The Future of Luxury Data? A Shift From Privileged Access to Advanced Personalization

The Future of Luxury Data? A Shift From Privileged Access to Advanced Personalization

After a long, competitive decade, luxury goods and services brands, as well as their agencies and consultants have finally mastered the rules of the digital marketing game; just in time for the game to change. Pandemics inspire reality checks of major proportions. The reality ...

December 2, 2020 - By TFL

The Future of Luxury Data? A Shift From Privileged Access to Advanced Personalization

Case Documentation

The Future of Luxury Data? A Shift From Privileged Access to Advanced Personalization

After a long, competitive decade, luxury goods and services brands, as well as their agencies and consultants have finally mastered the rules of the digital marketing game; just in time for the game to change. Pandemics inspire reality checks of major proportions. The reality is that for years, luxury goods and services brands have been playing a digital marketing game that serves the best interests of digital tech platforms and their proxies while failing to observe the privacy and best economic interests of their customers, society, and shareholders.

As demonstrated by the economics of dismally low ad-click and conversion rates, especially on mobile devices, digital advertising is a broken business model. Empirical research shows that digital ads inspire clicks from a majority of customers that would have purchased anyway. Digital advertising is rife with obsolete data, mistargeted messages, accidental clicks, deceptive metrics, bias, fraud, mistrust, privacy violations, consumer frustration, and growing ad blocking. The model is hugely profitable for the intermediaries, but fails economically for the two supposed beneficiaries: luxury brands and their valued consumers.

When luxury executives in the 2030s look back at the post-pandemic 2020s, the most flawed assumption in business in the pandemic year of 2020 will have been that individual personal data would always be coerced, owned, and controlled by the ad platforms and third-party brokers, instead of those who produce it: individuals. Future luxury marketers will wonder why brands and their advisors opted for superficial, unprofitable customer relationships. Brands are stuck in the myth that the current coerced data extraction method of digital advertising is the most effective way to generate value in the digital economy. Post-pandemic, digital leaders will adopt legal, consent-based, privileged access methods that yield rightful personal data control to customers in order to empower and accelerate relevant, rich, and real-time sharing.

In the post-pandemic personal data economy, a brand’s privileged access to relevant, rich, and real-time customer data will be the major driver of economic and competitive advantage. As customers are empowered by legislation, and a rapidly growing community of ethical, innovative entrepreneurs and trusted intermediary services to take full control of their personal data, they will be selective about sharing. There will be winners and losers in each category. Smart brands need to develop the optimal customer data strategic plan and operating model now in order to take advantage of the opportunity.

For almost three years, the Luxury Institute has been researching, investing, and building critical relationships and deep expertise in the emerging personal data economy. The Institute has designed a framework for planning and executing in the new era of Advanced Personalization. Here is its 10-step process for developing a strategy to kick-start the ethical, client-centric, advanced personalization process … 

Step 1: Identify the most valuable customer data  

To start the process, a brand will assemble and empower a multi-functional team of trusted internal and external experts. Their task is to dissect the current customer journey and brainstorm the top opportunities for advanced personalization. The team must determine what privileged personal data is required, how often it is needed, and where it will reside  in order to enable the first experiments. Start very simple and evolve. Some required personal data will be historical, some will be real-time. Depending on the category, required data can include historical category purchase and behavioral data, and/or real-time browser and location data.
Step 2: Install the legal and ethical framework for data sharing

Once the opportunities and the relevant data have been identified, a legal team must work with the customer-facing team to establish the legal framework.  It must meet the optimal legal requirements, and also go above and beyond the law to protect the consent, flow and storage of personal data and insights. Cybersecurity, privacy and seamless data access consent protocols must be put in place. Because it is a legal process, some brands get intimidated, but being legal, ethical, and serving the interests of customers is not complicated. It is super-simple for trustworthy brands.

Step 3: Establish the technology framework for data sharing

The most important requirements in the technology framework are the data sourcing, frequency of sourcing, data transfer facilitation, and data/insight storage and access technologies. The right data may need to flow by consent from the customer’s device to the servers of the brand. Or, the data may remain in the customer’s devices and be accessed by consent through on-device AI/edge computing, which is secure and preserves privacy, saves on costs, and will likely become the norm in AI. Both methods may be appropriate at different times. Surprisingly for many, all the required technology to execute this process already exists. It can be configured easily to create a seamless process.

Step 4: Power up the analytical AI engine 

The new analytical process is a continuous virtuous cycle of gathering and structuring data, using simple machine learning techniques plus expert human judgment, to identify actionable, timely insights and value-generating opportunities. The cost of using algorithms to make millions of rapid predictions and recommendations in real time at any company is plummeting to near zero. AI is now a low-cost prediction engine. That is why it is being applied everywhere. Besides talent, it is critical to remember that the right customer data is the most precious resource in the AI process.

Step 5: Turbo-charge the continuous customer value innovation platform  

The customer value innovation platform for each brand is comprised of asking the most powerful questions followed by using the right training data, generating insights, recommendations, decisions, and using the critical feedback data to adapt and innovate. The brand uses these to engage the customer in rapid testing and learning to determine what works for the customer, and what does not, quickly. Because the brand now has privileged access to relevant, rich, and real-time data from so many customers, it can use the generalized cohort learnings and insights to enhance the individual experience of each with far greater precision and accuracy than ever before. This innovation platform scales fast and delivers mutual cost efficiencies and value over time.

Step 6:  Implement a new customer communication strategy

Actionable insights and recommendations developed during the analytical phase must be humanized and delivered with the essential emotional intelligence pillars of expertise, deep empathy, trustworthiness and generosity. These make associates and customers feel cared for, and special. The brand team and each customer determine together what part of the journey is best automated, and what part of the journey is best delivered with rich human engagement. Customers can choose their favorite journey elements, and exceptions, in real-time. This is an omni-personal experience that transcends channels.

Step 7: Pilot testing and learning experiments

Once steps 1-6 merge into a smooth workflow, usually within 3-4 months, the brand can begin to experiment live. The brand can invite real customers to participate, and compensate and reward them, or select internal team members who fit the roles to be test participants. This allows the brand to start executing the steps of the process and identify experiences that need to be enhanced. The feedback, adjustments, and course-corrections will be valuable in optimizing the scale-up process.

Step 8: Reorganize teams for advanced personalization

One of the most important learning opportunities that brands will enable through testing the advanced personalization process is how to organize team members to collaborate to best serve the customer. Departmental silos may no longer work. Customer segment leaders may emerge to refocus the brand from products and channels into a customer-centric business model. The agility of the teams to stay open to learning and innovation and remain agile in the moment must be established as the norm.

Step 9: Scale up and measure what works 

As soon as the brand has established that customers are receiving highly personalized value, the brand can scale up advanced personalization in an efficient and effective evolutionary process throughout the enterprise. This is an endless journey of rich, open dialogue and continuous learning, continuous improvement, and rapid-fire innovation. Key metrics such as customer acquisition, retention, and referrals will be used, but those are merely outcomes. Entirely new input metrics will emerge as the brand abandons irrelevant Industrial Age measures of performance. For example, measures of customer trust, levels of access to the most relevant data,  algorithm prediction effectiveness and individual personalization accuracy will be used. New individualized measures of customer relationship length, depth and loyalty will replace the surface-level customer satisfaction metrics of today.

Step 10: Innovate an iterative test

The most exciting aspect of ethical personal data sharing is that the acceleration of the flow of quality and quantity of the right personal data uniquely delivers economies of scale (cost efficiencies), and economies of scope (higher share of customer spend). It can unleash unimagined levels of prediction and creativity with major cost reductions and profitability. To get these benefits, however, brands must create a culture of socially responsible, fiduciary purpose, where they transparently protect, enhance, and promote the best interests of  customers, associates and society continuously. It’s an ethical journey of innovation.

Surveillance capitalism is so 2010s. It is not just untrustworthy, it is economically unworthy. In America and Europe, 2020s consumers will be empowered by legislation and rapid innovation to assert full control over their individual data. Ethical brands will embrace this because it dramatically lowers their cost and risk of holding high quality personal data while reducing waste and improving profit margins. There will be exponentially more, and richer personal data, that each human will produce post-pandemic. They will happily share this precious resource when they are guaranteed the best cybersecurity, strict privacy, and clear, relevant recommendations and rewards.

The more their complex lives require them to make high-risk, high-value, high-investment, and high-emotion purchases and decisions, the more luxury consumers will be open to entrusting privileged access to their personal data to brands that offer personalized best-in-class solutions.

Post-pandemic, luxury brands will need new ways to vastly improve performance. These advanced personalization techniques dramatically reduce the cost of acquiring relevant, rich, and real-time data, leverage the plummeting costs and ease of use of AI, reduce customer acquisition and retention costs, reduce supply chain and inventory costs, and increase conversion, retention, and referral rates. The ethical and economic use cases are compelling.

The only question now is which brand leaders will step up to meet the changes and embrace the low-cost, high-performance breakthrough journey from privileged access to advanced personalization?

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