model-accuracy-in-logistic?xg_source=activity&utm_content=buffereab11&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

No - you can't call it a good model. In the domain you are talking about, we are more interested in catching a true churner than catching a true non-churner. Now from your data you can find - if you use the 0.8 as the cutoff - what %of true churners you correctly predict (true ve) and what % of true non-churners you wrongly label as churners (false ve). ROC tells you, what should be your cutoff and to get there how much false ve you need to tolerate.