Knowing your customers is no longer enough. To deliver the ultimate retail experience and keep them coming back, retailers need a highly personalised approach.
With access to huge volumes of customer data, retailers have unprecedented levels of insight into their customers’ wants, needs, pain points and buying habits. Using this data considerately and intelligently to deliver an effective data-driven customer experience is key. This means delivering what they want, in the way they want it, at the right time. When retailers achieve this, they gain customer trust and brand loyalty that endures.
What can personalisation add to the retail customer experience?
Ultimately, businesses want their customers to feel valued. When retailers add personalised touches to their CX strategy, shoppers feel understood and build a deeper connection with a retailer. A well-executed personalised shopping experience makes customers feel like a retailer is intuitive. The retail customer experience becomes stress-free and seamless and they feel a kinship of the kind that marketing alone can only dream of creating.
In light of its power, retailers shouldn’t underestimate the impact personalisation can have on customers. However, they mustn’t ignore the fact that getting it right isn’t easy. Collecting and utilising data comes with responsibilities, while AI requires a careful approach to avoid bias and maintain transparency. Privacy concerns are valid and need constant scrutiny to allay consumer concerns and regulatory constraints.
Delivering a personalised shopping experience requires a specialist and nuanced approach to be effective.
Here, we look at some of the opportunities and challenges in more depth and examine how personalisation can enhance the retail customer experience while generating efficiencies for retailers.
Personalisation and customer experience design - areas to consider
Data collection and integration
Challenges retailers face with data collection & integration:
Collecting and using customer data isn’t always straightforward. Firstly, there are regulation and compliance considerations, with GDPR and CPPA limiting and controlling data collection. Retailers will also find that utilising data that's stored in different incompatible formats is extremely difficult while making sense of data streams that are isolated from each other, often due to the limitations of specific channels, will also bring problems
Opportunities for ‘servitising’ data:
Shoppers interact with retailers across a variety of formats, including in-store, social media, e-commerce sites and mobile apps. By collecting and applying user data effectively, retailers reduce friction in the retail journey and create a seamless experience, joining the dots for the customer.
When customer data is collected and utilised effectively, retailers can develop deeper connections and enhance trust with customers. Shoppers might receive a hyper-targeted offer that applies specifically to an item they’ve been pondering both in-store and online. It feels like magic. Meanwhile, their carts sync perfectly across their devices, removing a point of friction from the retail journey and increasing their trust in the brand.
From a wider perspective, retailers can deploy customer data to introduce dynamic pricing. This enables them to introduce offers or even reduce inventory levels or staffing during quiet periods to drive sales and reduce costs and waste.
Without clever data collection and deployment, a shopper may find that they are recommended a product on a retailer’s mobile app that they recently bought in-store. They may visit an e-commerce store and be treated like they’re a brand new customer when they’ve been buying products there for years. Or they check online whether a product is available in-store, only to be told the staff aren’t aware of their inquiry and the product’s not available.
Who’s doing this well?
Starbucks uses data from a variety of sources, including its loyalty scheme, in-store purchases and its mobile app, to introduce personalised offers to customers who sign up. According to its 2022 earnings report in the US, Starbucks enjoyed a 40% increase in revenue from members of its loyalty scheme, driven by a 150% increase in its app uptake.
AI and predictive analytics
Challenges of balancing accuracy with bias:
AI’s limitations are well-documented and are as relevant to retailers, as they are to any other business looking to rely more heavily on machine learning. Bias is a factor that needs constant consideration, making sure that assumptions aren’t made based on inaccurate biases built into LLPs. Then there’s the balance that needs to be struck with regard to explainability. Retailers will need to be transparent about their AI assets and offer accountability and visibility to maintain trust.
Opportunities for predictive insights:
The benefits AI brings to the retail journey for customers are no secret. AI-driven predictive models can dramatically alter a retailer’s ability to recommend complementary products and provide relevant and timely promotions that take a shopper’s experience to a new level.
As well as enhancing CX strategy, AI and predictive insights can aid retailers in dealing with seasonal slumps, market trends and fluctuating consumer behaviours and demands. AI-driven insights, using techniques like social media sentiment analysis, help retailers to meet demand and optimise their processes.
Who’s doing this well?
Amazon has successfully applied machine learning technology to power its recommendation engine, which has increased purchases from recommendations by 35%. The tool leverages data from sources including browsing history, hovering time and purchase patterns to offer well-informed, intuitive recommendations that help shoppers discover products that deeply resonate with them.
Hyper-personalisation
Challenges of customer scepticism and regulations:
Alongside the demand for a data-driven customer experience is growing concern about intrusive data gathering and marketing, as well as privacy concerns. Hitting the sweet spot, where a retailer can offer a hyper-personalised customer experience, without being perceived to overstep the mark, is the ongoing challenge.
Retailers can be upfront and transparent about their use of consumer data, reassuring consumers that they are only using their data to offer them the most relevant deals. Another tool that helps retailers circumvent the challenges is using zero-party data. This involves the use of data gathered through surveys and ‘quizzes’ that consumers voluntarily take part in.
Opportunities for hyper-personalisation:
Younger consumers expect a deeper relationship with the brands they use. Social media marketing has enabled brands to become part of people’s daily lives and, in turn, consumers expect to be understood as individuals and for their customer experience to reflect this deep level of consumer knowledge.
For retailers that get it right, they can deliver targeted marketing and offers that help deepen trust and engagement - driving sales and brand loyalty.
Who’s doing this well?
Sephora effectively uses zero-party data in its Beauty Insider members program, but avoids invasively tracking their customers. Their approach builds trust and allows them to offer a hyper-personalised service, where customers receive perks that they truly value: a sense of belonging, access to high-quality beauty products and exclusive discounts. Beauty Insider now accounts for over 80% of Sephora’s sales.
Measuring ROI
Challenges with attribution gaps:
The risk with personalisation campaigns is that retailers will invest heavily in tactics that underperform. This can happen when sales are incorrectly attributed to ineffective personalisation.
Opportunities to measure ROI of personalisation:
Being able to demonstrate the effectiveness of any CX strategy investment is vital. Getting this right enables retailers to focus their spending on approaches that work for their specific offering and customer base. Identifying the tactics that make the biggest impact allows retailers to double down and refine these strategies to drive sales and deepen engagement.
Effective ways to measure a personalised CX strategy include linking programs to key metrics, such as repeat visit rates or conversion rates. Identifying approaches that make the biggest impact and using A/B testing to refine a CX strategy and improve ROI.
Who’s doing this well?
A/B testing (in this case, comparing two versions of a personalisation approach to see which is the more effective) is central to Netflix’s content recommendation offer. It uses AI to trawl the keywords attributed to each program and recommend content that the viewer may not have otherwise considered - deepening their customer’s trust and engagement with the interface.
Online retailers such as ASOS have mimicked this approach with great success, using A/B testing to hone their garment recommendation tool and help consumers discover styles they never even knew they wanted.
Ready to bring personalisation into your CX strategy?
When personalisation campaigns are executed effectively, they strengthen customer relationships, build trust, and drive sales by delivering seamless, intuitive experiences. However, achieving a truly personalised shopping experience is a fine balance. Retailers must navigate challenges like data integration, AI transparency, and privacy concerns while ensuring their strategies are measurable and impactful. By leveraging customer insights responsibly and refining their approach through data-driven testing, retailers can create a personalised shopping experience that not only meets but anticipates consumer needs - transforming casual shoppers into loyal brand advocates.
Ready to transform your customer experience and build customer loyalty? Get in touch, our CX experts would love to talk.