Elevate the customer experience with AI-powered personalisation

The evolution of AI personalisation

With AI-powered personalisation, businesses can unlock valuable data insights and deliver content tailored to each shopper’s preferences. In the UK, 72% of businesses are already using AI for personalisation, according to 2023 data.¹

Here’s a closer look at how AI can help to improve customer experience and operational efficiency.

The power of data in personalisation

AI can process data across various platforms to help businesses to understand customer behaviours and preferences. With this customer behaviour data analysis at their fingertips, businesses can better personalise shopping experiences according to customer preferences.

Imagine: A business notices that in-person shoppers who pay with digital wallets spend more. To help to boost revenue and streamline checkout, the business can accept a range of digital wallets. And make it quick and easy for customers to pay.

Real-time personalisation with AI

Businesses can use AI to better provide real-time personalisation during the product discovery and research process. And even after customers check out. Shoppers can receive immediate customer service and answers to their questions from AI chatbots. AI-powered commerce platforms can also provide automated updates about shipping and returns. So customers can stay informed about their orders each step of the way.

Dynamic content customisation

Businesses can deliver AI-powered dynamic content customisation with strategies such as:

• Content tailoring. AI can customise website content in real time, based on each user’s interests and preferences.
• Contextual personalisation. AI can use contextual data to tailor content to each customer’s experience. Such as using location data to set web page language.

Personalised product recommendations

AI-based product recommendations can help to enhance the online shopping experience. Which potentially improves customer satisfaction and loyalty. Customers can answer a few automated questions to receive tailored recommendations in real time. AI-powered platforms can also use the data from each shopper’s buying history to suggest relevant products.

AI can deliver real-time customisation through:

• Predictive analytics that suggest products a shopper is likely to buy.
• Collaborative filtering to recommend products to new customers based on the preferences of similar users.
• Continuous learning from customer data to improve recommendations based on user interactions.
• Cross-platform integration using AI to bridge the gap between channels. And provide personalised recommendations and consistent customer experiences across platforms.

The complexity of personalising end-to-end customer experiences

AI offers many opportunities for businesses. But it isn’t without challenges. Especially during implementation. It’s important that businesses understand potential roadblocks as they optimise their e-commerce strategies.

Data silos and integration issues

Data fragmented across disparate platforms can make it difficult to draw inferences. Or leverage data for consistent experiences. That’s why it’s important to ensure that AI-based solutions can be easily integrated with a business’s existing tech stack.

Challenges in omnichannel personalisation

Customers today seek experiences that span online, mobile, social, and in-person channels. Businesses need AI solutions that can deliver the seamless, personalised experiences customers want. No matter where they interact with a business.

Privacy concerns and ethical use of data

Businesses should be sure that they’re using secure and compliant methods to gather and store customer data. For example, businesses should obtain explicit consent to collect data from customers. And always ensure they’re meeting the latest compliance regulations.

Using machine learning (ML) for payment fraud detection can also help to protect businesses and their customers from scams. AI and ML can reduce false positives by up to 86%.² This can save resources and allow businesses to focus on real threats.

Scaling personalisation efforts

As businesses grow and customer expectations evolve, having the tools to scale personalised experiences can increase in importance. A successful AI personalisation strategy should be flexible enough to expand with your business and adapt to changes.

Measuring the impact of personalisation

It can be difficult to measure the impact of AI-based personalisation strategies that span channels and deliver vast amounts of data in real time. Businesses will need to consider which metrics can accurately quantify the success of their strategies. And optimise for future growth.

Better checkout, enabled by AI

AI’s continual evolution will provide businesses with new ways to fuel e-commerce personalisation and drive engagement.

Business can better meet shopper expectations at checkout by providing:

• Frictionless experiences. It’s important to identify unexpected points of friction. And optimise payment experiences to help to improve customer satisfaction. AI can simplify the checkout experience by using shopper data to auto-fill information. This helps to minimise friction and reduce cart abandonment rates.
• Payment personalisation. AI can provide shoppers with personalised payment options at checkout. And offer suggestions based on customer preferences and past behaviour.