Uncommon and unseen challenges posed by the current economic environment require exceptional, fast, and targeted interventions to the business execution in the Frontline. Margin, conversion, and volume are all driven by price. So how can organizations resolve multiple issues with one instrument?
Data, AI & Digital Tools are mainstream in the retail & consumer industry and now proliferating the industrial sector. These proven solutions can be leveraged to brings deep insights, differentiated solutions, and overall business optimization in short time spans.
The fundamental point is to predict churn, not to prevent churn. Subsequently, we anticipate the impact. The changed outcome method is useful. It changes the names in the dataset with the end goal that our model predicts. That information will have inclinations. In this way, we have to recognize relationships and causation. The causal induction strategy backward inclination weighting accomplishes that. That permits us to get quality feedback to retrain the model. Subsequently, we prevent churn from utilizing modeling, displaying, and support learning.
Issue: Predicting churn
As we want to prevent churn, it is a logical step to predict who is going to churn. We then target those who are likely to churn. To determine the best treatment, we could use a different treatment per segment of customers.
This approach separates the goal of preventing churn into two sub-goals. The data scientists determine who is going to churn. The customers who are likely to churn, are not necessarily easy to retain. They can be so-called lost causes. Predicting churn likelihood makes the challenge of treatment selection very difficult. For some customers it becomes impossible.
Customer Churn & Declining Share of wallet:
- Shrinking customer base with customers taking their business to competitors
- Volume attrition/decline at existing customers
- Tumbling prices to heavy competitive pressure from emerging markets/
- High acquisition costs associated with hunting and cultivating new business to profitable heights
Is it that bad?
You may believe that these lost causes are exemptions. Or then again more down streaming, that we discard those groups with additional business rules. Overlooking the applied techniques in the model outcomes in a negative impact.
Example: Assume we call a few people with a high score and can convince them to not churn. They will wind up in our future preparing dataset as non-churners. Retraining the model will bring about them getting a lower score. Subsequently, we figure out how to quit calling the persuadable clients.
Optimize for Business Value
Unfortunately selecting the treatment with the highest uplift is not good enough. That selection ignores the different costs of the treatments. Sending an email is for example much cheaper than visiting a customer’s house. Customers are also not equal; they bring in different revenues. To make the best choice, we need to maximize business value.
The loss of customers, also known as churn, can have a major impact on your revenue. Machine Learning (ML) can prevent this, but not every ML solution will be adequate. Some might even be harmful, letting you pursue the wrong path. Each business has its specific possible treatments. We can send them an email to give extra information about the product. Or we can call them to find out if there is an issue. The effect of each treatment depends on the customer’s situation. The goal is to find out which one will retain the customer.
Predicting Churn likelihood, Price Sensitivity & Retention Price to Equip Sales with insights needed to cultivate a sound customer base.
- Understanding contributors of Customer Churn & Decline SoW
Historical volume-price trends and customer events are used to determine drivers most commonly associated with customer churn or volume attrition.
- Building a Predictive Model to assess likelihood churn
Use historical predictors of churn to predict which customers are likely to churn or reduce their product volumes in the near future and flagging these.
- Actions & Recommendations to Prevent Churn
Conducting additional analysis on price levels, price aggressiveness, price sensitivity, CLV to propose price concessions necessary to retain customers.
- Actions & Recommendations to Increase share of wallet
Finding customers with free volume or volume win back potential and recommending nominal price reductions in order to increase volume & SoW
This arrangement has brought together strategies from various lines of thought. We focus on feedback and significant (prescriptive) predictions. We consider inclination and causality utilizing backward affinity weighting. We consider volume uplift with insights on customers with Up & cross-sell along with win-back potential, discount pricing for win-back & bundled pricing, net gains. Thus, we promise actionable insights for sales to execute a top-line strategy and ensure a steady customer base.