Monetising Your Data

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.

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 to achieve them using data science and predictive modeling.
Clear business goals and measurements are the cornerstones of a successful data project.

CUSTOMER, STORE AND PRODUCT SEGMENTATION

Cluster analytics

Improve marketing spend and Return On Investment (ROI). Create tailored marketing actions for each segment

PURCHASE LIKELIHOOD

Propensity model

Focus on most suitable audiences, prevent churn and build loyalty

CHURN ANALYTICS

Survival analysis 

Predict and reduce customer churn 

RECOMMENDATION ENGINE

Collaborative filtering 

Increase repeat sales and loyalty. Determine products or services most likely to trigger intent

CUSTOMER ANALYTICS

Recency, Frequency and Monetary analysis

Increase customer retention, response, conversation and revenue rate

MARKETING AUTOMATION

Mautic

Mautic marketing automation platform, customised using machine learning and data science

PRICING ANALYTICS

Optimization techniques 

Respond quicker to competition and determine your pricing "sweet spot"

SENTIMENT ANALYSIS

Natural Language processing

Build a positive social media sentiment towards your brand

CROSS SELLING & UP SELLING

Market basked analysis 

Increase basket size and maximise customer value

DEMAND FORECASTING

Multivariate time series models 

Reduce overstock, understock and cash locked inventory

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. 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.

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