About
Hi, my name is Rita and I am a Data Science, Analytics & Insights Specialist.
I have a strong passion and interest in anything analytics, and I believe in the power of data to drive smarter, data-driven decisions!
Through my work, I love uncovering patterns, predicting trends, and helping businesses make smarter decisions.
With 15+ years of experience in customer and growth analytics, I work with advanced analysis to help solve complex business questions. My foundation in Economics and Marketing, along with Data Science studies, allows me to bridge the gap between data and strategy.
🔹Specialisation: Statistical Analysis, Data Science, Machine Learning, NLP foundations, Time Series
🔹Tools: SQL, Python, Excel, 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, data needs and governance. Code, transform data to build views for analysis and insights. Feature engineering and ETL.
Data Science & Analytics Solutions
Modelling and Analysis of data. Extract trends, patterns and test hypothesis and validate results.
Insights & Opportunities
Generate actionable insights and recommendations in line with core business requirements.
Activation & Measurement
Support the implementation of automated tools and reports to monitor results and insights, and the deployment of business KPIs and targets.
Core Solutions
Audience Growth Optimisation with
Data-driven Insights
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 customer 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
-
Customer sentiments and reviews analysis
Main Techniques
-
Regression Models
-
A/B tests and experimentation
-
Survival Models
-
NLP techniques
Optimise Engagement and Retention strategies: Identify customers’ engagement drivers and how these link to retention and value.
Customers' Digital Funnel and Growth Analytics
Digital Funnel and Growth Measurement Framework
-
Data strategy to track relevant digital data signals
-
Set-up of digital 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
-
Attribution Models
-
A/B tests and experimentation
-
Time Series & Machine Learning
Portfolio
Some of my work:
Data Science & Advanced Analytics
Business case for an Editorial Publisher: the Factors and Clusters Analysis 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.
