Definition: The meaning of Big Data
Big data is a marketing concept that refers to the technologies and processes used to collect, store, organise, generate insights from, and take action on the large amount of customer information available thanks to the digital transformation of an industry.
While data analytics has always been used by businesses, the breadth and depth of customer information that is now accessible to luxury brands renders traditional analytical models and database technologies obsolete.
As such, big data analytics requires new skills and technologies to be successfully leveraged. One of the most immediate benefits of a proper big data workflow as part of a holistic marketing strategy is the capacity for luxury brands to identify and engage with their affluent consumers in more personal and timely manners.
Such marketing campaigns are proven to significantly outperform the now outdated mass marketing efforts. Big data insights can indeed help luxury understand their customers’ lifestyle and purchase behaviours to build profitable long-term engagement.
What is big data
The definition of big data is an evolving concept that generally refers to a large amount of structured and unstructured information that can be turned into actionable insights to drive business growth.
Big data analytics require a new set of processes and technologies to be successfully integrated into a holistic luxury marketing strategy.
Big data process
The concept of big data marketing typically encompasses five distinct stages of a process: collecting, storing, organising, generating insights from, and taking action on a large set of data.
We will explore each of these process stages in more detail below.
Collecting big data and generating actionable customer information
The first necessary step to leverage big data as part of a marketing effort is the collection of customer information. This can take place both online and offline, through customer surveys, loyalty program subscriptions, luxury brands memberships, etc.
Three elements are critical to ensuring that big data collection is done properly:
- Customers need to consent to their information being captured;
- The brand that collects those information needs to be transparent about its purpose;
- The data needs to be recorded in a way that will facilitate storage and processing at a later stage.
Storing big data with security and accessibility in mind
Next is the actual storage of the collected customer information. Big data storage comes with its own sets of challenges, as the information collected will often be in an unstructured format and of significant size. We’ll explore below the new technologies and systems available for luxury brands to store their customer data.
Two aspects are essential when planning big data storage capacity:
- Security: because of the private and confidential nature of the customer data that were collected, storing information in a secure fashion is critical. Encrypted databases, data segregation, and strict internal access policies are essential for a company to ensure that their customer information is safe.
- Accessibility: the sheer size and weight of the customer data that need to be stored can rapidly slow down a system that isn’t thoughtfully built with scale in mind. Luxury brands should carefully consider database redundancy and server capacity to ensure that their customer information is easily accessible to their marketing teams.
Organising big data and customer database management
While planning for its data storage and architecture, luxury brands need to consider how its customer information will be organised and managed in order to generate actionable insights. The main challenge comes from the fact that big data can be collected both offline and online in various structures (or sometimes no structure at all).
For that reason, big data need to be organised in a way that will ensure:
- Flexibility: certain customer information, such as name, surname, date of birth, address, etc. can easily be collected and stored in a standard structure way. But other customer data, such as their browsing history, their purchase habits, their communication preferences, will require a certain level of flexibility and adaptability in order to be collected and stored.
- Longevity: the needs of your marketing team for big data insights will evolve over time as new experiments are scaled and measured. As such, the organisation of big data analytics needs to based on a system that can be easily maintained and adapted as new technologies will emerge.
Generating actionable insights from big data
Big data intelligence, the stage when raw data becomes actionable insights, requires a new set of skill sets, often referred to as data scientists. At the crossroad between traditional marketing teams and strategic intelligence, data scientists are responsible for identifying valuable insights from the collected data and suggest specific marketing campaigns that can be executed to drive sales.
Big data insights are typically generated in three stages:
- Data scientists will start from a specific hypothesis. This hypothesis needs to be measurable and actionable based on the available data.
- They will then search for patterns in their customer data and segments consumers into groups that can help test their hypothesis.
- Once this is completed, data scientists will segregate customers into tiers (based on their purchase power for example) or cohorts (based on their acquisition timeframe for example).
Taking action on big data insights with marketing automation
The final step of a typical big data process is to take action on the insights generated by your data scientists. The end goal of this step is to drive measurable impact through personalised marketing campaigns by sending the right message, at the right time, to the right audience, and through the right channel.
Taking action on big data insights usually includes three broad stages:
- Building thoughtful and personalised marketing campaigns. Those need to be beautifully crafted with multi-devices in mind and an impactful copy.
- Scaling marketing campaigns in a way that will allow for rapid experimentation and automation when successful.
- Measuring a marketing campaign effectiveness against pre-defined KPIs.
- Closing the loop by providing specific and timely feedback to all the stakeholders involved in this process to improve future campaigns.
Big data technology
Big data analytics comes together with new tools and software to assist through all the stages of the process, from collection and storage, to organisation, insights generation, and marketing automation.
Broadly speaking, every luxury brands embarking on a digital transformation will need to decide between building custom-made in-house big data technologies and outsourcing to third-parties. Both options have pros and cons, so it’s important for luxury leaders to understand what their options are and select what is most appropriate for their available budget and timeframe.
We recommend you read our in-depth report on how big data drives luxury brands growth to further explore this topic.
Our take on big data for luxury
The digital transformation of the luxury industry and the incorporation of digital technologies into current business models is radically redefining success. Digital luxury pure-play new entrants are shaking up their industries and rapidly gaining market shares, while traditional luxury brands are cautiously experimenting with their brands on new channels.
Big data can help high-end brands create a seamless and integrated online customer experience, with the view to improve market outreach programs and overall sales performances.
