PredictRetail uses data science, predictive modeling and machine learning techniques to convert raw data into actionable insights and business value
Modern retail operations are powered by a variety of software and IT solutions, producing large volumes of raw data. Converting this raw data into business value and improved customer experiences requires specialist skills and an understanding of the retail domain and business processes.
Business analytics lifecycle is an iterative process of learning and continuous improvements.
We work together with your team, to define key business goals, 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.
Cluster analytics
Propensity model
Survival analysis
Collaborative filtering
Recency, Frequency and Monetary analysis
Mautic
Optimization techniques
Natural Language processing
Market basked analysis
Multivariate time series models
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. Then only 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.
