“Today, marketing segmentation needs to go beyond classic customer segmentations based on sociodemographic data. It should encompass a broader scope of information and provide a dynamic view of the customers, adapted to the company’s marketing needs. To achieve this goal, Big Data & Analytics are not only buzzwords, they provide key innovative assets by enabling to capture data from many different sources, work on various types of data and provide dynamic results."
The growing number of customer touchpoints and interactions is generating more and more information: CRM data, web logs, call center recordings, social media and email conversations, etc.
Big Data technologies enable to leverage these huge amounts of data, by offering capacity to capture and manage:
Analytics technologies, on their side, now offer the capacity to analyze these new amounts data and provide relevant insights, through:
Ability to leverage many formats of information that used to be complex or impossible to take into account – e.g. Deep Learning algorithms to understand the content of an image or video, and the meaning of text data,
New algorithms for segmentation, such as network mining and density-based clustering (as opposed to classic K-means),
Enhanced hardware & software performance on large amounts of data : segmentation jobs that used to run overnight in 10 hours can be optimized to run in 5 minutes,
Big Data & Analytics are often used to run one-shot studies (Proof of Concepts) in an exploratory mindset. It is now possible to have as many segmentations as business questions to answer. But their real value lies in the industrialisation of dynamic segmentations, updated automatically in real time, with a link to both Marketing and IT. This can be done by integrating segmentation to the service catalog offered by a company’s Data Lab, in addition to its exploratory role.
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