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Gone are the years where decisions were made solely based on historical data or pure intuition. Through AI use cases we are helping our clients anticipate different events and make better decisions. They can now segment and cluster customers in a more intelligent way, they can predict the propensity to certain behaviors such as buying a product or service, churn, fraud, predict the success of a campaign and much more.
Leveraging Tableau stories and annotations to explore household inflation in the UK.
Leveraging customer data to understand their behaviour, preferences and motivations can lead to significant return on marketing investments. However, with large numbers of customers, traditional business intelligence practice is to reduce customer data down to aggregated metrics, typically focussed on pre-defined segments (or cohorts) of the customer base. Whilst useful, this process discards some insight that can be revealed only by looking at individual customer-level records. In this blog, we discover a way to use Qlik Sense's On-Demand App Generation to study user-defined cohorts.
by Hannah Lissaman, Senior Consultant, and Louis Lindsay, SDA at Keyrus UK
Creating and deploying enterprise dashboards has never been easier. With Qlik Sense, a business user of a dashboard can even create their own content, simply drag and drop a chart and select from prepared master items. This is one of many features that make Qlik Sense a great platform for organisations promoting data literacy and self-serve analytics. However, sometimes the resulting one-size-fits-all look and feel of Qlik Sense dashboards can leave us feeling a bit limited in terms of design.
When creating reports for clients they can be financial penalties if they don’t meet the agreed standards, particular in the financial sector.