Customer Behaviour Modelling

Customer Behaviour Model is used to identify the behaviour and predict the similarities among various customers. Customer behaviour modelling illustrates similar behaviours in pool of customers. This model is entirely based on data mining of customers data and build to create a solution at the point of occurrence.

  • Complexity of Customer Behaviour Modelling

This model is used to predict the specific group of customer reaction on their marketing action. Creating a customer behaviour model is critical and high-priced task. Mathematical techniques are used to identify the common customer margin. Experienced and smart customer analytics experts only can able to build this model.

A model is complicated to change after its creation. It is very complex to change according to marketer’s perspective to identify the marketing action to be applied on group of customers.

In spite of these complexity issues, there are many customer models with relevantly simple. 

  • The RFM approach to customer behaviour analysis

There are various behaviour models that are based on an analysis of recency, frequency and monetary value. Analysis of customer’s money spent for the business purposes that indicates; spent recently will spend more than others, spend regularly will spend more than others and spent most of money on business will spend more than others.

Business marketers and managers can understand RFM with ease. RFM is well known and it is not necessary to have a specialised software. RFM can not able to generate accuracy levels, needed for marketers. RFM model only illustrates past of the customer. It cannot predict the future behaviours accurately. This model considers customers view at certain point of time and will not consider the past behaviour of the customer. 

  • A better approach to Customer behaviour modelling

New modelling methods are introduced into the market. These are more advanced and efficient than traditional methods. Merging various technologies into a closed loop system that helps to produce accurate customer behaviour analysis to marketers.

New behaviour modelling method is achieved by integrating these capabilities:

  1. Separating customers into different groups. Based on the customer behaviour, address individual customer. Use any pre -conceived notions or assumptions in-place of hard-coding to sort customer similarities.
  2. Track the customers, identify and analyse their move in different segments that includes customer life-cycle context and cohort analysis. Instead of identifying different segment customers, identify how they arrived.
  3. Predicting future behaviours of customers accurately through implementing predictive customer behaviour modelling techniques. 
  4. Advanced  calculations are used to identify the customer life cycle value of every individual customer and decisions based on it. 
  5. To improve the long-term value of individual customer, based on objective measurements the marketers actions should be implemented. 
  6. Inducing machine learning marketing technologies that will reflect the views and recommendations for customer marketing improvement. 

Traditional and conventional approaches can be distinguished. Traditional approach is like a frame without lens whereas, conventional approach is inducing frame to that empty frame.

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