Interviews, insight & analysis on digital media & marketing

Hyper-Personalised Customer Experiences: Key Strategies for Successful Retailers

Data – specifically the right data collected in the right way – offers retailers the power to create a true 360-degree buying persona of their customers. Rafael Alonso Clarke, Business Development Manager and Retail Lead at Keepler Data Tech,  a software company providing retailers with real-time data visibility for faster decision-making,  explores how companies can use this insight to move beyond simple product recommendations and into the realms of highly personalised shopping experiences.

The rise of ‘The Right Now Economy’ – characterised by instant access, on-demand services, and immediate gratification – is reshaping how consumers interact with businesses. Heavily influenced by the likes of Amazon and other ecommerce giants, not only do we crave speed, convenience and up-to-date product details, we also expect businesses to truly understand us. And with such an abundance of choice available – particularly in the world of online shopping – it’s no wonder demand for relevant recommendations and customised offerings is through the roof. 

However, to offer an exceptionally personalised shopping experience and facilitate swift and accurate transactions requires real- or near-real-time data, and lots of it. With data essentially shaping the customer experience, ecommerce operators are using reels of it to improve their offerings, but many are missing a trick when it comes to collecting the right data in the right way. 

For those retailers focusing on top-level demographic consumer data – age, gender, geographic location – they’re simply unable to offer the kind of personalised shopping experience today’s digitally-savvy consumers expect from brands.

This outdated strategy fails on two levels:

  1. It fails to truly understand the customer – from the kind of “fit” a consumer favours in clothes, to whether they are a bargain shopper, or if they like to make purchases on certain days of the week, as just a few examples.
  2. Without collecting, understanding, and implementing data focused on an individual, an unsatisfactory customer experience often occurs and companies miss the opportunity to effectively market other products or experiences. 

‘Why should I buy from you?’

Equipped with the mindset of ‘Why should I buy from you?’, consumers don’t just expect personalisation, but are increasingly willing to provide brands with more personal information when they perceive a greater value in return. 

Take US-based Stitch Fix, for example, an online personal styling service that leverages recommendation algorithms and data science to curate clothing items based on customers’ size, budget, and style preferences.

Stitch Fix asks customers to complete a comprehensive 20-minute survey during the onboarding process. While the length of the survey may seem significant, customers understand that investing time sharing their style preferences, fit requirements, and even personal anecdotes, Stitch Fix can deliver a personalised and curated wardrobe directly to their doorstep.

The perceived value here is twofold. Firstly, they benefit from a more efficient and convenient shopping experience, saving time and effort, and secondly, customers appreciate the personal touch and sense of exclusivity that comes from receiving items tailored to their specific style. 

With Stitch Fix, alongside other similar business models, having now established a presence in the UK, it’s clear UK consumers are in line with other countries when it comes to data-driven personalisation. 

A recent eConsultancy report (2022 Digital Consumer Trends Index: Consumer Attitudes and Trends in Personalisation, Privacy, Messaging, Advertising and Brand Loyalty) revealed that in the value exchange economy, UK consumers are rewarding brands that make hyper personalisation a priority, with more than half saying they will trade personal and preference data to feel part of a brand’s community. 

Simultaneously, there has been a substantial 60% rise in U.K. consumers experiencing frustration with brands that fail to acknowledge their individual desires and needs through personalisation efforts.

360-degree customer vision

So what are the challenges retail businesses face when it comes to mining data effectively and how can they overcome these to enable the delivery of true personalisation?

Unlocking data’s potential lies in how it is managed, and this is where we are seeing challenges arise. The volume of data and dedicated personnel has grown exponentially, so data access and processing are hindered. Meanwhile, data silos and legacy platforms mean that analytics are often drawn from out-of-date or incomplete datasets that fail to take into consideration many of the external factors that could help them protect profitability by making better-informed decisions.

To overcome this, retailers must establish a 360-degree customer vision, creating a single customer view across channels. This aggregated dataset identifies customer behaviour trends, enabling personalised recommendations, boosting conversion rates, and reducing costs.

This data exploitation also enables many other useful analytics, such as demand forecasting. Demand forecasting can be applied in different areas of the business, such as improving the customer experience by increasing the accuracy of deliveries, or making operational decisions such as the number of employees needed per store or distribution centres at different times of the year. At the same time, these operational decisions can be automated through appropriate models that take into account demand forecasting. 

With various data sources, such as point of sale, social channels, ERP systems, and customer reward programmes, providing valuable insights, it’s key to remember that regular data updates and model refinement are crucial for effective outcomes. High-quality, easily accessible data and a robust environment foster agile experimentation, testing, and deployment, while inadequate data access and weak environments lead to slow development and inferior results.

Emphasising better data governance, proper architecture design, cloud services, and advanced approaches like MLOps is essential. 

With consumers clearly willing to pay more for a richer customer experience, there is a real opportunity here for retailers to leverage data for lasting growth and future-proofing. Those that miss that or choose to ignore it, may risk losing customers to brands happy to board the hyper-personalised retail train – especially amid a volatile retail economy.