Data science has always been our core business (we even started in 2006, long before the hype). The large majority of our work evolves around prediction in a business context. For example, we predict which prospects to attract, what customers will buy next, which customers will leave, who will pay, which machine will fail, who risks a burnout, etc.
Yet besides prediction, we also gathered a lot of experience in creating customer segmentations and recommendation engines. We nailed projects in process mining and demand forecasting, where we optimize process efficiencies and logistics.
And while we are often included to create the first success story, we have great references in industrialising analytics in mature organisations.
With the growing popularity of data science, the scope of the domain has broadened. Where our original projects were often well-defined and well-structured, we see an increasing demand for data exploration projects. In a growing number of projects, we work in an agile way on exploring new data in a variety of formats and sources, often using new technology. These new data and technologies lead to novel applications such as image recognition, IoT analytics, location-based analytics, network analytics, etc.
We ensure we continuously innovate through dedicated development time, our connection with modern data science communities and our collaborations with academic programs and business schools.
Since the very start, we invested a lot of time in training and coaching for both managers and data scientists where we focus on the essential components of data science projects. We consider training to be an important factor for increasing adoption and maturity of data science.
We often conduct analytical roadmaps, leading to a concrete list of feasible and valuable project ideas. We offer intuitive, non-technical training for managers, as well as technical hands-on training for data scientists. And we coach both managers and data scientists in their challenges.
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A fantastic customer experience is of strategic importance for many companies today. In this age of information overload, customers expect to be targeted adequately, while marketing efforts are increasingly evaluated based on their return on investment.
Through Data Science projects, marketeers today are increasing targeting efficiency and relevance for different types of marketing campaigns over different channels (online, offline) and media. Typical applications in the marketing domain focus on new customer acquisition, cross-selling, upselling, customer retention and (re)activation.
In risk evaluations, it is widely understood that mere human judgment does not suffice to adequately estimate future risk.
Typical applications in the risk domain include credit risk (i.e. estimation of creditworthiness) and fraud risk (i.e. detection of fraudulent behavior). A growing range of organizations today make use of proven data-driven methodologies to analyze massive amounts of data, in order to provide reliable estimations of future risk.
Also in operations, a growing number of organizations deploy the abundance of internal data with modern data science and machine learning techniques in order to improve process efficiency. Such applications focus on optimizing stock volumes, maximizing production output or minimizing inputs, waste, pollution, etc.
Data Science has the leveraging potential to turn the currently existing massive information flows into value.
The newest applications in Data Science lie in the domain of human resources. Indeed, an increasing number of companies is using data to support evidence-based decision making in HR.
The most obvious applications are related to planning and forecasting, recruitment, development and retention of staff. We are extremely excited about our first experiences in this field.
01 Do you only use Python?
No, the name Python Predictions originates from the Greek mythology, where the Greek god Python resided at the Oracle at Delphi. Hence, it was the first Greek god capable of predicting the future. We chose this name in 2005 for that reason only.
Python, as a coding language, has become increasingly popular recently, also in our team. But we equally use R, or even commercial software packages such as SAS or IBM SPSS. In short, we use the solution that makes most sense for a specific client.
So while we also love Python – the Python in our brand name does not refer to the programming language.
02 How can we assess the opportunities?
Most clients address us with a specific problem. For example, some companies aim to improve customer retention, others aim to improve new client acquisition, others aim to reduce fraudulent behavior and others aim to optimize stock levels. However, we have also helped clients with more general questions, such as: ‘is my company mature to benefit from predictive analytics?’. The time needed to perform such assessments may vary from as little as one meeting or as much as a week. Most often, such questions result in a strategic roadmap for data science projects.
03 How much internal expertise is required?
While some of our clients have a large team of internal data science experts, we have equally worked with companies that have no previous internal experience. In the different settings, we work towards the same goal: offering powerful predictions that can be interpreted and trusted by the business users. This means that we always strive for an optimal solution, but we aim to adapt our communication and presentation to the interests of the user, balancing the focus between managerial and/or technical aspects.
04 How can we collaborate practically?
Depending on the availability of hardware, software and office space, we can perform our activities at our client’s location or at our offices. Our primary goal is to perform projects from A to Z, i.e. starting from the problem definition to the solution and recommendations. Furthermore, we also offer support and guidance during the implementation of the solution in the customer’s IT infrastructure. An initial data assessment may be useful to identify and quantify opportunities.
We also offer company-specific training and coaching in the areas of data science, predictive analytics and machine learning, and customer segmentation. These courses are adapted towards the (desired) level of expertise and interests of the client.
05 Do you invest in analytical communities?
Absolutely. Since the launch of Python Predicitons, we have invested both time and resources in analytical communities. Most recently (since 2015), we are actively collaborating with the Brussels Data Science Community and DigitYser where we offer training for managers as well as Data Scientists. Previously, from 2012 to 2015, Python we were responsible for programming Predictive Analytics World London, an international vendor-neutral event focused on bringing together managers and practitioners in the domain of Predictive Analytics. In ancient history (from 2006 to 2009), we were heavily involved in building baqmar.eu, a large vendor-neutral Belgian community for analytics practitioners.
06 Is data science useful for my industry?
Data science is useful across a wide variety of industries and applications. Although retailing, e-tailing, mail-order, banking, telecommunications and subscription services provide some of the most obvious players that currently profit from data science, the methodologies and technologies used are generic and industry-independent. However, we consider an accurate definition of the business question and an understanding of a company’s strategy and tactics crucial to deliver valuable results.
07 Are you connected to universities & business schools?
Absolutely. We regularly provide introductory sessions about our work at respected Belgian universities such as Ghent University and KU Leuven. And we have contributed to business school programs and workshops at Solvay Business School (Belgium), Vlerick Business School (Belgium), and IÉSEG School of Management (France). Moreover, we have a long-standing structural academic collaboration with Prof. Kristof Coussement at IÉSEG (France).
08 Data science or predictive analytics?
Many successful data science projects contain an important predictive component. Predictive analytics is a term more often used for challenges in a business context. It refers to the methodology needed to convert historical information into valuable and accurate predictions of future events, in order to improve decision making. In this way, banks aim to predict who will repay credits, retailers aim to predict who will visit the store, and which item they will buy next, etc.
While the majority of our projects still have an important predictive component, we have also embraced other activities related to modern data science projects – often of a more exploratory nature.