Cropland introduces 'behavibility', the data science that analyses entity behavior; it refers to the ability to predict and even, prescribe the behavior of customers as being individual consumers, governments or organisations. Typical questions to be answered: 

  • Who are your customers internal & external, people and organisations?
  • How do you differentiate?  What’s their ‘consumption’ behavior?
  • How does the purchasing decision process work?
  • Can we pro-actively involve them in our offering?
  • What’s their retention risk and should we act on churn, or not?

Data cases:

  • Customer behavior analysis including omni-channel purchase insights, purchase drivers, churn & loyalty analysis, fraud detection, credit scoring, etc.
  • Recommendation systems for optimizing product offerings and cross-sell opportunities.
  • Customer segmentation and profiling including lead generation & qualification.
  • HR related profiling such as retention analyses, profile search optimization, job–employee matching, etc.

Data story

"A manufacturer of raw materials in the construction sector wanted to generate a list with leads based on the end customers’ sector classification code. As they were unsuccessful in the past to optimize this lead generation, they asked Cropland for a new approach. Text-mining algorithms were used to explore different open data sources in order to match it with key words indicating a probability score of a fit/non-fit lead according the NACE-code. The success rate of outbound marketing activities based on the Cropland’s prospect list was significantly higher than other former attempts to find the right potential customer."

Data story (in combination with Traceability)

"In collaboration with Mobistar, the city of Antwerp wanted to explore the possibilities to monitor crowds during a series of public events: e.g. a fix location with a visiting period of 7 weeks or a sports event with very high density crowds during one day. Cropland analyzed the mobile data sets and proposed a methodology to separate local contacts, independent of the event, from visitors’ contacts. This approach allowed computing the real number of daily visitors and how they behaved during their journey. For the sports event, the crowd monitor tool was offered as a real time dashboard for local security"
Press release:

Belga NL:

Belga FR: