Customer value is the level of satisfaction of your customer has with your business. This value is often related to what they are willing to pay for your product; therefore, the value of a product or service will heavily influence buying decisions.
For enterprise software products, value is measured in monetary terms like:
- Time saved
- Cost saved
- Cost avoided
- Revenue improvement
The value of a software product or solution becomes tricky to quantify. For example, if a product can improve productivity, avoid cost avoidance, or improve revenue at various parts of a business process, adding time or cost savings at each point may overestimate the benefits and hence give unrealistic numbers that are difficult to communicate to a CIO
Therefore, communicating and calculating value is vital, as the impact of inaccurate value promises can result in lower customer satisfaction when the value is overestimated or losing business to the competition when it’s underestimated.
How to do the balancing act
Discrete event simulation (DES) is a method of simulating the behavior and performance of a real-life process, facility, or system. DES can estimate a process with multiple steps more accurately than first principle mathematics, which can miss out on dependencies.
DES takes care of benefits overlap and determines the benefit of the entire system rather than benefits at various points.
Let’s illustrate this with three scenarios:
- Step 2’s process time improved by 50% but the overall process time improved by 2%
- Step 4’s process time improved by 12% but overall process time improved by 16%
- Step 1’s process time improved by 50% but overall process time improved by 2%
In all three cases, the actual benefit is very different from benefits from each step of the process due to dependencies
Unmet needs in traditional value estimation methods
|Traditional method – unmet needs||How DES addresses these issues||Level of need|
|Estimate interval with confidence||Completely addressed: DES runs several times for a given set of scenarios and output can be recorded. A mean and standard deviation will be established.||High|
|Calling out dependency||Partially addressed: Based on the process relationship, the simulation can pinpoint the bottleneck or likely bottleneck in the process with the help of queue build-ups.||High|
|Impact of externalities||Partially addressed: When accounted for in the model, it can help to determine or at least factor for this.||Low|
|Decision consequences and sensitivity||Completely addressed: DES is precisely built for sensitivity analysis and can give a good measure of consequences in the long run.||Medium|
Though DES is a robust method, it has the following challenges:
- Requires a simulation expert to run DES
- Scenarios have to be very well thought through
- Output inference has to be simplified
Discrete event simulation is a very effective method to calculate value despite its initial effort and expert requirements. DES has been tested and validated with real scenarios, therefore, it is more dependable than most traditional techniques.