What is the Impact of Big Data & Artificial Intelligence
on Luxury / High-end Brands?

 

Data are to this century what oil was to the last one: a driver of growth and change. As data collection, analytics and the interpretation of that data become more readily accessible, Big Data & Artificial Intelligence will have an impact on every business in several important ways, regardless of what field a company operates in or the size of its business.

 

An explosion of data has been unleashed since society merged itself with the digital world. From trains and wind turbines to mobile phones and toasters – all sorts of devices are becoming sources of data, so that people will leave a digital trail wherever they go. If they make use of it or not, companies collect gigantic data quantities from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media. This data can be regarded as one of the most valuable assets a company can maintain[1]. Big Data refers to extremely large and diverse data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. The primary value from big data comes not from the data in its raw form, but from the processing and analysis of it and the insights, products, and services that emerge from analysis. Big data analytics can enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making.

 

Artificial Intelligence (AI, also machine learning) relies on Big Data and is one of the techniques in analyzing Big Data, besides other, also complementing advanced analytics techniques such as text analytics, predictive analytics, data mining, statistics, and natural language processing. AI relies on the idea of imitating human thought processes. Rather than have to be taught to do everything step by step, machines, if they can be programmed to think intelligently, can learn to work by observing, classifying and learning from its mistakes.

 

Nearly everywhere we look today, we already see intelligent systems talking to us (Apple’s Siri), offering recommendations (Netflix and Amazon), or providing financial advice (Schwab’s Intelligent Portfolio), and we see systems emerging to further improve voice recognition, image interpretation, face recognition, and even driving cars. AI systems make use of three major components of human reasoning: assessing, inferring, and predicting.

  • Assessing: Most of the consumer systems in use are assessing us. Amazon, for example, puts together a detailed picture of who we are so that it can match us against similar customers and create a source of predictions about us. The data for this assessment is transactional: what we touch (look at, like, etc.) and what we buy.
  • Inferring: Once you know a little about the world, you can start thinking about extending that knowledge and start making inferences, which includes assessing similarity, categorizing, and amassing points of evidence. AI systems are reasoning in the following way: “Both you and Bob like snowboarding, kick boxing, and skydiving. I know Bob likes water skiing. I bet you would too.”
  • Predicting; Systems aimed at predicting specific outcomes such as customer churn and equipment down time almost always make use of historical information and rules related to those issues – and are able to anticipate, and therefore avoid, problems before they arise. AI systems are able to connect visible features at one point in time (too many dropped calls) with events that need to be predicted (we’re going to lose this customer) by looking at the frequency of the initial features compared to examples of what the system is trying to predict.

 

In recent years, companies such as Facebook and Google have discovered that data can be turned into any number of AI-based services, some of which will generate new sources of revenue, e.g. translation, visual recognition and assessing someone’s personality by sifting through their writings. AI-based tools such as Google’s digital assistant get better at performing tasks the more they are used.

 

Big Data & AI Software

One of the market and innovation leaders is SAP HANA (see Burberry case study below) and a major competitor is IBM Big Data & Analytics (see Luxottica case study below). Marketing database applications such as SAS and Unica, and integrated marketing automation systems like Oracle’s Eloqua, Marketo and Salesforce.com’s Pardot, allow marketers to determine the factors that identify, explain and influence a consumer’s decision in real-time.

 

Opportunities for High-end Brands

Luxury companies can tap into existing Big Data solutions and may also initiate cooperation with other brands to reduce costs and overhead. They can benefit from Big Data mainly in the following areas:

 

  1. Supply chain operations: AI-based data analytics is used to manage workforces (e.g. identify suspicious behavior and pinpoint potential high-risk employees) and production processes as well as for predicting faults before they occur, therefore enabling predictive maintenance. As a result, high-end manufacturers can further boost quality and output while minimizing waste and energy usage – processes that are key in today’s highly competitive market.

 

  1. Product & service innovations: In the consumer world, more and more of the technology that we are adopting into our everyday lives is becoming powered by AI, such as smart phone assistants. In the high-end market, the new TAG Heuer's Connected Watch and the Montblanc Summit smartwatch are AI-driven with functions such as travel directions, translations and assistance for dictating emails.

 

  1. Market Research: Big Data analytics enable companies to collect more and better marketing data about consumers, to predict what they want (even before they become aware of it) and what channels they use to buy, which allows for a better consumer segmentation. For instance, Luxottica uses IBM Big Data & Analytics to identify its highest value customers and most effective sales channels for reaching them, and to create individualized campaigns directed at customers who are more likely to purchase. Ai can also help to capture and analyze social media comments and to simulate and predict the best pricing scenarios to increase revenue and margins. Consumers going through major life events often don’t notice or care that their shopping habits have shifted, but retailers notice. For instance, a father had complained to British supermarket chain Target that they would send emails for baby products to his teenage daughter ‘to encourage her to get pregnant’ – but later it turned out that she actually was pregnant, which Traget had detected based on a comparison of shopping habits of pregnant women[2].

 

  1. Retail: Retailers need to know the best way to market to customers, the most effective way to handle transactions, and the most strategic way to bring back lapsed business. Big data remains at the heart of all those things. For instance, Montblanc used data-driven personalization to improve customer experience in stores. In collaboration with RetailNext, they deployed video analytics in their retail spaces, generating maps that show where customers spent most of their time in store. The company was able to identify where to place their various product lines and sales staff, which helped increase sales by 20 percent[3]. As entry-level items such as wallets and sunglasses made up a large proportion of sales, Versace made use of Big Data analytics to find out how to reach customers who buy larger-ticket items like clothing and handbags. Targeted prospects who visited the web site were scored in real time based on their purchases to help refine the prospect model. The campaigns delivered a 35 percent increase in online sales and a 300 percent average order value increase[4]. In addition, facial recognition software is now available in retail settings, which allows luxury brands an instant identification of VIP clients on a global basis[5].

 

  1. Customer Relationship Management & Retention: Luxury brands can deliver personalized content and connect with their consumers as a result of insights generated by Big Data. Marketing professionals can tailor their outreach based on prediction models of when a specific customer is most likely to make their next purchase, or how much this customer is willing to spend per item. Some brands such as Jaeger-LeCoultre are using AI-based consumer-brand interactions with chatbots answering client questions – without the need for a human. This has the potential for “conversational commerce” where brands can talk via chatbots to clients and use AI-based Big Data analytics to better understand trends, customer emotions and sentiment, and adjust in real-time product management strategies accordingly.

 

  1. Fighting counterfeiting: Burberry uses technology provided by Entrupy which is based around image recognition, and capable of determining from one photograph of a tiny section whether or not a product is genuine. It does this through examination of minute details in the texture and weaving, and can reportedly spot a counterfeit with 98% accuracy.

 

Big Data & AI used in Practice: The Case of Burberry

Burberry makes use of Big Data and Artificial Intelligence (AI) to boost sales and customer satisfaction[6]. They are asking customers to voluntarily share data through a number of loyalty and reward programs. Customer profiles are built also based on what garments (tracked by RFID tags) the customers have tried on in stores. Big Data analytics by relies on SAP HANA is used to offer personalized recommendations, online and in store. When an identified customer enters a store, sales assistants use tablets to greet them by name and offer personalized buying suggestions using predictive analytics fueled by their customers’ purchase history as well as their Twitter posts and social media activity, as well as fashion industry trend data. If Burberry knows that a customer has recently bought a particular coat, for example, then assistants may be encouraged by the app to show them a handbag which is popular with other buyers of the coat. In 2015, the company announced that their investment in personalized customer management programs had resulted in a 50% increase in repeat custom. Burberry has learned to make the experience of going to a high-end luxury shop -- where products were historically kept behind glass – more comfortable and personal. Burberry’s senior VP of IT, David Harris, said “We are formulating our AI strategy now … we believe that AI can deliver business value through making better products, faster, cheaper processes and more insightful analysis” (see Forbes 2017). Possible uses include insights from pattern recognition, scenario modelling for logistics purposes, as well as increasing security and preventing fraud[7].

 

Challenges

Complexity and costs: Providing exclusivity with a delightful experience through the use of data analytics becomes a competitive advantage. Well-known luxury brands have no trouble driving traffic through their digital channels, but what they fail to do is ensure they are driving the right traffic, which is traffic that leads to active and loyal customers. However, Big Data analytics software is costly: SAP HANA can also be regarded as a luxury product, which small and medium-sized high-end brands can hardly afford (although investments may well pay off).

Data protection in the EU is becoming stricter: The General Data Protection Regulation (GDPR) imposes new requirements on personal data and grants stronger rights to individuals, which influences the use of Big Data.

Data analysts with the required skills to manage and analyze Big Data are in high demand. Luxury brands are competing with tech companies to attract and retain the best talents.

 

 


[1] The Economist, May 2017, Data is giving Rise to a new Economy, read here.

[2] Forbes, Feb 2016, How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did, read here.

[3] Luxe Digital, February 2018, Big Data Drives Luxury Brands Growth Beyond Digital, read here.

[4] WWD, May 2017, Luxury Brands Must Embrace Data-Driven Marketing, read here.

[5] Brand Channel, July 2013, More Retailers Tapping Into Facial Recognition Tech to Improve Customer Experience, read here.

[6] Forbes, Sep 2017, The amazing Ways Burberry is using Artificial Intelligence & Big Data to Drive Success, here.

[7] Forbes, Oct 2013, How Fashion Retailer Burberry keeps Customers coming Back for more, read here.