Did you know that customer churn is sometimes good ?

Hello AI 4 HUMANS Community,

Efficiency does not replace Effectiveness. It is nowhere more profound than in the world of AI, where people mix between efficiency and effectiveness. Effectiveness is Human, initiated by humans and consumed by humans.

A fast and accurate AI model does NOT necessarily mean it solves your problem. The importance of assessing the effectiveness of the solution can never be overstated. It requires ample initial enquiry to ensure alignment with the problem statement at hand. Many times, the initial proposed solution is NOT the best solution for the underlying problem.

Use case example when Churn is Good:

Take for instance the following example: We were hired to produce a solution for a Customer Churn problem. After our insistence on digging deeper into the functionality of the client's problem, we found that 80% of their churned customers is GOOD for them. How is churn good? Well, the customers that do not resonate with their best interest were the churners, however, they did poorly in maintaining and ENTERTAINING their ideal customers. So, instead of working on churn, we worked on identifying the features and anomalies of their ideal customers, with their likes and dislikes as described by customers themselves in their communication with customer service. So, we deployed a GPT model with a customer NLP knowledgebase, encompassing ALL customer interactions with the service provider, classifying all relevant keywords, ready for retrieval by our developed algorithm that produced a predictive model, based on accurate customer feedback.

Moreover, not every use case is a functional one. Many projects rush the initial process of use case discovery and functionality attainment, which renders the project inappropriate for the attempted solution. The main thing lost here is effectiveness, while the model may be fast and accurate towards the parameters it was built on. This is a BIG problem for many projects and data scientists.

Our Message to both Users (customers) and solution providers: Take your time to find out the REAL ISSUES so that your medicine will work well.

WARNING: Techies only

Never forget to not rush the “Problem Statement” initial stage and expect to guide end users of the project. One of the major reasons for flawed projects is the rushing to final solution output design. Another fatal aspect of projects is the non-functionality of the problem statement, which really means either an unsubstantiated problem statement with data or tackling an incomplete dataset.

A thorough and careful business dilemma discussion is an absolute MUST to arrive at a highly effective and functional problem statement, way before you start fetching the dataset requirement. Watch out, many times the end users are the ones rushing this initial process because they are fed up of the adverse scenarios they were facing and want to jump to the fix.