Personalisation has become a critical factor in retail, shifting the sector from a mass-marketing paradigm to a highly customer-centric approach. Consumers are increasingly seeking tailored experiences that engage them individually. Whether it is a knowledgeable shop assistant helping customers make informed decisions or a fully immersive in-store event, brick-and-mortar shopping is now firmly built on the foundation of personalised customer experiences.
Consumers, Artificial Intelligence, and Personalised Experience
Today’s retail consumers seek three key things: convenience, relevance, and personalisation, whether shopping online or in-store. They expect smooth, effortless interactions that integrate seamlessly into their lives and they desire relevant products, promotions, and recommendations that align with their preferences and past behaviours. Personalisation adds a crucial layer to this, as shoppers increasingly look for tailored experiences that make them feel understood and valued as individuals. Research from New Epsilon reveals that 80% of consumers are more likely to make a purchase when brands offer personalised experiences—an overwhelming majority that underscores how essential personal connections with a brand are to customer decision-making.
As data collection becomes more prevalent, particularly in retail, retailers now have unparalleled insights into customer behaviour and preferences. Interestingly, public perception of data security has evolved, spurred on by advancements in AI. A recent survey by Jack Morton, a global experiential marketing agency, found that before the rise of ChatGPT, 61% of respondents wanted their data to remain private. This figure has since dropped to 52%, reflecting growing acceptance of the use of data in retail and the potential for AI-driven experiences. In fact, the same survey revealed that 48% of consumers are willing to exchange their data for enhanced AI-driven brand experiences.
With access to more data than ever and increasing expectations for personalised experiences, how can retailers effectively collect and leverage this information? What are the key tactics for implementing personalisation, and what are some examples of retailers creating exceptional personalised experiences for their customers?
Key Tactics for Data-Driven Personalisation
The first step in leveraging data to enhance customer experiences is tracking the right data points. While brands can gain various insights about their customers, identifying the most meaningful and actionable ones is key. For instance, a home goods retailer might analyse abandoned carts on their website and offer personalised deals to recover these sales. Conversely, a beauty brand might monitor social media interactions to predict trends and manage inventory effectively. Different consumers, different data points, and different reactions from retailers; this is the challenges of personalisation.
Launching a data-driven personalisation program to create experiences can feel daunting, but there are three main tactical challenges to address:
Data Management and Analysis: Managing customer data securely and integrating it effectively is essential. Once collected, data must be analysed and organised to yield actionable insights. Building an in-house team for this can be costly, while outsourcing may limit customisation and control.
Alignment Across Functions: For larger retailers, aligning internal teams to act on data insights can be challenging. Personalised experiences must be unique and relevant to each customer rather than repetitive or generic.
Tools and Technology Enablement: The tools and technology available to a retailer significantly impact the success of data-driven personalisation. This includes tools for both gathering data and executing tailored customer experiences.
These challenges are significant, as highlighted in a McKinsey survey, which found that 67% of retailers struggle with gathering, integrating, and synthesising customer data. Additionally, 48% cite difficulties in building and maintaining an analytics team, while 67% report lacking the tools needed to implement personalisation at scale.
Creating Personalised Experiences
Once customer insights are gathered, brands must use them to create relevant, personalised experiences. Different demographics desire different experiences, so segmenting audiences based on data helps streamline this process.
Start by identifying a list of impactful, audience-specific use cases that are easy to implement. Launch these experiences, gather customer feedback, and use the data to refine future activations.
AI has played a transformative role in this area, not only in analysing customer data but also in crafting personalised experiences. AI algorithms can deliver tailored product recommendations, generate personalised emails, and even respond to customer inquiries via virtual assistants or chatbots. Additionally, AI can optimise pricing dynamically and offer personalised discounts to maximise revenue. For example, during a project involving customer outreach here at Agile Retail, our data indicated that younger customers respond much better to text messages, while older generations prefer phone calls. Adjusting our communication approach based on these insights led to a significant increase in customer engagement.
Approaching customers in the right way, at the right time, and with the right offer can have an enormous impact on their loyalty to your brand and their inclination to purchase your product. In fact, another McKinsey study found that personalisation can reduce customer acquisition costs by up to 50%. According to Statista, however, only 11% of customers agree that brands always offer personalised experiences. If the gap between customer expectation and brand execution is this wide, then no wonder brands are rushing to create personalised experiences in flagship stores around the world. But who is doing it best today?
Who’s Doing It Well?
Sephora exemplifies personalisation through its mobile app, which offers a personal customer service portal. Customers can book in-store experiences (like the Skincredible machine for personalised skincare insights), check product availability, and try on items virtually. The app not only enhances the shopping experience but also provides Sephora with valuable data to further personalise offerings and audience segmentation.
Nike has embraced personalisation to re-engage customers amidst increasing competition. Initiatives like the Nike House of Innovation stores in major cities allow customers to design and customise their shoes, providing a deeply personal touch. Additionally, Nike’s flagship stores feature treadmills for customers to test shoes before purchasing—a simple but impactful feature. Much like Sephora, Nike’s app integrates seamlessly with its omnichannel strategy, enabling customers to scan items, check stock, and engage more deeply with the brand.
Nespresso has proved a force of innovation in the coffee sector. They have seamlessly integrated data-driven insights with a luxurious, personalized retail experience, creating a sensory-rich environment that benefits both customers and the brand. Through its loyalty program, the company collects data on purchase history and preferences to offer tailored recommendations, exclusive invitations to events, and optimized in-store stock. In-store interactive elements, such as tasting counters and product demonstrations, allow customers to refine their preferences and explore customized coffee solutions. This approach enhances customer satisfaction by delivering curated experiences and it will strengthens brand loyalty and drives sales, highlighting how effective data use can elevate physical retail spaces beyond simple transactions.
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