- Propensity to collect (am) = 70 percent
- Propensity to collect (pm) = 50 percent
- Propensity to collect (am) = 40 percent
- Propensity to collect (pm) = 10 percent
Optimising your dialler operation maximises performance by determining which customers to call, how often, and when.
Significant increases in collections have been delivered through understanding the differences between the available actions – and adapting the dialling strategy accordingly.
Our solution can inform you of the optimal way to allocate your available dials on an account by account basis, thereby maximising the amount collected without changing the number of dials you need to make or increasing your costs.
If one customer were to be selected for a call in the morning and the other for a call in the afternoon, a hierarchical approach will assign a morning call for customer A and an afternoon one for customer B. However, if these are swapped round, the result is a higher total response. This is a simple example, but as the number of accounts being considered increases, the problem becomes exponentially more difficult to solve. This quickly reaches the point where the best solution cannot be arrived at manually.
Alternatively, what about a situation where a dialler has capacity to make just ten dials to two accounts and the objective is to maximise the amount collected after those ten dials? Each account could be dialled 5 times, one 6 and the other 4, one 10 and the other not at all, and so on. Analytics can be used to predict how likely each account is to pay after each dial and, from this, the ten dials allocated as effectively as possible. Again, as the number of accounts and the number of dials increases, it becomes exponentially more complex to allocate the correct amount of dials to each account.
Optimisation is used to solve business challenges like these.