Data Driven Decision Support for Retailers

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.
Data science gives sales and marketing teams the ability to understand their audience on a very granular level. With this knowledge, an organization can create the best possible customer experiences. A variety of data modeling techniques and algorithms are applied to extract insight, provide decision support and improve overall profitability. The most effective approach is dictated by the Velocity, Variety and Volume of data available for processing, and there is rarely a “one size fits all” approach

Use cases and benefits

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 achieve them using data science and predictive modeling. Clear business goals and measurements are the cornerstone of a successful data project.
How What Why

Recommendation engine

Determine products or services most likely to trigger intent
Increase repeat sales and loyalty Personalise customer experience

Market basket analysis

Determine which products are often bought together
Increase basket size and Maximise customer value

Customer micro segmentation

Cluster similar customers using machine learning
Improve marketing spend and ROI Target most relevant audiences

Lifetime value analysis

Recognise and retain your most valuable customers
Improve acquisition, retention and Maximise long term profits

Propensity modeling

How likely will your customer buy or churn

Focus on most suitable audiences, Prevent churn and build loyalty

Demand forecasting

Predict supply and demand more accurately
Reduce overstock, understock and cash locked underperforming inventory

Price elasticity

Determine pricing “sweet spot”

Increase sales and Improve profitability

Sentiment analysis

Monitor social media sentiment towards your business
Improve your brand and build loyalty


  • Tools infrastructure
  • Techniques
  • People


  • Cost impact
  • Value impact

Model development

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 and 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.

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