WHAT WE DO
Predictive Insights is a leader in behavioural science and artificial intelligence to improve business efficiency and profitability. Through a combination of data science, machine learning and behavioural insights, we help customers to accurately predict sales, staffing and stock levels. Our solution improves sales forecasting on average by 50 percent. We operate in Africa as well as Europe, Middle East and India in the restaurant, food processing, retail and financial service sectors.
We are part of Alphawave, a specialised technology investment group supporting businesses seeking to do things that are complex to replicate.
Our demand forecasting product accurately predicts store and product level sales on an hourly, daily or monthly basis. These forecasts can then be used to optimise stock and staff levels.
Our stock optimisation tool makes sure that optimal stock keeping units (SKUs) are available at the branches where they are most likely to sell over future periods of time.
Our sales insight module is used to identify different types of customers, potential target markets or product complements, and inform marketing about up-selling or cross-selling opportunities.
Our staff management tool provides suggestions for the optimal staffing schedules, based on the organisations criteria that may include minimising overtime or absenteeism, while maximising sales.
We work with our clients to understand the business problem that needs solving, the information and data they have which can help, and how the solution could fit with their own processes.
We design a solution which might draw on one of our existing products or might require the development of a bespoke product. Given our extensive experience and suite of existing products we can quickly build an effective solution.
We then maintain the solution or product to make sure that it continues to deliver value. If the client wants to build internal capacity we work together to transfer the ability to maintain and modify the product to the client’s team.
What data to collect?
How to extract data?
How to combine & structure for insight?
Cross-sectional and panel statistics
Time series analysis
Machine learning approaches
Trees and forests
Support vector machine
Deep learning methods
Using data for business decisions
Financial & risk modelling
The role of markets & prices
Timely reporting on results
Connect insights to strategy
Rulof is an econometrician, game theorist, and behavioural and labour economist. He has a part-time associate professor position at the University of Stellenbosch, a doctoral degree from the University of Oxford, and master’s degrees from the University of Cambridge and the University of Stellenbosch.
Neil is focused on making the most effective use of data within an organisation and on building solutions and teams to make this possible. He has degrees from the University of Cape Town and Simon Fraser University, and a doctorate from the University of Oxford.
Kevin is primarily interested in the modelling of variables that evolve over time and has developed several predictive models. He completed his graduate studies after receiving a doctorate from the University of Stellenbosch and has over twenty years of corporate and academic experience.
Richard is engaged with the development of customer centric solutions and the use of the Internet of Things to capture data for further analysis. He has a computer science and electrical engineering background; and completed his graduate studies at the University of Stellenbosch. He is also responsible for new business developments and building client relationships.
Cobus enjoys building and applying probabilistic models which have been applied in a variety of contexts. He has taught graduate econometric courses at the Universities of Cape Town and the Western Cape. He completed his graduate training at the University of Oxford and the University of Stellenbosch, and is in the process of completing a post doctoral fellowship.
Hendrik van Broekhuizen
Hendrik is interested in data management, data science workflows, and the visualisation of results. He completed his doctoral studies at the University of Stellenbosch where his research focussed on the linkages between education, skills development, and labour market outcomes.
Diana is interested in intelligent systems, natural language processing, chatbots, and integrating machine learning models into web applications. She has nine year experience lecturing at the Tshwane University of Technology, where she completed her MTech in Intelligent Industrial Systems. She also holds an MSc in Electrical and Electronic Systems from the French-South African Institute of Technology.
Michael is involved across the data science pipeline from data extraction and ingestion, through exploratory analysis, building interactive data visualisations and maintaining machine learning models in production. He completed his master's degree in economics at the University of Stellenbosch.
Mahlatse has a keen interest in machine learning models, reporting and data visualization, and how data science can be used to solve real business problems. She holds a degree from the University of Cape Town, where her focus areas of study were in economics and statistics. She is in the process of completing her BCom Honours degree in Financial Analysis and Portfolio Management at the University of Cape Town.
Dennis has a knack for spatial data collection and analysis, and is particularly interested in how people and technology come together to create and use this type of data. His interests include geographical information system (GIS) tools, database design, and data science workflows. Dennis is one of the 2018 YouthMappers Research Fellow, a program funded by USAID, which is designed to enlist, enable, and showcase the contributions of open geospatial data for research on resilience of vulnerable populations around the world, and is very active in the AfricaR community.
First Floor, Rhino House
23 Quantum Street, Technopark