How online retailers can use algorithms to grow their business

Product recommendation engines are now a common feature on ecommerce sites. Using algorithms, they are used to surface products for customers based on various factors.

They benefit retailers by showing visitors the products that, based on the data fed into the engine, they’re most likely to buy. For the shopper, the improved relevance means a better shopping experience.

These engines work well too. Indeed, Netflix says its recommendation engine is worth $1 billion a year to the company. This is because it helps users to find a show or movie they want to watch, and quickly, before a customer decides to give up the search.

The company’s research found that Netflix users lose interest after 60 to 90 seconds of choosing. The algorithms that learn from their behaviour and preferences can surface relevant content more quickly than if they had to search and browse, and keeps them subscribing.

They can be used in various ways. For example, to show trending or best-selling items, or those which visitors are viewing more than others. Amazon’s homepage, for example, provides a wide range of AI generated recommendations, from videos to products.

Product recommendations can be more advanced, finding relationships between products in different ways.

Some estimates credit recommendation engines for 35% of Amazon’s sales, and this seems very plausible given the number of recommendations Amazon makes, and the different ways it uses data to present products.

A range of data is used to produce these recommendations, from personal shopping history, to the purchase journeys of other site users. In this way, recommendation engines can produce recommendations accurately and at scale, something which would be impossible to do manually.

According to McKinsey, only one in ten online retailers are using AI recommendations, so there is an opportunity for many to improve conversions.

Site search is a key yet often overlooked feature in online retail. However, when implemented well, it can improve the user experience, and ensure that customers can quickly find the products they’re looking for.

Traditional site search is relatively basic, and relies on finding an item in the product database which matches all or some of the shoppers’ search query. This can produce good results for the user, but it can fail, and often also relies on the shopper spending time tweaking queries and filtering and sorting returned results.

Machine learning algorithms can be applied here, which allow additional data such as add to cart and purchase behavior around products to influence the sorting of results, meaning shoppers will see more relevant results, and those which other shoppers have a higher propensity to purchase.

Often referred to as dynamic pricing, algorithms can be used to control and set pricing levels for online retailers.

For the retailer, it can help them find the right price point for their products and maximise profitability. It can take into account several variables to get, testing for different visitors, before finding the best blend.

For example, product seasonality, competitor prices, product margins and factors like stock levels can be considered by the algorithm.

Dynamic pricing can have negative connotations, when associated with Uber’s surge pricing for example. However, it can actually deliver better deals and more competitive pricing to the individual shopper in some cases.

Machine learning algorithms can also forecast inventory levels retailers will need, using demand estimation engines.

By using various factors to forecast demand, retailers are able to order the right amount of stock at the right time to cope with differing levels of traffic.

Chatbots are designed to simulate online conversations with users, and have the potential to reduce pressure on customer service teams and speed up live chat interactions.

Since chatbots first arrived, they are getting closer to replicating human performance, and customers are becoming more used to them. 

One example comes from North Face, which uses IBM Watson to help shoppers find the best jacket for them. It asks customers questions about when and where they’ll be using the jacket, and what for, preferred styles, and crunches this data to find the best match.

As well as chatbots, AI can be used to support customer service agents, acting as a co-pilot to provide key information at the point where it is needed.

It analyses calls using natural language processing to detect sentiment, trends, and common concerns. From these interactions it can predict customer needs, personalise responses, and optimise workflows, improving efficiency, accuracy, and customer satisfaction.

Email remains a massively important marketing channel for retailers, and subject lines are a key aspect of this.

A good subject line can make the difference between the recipient opening or not opening an email, and so it pays to test and find the best performing copy.

Through A/B testing and a little creativity, marketers can improve performance, but companies like Jacquard are using AI algorithms to beat the results achieved by humans.

Language generation algorithms can use a brand’s language to produce subject lines specific to them and relevant to customers, testing variations to find the best performing subject lines.

Personalisation can be used in various ways on ecommerce sites and in marketing emails, with the aim of improving relevance for the shopper.

When used well, personalisation improves the experience for the shopper, as communications, product recommendations and even whole pages on the site are tailored to them according to their preferences.

It also helps the retailer, as an easier shopping process and better experience means customers are more likely to buy.

Personalisation, as it has been implemented so far, has often been basic. Using a customer’s name in emails, recommend products based on those a customer has bought before. These are tactics that can work, but AI algorithms offer the opportunity to personalise on a different level.

This is a variation on the chatbot, which uses AI to recommend products to shoppers, based on their previous history, and direct interactions.

For example, fashion retailer Bash has an AI shopping assistant which will provide recommendations based on the customer’s input. On Black Friday, customers who interacted with the AI assistant had 35% higher conversion rates than the average. 

Segmentation has been used for years in ecommerce in various ways, so that marketers can sell to different customer groups in different ways.

Common segmentation methods include:

• Customer demographics – data such as age, gender, income and occupation can be used to target different customer groups in different ways
• Customer lifetime value. It’s a good tactic to target the most valuable and most growable segments amongst your customers
• Customer behavior – previous purchase history, browsing behavior on site etc
• Devices and channels used. Customers can be grouped according to the devices they use to access your site, or the preferred channels

AI powered algorithms have the potential to analyse the various data sources, making connections that traditional segmentation methods may miss.

For example, a learning algorithm might identify alternative means of segmentation, such as online behaviour or preferences, that can serve as a more accurate predictor of interests or tastes than standard methods.

Machine learning algorithms can help with image recognition, identifying images and finding best matches for those uploaded by customers.

This has a lot of exciting uses for ecommerce sites, allowing for applications which improve shopping experiences or new product discovery. For example, image recognition can be used in site search to find visual matches for products entered and show relevant results for users.

As technology progresses, the power of AI-driven visual search continues to improve. It’s a growing market, one estimated to be worth $33 billion by 2028.

For example, Amazon StyleSnap allows users to upload a photo of some clothing, or an outfit, showing recommendations for similar products.