Client Engagement Prediction Modeling

Client Engagement

Understanding client ecosystems will allow you to shape client engagement and attrition predictors. Client engagement is the connection between your organization and your clients. This is facilitated via a number of channels where many data points are being recorded and used to predict future interactions. These data points are used to determine the style of engagement, energy of interaction, and overall sentiment. We use a machine learning approach and a number of algorithms to determine the most optimal interaction approaches. The interaction style is tracked and differs between glass, voice and human interactions.

  • ­Moving beyond the individual by understanding customer networks.
  • ­Front-end retention analysis show areas of improvement when selling and onboarding customers.
  • ­Predicting when customers are about to switch to alternatives.

The process of onboarding clients, and ensuring ongoing service delivery, requires a combination of algorithms to be assembled and deployed as an ensemble to achieve prediction quality.

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Predict Client Engagement

Our prediction enabled engagement process analysis and predictor deployment enable your processes with intelligent technology.


Strategy is about exploring options for the future. Your digital strategy is used to shape the ongoing approach when using intelligent technology to capture a new future. The implication of using conversational intelligence, automatic categorization, cross-selling and up-selling, or other key areas of intelligent automation all require a different way of thinking.

We can assist you in shaping this future that’s best for you. A consultative and workshop style of interaction is used to gain understanding of your strategic challenges and automation requirements. This will inform our process.


The first step is to create a project with specific deliverables. Projects shape the collective execution of all the elements that are needed to deliver on a deployable predictor. Prediction projects normally have a specific structure to ensure that you can successfully move from scope, to data munging, model preparation, and to your deployable predictor.

We provide you with a set of templates and an approach to get your prediction project executed. The ultimate measure of success is determined once you have your project measures validated.


Various client engagement challenges are represented in models. Each model has a specific purpose and calibration point. Typically the purpose of the project would be quite specific. You might want to predict when clients are switching to new providers, or maybe to predict the clients’ happiness with your service or product.

The eventual outcome of this phase is to find the most accurate predictor. You will then assemble these models as an ensemble.

Review Results

Many algorithms use supervised learning models that are dependent on training data sets. Your project needs a phase where you can solve any prediction inaccuracies through an iterative process of review and data preparation. We provide you with a set of data enrichment tools to assist you with this process of finding the optimal predictor.

Review test results and compare against the deployed predictor. Even in the world of AutoML you might find that the ongoing iteration of data selection and preparation, and model calibration for computational efficiency, becomes an ongoing process.


Your project does not really end here, but you will use the deployed predictor to determine your success. Your client engagement metrics will be used to determine if the intelligent automated model deliver on the benefits intended.

The generated runtime engine will be deployed to your IT organization while the data science team continues the model development process.