I am recently switched from using MAPE to MASE for comparing forecast accuracy after reading the benefits of MASE over MAPE. But I am not sure how to read the values in simple terms. For example, MAPE of 6.7 would mean the error between actual values and forecasted values is only 6.7%. But if we have MASE of 0.53, how do we read that in plain English to explain to non-technical team (instead of using terms like "in-sample" or "naive forecast")?

The distinct advantage of MAPE is the percentage interpretation; this attractive feature is built-in. By contrast, MASE is [S for scaled] compared to a naive or seasonal naive forecast; for each individual forecast, numbers greater than one (in absolute value) imply a worse forecast than the NAIVE/SNAIVE and those less than one (in absolute value) imply a more accurate forecast. We then take the mean of those ratios with the absolute value transformation guaranteeing that each component is non-negative.

It is worth noting that one could derive a percentage for MASE by creating a table based on the frequency of ratios between -1 and 1 [or 0 and 1 post abs()] and it would answer what proportion of the forecasts are better than NAIVE/SNAIVE though sans a metric giving any idea of by how much; the how much, on average in the metric of y, is the exact quantity provided by MASE.

Thanks @rwalker ! I am trying to find ways to explain this to non-technical team where they can look at MASE of 0.53 as 53% or 47% accuracy, which of course is not the case and at the same time it will be a challenge to explain about the comparison with naive forecast. Just trying to find simpler way to explain it.