Value of Information

We start the analysis by reviewing the different expected BI scores from our model, or our expected value with uncertainty. 

We then calculated expected value (EV) with uncertainty with the existing probabilities of each conversion scenario, also displayed in our existing decision tree.

EVuncertainty = (p(low) * net value low) + (p(moderate) * net value moderate) + (p(high) * net value high)

We then calculated the expected value with perfect information about what the conversion rate will be in the metaverse for luxury brand goods. Here we use our existing model for BI where BI = expected traffic * expected conversion rate * BIC where expected traffic is kept at 250,000 visitors and BIC is held equal at 1.38.

From the evaluation of our decision tree, we can evaluate perfect information if we know for certain if our low conversion rate, moderate conversion rate, or high conversion rate were to occur. We can conclude that when the conversion rate is absolutely moderate or high we will move forward with the metaverse decision as they result in expected values greater than 800. However, if Flourish management was to know the low conversion rate was absolutely to happen, they would decide to move forward with their traditional marketing efforts that now result in an average brand image score of 800.

Overall, the expected value with perfect information is 897.

Finally, we can find the value of Flourish management having perfect insight into the conversion rates by taking the difference of the expected value with uncertainty and the expected value with perfect information.