How AI personalisation transforms customer experience in retail

Using AI personalisation for retailers isn't new, but with more recent advanced AI, we see new capabilities in how enterprises interact with customers through all channels. With the new AI technology, it is nearly impossible for you to know whether you're communicating with humans or computers.

For enterprises, personalised shopping isn't just a trend - it's becoming a critical necessity in a market where consumers expect experiences tailored to their specific preferences and needs. The leading companies are squeezing the last drop out of these new technologies.

This article explores how new forms of AI-powered personalisation are reshaping the retail sector for enterprises. It highlights the significant benefits that justify the investment and addresses the challenges that may arise during implementation, including integrating AI with marketing and other business processes.

New opportunities with AI-powered hyper-personalization

AI-powered personalisation or hyper-personalization in retail involves integrating various artificial intelligence tools in real time to create customised customer shopping experiences. This sophisticated approach uses advanced algorithms and machine learning capabilities to process vast amounts of data, including browsing history, purchasing behaviour, and customer preferences. This is nothing new but has gradually become more advanced, given the new possibilities from the development of deep learning.

All these technologies offer insights, predictive analytics, and personalization opportunities for retail enterprises. This has been done for decades. But the new magic is how we use these insights and communicate with our customers by leveraging natural language processing and other Gen AI capabilities. We will focus on this specific area of hyper-personalization - the new generation 2 capabilities.

A few key AI technologies enabling hyper-personalization

With the new AI tools emerging in the last two years, we see additional ground-breaking features adding to the capabilities that have been there for over a decade. Let's dive into three of my top favourite use cases:

1. Product Recommendation AI: Tailored to Your Cultural Context

Product Recommendation AI has got a huge uplift from previous abilities. Enterprises with large product catalogues can now analyse millions of previous purchases and browsing histories to suggest products tailored to individual consumer tastes and offer them tailored to consumers' cultural and language backgrounds. In other words, the key to maximising the impact of these recommendations lies not just in their accuracy but in their presentation.

Understanding the language and context in which recommendations are delivered can significantly enhance the customer journey and increase the likelihood of purchase. For example, you offer your consumer a product description tailored to their profession or hobby interests. You can adjust the product name in real-time during dialogue with your customer to fit their preferences.

The key takeaway for product recommendations is that they do NOT all fit everyone. In hyper-personalization, we think that every single consumer could have their own specific product recommendations communication, using particular words you would use or cultural jargon for your region, demographic, gender, etc.

2. Generative AI Assistants: Your Virtual Shopping Companion

Generative AI Assistants are transforming customer service and sales support with their human-like communicational abilities. These advanced chatbots and virtual assistants can simultaneously handle thousands of consumer queries, from explaining purchase processes to recommending products and addressing post-purchase inquiries.

This capability allows businesses to streamline the sales process and customer communication at an unprecedented scale, far surpassing what would be possible with human agents alone. It's now possible to do this with real-time human avatars, making it nearly impossible to discern AI from humans. You can read more about generative AI chatbots here.

3. Augmented Reality (AR) and Virtual Reality (VR): Try Before You Buy

Retailers are increasingly adopting AR and VR experiences to enhance the online shopping experience. In the not-so-distant future, most consumers will have either a set of AR glasses or other wearables, which will augment the consumer's visual and audibility in the moment of consumption.

These immersive technologies enable customers to virtually try on clothing or visualise furniture in their homes before purchasing. Perhaps during a chat with a virtual human-like agent standing almost in front of you in your home, guiding you through the process in your language, reading your body language to adapt as the perfect salesperson, making you feel like you're talking to someone you've known forever. Walking around in your shopping area where your interest filtering is on for your wearable will direct your attention with audio or visual promotions or information to let you know of offerings close by that are tailored to your interests.

For businesses with extensive product lines, AR and VR can significantly reduce return rates and enhance customer satisfaction across various items, bridging the gap between online browsing and in-store experiences. They will also remove the necessity of investing in physical 3D facilities such as stores or other exhibitions, making it possible for you to offer your products worldwide in minutes.

These technological advancements are improving individual aspects of the shopping experience and working in concert to create a more personalised, efficient, and satisfying customer journey from browsing to purchase and beyond. They also allow all people to have similar experiences and access to products without being hindered by possible impairments.

Some challenges of implementing AI

As retailers increasingly use AI-driven technologies to enhance customer experiences and boost sales, they face new challenges that must be carefully navigated. Let's break down these hurdles:

1. Data Privacy and Security:The critical issue of privacy and security is at the forefront. To make AI effective, we must process vast amounts of customer data, and maintaining trust is paramount. This demands robust security measures, transparency in data processing methods, and strict compliance with data protection frameworks such as GDPR in the European Union.
   
   Retailers must walk a fine line between leveraging data for personalised experiences and respecting customer privacy, a balance that is crucial for long-term success in the digital marketplace. Today, consumers would be more hesitant if they truly knew the apparatus behind the scenes and the collected data. How businesses gather and sell this information to other companies is sometimes not fully transparent. It will become less evident in a world where we will most likely store all data about everyone, everywhere.
2. Integration with Existing Systems: Many retailers have difficulty integrating these best-of-breed AI technologies into their existing infrastructure. Legacy systems, the backbone of many retail operations, often prove incompatible with modern AI solutions requiring real-time data access. This technological mismatch presents a significant hurdle. Usually, AI projects start with implementing a modern cloud data platform or requiring retailers to undertake complex and costly integration processes to an intermediate database for AI-driven insights data. The challenge lies in the technical aspects of this integration and in managing the operational disruptions that may occur during the transition.
3. Financial Investment: Compounding these issues is the substantial financial investment required to develop or acquire AI solutions at an enterprise scale. The costs involved are not limited to the technologies but extend to hiring and retaining highly skilled specialists capable of implementing and maintaining these advanced systems. While the initial outlay can be daunting, forward-thinking retailers recognise the potential for significant returns on investment over the coming years. It is important to strike a balance between key resources you need internally and the ones you can hire externally when executing different projects.

As retailers navigate these challenges of data privacy, infrastructure integration, and financial investment, they must adopt a strategic, holistic approach. Success in this new AI-driven retail business development requires technological adoption and a fundamental shift in organisational thinking and processes. Those who can effectively address these challenges stand to gain a significant competitive edge in an increasingly digital and personalised retail environment.

So, in my opinion, how do you do this?

1. Define Your Digital Transformation Goals:Create a well-defined digital transformation project that clearly defines the capabilities you wish to add or transform with this implementation.
2. Start Small, Think Big:Do things in increments where less is more.
3. Build Modularly:Take a modular transformation approach, avoiding putting too much logic and dependency into one specific vendor, technology, or application. Be sure you can change your organisation's business-critical areas in as short a time frame as possible.
4. Stay Flexible:This technology space is more uncertain than any other, making it nearly impossible to choose the right path for over a year. Beware that the future will make more and more minor changes more often than we have historically seen.

Future AI personalisation use cases improving customer experience

Previously, we mentioned a few things that would entice you to start today with extended possibilities within AI personalisation. But what about what is coming next? Soon, we will see a lot of new features, but here are some of them:

1. Personalized Marketing Campaigns at Scale: AI will react in real-time to market changes, and in the same way automated trading exists in the stock market - we will have robotic marketing automation where some campaigns might include just a couple of people to millions of people and duration from seconds to days. Today, consumers have no clue we are the target for a campaign, but shortly, the only ones knowing why a campaign is running are the neural network operating the AI. With this change, businesses will have less understanding of why they are making money and are required to trust AI more and more over time.
2. Dynamic Real-time Pricing Strategies:For businesses with vast product catalogues, AI-driven dynamic pricing models can adjust prices in real time based on demand, stock levels, competitor pricing, and individual customer purchase history. This is nothing new - but what if we change pricing based on what you say? Imagine AI conversing with you, understanding precisely the probability of you making a purchase. Perhaps with the same stunning accuracy as a car salesman in front of you. Where the price suddenly offered is spot on within your price range - enabling you to buy!
3. Customer Journey Optimization:AI can map and optimise each stage of the customer journey, from initial website visit to final purchase, across multiple channels and touchpoints. Depending on what emotions your customer is expressing - some people are in a hurry, and others like to take their time. As a human reading your customers, we would easily understand where this person would be in the process - but I believe AI can improve ways over our capabilities so the steps in the journey from initial lead to cash will be adjusted on your buying signals from not only your previous buying patterns but your current mood, etc.
4. Home personal shoppers, Virtual Fitting Rooms, and Augmented Reality: Fashion and home goods retailers can implement virtual fitting rooms and AR technologies across their entire product lines, allowing customers to visualise products before purchase. This is something that has been suggested since the boo.com crash - but the new features with new AI let us interpret and create professional personal shoppers trained explicitly for your needs. These virtual helpers appearing in your home through augmented reality will know everything about you and your family, guiding you to a purchase that fits your current home and preferences. This sounds like it is for most people and not for everyone, but there is a trade-off between getting good, flexible service and sharing data about your preferences.
5. Predictive Inventory Management:For retailers managing extensive inventories across multiple locations, AI can analyse customer behaviour trends and external factors to optimise stock levels, reducing stockouts and overstock situations. But imagine a future where these AI systems are so confident in their predictions that they initiate orders from vendors or manufacturers before customers click the "buy" button.
   
   These advanced AI systems could calculate the probability of a specific customer making a purchase within a certain timeframe. If the likelihood is high enough, the system might proactively start processing the order. For instance, if the AI determines there's a 95% chance you'll buy a particular item within the next 48 hours, it could trigger the supply chain to begin preparing your order, effectively getting a head start on fulfilment.
   
   This preemptive action could dramatically reduce delivery times and further optimise inventory management. However, it also raises questions about data privacy, the accuracy of predictive models, and the potential risks of anticipating purchases that may not materialise.
6. AI + IoT Integration:Retailers could link AI-driven personalisation with smart home devices and IoT gadgets, bridging the gap between online preferences and physical shopping experiences. To link buying experiences to edge devices like mobiles, watches, or even your dishwasher. AI home systems will manage all AI home-oriented purchases like food, electricity, etc. - you no longer need to think about when and how to make reoccurring purchases unless you genuinely wish to.
7. Voice-Activated Shopping at Scale:Imagine all customer support knowing who you are just after saying hello. As AI voice assistance technology improves, retailers can implement voice-activated shopping across their entire product range, making it even more convenient for customers.

Get started with Hyper personalisation

Integrating AI-powered personalisation in retail isn't just an evolutionary step - it's reshaping the entire industry. As we've explored, from hyper-personalized product recommendations to immersive AR/VR experiences and predictive inventory management, AI is touching every area of the retail ecosystem.

For enterprises, the message is clear: AI personalisation is no longer a luxury - it's a necessity for survival and growth in a competitive and digitalised marketplace. The potential benefits are obvious:

• Enhanced customer experiences that drive loyalty and sales
• Optimized operations that boost efficiency and reduce costs
• Data-driven insights that inform strategic decision-making
• Innovative shopping modalities that differentiate brands in a crowded market

However, the path to AI integration is not without its challenges. Retailers must navigate complex data privacy issues, system integration, and significant financial investments. Yet, these hurdles are not impossible. By adopting a strategic, modular approach to implementation and maintaining a commitment to ethical data practices, retailers can harness the power of AI while building trust with their customers.

Looking ahead, the future of retail is one where the lines between physical and digital shopping blur, where AI acts as an invisible shopping companion, anticipating needs and personalising experiences in real-time. From voice-activated purchasing to IoT-integrated inventory management, the possibilities are limited only by our imagination.

With this new AI-driven retail generation, the question for enterprises is no longer "Should we implement AI personalisation?" but rather, "How quickly can we adapt and innovate?"

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