At the recent Dialog Summit, the European forum for e-mail and data-driven marketing in Frankfurt I was really stuck at how the term context analytics and indeed the concept of context, is now being used to redefine how targets are set in CRM, in a move away from focusing on customer lifetime value.

Though definitions vary, context analytics focusses on the importance of taking in to account the contextual history derived from data insights about entities (people, places, and things) when reaching business conclusions. By combining context with big data, organizations can derive trends, patterns and relationships from their structured and unstructured data.

To put it simply, without context a customer, who has already bought several headsets from an online retailer over the past 12 months, will keep receiving recommendations for more headsets rather than suggestions for possible video games to play with them.

At DataLab, we apply context analytics to verify the strength of the dataset, the validity and the significance of patterns and of course for predictive modelling. This is how we transform a precise analysis into an actionable piece of information.

Using context to define targets can be dangerous and could for example, lead to setting a lower customer target because the context has changed, thereby decreasing the potential. At DataLab, we do not use context to set targets. We use it to allow you to become more customer-centric in your product portfolio, your pricing, promotions and communications.

Let´s start placing customer insight in a relevant context to create a deep understanding about customers. Meeting their present and future needs will revolutionise the way you do business. 

At the recent Dialog Summit, the European forum for e-mail and data-driven marketing in Frankfurt I was really stuck at how the term context analytics and indeed the concept of context, is now being used to redefine how targets are set in CRM, in a move away from focusing on customer lifetime value.

Though definitions vary, context analytics focusses on the importance of taking in to account the contextual history derived from data insights about entities (people, places, and things) when reaching business conclusions. By combining context with big data, organizations can derive trends, patterns and relationships from their structured and unstructured data.

To put it simply, without context a customer, who has already bought several headsets from an online retailer over the past 12 months, will keep receiving recommendations for more headsets rather than suggestions for possible video games to play with them.

At DataLab, we apply context analytics to verify the strength of the dataset, the validity and the significance of patterns and of course for predictive modelling. This is how we transform a precise analysis into an actionable piece of information.

Using context to define targets can be dangerous and could for example, lead to setting a lower customer target because the context has changed, thereby decreasing the potential. At DataLab, we do not use context to set targets. We use it to allow you to become more customer-centric in your product portfolio, your pricing, promotions and communications.

Let´s start placing customer insight in a relevant context to create a deep understanding about customers. Meeting their present and future needs will revolutionise the way you do business.