide-spread adoption of artificial intelligence in product search is the scarcity of usable data. During periods of lower than average traffic, collecting the volume of information required to gain a deeper understanding of consumer behaviour can be difficult. If visitors are only returning to your site once every couple of months, the information gathered on the previous occasion may no longer be relevant.

Consequently, artificial intelligence, which was originally built for data-rich companies like Netflix, struggles to deliver. It can't provide customers with satisfying search results or generate the actionable insights businesses need to better understand consumer behaviour. In such situations, demographic data — age, gender, annual income — is far more valuable.

Demographic data provides a foundation upon which you can build more effective rules for your AI. This, in turn, enables you to design a better, more relevant user experience. However, data of this nature has its limitations, too.

Studies show that demographic data alone is insufficient when it comes to predicting the likes and dislikes of your audience. Think of it this way: just because your neighbour is the same age as you, drives the same car, and earns a similar salary, it doesn't necessarily follow that they also have the same taste in fashion. Pigeonholing visitors in this way can have a detrimental effect on the overall customer experience.

The Two Fundamental Hypotheses of AI

In order to take e-commerce search to the next level, we must understand two key concepts:

1. How the products in a catalogue relate to each other
2. The common features that connect them
 
Alongside deep architecture, it's absolutely vital if you wish to leverage the behavioural data gathered from your customers and improve the operational efficiency of your AI.
 
In an effort to achieve this goal, our AI is built around two fundamental hypotheses:
 

1. Machines Learn from Intentions 

The first states that every visitor session, and the intentions behind it, is unique.

The user is searching for a specific product to meet a specific need. It's our job to understand what the intention is as soon as possible in order to optimise the customer experience.

2. Machines Learn from Preferences 

Our second hypothesis revolves around the AI's ability to learn from each visit. This includes trademark preferences, low/high price sensitivity, and demographic information — anything that helps the AI connect users to the products they're looking for every time they visit.

The combination of these two principles and our unique text-matching technology has enabled us to develop highly personalised e-commerce technologies. Loop54 uses the behavioural data gathered during every search to not only provide more relevant results, but actually remember each individual customer's personal preferences. 

Our advanced e-commerce machine learning algorithm is taking us one step closer to true artificial intelligence. Our algorithms model high-level abstractions from data using a deep architecture that utilises multiple processing layers. This enables the software to effectively train itself. As a result, machines are able to make viable decisions using significantly less consumer data than would usually be required.

The Loop54 Machine Learning Model 

E-commerce sites can be complicated places to do business. As the competition for space increases, so too does the cost of attracting and retaining customers. In this day and age, an alternative supplier is often just one click away. The onus is therefore on e-commerce sites to establish robust connections with their customers, whilst also making their services relevant, coherent, and easy to understand. But that's difficult to achieve if the data required to forge these bonds is lacking.

Learning how to use artificial intelligence effectively solves this problem. More than anything else, it gives e-commerce businesses access to the tools and information they need to develop the insights that help them build better, more engaging customer experiences.

Learn more about Loop54's machine learning model and discover how it can add value to your business.

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