Rita Bini - Data Science

Data Science & Insights is the art of extracting compelling information from a business’ data that allow to reshape strategic decisions and drive growth.

Get Started

About

Hi, I am Rita, a Data Science & Insights Specialist.

I have a strong passion for data, and believe in the power of data science and machine learning to drive smarter decisions and meaningful business impact.

With 15+ years of experience across data science, machine learning and predictive modeling, I design data-driven solutions that help organisations solve complex problems, optimise performance, and unlock growth. My background in Economics and Marketing, combined with advanced Data Science studies, allows me to bridge the gap between data and business strategy.

I am particularly passionate about designing and delivering end-to-end solutions, from data and modeling, to ML pipeline architecture design, experimentation and scalable insight delivery, helping businesses adopt data science to create measurable impact.

🔹 Specialisation: Statistical Analysis, Data Science, Machine Learning, ML Ops, NLP Foundations, Time Series and Forecasting
🔹 Tools: Python, SQL, Data Visualisation

Industry Relevant: Member of the Judging Panels for the "Data and Insights" Category at 2022 DMA (Data Marketing Association) Awards UK, and at 2014, 2015, 2016 Database Marketing Awards UK.

What I Can Do

Data Science to assist Business
and Marketing Decisions
End-to-end approach to unlock a 360 view of customers behaviour, preferences, drivers (or pain-points) for growth and engagement.

Ideation & Hypothesis

Discuss brief requirements by addressing relevant business questions, goals and challenges.

Data Strategy & Views

Consult on data strategy, and governance. Code, transform data to build views for modeling and insights. Feature engineering and ETL.

Data Science & Machine Learning Solutions

Modelling and Analysis of data. Extract trends, patterns and test hypothesis and validate results. Design and implementation of machine learning pipelines to automate the modeling work-flow.

Activation & Measurement

Support the implementation of automated tools and reports to monitor results and insights, and the deployment of business KPIs, forecasts and targets.

Core Solutions

Growth with
Data-driven Solutions

Customers' Groups and Behaviour Modelling

Segmentation Solutions

Customer Cohorts and Profiles
Consumer Product Preferences
Customer Value groups

Predictive Modelling and

Classification Modeling

Customer Conversions
Risk of lapsing and Churn Models
Upsell and Cross-sell opportunities
Reactivation and re-engagement

Main Techniques

Cluster Analysis
Principal Components
Regression Models
Decision Trees
Neural Networks
XGBoost
Machine Learning

Improve strategic targeting and personalisation: Understand your customers groups by identifying similarities. Predict behaviour and generate actionable insights.

Engagement and Retention Models

Engagement and Retention Analytics

Product Engagement landscape analysis
Engagement and Retention correlations and trends
Survival Models
Sentiment and reviews analysis

Main Techniques

Regression Models
A/B tests and experimentation
Survival Models
Product preferences forecasts
NLP techniques

Optimise Engagement and Retention strategies: Identify engagement drivers and how these link to retention and value.

Growth & Optimisation Modeling

Growth Modeling and Measurement Framework

Data strategy to track relevant data signals
Marketing Channel attribution and optimisation
KPIs to reflect funnel and conversion journey
Time Series Modeling

Data insights that lead to breakthrough growth moves. Understand the journeys and factors to growth.

Main Techniques

A/B tests and experimentation
Time Series & Machine Learning
Media Mix Modeling Machine Learning

Projects

Some of my work:

Data Science & Advanced Analytics

Business case for an Editorial Publisher: the Factors and Clusters Analysis (K-Means) helped segment subscribers with similar editorial content preferences, gaining deeper intelligence about the customer base.

Business Questions: Can we use data to identify product preferences segments? What is their profile? How do they link to value and overall performance KPIs?

The model allowed to group subscribers with similar characteristics and to gain deeper insights about their size, profile and KPIs performance.

Distinct Cohorts & Profiles

Outcome: A detailed information about customer’s product preferences and a dedicated reporting tool, to help monitor profiles, monitor performance and inform strategic decisions. The model allowed to create targeting lists and recommendations for personalisation to use for Marketing strategies, resulting in an uplift in communication response and overall performance over time compared to traditional targeting.

A look-alike model was also implemented to automate the groups, to monitor the profiles generated and insights on a monthly basis, and to project the work on new acquired customers.

In this business case experimentation analysis was used to identify customers engagement KPI and establish its relationship with retention projections (Survival Models).

Business Questions: How can customer engagement be measured? How many customers do we expect to retain if we reach optimal engagement? Can we create customers’ lists using the new engagement KPI?

Once the new engagement KPI was identified, it was also possible to quantify and project retention gains (success) by using Business scenarios that were deemed to improve engagement.

Engagement KPI landscapes

Survival Curves

Outcome: The statistical results allowed to create Customers’ Engagement lists relevant for strategic targeting, to generate a reporting tool to monitor Customer Engagement movements over time and gather insights for the Business.

The Survival Curves allowed to understand how each Business Product correlated to Retention, and from the model new targets KPIs and scenarios for growth were derived.

In this use case statistical data analysis was performed to gain deeper intelligence into customers behaviour and engagement drivers using Percentiles Techniques to generate insights.

Business Questions: What opportunity areas can be identified to increase engaged customer base? What are the key drivers of engagement?

With the use of advanced data mining techniques, it was possible to generate engagement cohorts and provide an overall landscape by specific products and business factors.

Products Engagement Landscapes

The new analytics methodology and data view created, allowed to generate a rich set of information and insights around opportunity areas for engagement growth and also to identify pain-points, gathering useful recommendations for the business.

Insights & Opportunities Uncovered

Outcome: A reporting tool was implemented to monitor the new insights and to help inform business decisions on the Touchpoints and Products that were more linked to Engaged Customer Groups, and to extract also pain-points.

The data was also overlaid to Customer Product Preferences Clusters to enrich behavioural insights and link those to overall Engagement and Retention performance.

The insights produced were adopted by the Business, informing priority areas and overall retention strategy.

Business case for an online retailer who recently launched a new Digital Experience for its customers, expecting acquisition and conversions to Membership Program. A Digital Funnel data operating model was set-up to measure performance and gather funnel movements learnings.

Business Questions: How can we create a measurement framework that reflects our new online journey? What can we learn about initial results to customers growth?

The work started by assessing the data tracked on the new online journey, by consulting the business on required data signals for modelling the Digital Funnel.

A combination of digital data from Marketing, Product and Audience strategies was modelled and a Scenario Planner was implemented on the back of the insights, to create business recommendations, new targets and projections.

Outcome: The Digital Funnel model proved to be effective, and the data results gained helped roll-out the webpage Globally. The operating model also allowed to produce insights around acquisition and conversion levers to inform more effective Marketing and Product Strategies, improving Marketing CPAs and On-site UX.

Business case for Telecommunication Business: the Propensity Model helped identify product subscribers that were at risk of churning/cancelling their subscription and generate targeting lists for campaign optimisations and personalisation.

Business Questions: Can we predict who are the customers likely to churn and target them for retention activities? What can we learn about these customers?

By modeling historical data it was possible to predict lapsing customers, quantify the size of this group, and create targets. The model was built by adding factors that could be actioned by the Business for retention strategies.

Probability Prediction (traffic light model)

Model Insights

The results and insights also allowed to identify factors that described the risky group, obtaining important information for product and Business Strategy.

Outcome: The Churn model improved targeting performance with a 2.5 uplift from traditional methods, increasing campaign response and retention results. It also allowed to generate strategic insights to help inform Marketing, Product and Business retention strategies.

The model was also sent to production to generate new campaign targets and insights on a weekly basis, and monitored in its performance over time.

Business case for a Grocery Retailer interested in analysing data to unlock opportunities to improve communication and Marketing strategies across the UK Market. A Cube Data Model was implemented to generate recommendations.

Business Questions: How can Marketing at store level become more efficient? What can be learnt from the data about growth opportunities for store-level strategies?

Three major customer performance metrics collected at store level were analysed and overlaid to create a “Frequency Index” using client’s UK supermarkets data.

Stores Grouping

Model Insights and Growth Areas

The model allowed to identify areas where an increase in customer performance was more necessary.

Outcome : The analysis generated Stores lists for targeting and performance measurement, by grouping stores based on their Frequency Index. This allowed to optimise targeting and Marketing investment by Region and by Store. The model was also combined with product preferences data, allowing to produce insights for communications personalisation at Store level, generating an uplift in Frequency Index over time.

ritabinianalytics@gmail.com

UK +44 7768702994

Rita Bini - Data Science

About

What I Can Do

Data Science to assist Business
and Marketing Decisions
End-to-end approach to unlock a 360 view of customers behaviour, preferences, drivers (or pain-points) for growth and engagement.

Ideation & Hypothesis

Data Strategy & Views

Data Science & Machine Learning Solutions

Activation & Measurement

Core Solutions

Growth with
Data-driven Solutions

Customers' Groups and Behaviour Modelling

Engagement and Retention Models

Growth & Optimisation Modeling

Projects

Data Science & Advanced Analytics

Get In Touch

RB Analytics

What I Can Do

Analytics Briefs

Data Strategy & Data Views

Insights & Recommendations

Measurement, Targets, Reports

Data Science & Machine Learning Solutions

Quick Links

Home

About

What I can do

Core Solutions

Portfolio

Contact

Contact

ritabinianalytics@gmail.com

UK +44 7768702994

Rita Bini - Data Science

About

What I Can Do

Data Science to assist Business and Marketing Decisions End-to-end approach to unlock a 360 view of customers behaviour, preferences, drivers (or pain-points) for growth and engagement.

Ideation & Hypothesis

Data Strategy & Views

Data Science & Machine Learning Solutions

Activation & Measurement

Core Solutions

Growth with Data-driven Solutions

Customers' Groups and Behaviour Modelling

Engagement and Retention Models

Growth & Optimisation Modeling

Projects

Data Science & Advanced Analytics

Get In Touch

RB Analytics

What I Can Do

Analytics Briefs

Data Strategy & Data Views

Insights & Recommendations

Measurement, Targets, Reports

Data Science & Machine Learning Solutions

Quick Links

Home

About

What I can do

Core Solutions

Portfolio

Contact

Contact

Data Science to assist Business
and Marketing Decisions
End-to-end approach to unlock a 360 view of customers behaviour, preferences, drivers (or pain-points) for growth and engagement.

Growth with
Data-driven Solutions