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The Future of Personalised Ecommerce

Machine Learning Customer Experience Search Best Practices Product Discovery
Posted on:
November 5, 2024
Author:
Constructor Team
future of personalised ecommerce
Table of Contents:
future of personalised ecommerce

This post was guest written by Street Agency and is about redefining shopping experiences to meet evolving consumer expectations. 

As consumer expectations shift rapidly, retailers face growing pressure to provide outstanding online shopping experiences, especially in search functionality and product discovery. According to a recent survey report by Constructor, 42% of shoppers rate their current search experiences as average or below, revealing a substantial gap between shopper expectations and what retailers deliver today. This divide presents a significant opportunity for retailers to innovate and secure a competitive edge.

What steps are you taking to differentiate your brand, stay relevant, and meet the needs of tomorrow's shoppers?

soho-house-breakfast@2x
On the morning of October 23, 25 retail brand leaders and innovators convened at Soho House in Soho for a breakfast gathering. They explored practical strategies and insights from Constructor's 'State of Ecommerce Search and Product Discovery 2024’ report, focusing on how to develop personalised, customer-centric experiences that not only fulfill but surpass shopper expectations, ultimately boosting satisfaction, loyalty, and revenue growth.

The panel was moderated by Marlies Riepl, Global Partner Marketing Lead at commercetools. Joining her were:

  • Fabrice Khullar, Director of Product, UX, and Optimisation at Boots
  • Nate Roy, Director of Brand & Content at Constructor
  • Vinny Masters, Composable Commerce expert and Account Executive at commercetools

The following summarises the morning's key discussion points:

The Role of Data in Personalising Customer Experiences

Data is increasingly central to crafting personalised customer journeys, and businesses are leaning more on customer data to deliver tailored experiences that meet consumer expectations. But personalisation isn't simply about collecting data. It's about having the right architecture to make data actionable. This is particularly important as businesses move toward composable commerce — a flexible, modular approach to digital infrastructure.

Composable Commerce: Enabling Data-Driven Personalisation at Scale

Companies like John Lewis and Clarks have successfully transitioned to a composable architecture, enabling frequent updates and releases, as well as data-driven insights at scale. With such infrastructure in place, John Lewis reported a significant uptick in feature releases, while Clarks saw improved conversion rates — though not purely from personalisation, these outcomes underscore the necessity of a solid digital foundation for data-driven strategies.

One notable case study was Primark, which uses a composable setup to implement 'Click & Collect' and other in-store engagements. As a retailer historically focused on in-store sales, Primark wanted to collect data from online interactions to drive customers into physical stores, effectively creating a hybrid approach to customer engagement.

The overarching message is that effective personalisation relies on both data insights and a robust, adaptable digital foundation. Only with a flexible infrastructure can businesses fully leverage data to drive meaningful, personalised experiences across channels.

AI and Machine Learning: Transforming Personalisation Through Advanced Technologies

Many top retailers have been using AI and machine learning tools long before the recent surge in AI interest. However, despite years of advancement, many consumers still feel their online shopping experiences lack true personalisation. This raises the question of whether current AI models truly understand and respond to human complexity.

AI agents could be a breakthrough in personalisation. These agents could 'live' on customers' personal devices, tracking their preferences, behaviours, and habits across platforms, making it possible to deliver highly accurate, personalised recommendations regardless of which device or platform a customer uses. This approach would elevate personalisation beyond current capabilities, allowing AI to anticipate shifts in a customer's preferences over time and provide an experience that feels uniquely attuned to the individual.

As an example, Salesforce's recent advancements in conversational AI, where AI agents have been tested in customer service roles, have been delivering responses nearly indistinguishable from human interactions. This 'leapfrogging' effect — where AI rapidly improves — could bring about a profound shift in customer expectations and experiences.

Expanding the Definition of Personalisation: Product Creation and Journey Innovation

Personalisation is not just about improving existing customer journeys but also influencing product innovation and discovery. A brand can now use customer data not only for recommending products, but also to inform new product combinations and designs. This customer-driven approach is shifting traditional product development, whereby consumer insights are being used to guide future product lines.

This approach represents a more nuanced view of personalisation, where companies are increasingly looking to predict and meet customer needs before they even arise. The panel agreed that companies employing such proactive personalisation strategies would likely see a positive response from customers, as they're offering not only recommendations but shaping the product journey itself to align with customer preferences.

Cross-Industry Expectations and the Competitive Benchmark for Personalisation

One of the biggest challenges facing retailers today is meeting consumer expectations shaped by the best experiences across industries. When customers interact with a retail site, they aren't only comparing it to other retailers but also to their bank's website, their social media platforms, and even work software they use every day.

This cross-industry comparison has raised the bar for personalisation in retail. As more companies invest in AI and personalisation technologies, the top 1% of user experiences are continually improving. Again, this 'leapfrog effect' means that companies at the forefront raise the bar while compelling others to catch up or risk falling behind. One area where this is particularly evident is search functionality, with 61% of UK shoppers reporting dissatisfaction with search experiences in 2023. The takeaway here is that personalisation doesn't operate in isolation. It's part of a broader ecosystem of customer experiences.

Privacy and Personalisation: Navigating Data Collection and Regulatory Compliance

As data regulations such as GDPR evolve, companies are required to handle customer data more responsibly. In recognition of this, the discussion moved towards the concept of 'zero-party data,' where consumers voluntarily provide personal information. This method — often implemented through product finder quizzes or preference surveys — enables retailers to gather insights directly from customers with their consent, setting clear expectations for how the data will be used.

However, gathering zero-party data presents a unique challenge in that it requires an infrastructure that can respond in real-time. For instance, if a customer completes a quiz to receive personalised recommendations, they expect those recommendations to be relevant. If the system fails to tailor suggestions accurately, customer trust erodes, making them less likely to engage in future data-sharing activities.

Cookie-less technology is another approach to handling privacy in personalisation. With Google's phased approach toward a cookie-less future, the focus is shifting to technologies that maintain privacy while delivering tailored experiences. Cookie-less personalisation methods allow businesses to offer tailored experiences without needing to store personally identifiable information, ensuring compliance with privacy regulations without compromising on personalisation.

Infrastructure and Future-Proofing: Preparing for Emerging Channels and Devices

All the panellists emphasised that personalisation efforts will need to evolve to encompass emerging digital channels, such as VR and in-car commerce. While web-based personalisation remains relevant, businesses must be prepared for a future where customer interactions happen across a broader array of devices and environments. Commercetools is exploring VR applications, envisioning how commerce might look as immersive digital environments gain popularity.

For personalisation to be effective in these new channels, businesses will require a composable architecture that can integrate with any platform or device, from in-store kiosks to wearable tech. Preparing for this multi-channel future is essential. Moving into the near future, companies with flexible, modular technology stacks will have a significant advantage, as they can more easily adapt to new channels and incorporate the diverse data these channels provide to offer consistent personalisation.

 

#

Commercetools is a leading provider of cloud-based, headless commerce solutions. Founded in 2013, it enables businesses to create customised digital shopping experiences through its flexible and scalable platform. By leveraging microservices architecture, commercetools empowers brands to innovate rapidly, integrate seamlessly with other systems, and deliver personalised customer journeys across multiple channels.

Boots is a prominent UK-based pharmacy and health and beauty retailer, established in 1849. It offers a wide range of products, including prescription medications, skincare, cosmetics, and personal care items. With a strong presence in both physical stores and online, Boots is committed to health and wellness, providing essential services like vaccinations and health consultations.

Constructor is a leading search and product discovery platform designed for ecommerce businesses. It enhances online shopping experiences by delivering personalised search results and recommendations using advanced AI and machine learning technologies. By optimising product discovery, Constructor helps retailers boost conversion rates and customer engagement, ultimately driving sales growth.

 

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