Luxury brand consumer journey

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01. Online & Offline data

02. CRM best practices

03. Data sharing with partner retailers

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01. Online & Offline data

Online data collection

Online tracking (on brand’s website and app)

Before sale: consumer profile, browsing history, wish list, cart, live chat...

During sale: transaction records, purchasing habits...

After sale: feedbacks, comments, consumer preferences...

Online campaigns

Emails to send invitations or feedback

Live shows: traceable traffic source (from instagram, official website, etc.)

Livestream: real time feedback from bullet comments

Online surveys: on social media, over email or phone, and in pop-ups on brand’s website

Social media monitoring: follower list

Tech tools

Google Analytics

Respect data privacy laws!

Givenchy

Givenchy entered RED and held its first livestream in 2020

The livestream gained more than 1.01 million viewers, and the interaction rate reached 30%. After the live broadcast, the number of fans of the brand's account increased 5.5 times compared to the day before

A great method to run the campaign, sale and collect consumer data at the same time

Offline data collection

Method

Contact information

Sales usually ask the customers about their contact information, they will then inform the clients when the new collection arrived (via email or social apps).

Create profile

They may ask clients to create their own file at checkout, so the brand can know their purchase history, product preferences, demographic location, even inquiries or complaints. Technology makes it easier to collect those information rather than paper form.

Technology

Camera Recognition Divided into 2D and 3D lenses. Count through the video images captured by the lens.

Thermal Sensation Count by grabbing the area of human body’s heat sensation and body temperature.

Infrared Sensing Divided into two types, cross-beam & single-beam. Customer touches one of the two infrared induction wires first, and then touches the other, it will be counted by the system. The cross shot is used for doorway counting, and the single shot is used for window showcase calculation.

02. CRM Best Practices

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Digital Black Books

Detailed customer profiles based on customer data (contact information, recent purchases, repair history, affiliations, etc.)

Targeted Marketing

Unique customer segmentation based on behavioral and demographic characteristics

Brand sentiment analysis through social media engagement

Points of sale

Online meetings and events to increase POS attractiveness

Mobile POS integration to improve shopping experience and analyze customer behaviour

Limited editions launch and instant purchase on social media platforms

Omnichannel Engagement

Offer web-to-store as well as store-to-web

Targeted advertisements on consumers’ preferred social media platforms

Chatbots to improve quality of digital service

Burberry digital strategy

to collect data

Reward programs:

They are asking customers to voluntarily share data through a number of loyalty and reward programs.

Tablets in store:

Sales assistants use tablets to identify client by their name

Clothes tracked by RFID tags:

They can build the customer profile based on what the client tried in stores.

Predictive analytics : SAP HANA (high-performance analytic appliance) offer personalized recommendations: online and in store based on the customers’ purchase history :

Twitter posts

Social media activity,

Fashion industry trend.

Prada Group & the use of Big Data

Collaboration with Adobe

This collaboration aims to deploy advanced customer experience management solutions at a global scale :

Analyse the interactions between brands and consumers across all of its properties, including social networks and the Group’s global retail network (634 stores worldwide).

Support its marketing and multi-channel communications with consumers to help integrate offline and online channels and deliver a more personalised experience to customers.

Use of AI

A dedicated internal team of data scientists at the Prada Group will use Adobe Sensei, Adobe’s artificial intelligence (AI) and machine learning solution to gain deeper and richer customer insights, and deliver high-quality content faster.

Estée Lauder’s use of Augmented Reality

No. 6 Mortimer Store, London

Shopping bot for the holiday season

Unveiled a lipstick-advising chatbot

https://www.marketingdive.com/news/estee-lauders-ar-chatbot-offers-advice-on-lipstick-colors/447096/

Estée Lauder’s offering for No. 6 Mortimer is the first time that a beauty group in the U.K. and Ireland has used a bot, and it lets a customer complete a purchase within Messenger by paying with PayPal, without going to an outside web site. The bot lets customers browse products from brands such as Estée Lauder UK, La Mer and Clinique, then choose to pick up purchases in the store, have them delivered by courier or delivered by mail.

Ramsha: Estée Lauder launched a shopping bot for the holiday season to bridge the gap between online and offline. The brand partnered with No.6 Mortimer, a pop-up store based in London selling beauty products. This chatbot gave the opportunity to anyone within one hour of London to browse products, shop and buy various presents by simply messaging the bot. The bot was really straightforward to use, simple, with limited functions and rudimentary vocabulary, but it worked well and was perfect for someone looking for that last-minute inspiration to buy a gift to its loved-ones. Once the choice was made, customers could choose between picking-up the gift directly in the store or different delivery options, including express delivery.

Luxury consumers already spend a huge part of their time on social media platforms, and more and more time on messaging apps such as Facebook Messenger, Whatsapp, WeChat, and the like, so it’s no surprise that brands which pride themselves on their attentiveness to customers would follow suit.

Louis Vuitton

Valentine’s Day exclusive pop-up store via a WeChat mini-program.

Exclusive offline promotions to customers via QR code.

Louis Vuitton doubled its online sales compared to the previous year’s Valentine’s season.

“Every paused journey will eventually restart. Louis Vuitton hopes you and your beloved ones stay safe and healthy.”

Ramsha: Advanced data analytics can help to document and improve the consumer experience by generating insights and making sure that each customer always receives personalized service. As an example: Imagine that you regularly shop at your local Louis Vuitton store, let’s say in Shanghai. They know you, and the service experience is tailored to you. Then you travel to Hong Kong on a business trip. Ideally, the store staff should know all about your history and preferences, and you should feel at home. What you don’t want is to feel like a stranger just because you are not at your home store or because you access the brand online. All touchpoint experiences need to be connected and seamless. But that’s not yet AI, that would be a holistic CRM [customer relationship management] system.

On top of that CRM infrastructure, AI can now help you identify complex patterns, predict what you may like, and trigger personalized customer journeys, perhaps through an automated email with specific content based on predictive analytics. Hence, the AI system ideally triggers an interaction precisely at the right moment. The ideal result is a personalized experience that excites Louis Vuitton launched a Valentine’s Day exclusive pop-up store via a WeChat mini-program that allowed customers to place orders online. Store associates were able to share exclusive offline promotions to customers via QR code. The brand moved pre-sale consultations and post-sale customer services online and partnered with SF Express to ensure smooth delivery. Despite the outbreak, Louis Vuitton doubled its online sales compared to last year’s Valentine’s season.

On February 7, Louis Vuitton posted a heartfelt message to Chinese customers across Little Red Book, WeChat, and Weibo: “Every paused journey will eventually restart. Louis Vuitton hopes you and your beloved ones stay safe and healthy.” The message is consistent with the brand image that is positioned as a purveyor of fine luggage.

consumers across all touch-points with relevant content.

Gucci’s partnership with Farfetch

Farfetch partnered with Gucci to launch “The Store of the Future“.

Farfetch helped Gucci collect data on its customers online and offline.

Farfetch showcased in-store technology that enables luxury shoppers to use their smartphone to log in when they enter a store in order to receive personalised recommendations from the retail staff. The staff themselves would be able to access their affluent customers’ profile, including purchase history and product wish lists.

Valentino partnered with Yoox Net-a-Porter Group to create an Al-backed omnichannel business model, 'Next Era'.

This model enables customers to check the availability of inventory at all its fulfilment centers and stores.

Its in-store features include quick checkout and detailed product information.

Valentino’s partnership with Net-a-Porter

03. Data sharing with partner retailers

Benefits of data sharing

Brand & Retailers

Brands can best leverage data by working with retailers on data collection and insights.

Sharing data with retailers will help retailers improve their services, leading to increased sales. Retailers are the primary point of contact with clients and this experience determines brand perception as well.

Retailers can help brands identify the best way to capture data, given their frequent interaction with clients.

Based on insights from data, brands can test numerous strategies when working with retailers

Brand & Clients

Luxury brands can use data to improve their offerings

Brands can gain insights to improve customer experience.

Eg. Montblanc used video analytics in offline stores to identify areas where customers spent the most time

Brands can build customer profiles to offer more personalised experiences in the future. This luxury service will increase the feeling of exclusivity as well.

Montblanc, a German manufacturer of high-end watches, leather goods, and writing instruments, deployed video analytics in its offline retail spaces to generate heat maps that showed where customers spend most of their time while browsing items in-store. The company managed to increase sales by 20% by using the data to identify the best places to position their product lines and sales staff. https://etaileast.wbresearch.com/blog/why-luxury-brands-need-to-embrace-big-data-to-drive-growth-strategy

Risks, Limitations and Management of Data Sharing

Brand & Retailers

Loss of Exclusive Data: Sharing data with retailers might give an impression of losing not only client specific data but also the competitive advantage. Additionally, shared data might lead to conflict of interest over usage and accountability conflicts over misuse of data.

As a response, the brands and retailers needs to draw boundaries by:

Determining means of data sharing and the roles of third party data aggregators.

Laying strong contract to monitor lawful and protected use of data.

Laying out terms on shared data after contract expiry or termination.

Brand & Clients

Maintaining Customer Trust: It would require added diligence by brand & retailers to allow clients keeping their trust, in both parties, when pooling of data takes place.

The risk of alienating clients here is quite high and could be mitigated by:

Creating safeguards by informing clients about the purpose and allowing easy opt-out.

Asking explicitly for consent before using data and demonstrating strong intent towards data protection.

Being transparent about collected and shared data and also allowing clients to have control over information being collected.