Big Data’s Role in the Challenge of Maintaining Luxury Brands: Exclusivity vs Democratization

Download the pdf here

March 9, 2020                                                                 Author:  Celia Banks, Ph.D. | CEO, I-Meta Inc

Abstract

Big Data is an integral aspect of predictive analytics for luxury retail.  The complexity of social media presents an abundance of feedback channels that make real time 360 customer feedback challenging.  Added to the challenge of real-time feeds is the distortion that reselling has created for luxury brands.  It is difficult to discern whether a customer’s feedback relates to the actual brand quality or to the reseller experience.  Also, once an item is sold, luxury manufacturers do not benefit from any ensuing resells.  With the luxury reseller market attracting sustainability conscious shoppers and a broader range of shoppers who love luxury brand names/quality but cannot afford the high price tag, it will be increasingly challenging for luxury manufacturers to sustain their exclusivity.

Introduction

Luxury brands are synonymous with premium and quality.  These brands enjoy a high value for their exceptional quality and aesthetic appeal.  From the days in the 1800s when Thierry Hermès was called upon to make saddles, bridles and leather riding gear for royal horse carriages, and also in the 1800s when 16-year old Louis Vuitton became a trunkmaster and  designed sturdy yet attractive luggage pieces for the wealthy traveler, luxury brand manufacturers have done a tremendous job to separate their perceived and intrinsic values from competitors.  It is not surprising that year-after-year, the same luxury brands have ranked as the top market influencers. With the advent of social media, retail in general has been able to closely connect with voice of customer.  There are moments that this has been good and moments where it has been not so good.  The real-time feeds from around the world have given good press and bad press.   When it is good press, a brand benefits with surges in product demand.  When it is bad press, brands not only lose market share, but damage to reputation can prolong the loss recovery.  Managing social media trolling is important to luxury manufacturers seeking to maintain their brand exclusivity.   The scatter plot below illustrates an example of a positive tweet data point.

Positive Tweet Data Point

The Meta Data Experience

Amassing customer data from a myriad of sources provides rich insight for predictive analytics.  With an eye on privacy, customer data must not share personal elements like name, social security number, payment information, etc.  As countries instill strict guidelines on data privacy, meta data will be employed more to allow for data to be freely shared.  Meta data is essentially data about a customer that is stripped of personal elements.  The Sankey diagram below is an example of meta data about global Spice Chip membership demographics.

Sankey Diagram

Big Data meta data has massive insight potential for customer analytics.  A lot can be gathered about a brand that can shape future buying outcomes.  An example of this is seen in the correlation and predictive plots below.  A correlation is indicated between income and type of Mercedes Maybach® preference.  Then, a predictive equation is identified to predict preference of type of Mercedes Maybach® of future customers within income levels.

Correlation and Predictive Plotting

Through the use of Big Data in analysis, we can acquire data points from real-time data, processing current data and historical data in business operations.  The ability to accomplish successful analysis of the data points in a continuous learning cycle is what is coined as Continuous Intelligence, and introduces machine learning and artificial intelligence methods in predictive analyses. With automation taking such a prominent role in analytics, luxury retail manufacturers should be encouraged to expand their data sources to include not only data points about their respective products but also those of equal competitors.  Doing so will allow comparative analyses across luxury brands that can lead to product development partnerships like the Hermes® Apple® Watch.

Brand Democratization and Exclusivity

Luxury retail manufactures have defined their respective brands on high prices and the premises of scarcity and exclusivity.  To achieve this aim, a select customer segment is targeted that can afford higher than normal prices for products of high quality.  This customer segment is exploited on the basis of social status and conspicuous consumption.   By limiting the production of certain high-priced goods, this customer segment will accept delayed gratification as an assumption of scarcity.  The exclusivity model has proven successful with the Baby Boomer generation, and luxury retail has grown to a multi-billion dollar industry.

While there is no clear relationship between income and democratization, generations beyond the Baby Boomers have redefined their consumption habits.  Gen X and Millennials emphasize sustainability and are willing to acquire personal goods through consignment channels.  The emphasis is true across income strata.   To attract these consumer groups, luxury retail manufacturers will need to refine their exclusivity approach or offer something in the alternative.  As example, luxury car companies have expanded their lines to include affordable offerings like the Mercedes® A class.  Recently, Lambroghini® released an SUV version after long maintaining its signature line of luxury sports cars.  Meanwhile, Ferrari® has remained true to its brand and stayed in its lane of luxury sports cars.

In the luxury apparel and accessories arena, brand democratization has led to a proliferation of consignment resellers of high-end luxury goods.  This market has successfully bit into the luxury retail manufacturing profits by selling salvaged high-end goods at lower price points due to their consignment status.  The consignment reseller channel also provides customers who are willing to pay high prices the ability to acquire vintage luxury items like older models of Hermès® Birkin bags.   More and more, luxury retail manufacturers will need to step up their game to continually refresh their branding and product offerings that cater to their Baby Boomer stalwart customer base and bring in Gen X and Millennials.

It remains to be seen how far luxury retail manufacturers will go to acquire loyal Gen X and Millennial customers.  Regardless of the decision to acquire these customers, luxury retail manufacturers will need to monitor brand sentiment from wherever it comes.  Social media has given rise to consumers sharing their opinions in real time.  Positive comments are helpful to promoting brands while negative comments can sway customers away.  Customer loyalty is constantly challenged by momentary ticks and tweets.  Assessing the impact of sentiment and discerning if the sentiment is directed toward the brand or toward the experience with acquiring it is necessary for luxury retail manufacturers to face the challenge to brand exclusivity that consignment reselling poses.

Sentiment Analysis and the Taguchi Loss Function

The Loss Function is part of a methodology to improve quality and reduce costs developed by Genichi Taguchi, a Japanese engineer who studied industrial signal-to-noise ratio experiments.  The Loss Function is a quadratic equation that when plotted produces a hyperbola.  The model below illustrates the concept of how an increase in variation away from specification limits (represented by lower or L.S.L. and upper or U.S.L., respectively) will exponentially increase customer dissatisfaction. 

Taguchi Loss Function

Applying the Loss Function to our discussion about brand democratization and exclusivity involves analyzing emotionality in texts by measuring sentiment and intensity.  Also needed is to discern where the sentiment is directed – on the brand or on the purchase experience – measured through adding the variable of purchase location.  For emotionality in texts, we could assign weighted values to a spectrum of intensity on a scale of negative to positive.  For our Loss Function, the target value would be a positive tweet about the brand having purchase experience with the manufacturer.  The outer or specification limits would be negative tweets (L.S.L.) or positive tweets having purchase experience through alternative channels like consignment resellers (U.S.L.).  So, any tweet we encounter that falls in the upper and lower specification regions will be deemed as a contributor to the loss of customer that the manufacturer will experience.  The hyperbola of a loss example is shown below.

Brand Loss Function

Conclusion

This discussion has presented the challenge of maintaining luxury brand exclusivity as new generations change views about their shopping experiences.  The Baby Boomer generation was a loyal following to luxury brands and this contributed to the multi-billion dollar establishment luxury retail has become.  With differing views about sustainability and cause-related buying habits, Gen X and Millennials are emphasizing brand democratization.  With this challenge to brand exclusivity, luxury retail manufacturers must rely more and more on Big Data analytics to guide them through the complexity of social media in the abundance of feedback channels. 

References

Jain, Vaibhav, Bernal, Jonathan, and Gupta Jain, Pranaay. [undated]. ‘Big Data Drives Luxury Brands Growth Beyond Digital.’ Retrieved March 8, 2020 from https://luxe.digital/business/digital-luxury-reports/big-data-drives-luxury-brands-growth-beyond-digital/

Mulligan, Sharmila. 2018. ‘What is Continuous Intelligence?.’ October 18, 2018. Retrieved March 8, 2020 from https://www.forbes.com/sites/forbestechcouncil/2018/10/18/what-is-continuous-intelligence/#2a5ba747d251

Radon, A. (2012). Luxury Brand Exclusivity Strategies – An Illustration of a Cultural Collaboration. Journal of Business Administration Research, 1(1), 106–110. doi: 10.5430/jbar.v1n1p106

Rød, E.G., Knutsen, C.H. & Hegre, H. The determinants of democracy: a sensitivity analysis. Public Choice (2019). Retrieved March 7, 2020 from https://doi.org/10.1007/s11127-019-00742-z

Ruth, Joao-Pierre S. 2020. ‘How Continuous Intelligence Enhances Observability in DevOps.’ February 5, 2020. Retrieved March 8, 2020 from https://www.informationweek.com/devops/how-continuous-intelligence-enhances-observability-in-devops/a/d-id/1336941

‘Taguchi Loss Function.’ In Lean Six Sigma Definition glossary.  Retrieved March 8, 2020 from http://www.leansixsigmadefinition.com/glossary/taguchi-loss-function/

ABOUT THE AUTHOR

Dr. Banks is CEO of I-Meta Inc, a company specializing in Big Data and retail analytics.  She combined her passions for data science and luxury shopping when she invented the patented Spice Chip® technology system.  She holds several degrees and certifications with over 20 years of experience in deploying and optimizing global systems.  Dr. Banks has served on the Board of Examiners for the Malcolm Baldrige National Quality Award and understands the connecting points that successful organizations follow to meet the needs of their customers.  Her other government service consists of having served honorably in the U.S. Air Force and worked for the Central Intelligence Agency.  In her previous job, Dr. Banks was Director of Continuous Improvement and Innovation for a biopharmaceutical company.  Her industry experience spans biopharma, retail, software, and gaming, to name a few.  Throughout her career, Dr. Banks has developed applications and managed the deployment of large-scale database systems and global technology projects.

Leave a Reply

Your email address will not be published. Required fields are marked *