Data Science Service

Deep Understanding

Customer behavior is not irrational or random. Make sure customer experiences happen the way they want them to. To do this, we work together to leverage your operational business data and link it to new data that captures customer experiences.

Machine learning

Then, together with machine learning, we develop algorithms that interpret their data in a fully automated way. As a result, you personalize customer interactions, anticipate future customer experiences, and make better decisions.

Turn experience data into information to gain deep insights. Discover what really matters to your customers and customize your services with data-driven solutions.

Implementation process

Data-driven improvement takes place in three steps: Explore, Analyze, and Execute.

Explore

  • Review existing databases.
  • Determine metrics of experience.
  • Capture (in)conscious emotions.
  • Identify customer personality.
  • Find frustration triggers.

Analysis

  • Prioritize actions.
  • Anticipate future behavior.
  • Segment customer groups.
  • Leverage machine learning.
  • Connect operational data with experience data.

Execute

  • Focus on customer requirements.
  • Implement potential for improvement.
  • Integrate emotional highlights.
  • Adapt to customer feedback.
  • Ensure continuous optimization.

Get in touch now

Are you looking for a way to get psychological concepts from your data set?
Let us clarify the possibilities in a non-binding consultation appointment.

Classifications

Data Science deals with the processing and analysis of large data sets. Neurolution combines the methods of Data Science with behavioral economics, psychology, neuromarketing and user experience. Algorithms and artificial intelligences analyze correlations and generate profound insights from your data sets, which are unmanageable for humans.

Specifically, models are developed that help you statistically segment your customers and potential customers and thus assign them to target groups in a mathematically sound manner. It can be determined for all target groups which characteristics distinguish groups (clusters) and which behaviors are the same across all groups and not suitable for differentiation. Based on these results, you can be sure to address your customers in a personalized way and to convince them with relevant arguments.

Forecast models

A second application area for Data Science is predictive models based on probability theories (stochastics). These models are trained, tested and validated with past and present data. Subsequently, it becomes clear which parameters from your data have a significant influence on variables such as purchase probability, price willingness, profit or delivery quality.

Built models can precisely determine a customer’s future behavior and how it can be positively influenced. In this way, candidates for cancellation can be precisely addressed before they cancel, or prospective customers can be encouraged to buy with the right measures. These and other insights can be calculated from the data that most companies already have at their disposal.