Become a frontrunner with predictive analytics

In today’s world, data is the key to tomorrow's success! 

However, due to the endless number of application areas and a lack of knowledge about the data science field, it is challenging to turn your data into actionable insights. Hammer supports your company in maximizing value from your current data flow.

‍

Three-phase problem approach

In our predictive analytics projects, we use of our expertise in the field or AI (Artificial Intelligence). Our approach to a problem usually consists of three phases:

1. We want to understand the intelligence need as clearly as possible: What information does the customer want to have after this project and what decision needs to be substantiated?
   ‍
2. Understand the currently available data as clearly as possible and try to expand the data pool in areas that might provide additional insights. These additional insights are usually provided by external variables.
   ‍
3. We decide which machine learning models will provide the most valuable results, considering the availability of the data and the intended use of the project.

It is essential to work closely together during the first phase of the predictive analytics project, this will kickstart the second and third phase. There is a lot of going back and forth between phase two and three since different models might have different data requirements.

Application area's

The opportunities with predictive analytics are endless. Below are three examples of common applications to provide an idea of the versatility of predictive analytics projects:

• Pricing methods: Predictive analytics offers valuable insights when determining the optimal pricing of a range of products or services. In this field there is usually a lot of data available that can be used as input data (think about historical pricing, sales etc.). This enables the power of machine learning assisted models in predictive analytics. The availability of this data makes it increasingly difficult to get a competitive advantage without using predictive analytics.
  ‍

• Inventory analysis: Inventory management is a vital part of many businesses, optimizing is key to stay competitive. Combining input data related to product lifecycles, sales trends and seasonality can often provide a solid base for machine learning models.This model can then be applied to optimize inventory, avoid overstocking and improve the buying process.
  ‍

• Predicting & managing crowds: Predictive analytics can be useful in predicting crowd behavior. Understanding crowd movement used to rely on fixed data (e.g., ticket sales), but with the increasing availability of data and the development of predictive models, the role of predictive analytics in this field is increasing. A combination of input data like GPS data, weather data, and/or historical data can give a much more accurate understanding of the crowd behavior. Last year, we supported VisitVeluwe in predicting and managing crowds in the touristic Veluwe region. Curious about this project?

Of course, predictive analytics is not limited to these areas. If you feel that you have an interesting use case where we can assist you with our expertise in predictive analytics, feel free to contact us to explore the possibilities.

‍

Our team at Hammer

Together with our Hammer team consisting of different disciplines (data scientists, consultants & business analysts) we support your company to maximize value from your current data.  

Want to discuss your project? Get in touch!

About Hammer Market Intelligence

Hammer supports strategic decisions by data-driven market insights. We work for start-ups, SMEs, and leading multinationals. Why Hammer?

• Total client commitment and a passion for winning
• Providing clear and understandable insights
• Demonstrated skills and experience