Nowadays, both companies operating in B2C and B2B markets realize the need for personalized customer relationships. In this age of information overload, customers expect to be targeted adequately, while marketing efforts are increasingly evaluated based on their return on investment.
Using Predictive Analytics, 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 statistical 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 analytical techniques in order to improve process efficiency. Such applications focus on optimizing stock volumes, maximizing production output or minimizing inputs, waste, pollution, etc.
Predictive analytics have the leveraging potential to turn the currently existing massive information flows into value. The used analytical techniques are generic and can be applied throughout different settings whenever historical data are available.
The newest applications in advanced analytics lie in the domain of human resources. Indeed, an increasing number of companies is deploying advanced and predictive analytics to support 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.
Data science is the art of converting data to value. While the majority of our projects have a predictive component, value comes in many forms: from creating valuable insights to customizing product recommendations and even to creating data-driven products. Our Data Scientists can help in project mode (sharply defined objective) or reinforce your team temporarily to work on your data science challenges.
Why would you choose for our Data Scientists in your team?
– Our proven track record in a wide range of industries
– Our people are focused on getting actionable results
– Our data scientists have learned to communicate in a non-technical way
– They are highly involved in solving interesting challenges
Since 2006, we offer trainings for managers and analysts where we focus on the essence of predictive analytics. We consider training to be an important component for increasing adoption of advanced analytics. We offer both intuitive, non-technical training as well as technical training – depending on the audience and goal of the training.
Subscribe to our training newsletter to receive instant notifications of newly scheduled trainings, or contact us at email@example.com to receive a quote for in-house training for managers or analysts.
01 Creative, Custom-made Solutions
Each problem is unique. An optimal solution is dependent on numerous elements, such as organizational environment, industry, amount of available data, varying end-user needs,… Python Predictions chooses a problem-focused approach and offers custom-made solutions to address the specific needs of its clients.
02 Quantitative Expertise
Our expertise lies in predictive (database-driven) analytics, an application in which past events and behavior are used to predict future events and behavior (responding to promotional offers, product purchasing, customer defection, credit repayment, fraudulent behavior, etc). All projects are closely monitored or executed by PhD’s trained in economics, marketing, statistics and quantitative modeling. We consider the integration of these domains to be crucial for the success of our projects.
03 Actionable Results
We are committed to deliver actionable results which can be directly implemented into existing strategies, procedures and methods. An entire project is worked out in close collaboration with the sponsor, the user and all other departments involved. Our findings are extensively validated on real-life data to assess their future impact, and to ensure practicability.
All team members are passionate about analytics, and are highly involved from the very start until the very end of each project. A team is dedicated to executing the whole project, from the first step of business understanding to the last step of implementation. This guarantees our ability to stay focused and develop an excellent understanding of the problem at hand.
01 What is predictive analytics?
Predictive analytics refers to the business process in which analysts use all information available to predict 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. Telecom operators predict the probability of a client leaving the company, etc. Predictive analytics refers to the methodology needed to convert historical information into valuable and accurate predictions of future events.
02 Why build custom-made solutions?
At Python Predictions, we fully recognize that every challenge is different, because business requirements, internal expertise, industry, scope,… may differ substantially. Hence, we do not believe that the most important challenges in our domain should be solved using mass-production pre-packaged solutions, and we consider it as our motivation and mission to deliver projects that satisfy the problem definition to its fullest extent.
03 How can we assess the opportunities?
Most clients address us with a specific business problem. For example, some clients 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, Python Predictions has also gained experience with more general questions, such as: is my company mature to benefit from predictive analytics. The time needed to perform such assessments largely depends of the strategic importance of analytics, and may require as little as one meeting or as much as a week.
04 How much internal expertise is required?
While some of our clients have a large team of internal analytical experts, we have equally worked with companies that have no previous internal experience in terms of predictive analytics. 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 commercial and/or technical aspects.
05 How can we collaborate practically?
Depending on the availability of hardware, software and office space, we can perform our projects at our client’s location or at our offices. Our primary goal is to perform well-defined analytical projects from A to Z, i.e. starting from the definition of the business problem to the solution. Furthermore, we also offer support and guidance during the implementation of the analytical 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 predictive analytics and segmentation. These courses are adapted towards the (desired) level of expertise and interests of the client.
06 Is predicting behavior possible?
Predicting the future is challenging. It is like predicting tomorrow’s weather. Knowing that there is an 80% chance of sun does not necessarily mean you will stay dry without an umbrella. The great advantage of predictive analytics however, lies in the fact that one does not make only one prediction. In marketing, for example, predictions of the behavior of every single customer can be made for a large customer base, whereby the joint performance will be related to the return on investment. Hence, when predicting behavior, it should not be expected that all predictions turn out 100% correct, but in important projects, a small difference in predictive quality can be reflected in large monetary benefits.
07 Do you invest in analytical communities?
Absolutely. Since the launch of Python Predicitons, we have invested both time and resources in analytical communities. From 2006 to 2009, we were heavily involved in building baqmar.eu, a large vendor-neutral Belgian community for analytics practitioners. More recently, from 2012 to 2015, Python Predictions was 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. Since 2015, we are actively collaborating with the Brussels Data Science Community where we offer executive training.
08 Is predictive analytics useful for my industry?
Predicting customer behavior 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 predictive modeling, the techniques are generic and industry-independent. However, we consider an accurate definition of the business question and an understanding of a company’s (marketing) strategy and tactics crucial to deliver valuable results.
09 What are the necessary requirements?
Some requirements exist to allow for predicting future events. In order to predict the future, one must gain a good understanding of the past. This will very often imply that it is necessary to recreate the full history of similar events in the past. It is only by understanding previous behavior that one can make informed decisions about future behavior. Contrary to popular belief, the methodology of predictive analytics does not apply to Big Data problems only. There is no absolute minimum of records needed to engage in valuable predictive analytics projects.