Python Predictions has solved over 500 business data-related challenges. We predict future behaviour of humans and machines. We segment clients and employees. We forecast product demand, build recommendation engines and we analyse your processes. And we always seek truth and beauty in solving business challenges in a data-driven way.
From structured problems to exploration
Where our original projects were often well-defined and well-structured, we also cooperate regularly on less-defined, exploratory projects. Where needed, we partner with our clients to refine project definition, translating the business challenge to a concrete, solvable analytical challenge. In this process, analytical translation skills are key. After we solve the puzzle and implement the solution, we guide clients in how to use the output to create real business results. And we increasingly tackle those challenges in an Agile way.
Building data-driven segmentations is one of the core things we do at Python Predictions. The tendency towards more personalised marketing and to better understand customers, puts a data-driven segmentation at the core of a company’s action plan.
Using predictions is all about making better-informed decisions. Find an answer to questions such as “Who to target? Who will churn? What drives our churn?”. How? By using predictive analytics, increasing efficiency in a broad range of applications. (from marketing to operations, risk and HR).
Companies collect massive amounts of data on the behaviour of their customers. How can you benefit from this data to offer your customers the best-personalised recommendation on their next action?
Most companies are aware of yesterday’s business results, but how can you learn from these figures to predict what will happen tomorrow? And how can you make the right decisions to influence the future and outperform yesterday’s results?
Organisations are collecting massive amounts of data. Most of the time, they focus on finding the trends that will help drive business decisions. Yet, in some cases, the most valuable information is found in those cases that deviate from the trend.
Social networks are at the heart of our daily lives. However, using Networks Analytics to tackle business problems is often neglected. We’ve seen great value in its power to visualise data and reveal complex patterns.
“You can’t improve what you don’t measure” lies at the core of process mining. How can you start redesigning your processes, if you have no view on how your processes run in practice? Process mining is the perfect tool to unlock the as-is situation based on data to identify improvement points.
Maintenance is an important yet costly activity in many industries. Increasing the efficiency of maintenance processes is a key priority and offers a lot of opportunities. How? By shifting from a reactive to a proactive way of working.
Many companies already collect a lot of structured data and store it in a data warehouse. However, most companies also have a vast amount of data in the form of text. Getting information out of such data is much harder, but could potentially add a lot of value. How can we tap into this hidden potential?
Data comes in many forms, in this case in the form of digital images or videos. Extracting information from digital images can help you better understand and automate business processes and decisions.
More and more connected devices are invented, improved and becoming part of our daily lives. “Connectivity” means “data” and where there is data, there is analytics. IoT devices produce huge amounts of information that can be analysed.
Focus on data innovation
The most important data a company has, is often well structured. However, we have gained experience in analysing new data in a variety of formats and sources, often using new technologies. 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.
We strongly believe that every business and organisation needs a data strategy. Data has become one of the most important business assets for companies. It is important to approach this asset strategically, in order to figure out what data is needed and how this data can be used to improve your business performance.
In the current changing business environment, directors and managers are constantly under pressure to make fast and accurate decisions. Faster decision making and identification of patterns or new business opportunities has not only become a competitive advantage but also a need for reaching your company’s strategic goals.
Data science puts the intelligence in AI. The majority of our work revolves around building and using algorithms in a business context. But solving concrete business challenges is not the only lever to business value. We strongly believe that making data science accessible, and making talent available are key assets in our increasingly data-driven business world.