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The enormous impact of AI and machine learning on eCommerce

By Contributor
Tue 1 Sep 2020
FYI, this story is more than a year old

Article by dotdigital head of marketing Aparna Gray.

Australians love online shopping and continuously look for a great purchase experience each time they check out. As with any mature market, customers are spoiled for choice, and competition is fierce. After all, the competitor is just a click away. 

In a volatile retail environment, the value of a satisfied, loyal customer cannot be underestimated. Customer experience (CX) is now the new arena where customers are won or lost. Increasing customer engagement and loyalty is the prize, and if retailers don’t have their eyes firmly on the prize, they won’t survive. 

The latest figures reveal Australians spent $30.14 billion on online retail in 2019, which represents around 9.2% of the total Australian retail trade estimate. eCommerce has witnessed a growth of 9.4% since 2018.

The advent of technologies like AI and machine learning (ML) has enabled businesses to have dynamic customer engagement and retention strategies in place. Statista predicts there will be 20.9 million Australian customers online by 2024. 

With an increasing potential customer base, AI and ML are crucial game-changers in the marketing and sales toolkit. So how can businesses best use these technologies to surface and funnel data effectively for enhanced customer experiences? 
 

Personalised recommendations 

There is no doubt that personalisation and optimisation both play a crucial role in creating a superior customer experience.

A memorable purchase journey in which the customer feels valued, their preferences are understood, their data is stored securely, and the whole experience is consistently smart, intuitive, and straightforward.

Four out of five shoppers are more likely to buy from a company that offers personalised experiences, and 71% of consumers express some level of frustration when their shopping experience is impersonal.

Machine learning is the secret to unlocking online sales, combining the sophistication of AI with traditional sales tactics of upselling and cross-selling. 

AI and machine learning use visitor profiles to create more powerful and personalised product recommendations that lead to incredible results. 

By matching the product catalogue and inventory with intelligent visitor profiles, AI can create hyper-specific targeted promotions designed to increase conversions and encourage higher-order values. Predictive recommendations can also be used post-purchase to drive customers back to the store. 

These recommendations increase a retailer’s relevancy and create a personal touch – something quite rare in the offline retail environment. 
 

Inventory management

AI helps retailers make better future predictions about sales and improve inventory management. 

Businesses can benefit from AI-powered analytics – including average order value (AOV) trends, the effectiveness of promotions and sales events, colour and style preferences, sizing, and location information. 

Armed with this intelligence, retailers can keep ahead of customer demand, plus improve their product offering and stock levels to reduce the amount of unsold stock.    
 

Better customer support

Machine learning is the driving force behind smart searches and enhanced customer service around the clock, including live chat functionalities. 

Research has found 82% of people want an ‘immediate’ response to their question while interacting with a brand. Live chat establishes an instant connection with the customer by answering questions promptly and creating a vastly improved customer experience, night or day.
 

Retargeting - hooking the one who got away

Targeting and retargeting can be a successful strategy or an expensive mistake – the difference is AI which captures and funnels data to identify customers who perhaps abandoned a cart and are most likely to complete their purchase with the right message. 

Using the RFM model (recency-frequency-monetary value), AI segments individuals based on their behaviour, identifying those who would be more engaged and motivated to return.

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