The average frequency of customer engagement is approximately 4.17 interactions per customer, while the average recency - the time since the last interaction - is around 205.45 units.

Insights:

1. Customer Engagement Patterns:

   • The average frequency indicates that customers tend to engage with the business around 4 times on average.
   • The average recency suggests that customers, on average, make a repeat purchase or interaction after about 205 units of time.

2. At-Risk Customer Groups:

   • Customers who have not engaged in a longer time period than the average recency are potentially at risk of churn or disengagement. Identifying these customers and re-engaging with them could be crucial to retention efforts.

3. Suggestions for Different Frequency-Recency Combinations:

   • High Frequency, Low Recency (e.g., frequent buyers who haven’t engaged recently):

     • Re-engage with personalized offers or reminders to bring them back.
     • Offer exclusive deals or loyalty rewards to incentivize repeat purchases.

   • Low Frequency, Low Recency (e.g., occasional buyers who haven’t engaged recently):

     • Send targeted promotions based on their previous interactions to encourage them to return.
     • Implement a reactivation campaign with appealing incentives.

   • High Frequency, High Recency (e.g., loyal customers who make frequent purchases):

     • Show appreciation through thank-you messages or rewards for their continued loyalty.
     • Invite them to join loyalty programs for further engagement and rewards.

   • Low Frequency, High Recency (e.g., rare purchasers but recently engaged):

     • Provide product recommendations tailored to their past purchases to rekindle interest.
     • Seek feedback on their recent interaction to understand how to improve their experience and encourage future engagement.

By understanding these frequency-recency patterns and acting upon them with targeted interventions, businesses can better manage customer engagement, improve retention rates, and drive overall customer lifetime value.

The probability alive distribution indicates that on average, customers have a 58.19% chance of being active. This metric provides insights into the likelihood that a customer is engaging with a business, product, or service.

Given that 77.69% of customers are currently active, it suggests that a majority of the customer base is already engaged. However, to maintain this high level of activity, it is important to monitor and potentially re-engage with the remaining 22.31% of customers who are not currently active.

Segments that may need reactivation campaigns would include those customers who have a low probability of being active based on the distribution. By targeting these segments with tailored marketing strategies, businesses can potentially rekindle their interest and boost overall customer engagement rates.

It is important to strike a balance between active and dormant customers. While active customers drive revenue and contribute to the vitality of a business, dormant customers represent a latent opportunity for reactivation and can help prevent customer churn. Therefore, businesses should aim to strategically allocate resources to both retain existing active customers and re-engage dormant ones to maintain a healthy customer base.

The Mean Absolute Error (MAE) of 5155.98 indicates that, on average, the model’s predictions deviate from the actual values by approximately $5155.98. Additionally, the Root Mean Square Error (RMSE) of 10049.12 suggests that the model’s predictions have larger errors, with greater emphasis on outliers due to the squaring effect.

The presence of a holdout dataset implies that there was a separate subset of data used for validation, which is a good practice to assess model performance on unseen data. However, further details on the holdout set would be beneficial for a more comprehensive evaluation.

The relatively high MAE and RMSE values may indicate that the model needs improvement in its predictive accuracy. To improve reliability and reduce biases in Customer Lifetime Value (CLV) predictions, the following steps could be considered:

1. Feature Engineering: Including more relevant features or capturing nonlinear relationships could enhance the model’s predictive power.
2. Tuning Model Parameters: Adjusting hyperparameters through techniques like grid search or cross-validation may help optimize model performance.
3. Model Selection: Exploring different algorithms or ensemble methods can potentially yield better results.
4. Addressing Outliers: Understanding and treating outliers appropriately can mitigate the impact on model performance.

In summary, while the model has provided insights into predicted versus actual values, there is room for enhancement to increase its accuracy and reliability in CLV predictions. By addressing systematic biases and implementing improvements, the model’s performance could be improved significantly.