Data Analytics in the Luxury Industry

Data Analytics in the Luxury Industry

Over the past 12 months, in confidential face-to-face, zoom, and phone conversations with the CEOs of approximately a dozen large, medium, and small luxury goods and services brands, Luxury Institute CEO Milton Pedraza heard one recurring theme: most are dissatisfied with the state of their group's or brand's data and analytics resources and capabilities.

When Pedraza mentioned to a top luxury retail CEO that his brand could now access the most descriptive and predictive digital platform (Google, Instagram, Facebook) data directly from its customers via the Advanced Personalization Xchange (APX), the CEO threw up his hands in frustration and said, "We don't have the internal expertise or tools to do anything meaningful with the data we have now, let alone richer and higher caliber customer data. I feel like we are amateurs, while everyone else is playing major league."

The CEO was wrong. Most brands in luxury, across all categories and levels, appear to also be playing at "aspirational" levels in data and analytics capabilities, at best. That sums up the findings of a recent survey conducted by Luxury Institute's Affluent Analytics Lab (AAL). This survey was conducted with luxury goods and services brand executives and their top consultants across the globe. The results indicate that data and analytics processes across most luxury enterprises are broken.

Here are some of the study highlights:

Data collection capabilities

When asked to rate their, or their client's brand's, data collection capabilities, a majority (56%) are neutral (34%), dissatisfied (20%), or very dissatisfied (2%). A scant 2% are very satisfied, while 42% are satisfied that their brand's data collection is adequate. Data collection is the one area in which participants provide the highest ratings in data capabilities. After that, internal enterprise data and analytics capabilities ratings go downhill.

Data integration capabilities

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% of responders state that this critical step has only been partially addressed, while 15% say it has not been addressed. Only 13% feel this need has been addressed adequately by the enterprise.

Data accuracy and quality for analysis

When rating whether their customer data from internal and external sources is accurate and requires no cleansing or fixing to be used for analytics, 69% of responders state that it is only partially addressed, while 21% say it is not addressed. A small group (10%) state that data accuracy and quality are fully addressed.

Data access for analysis

The ability to access data that is internally stored in one place such as a data lake or warehouse is important for the various groups within the enterprise such as 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% respond that this is only partially addressed, 28% report that it is not addressed, and a minority (18%) say it is fully addressed.

Data timeliness for analysis

Data timeliness is the act of making the data available for data analysts to conduct their critical analytics work on a timely basis. Data timeliness deeply affects the adaptability and agility levels of the enterprise. A large majority of responders (64%) feel this is only partially addressed, while 28% feel timeliness is not addressed. Only 8% of executives feel this critical capability is addressed.

Analytics culture

With respect to having cultivated a data-driven, analytics-first mind-set and brand culture in their enterprise, a full two-thirds of responders (67%) state that this is only partially addressed, while 26% feel it is not yet addressed at the company level. Only a low 8% feel their enterprise has an analytics culture.

Analytics capabilities

It is no surprise then, given the prior reported lack of an analytics culture in most enterprises, that a strong 70% of survey responders give a neutral (39%), dissatisfied (29%) or very dissatisfied (2%) rating to the analytics capabilities of the brand. A scant 2% are very satisfied while 29% are satisfied.

Analytics expertise

Most luxury brands report that they lack analytics expertise. Only a scant minority (5%) report that the brand has personnel with modern analytics training and skills such as data science, AI, and machine learning to execute their analytics. A whopping 95% report that this critical need is partially addressed (56%) or not addressed at all (39%).

Analytics tools

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

While data collection is an area where there is the highest level of satisfaction reported by luxury enterprises, still, only a minority of brands report being satisfied. Once the data is collected, however, most enterprises report 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 use data for basic and rudimentary tasks such as to measure outputs (email campaign results, total sales) versus generating high-performance inputs that accurately define and respectfully target pinpoint, high propensity audiences.

Content development is another area where data is used, but it is used in very rudimentary ways that fail to deliver personalized, sharp, compelling content and offers that resonate with well-defined customer segments. Without the right data management and analytics skills and without the right processes in place, executives tell us their luxury brands are using data and analytics to execute what amounts to mass marketing and selling in a digital format. Luxury brands are failing to innovate and create sustainable competitive advantage.

As a result of the study findings, Luxury Institute's Affluent Analytics Lab (AAL) is providing an exclusive offer to large, medium, and small luxury goods and services brands. Led by Pedraza, who created the first CRM project for Citibank and a $20B consumer goods and services conglomerate, AAL will conduct a data and analytics audit to discover and determine the current state of a brand's data collection, data management, and analytics processes. Luxury Institute's team includes top data scientists and expert legal advisors, formerly of Google, Forrester, and other top-tier institutions. Its attorneys are helping to advise on privacy and data rights laws in Washington.

Based on the findings, Luxury Institute will deliver actionable conclusions and recommendations to immediately address brand data and analytics needs and improve team processes and results. If the brand desires, the AAL team will remain as an objective advisor to the CEO or Board of Directors to help oversee the brand's data and analytics capabilities implementation progress.

Article published by icrunchdata
Image credit by Getty Images, Moment, yuanyuan yan
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