What is next-best-offer/next-best-action modeling?
Posted: Sat Dec 21, 2024 5:24 am
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Next-best-offer/next-best-action modeling
Now you have insight into the value of your customers and the behavior they will exhibit. In the next step, you can work on influencing the behavior of your customers, in order to create the desired behavior. You can do this using next-best-offer/next-best-action modeling.
Using NBO/NBA modelling, we predict the most suitable offer or action to influence customer behaviour. We look at past actions or offers and their effect. We train a machine learning algorithm on these cases, predicting conditional probabilities of behaviour given the offer or action. This allows us to make effective choices in how we approach a customer on an individual level. Suppose we focus on customers who have a high chance of leaving, and we want to influence this chance. We then calculate the chance of leaving, given whether or not certain marketing actions are carried out.
Example insurer
Below is a fictitious example of an insurer. This insurer has various actions they can take to retain customers. For example: no action, a discount or a loyalty call and giving a discount. By modeling the effect of these actions on the churn probability, we can make very effective choices per customer.
In this example, we see that scenario 1 represents the chance of churn if we were not to set up an action. We also see that the chance of churn remains the same for client 1 and even increases for client 4 if we were to give a discount. In this case, the prediction therefore shows that it is better not to set up these actions for these customers. However, for the other customers, we see a decrease in the chance of churn and we have a valid reason to set up the actions. After all, it laos telegram data ensures that the chance of churn decreases. You do these calculations for all possible actions and combinations of actions, which gives you different scenarios per customer.
Of course, we choose the action (or combination of actions) that reduces the chance of churn the most. We calculate the insights obtained from the CLV model to assess whether a customer is worth the action. Of course, you can also calculate the total marketing budget, so that you make the right choices within the budget. The combination of churn, CLV and NBO/NBA modelling ensures large decreases in the churn ratio. Just calculate how much that yields in extra turnover.
table retention strategy
Possibilities other industries
Of course, you can also do this for other sectors, such as retail. You calculate whether the chance of purchase increases (renewal) when you offer a specific product. You then make the offer (or combination of offers) with the highest chance of purchase. It is very important to look at products that have the highest chance of purchase after purchasing a previous product.
Next-best-offer/next-best-action modeling
Now you have insight into the value of your customers and the behavior they will exhibit. In the next step, you can work on influencing the behavior of your customers, in order to create the desired behavior. You can do this using next-best-offer/next-best-action modeling.
Using NBO/NBA modelling, we predict the most suitable offer or action to influence customer behaviour. We look at past actions or offers and their effect. We train a machine learning algorithm on these cases, predicting conditional probabilities of behaviour given the offer or action. This allows us to make effective choices in how we approach a customer on an individual level. Suppose we focus on customers who have a high chance of leaving, and we want to influence this chance. We then calculate the chance of leaving, given whether or not certain marketing actions are carried out.
Example insurer
Below is a fictitious example of an insurer. This insurer has various actions they can take to retain customers. For example: no action, a discount or a loyalty call and giving a discount. By modeling the effect of these actions on the churn probability, we can make very effective choices per customer.
In this example, we see that scenario 1 represents the chance of churn if we were not to set up an action. We also see that the chance of churn remains the same for client 1 and even increases for client 4 if we were to give a discount. In this case, the prediction therefore shows that it is better not to set up these actions for these customers. However, for the other customers, we see a decrease in the chance of churn and we have a valid reason to set up the actions. After all, it laos telegram data ensures that the chance of churn decreases. You do these calculations for all possible actions and combinations of actions, which gives you different scenarios per customer.
Of course, we choose the action (or combination of actions) that reduces the chance of churn the most. We calculate the insights obtained from the CLV model to assess whether a customer is worth the action. Of course, you can also calculate the total marketing budget, so that you make the right choices within the budget. The combination of churn, CLV and NBO/NBA modelling ensures large decreases in the churn ratio. Just calculate how much that yields in extra turnover.
table retention strategy
Possibilities other industries
Of course, you can also do this for other sectors, such as retail. You calculate whether the chance of purchase increases (renewal) when you offer a specific product. You then make the offer (or combination of offers) with the highest chance of purchase. It is very important to look at products that have the highest chance of purchase after purchasing a previous product.