Beyond Model Estimates : Reporting Effects in A Visual Way

Beyond Model Estimates : Reporting Effects in A Visual Way

Established ongoing relationships with existing customers can be a significant source of revenue for many businesses losing customers to competitors can significantly cut into a company’s revenue.

Introduction

Established ongoing relationships with existing customers can be a significant source of revenue for many businesses losing customers to competitors can significantly cut into a company’s revenue. Managing this phenomenon, taking active steps to prevent customer “churn” is a high priority for many businesses. Thus, it’s critical to communicate data science insights to decision-makers in the organisation. Most decision-makers in organisations are not data scientists, but these individuals make important decisions on a day-to-day basis. Thus, this is of utmost importance those of you who are eager to use machine learning techniques needs to avoid the black box syndrome: a very accurate model is nice but when you’re trying to understand a situation, an insightful model is better. And the challenge with more advance models is usually that their output is more complex to interpret.

Data Science and ML have been one of the most talked-about trends in 2019 and without any surprise, they will continue…

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Business Analytics and Machine Learning

Data science is a craft. As with many crafts, there is a well-defined process that can help to increase the likelihood of a successful result. This process is a crucial conceptual tool for thinking about data science projects. To illustrate how complex machine algorithmcan be applied to business analytics, consider a set of questions that may arise and the machine learning algorithms that would be appropriate for answering them. These questions are all related but each is subtly different. It is important to understand these differences in order to understand what machine learning algorithms one needs to employ and what people may be necessary to consult.

  1. Who really are the customers?
  2. Can I characterise them?
  3. Who are the most profitable customers?
  4. Is there really a difference between the profitable customers and the average customer?
  5. Will some particular new customer be profitable?

Customer Churn Prediction Models

As a data scientist typically at‐ I tacked a project by decomposing it such that one or more of these canonical tasks is revealed, choosing a solution technique for each, then composing the solutions. I have introduced concepts of predictive modelling which entails the above-mentioned questions, one of the main tasks of data science, in which a model is built that can estimate the value of a target variable for a new unseen example. In the process, we introduced one of data science’s fundamental notions: finding and selecting informative attributes as well as building top-notch machine learning models. For example, we gather historical data on which customers have or have not left the company (churned) shortly after their contracts expire, attribute selection and eventually machine learning modelling was performed on that dataset.

One can refer to my previous blog “Unlocking Behavioural Secrets to Overcome Churn Extremes: UnderstandPredict, and *Minimise *Customer Churn” to understand the various perceptions of churn that exist among businesses.

modeling visualization churn customer-retention data-science

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