Machine learning refers to a branch of artificial intelligence that processes patterns and decisions with minimal intervention from humans. This is based on the notion that systems can learn from data and develop their own form of ‘intelligence’ in order to make smarter choices. For e-commerce retailers, this can allow on-site search engines to recognise consumer behaviour while producing search results that are accurate and more likely to convert.
How Does it Work?
By utilising machine learning, e-commerce-specific algorithms can find complex patterns in the metadata of retailers’ products – and use those patterns to build an interconnected, multi-dimensional map. This allows the system to plot how all products relate to one another. Much like merchandising a physical store, this can help the system to automatically determine where each product belongs.
Sorting Apples from Oranges
In a physical store, we know the humble ‘orange’ would be most at home in the ‘fruit’ aisle. Machine learning takes this process a step further by utilising metadata patterns combined with previous consumer behaviour to automatically deduce that an ‘apple’ would be in the ‘fruit’ aisle too.
The interconnected product map grows as the system learns more complex relationships between products, for example, an ‘apple’ and ‘apple pie’ are related, but would not be found in the same aisle. This process permits the system to train itself to perform search tasks with more accuracy.
Of course, as the complexity and intelligence of this processing grows, the accuracy of the search results grows along with it, increasing the relevance of the suggestions offered to users. This can have a number of core benefits – enhanced accuracy can help to increase basket size, by showing website visitors relevant results that are more likely to convert – and even better, the algorithm learns from these conversions to further enhance the search process for the next customer.
In What Way is Machine Learning Different?
Without learning algorithms, on-site search is predominantly 'text-matching'. Text-matching requires users to enter the exact keywords that match a product's description to find what they're looking for. This makes it harder for users to locate items, as this basic on-site search does not account for common issues such as spelling mistakes, synonyms, or context.
For example, a visitor using the search term ‘crisps’ (UK term) when the site lists ‘potato chips’ (US term) or misspelling their query as ‘chiips’ would find that with text matching-based search, both these sorts of terms would produce null results.
In contrast, machine learning can define a match type for these types of search queries and use fuzzy matching to transform the query to the correct match by utilising learnt behaviour. For example, automatically identifying a misspelt word, and matching it to the correct one from its self-generated product map, or, most importantly, identifying thematic search terms. In fact, of the top 50 e-commerce sites that failed to utilise machine learning, 60% did not support thematic search queries and produced no results, despite how common they are.
How does Machine Learning Improve E-commerce Conversion?
It's clear that machine learning has numerous positive effects on online site-search, but how do these smart algorithms translate to improved e-commerce conversion rates?
1. Utilise consumer behaviour
When a user makes a search query, machine learning algorithms can collate previous customer search behaviour to determine which products are best suited to the search query. This means that algorithms no longer rely on text-matching, but can refer entirely to past behaviour learnt through machine learning. This includes the most clicked on or accurate results for a certain search term – and those are more likely to convert.
With a wealth of rich data that can be used to determine accurate search results, visitors can find what they’re looking for almost instantly, even if their query phrase is not an exact match to their desired product's metadata.
2. Advanced related results
Machine learning algorithms can learn from consumer behaviour to create a list of related results. The system will know that certain results are related, as each product's feature weights will resemble the direct search results. For example, brands with similar product lines or uses will have similar feature weights to the initial search query results. Additionally, smart systems can learn which products are related to the initial search query by remembering what visitors like to click on or search for that is similar, providing more chances to increase basket size.
3. Understand purchase intent
Personalised recommendations based off purchase intent account for 35% of Amazon's sales, highlighting the importance of machine learnt predictive algorithms to drive conversion rates. There's a lot to be learned from the world's largest online retailer, so e-commerce sites should consider mimicking this success by utilising machine learning to optimise their conversion rate processes.
For example, machine learning algorithms can identify that if a user searches for ‘dresses’ and selects ‘formal-style’ options, this is a preference for the current session, and can suggest more ‘formal-style dresses’ options.
4. Support and assistance
One of the most obvious versions of machine learning is chatbots. Not only do they allow your business to offer excellent, friendly customer service without eating up valuable staff time, they can also give your business a deeper insight into consumer behaviour.
Effective chatbots deal with customer queries, give relevant recommendations, and assist customers with navigation or purchase. In fact, 85% of people would rather interact with a chatbot than a human when they're in a hurry, giving businesses a great opportunity to derive insights into consumer behaviour. Chatbots can learn from this behaviour, and react appropriately to given tasks. They can also deduce what are the most common questions, searches, or issues on your website.
5. Learn specific customer preferences
Last – but not least – machine learning algorithms can remember specific customer preferences and save them for next time. Studies show that 91% of consumers are more likely to shop at places that recognise and remember them, and provide relevant recommendations. And although purchase intent can change per session, it’s common for users to still have certain preferences such as size or gender. A smart algorithm that remembers customer preferences is more likely to increase e-commerce conversions, simply because most users appreciate the gesture – and find it easier to find what they're searching for.
Businesses can take advantage of the best of machine learning by utilising effective e-commerce site-search platforms that intelligently devise smart algorithms based on consumer behaviour to build accurate and concise search results. For more information on what exactly some of the most popular platforms offer, download our e-commerce site-search platform comparison cheat sheet.