Big data analytics & customer advocacy

Big data analytics & customer advocacy


  • The biggest challenge today of any organization is to understand what their customer wants; and be able to be one step ahead of him. Luckily the combination of technology and big data analytics tools makes this dream come true.


  • For the past 25 years, KPA’s team of experts has been dealing with using statistics in an increasing variety of ways whilst using different types of data. This means generating insights through analytics.


  • Using our expertise in big data analytics will gain you the following advantages:
    • Increase of up-sale/cross-sale
    • Increase usage of your product/system
    • Increased loyalty of your customers
    • Ability to prevent customer attrition


  • The concept is to integrate big data analytics tools and techniques with Customer satisfaction/ Advocacy initiative. In order to achieve this we propose the following milestones:
    • Setting up the objectives and goals
    • Collecting the relevant data from various sources
    • Analyzing the data using big data analytics techniques and tools
    • Integrating expert opinion with the big data analytics findings
    • Setting up control mechanisms, such as Dashboard/Hot alert/CDNEWS


  • Our team of experts know how to handle the most challenging data analytics tasks:
    • Customer profiling
    • Understanding customer needs
    • Identifying current behavior patterns
    • Predicting preferences and expectations regarding future use of features/materials
    • Setting up alerts’ rules
    • Defining operational targets


  • In order to achieve this, KPA experts team will:
    • Analyze data generated from various sources:
      • Customer surveys data – satisfaction and perceptions data
      • CRM – sales data
      • Operational IT – operational/service data
      • Sensors (Internet of Things) – usage data
    • Use predominant statistical programming languages such as R, currently the most widely used programming language, for predictive analytics and statistical computing. R can scale across big data systems due to its parallel computing and distributed processing facilities.
    • Use advanced statistical methodologies and models; such as:
      • Classification models (e.g. logistic regression, decision trees, random forests, discriminant analysis, support vector machines, naïve Bayes, k-nearest neighbor, neural networks)
      • Clustering models (e.g. k-means, EM algorithm, hierarchical clustering, principal component analysis, canonical correlation analysis)
      • Regression models (generalized linear models, non-linear models, ANOVA models)
      • Network models (e.g. Bayesian networks)
      • High-throughput statistical testing in complex studies


Contact us today to see how KPA’s expertise in big data analytics can take your understanding of your customer’s behavior and needs to the next level!

Contact us for a free consultation or fill out an enquiry form