KUBIK Intelligence

Co-Founder and Chief Data Scientist

University of Nottingham

Advisory Board Member

As a researcher, I was in charge of the following projects that involved using machine learning for social good:
- detecting anxiety purchases in FMCG data and their prevalence across the UK;
- detection of menstrual pain from big data (transactional data) and assessing their links to deprivation;
- classification of complaints in a food sharing app using linguistic features of a text;
- mediation analysis to assess the impact of stigma on adherence to asthma medication;
- predicting non-adherence and the most relevant features using a novel method (Grouped-MCR)

PHD Researcher

DAMSL Ltd

Providing expertise and logical thinking in a way that helps to develop exciting new analytical solutions for intricate business obstacles. This work includes international projects across multiple sectors including retail, telecommunication and healthcare.

In the most recent projects I:
- created a detailed psycho-behavioural segmentation of customers based on survey and transactional data; 
- created a credit score rankings based on customer behaviour;
- used neural networks to extract customer perceptions from Twitter;
- used linguistic features to predict complaints in a food sharing app;
- used machine learning tools to discover hidden styles in a wardrobe for a fashion start-up.

Data Science Consultant

Muscle Foods Ltd

During this role I created a data team from a scratch and helped customer insight to become the heart of MuscleFood's business, strategic and tactical decision-making;
I recruited other data team members and as a Senior member, I helped managing and organising the team;
I developed the reporting system for the company that includes daily, weekly and monthly trading dashboards as well as other business-related forms of reports (such as Google Analytics reports).
Using advanced SQL and data science techniques I developed several innovative pieces of analysis:
 - Single Customer View (removing all the duplicates from the database);
 - In-depth retention analysis that helped business understand the value of retention;
 - Customer Life Time value based on machine learning predictions;
 - Segmentation Based on Latent Class Analysis through surveying customers;
 - Segmentation in the form of Clusters of customers, based on items they purchase, using machine learning.
 - In-depth qualitative analysis of single order customers.

My work brought valuable insights about MuscleFood customers, their habits and underlying patterns in purchasing behaviour that then informed Marketing, New Product Development, Finance as well as overall business actions.

Senior Customer Insight Analyst

The University of Nottingham

During this project I worked on anonymised FMCG retailer's transactional data as a part of neo-demographics team in Horizon, the research institute. Using data science tools such as SQL and machine learning techniques I developed a methodology and a predictive model that predicts who are the future customers of a brand. This enabled me to develop my analytical skills and get richer insight into customers’ mind-set and reveal what are the exact triggers that drive brand switching. The factors I analysed included both transactional data and psychological traits of customers. 

The approach to this problem is unique as it endorsed several perspectives and for the first time, psychology, machine learning and both qualitative and qualitative research were immersed with the aim of achieving deeper understanding of connections between social patterns and predictions extracted from the data.

Throughout this project my role as a project manager included: predictive modelling; generating predictions; analysis of the results; creating creative brief; presenting and communicating results in a clear and succinct manner; managing stake holders and training others as well as myself, using the budget I was provided with.

Eventually, the results I accomplished here were:
•	Developed methodology for product categorisation based on the factors that affect people to start purchasing a particular product of a brand
•	Detailed description of people who are most likely to start purchasing products/brands and hence -
•	Personalised recommendations for each product/brand regarding targeting
•	Explanation of factors that drive the behavioural shift
•	Creative brief and recommendations for product implementation strategy

Data innovations manager

Age UK Nottingham & Nottinghamshire

Market Research Consultant

Nottingham City Council

As part of a consultant team comprised of current students on the MSc Marketing course at the University of Nottingham, we were responsible to help Nottinghham City Council to create an effective marketing strategy to increase the use of their online channels.

Marketing Consultant

MCF MegaCom Film

Marketing Internship

I create insights about the rich customer experience - using data science and psychology.

After years of involvement in academic research, I co-founded Kubik Intelligence. Our goal is to humanize data to drive sales and loyalty by uncovering customer secrets and solving industry problems.
 
I am a data psychologist, thought leader, serial entrepreneur, advertising enthusiast, public speaker, published author, UoN guest lecturer, ENTJ type and an avid jazz listener.

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