Transactional data
(eg. weather, exchange rates, economy)
(eg. temperature, foot traffic, facial recognition)
Business analytics lifecycle is an iterative process of learning and continuous improvements.
We work together with your team, to define key business goals and KPIs, and help you to achieve them using data science and predictive modeling.
Clear business goals and measurements are the cornerstones of a successful data project.
Increase basket size and maximise customer value
Respond quicker to competition and determine your pricing “sweet spot”
The data modeling process goes through multiple phases. Each phase refines the data further to ensure data is clean and well structured. Given that data is the fuel that powers machine learning and predictive modeling, you could say that data quality is the most important ingredient in any advanced analytics project. Data must be cleaned, merged and preprocessed, relevant features extracted and/or created. Only then the process of training and evaluating models can begin.
Once the model is evaluated and deployed, applications and business processes are connected to begin extracting value. The value extraction process typically generates new sources of data, which is fed back into the model to improve the efficiency and accuracy on an ongoing basis.
Our development and integration teams will plug the models into your existing systems and business processes to minimise change management.
Or alternatively, we will deploy application such as Mautic to give your teams the power to action the insights and convert it into tangible business value.
