Consumers today are overwhelmed by choice across products and services.

Seventy-four percent of customers report walking away from online purchases due to the sheer volume of choice, according to BoF & McKinsey’s The State of Fashion 2025 report. This behaviour is felt by global brands and retailers experiencing a decline in engagement and conversion rates in an already highly competitive market.

Discretionary spend categories like fashion and luxury are further impacted by consumers’ increasingly cost-conscious practices, driven by ongoing price hikes in the sector and across essential products and services like food and energy. Individuals across wealth brackets are trading down in search of better value for money — 64 percent of US shoppers traded down in the third quarter of 2024, according to The State of Fashion 2025 report.

As a result, the need to cultivate and leverage first-party data to better understand and serve customers is proving a critical hygiene factor for brands and retailers looking to capture consumer attention in 2025.

Last year, BoF’s Marc Bain explored this opportunity in a case study “How to Turn Data Into Meaningful Customer Connections,” with a spotlight on New York-based Tapestry — parent company of Coach and Kate Spade — which has made consumer data central to its strategy back in 2020. Combining advanced analytics with immersive research, Tapestry’s approach has fuelled growth at Coach in particular, driving 6 percent growth year-after-year and driving 2.4 million new North American customers by early 2024.

This data-backed approach is fuelling retail strategies of global conglomerates, retailers and independent brands alike — with physical stores primed for data-led innovation to deepen relationships with consumers. 60 percent of shoppers cite poorly trained or prepared staff as a cause of discontent with store experiences.

The State of Fashion 2025 report highlights how data-driven insights and tools boost engagement, from CRM-enabled technologies providing associates with real-time information about a customer they are interacting with, to tracking movement about a store.

For example, Canadian retailer Aritzia bases its ​store scheduling decisions on footfall data to improve the shopper-to-associate ratio and sales productivity. American retailer Levi Strauss & Co. has partnered with Google Cloud to analyse data across product popularity and weather patterns, tailoring their inventory to meet consumer needs. Kering’s clienteling app Luce provides tailored product recommendations for store associates to use in interactions with customers. The app has boosted the average order value by between 15 and 20 percent.

But data alone isn’t enough — a 2023 Salesforce survey found a third of 10,000 business leaders struggle to generate actionable insights. True success, it seems, comes from integrating data learning across product development, merchandising and marketing, as well as omnichannel retail strategies.

Looking ahead, artificial intelligence is naturally poised to take these data capabilities even further. As brands refine their ability to harness both internal and external data sets, AI will enable much more sophisticated consumer insights, predictive as well as generative analytic capabilities — allowing for hyper-personalised experiences at an even greater scale.

Indeed, the brands that invest in this type of technology — and hone their approach to data — will be well-positioned to stay ahead of the curve.

In the second edition of this four-part series, in partnership with GGP, we unlock some key insights and examples from BoF’s proprietary case study “How to Turn Data Into Meaningful Customer Connections.” We share learnings for brands looking to harness data as a tool for tracking customer behaviour and building long-term success.

01 — Adapting Quickly Yet Strategically

To be successful in any retail endeavour, data is critical to continued success and growth — and increased capabilities in data capture and integration can provide a decidedly competitive edge for brands and businesses.

Those that successfully integrate data into their value chain and retail operations — from customer movement about the store to factors such as store opening decisions — will reap the most reward. For instance, McKinsey & Co. estimates that optimised stock and store planning can increase sales by 10 percent, inventory costs can be reduced by up to 15 percent, and personalised e-commerce experiences can drive digital sales growth as high as 50 percent.

Data also transforms marketing strategies. For years, brands have relied on platforms like Google and Meta to reach shoppers through digital advertising, but these channels have become less effective. Performance marketing returns have dwindled due to intensifying competition and digital privacy shifts that make consumer tracking more difficult. In 2023, 33 percent of consumers reported always blocking tracking data, up from 20 percent in 2021, according to consultancy Gartner.

For brands looking to upgrade their data capabilities, chasing the latest technology can be tempting. However, experts emphasise the importance of establishing a strong data foundation first — both first-party and third-party data play critical roles in a brand’s data strategy. Ensuring information is centralised, harmonised and actionable. Without that groundwork, even the most advanced tools won’t deliver the meaningful results brands are seeking.

02 — Implementing Sophisticated Data Collection

The level of sophistication in data collection has advanced significantly. Just a few years ago, brands primarily captured basic demographic details and transaction histories. Now, they seek to measure emotional connections with their brand, understand how consumers dress for different occasions and analyse broader lifestyle patterns, for example.

As BoF previously reported, the foundation of Tapestry’s data capabilities is driven by in-depth consumer research. The company wants to understand its consumers’ lives beyond their purchase history — what their dreams are, how they use their devices, and how they approach fashion more broadly.

The company collects all the standard information on transactions, demographics and online interactions. But it also conducts surveys, tracks online searches and social conversations, and interviews both customers and non-customers — even going as far as examining their closets and accompanying them on shopping trips.

The goal is to anchor its strategies in consumers’ needs and desires, turning numbers into deeper insights. Quantitative data reveals what consumers do — qualitative data explains why they do it. This information also allows Tapestry to segment its consumers in more sophisticated ways beyond age, gender or product category — an increasingly valuable advantage as traditional mass marketing loses effectiveness.

Indeed, as brands seek to rethink customer segmentation, they are moving away from age-defined customer segments and instead leveraging data to identify the values and preferences that unite customers across age groups. This insight can be used to inform marketing and communication strategies. For luxury brands, it can also enhance clienteling, helping to tailor interactions with high-income VIP consumers who drive a disproportionate share of sales.

03 — Turning Data into Actionable Insights

When it comes to making more informed decisions, the case study calls out Tapestry’s approach to “data fabric” — a system which transforms raw and scattered data into real, tangible insights. It organises, analyses and presents the information in a way that is most digestible for all teams across the company.

The company incorporates AI and data science into this process, enabling different teams to access and manipulate the data in various formats. This system pulls together technologies from third-party providers like Snowflake, a cloud-based platform that helps make all of this possible.

As AI adoption accelerates, brands that leverage it effectively can enhance product assortment planning, optimise markdown strategies and unlock new opportunities for personalised customer service. However, all of these require strong customer data input to drive value for different teams across the business.

A standout example of how this data-driven approach has led to success is the Coach Tabby bag — a favourite among Gen-Z consumers. Their consumer research, including shop-alongs to Coach stores, revealed that while Gen-Z shoppers weren’t fully convinced by the Coach brand as a whole, they were drawn to the Tabby. Following this insight, Coach briefed its design team and began releasing new iterations of the Tabby, offering a range of sizes and introducing more affordable versions to appeal to a broader range of customers.

Searches for the Tabby in the US surged by 368 percent in 2022, according to data from Trendalytics, a retail intelligence platform. Global engagement on platforms like Instagram, TikTok, Facebook, and Pinterest spiked with each new campaign, with the biggest surge occurring in April 2023 during a pop-up tour and a campaign featuring Jennifer Lopez.

04 — Key Takeaways

1. Data should not be viewed as mere numbers in a spreadsheet, but all information needed for decision-making. Transaction records and demographics are valuable, but they can become even more so when they are combined with insights into consumer emotions and behaviour, especially within the fashion industry, where emotional connections have been proven to drive brand loyalty.

2. The best place to begin isn’t with the data at all, but rather to consider the problems that need solving. Then determine the data that would help and how to obtain it. That approach ensures the data is working in service of the business and not the other way around. It also has the advantage of keeping data projects manageable, both in terms of scope and cost.

3. Companies should make small adjustments before scaling. If it works, the value they’re getting out of it can help fund further changes, and they must integrate ways of gleaning data-learnings across business functions. If analytics remain confined to one particular team, their impact is limited.

4. Maximising consumer data is an ongoing process and will request continuous testing and iterations. Going forward, companies must regularly evaluate how to enhance their capabilities and refine their approach.

Stay tuned for the upcoming articles in this series and check out www.ggp.com for additional retail insights.

This is a sponsored feature paid for by GGP as part of a BoF partnership.