How Are Luxury Brands Using Data in the Wake of the Pandemic?



No longer operating the way that they did in a pre-COVID world, fashion and luxury brands have been forced to face the reality that they cannot stay ahead without increasing their data capabilities. “Data will be the key to unlocking the insights needed to adapt to change and to reengage customers in the coming months and years,” McKinsey analysts stated in a note last year, reflecting on the impact of the pandemic on fashion and luxury brands. However, they asserted that the pandemic also “exposed a major shortfall in data gathering and analysis across much of the [fashion and luxury] industry,” meaning that “the sooner fashion and luxury companies learn to harness the power of data, the better.”

The notion that fashion and luxury brands have access to a wealth of structured data on their customers that can be collected and processed to drive sales, but that they are “often under-utilizing this data, and, furthermore, ignoring the vast reams of unstructured data (such as consumer comments on social media, affluent influencers’ photo feeds on Instagram, and engagements across multi-channel customer journeys) that can be mined to glean invaluable insight into customer lifestyles, shopping preferences, and purchase behaviors,” is enduring in a post-pandemic world.

A survey conducted by the Luxury Institute’s Affluent Analytics Lab reveals while some brands may have some out of the pandemic stronger thanks to their use of big data, most brands in luxury, across all categories and levels, are not there yet. Most brands appear to be playing at “aspirational” levels when it comes to their data and analytics capabilities, the Affluent Analytics Lab found in connection with a survey of executive-level figures from an array of global luxury goods and services brands, as well as their top consultants, this spring.

The results indicate that data and analytics processes across most luxury enterprises are “broken,” the Affluent Analytics Lab states, which points to the following as some of the key highlights from its survey …

Data Collection & Integration Capabilities
When asked to rate their – or their client’s brand’s – data collection capabilities, a majority of respondents (56 percent) are neutral (34 percent), dissatisfied (20 percent), or very dissatisfied (2 percent). A scant 2 percent said they are very satisfied, while 42 percent are satisfied that their brand’s data collection is adequate. While data collection is the one area in which participants provide the highest ratings in data capabilities, beyond that, internal enterprise data and analytics capabilities ratings go downhill.

When asked to rate whether data collected from various internal (e.g., transaction and website navigation data) and external sources (e.g., vendor third party data) has been integrated into one seamless view of the customer, 72 percent of survey responders stated this spring that this critical step has only been “partially addressed,” while 15 percent stated that it has not been addressed. Only 13 percent said that they feel that this need has been addressed adequately by the enterprise.

Data Access for Analysis
The ability to access data that is internally stored in one place is important for the various groups within the enterprise – including (but not limited to) logistics, finance, marketing, and sales – to be able to use the data readily. This is also known as data democratization within an enterprise. On this important process, 54 percent of survey participants responded that this is only partially addressed by their employer-company, 28 percent reported that it is not addressed, and a minority (18 percent) said that it is fully addressed.

Analytics Culture & Capabilities
With respect to having cultivated a data-driven, analytics-first mind-set and brand culture within their enterprise, a full two-thirds of responders (67 percent) state that this is only “partially addressed,” while 26 percent feel it is not yet addressed at the company level. Only a low 8 percent feel their enterprise has an analytics culture. With the foregoing in mind, it is no surprise then, given the prior reported lack of an analytics culture in most enterprises, that a strong 70 percent of survey responders gave a neutral (39 percent), dissatisfied (29 percent) or very dissatisfied (2 percent) rating to the analytics capabilities of the brand. A scant 2 percent said that they are very satisfied – while 29 percent said that they are dissatisfied.

Analytics Tools & Expertise
Most luxury brands report that they lack analytics expertise. Only a scant minority (5 percent) reported that the brand has personnel with modern analytics training and skills, such as data science, AI, and machine learning, to execute their analytics. (In other words, companies are lacking key data architect, data scientist, and data steward professionals to help “ensure that core decision makers, such as designers, merchandising teams, and e-commerce teams, can translate data and analytics to fit business needs,” per McKinsey.) A whopping 95 percent reported that this critical need is partially addressed (56 percent) by the company or not addressed at all (39 percent).

And only 8 percent of luxury brands executives revealed that they use modern analytics tools, such as data visualization and powerful, self-service business intelligence tools, to conduct customer analytics. An overwhelming 92 percent of the responders stated that the need is not fully addressed (67 percent) or not addressed at all (25 percent).

Ultimately, data collection is an area where there is the highest level of satisfaction reported by luxury enterprises, according to the Luxury Institute. Yet, only a minority of brands reported being satisfied. Once the data is collected, most enterprises reported “systemic failures across all elements of the data management and data analytics processes and capabilities.” Qualitative responses as to how the data is used indicate that luxury brands primarily use data for “basic and rudimentary tasks, such as to measure outputs (email campaign results, total sales, etc.)” as opposed to generating high-performance inputs that “accurately define and respectfully target pinpoint, high propensity audiences.”



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