Goto: 50% More Expensive. A Bad Case of Data Analytics?

I've been a loyal customer of the Goto company, whether if I shop for food deliveries or transportation. This company is perhaps an equivalent to Uber in the US that provides similar services.

In fact, I'm so loyal that I almost exclusively shop for food deliveries with the company and exclusively for my transportations especially when I'm in another city.

Tonight, I just got back from another city. It was late at night, raining, and I just want to get home as quickly as I can. So, as usual, I opened the Gojek app and order a car to take me home.

When I was ordering, I took notice that the price was unusually high but as I was already so tired, I just went ahead and ordered.

While I was waiting to be picked up, I was curious as to how much an alternative ride with another company would have costed me. To my dismay, it's 33% less expensive! Or from the lower price perspective, the current ride was 50% more expensive! I was very tempted to cancel but since the driver was already on his way, I decided not to cancel.

In this situation, of course the price played a part as to making me feel agitated. However, more than that, to me, I felt betrayed. I've been a loyal customer, trusting the company to give me a great service for the best price. But when I feel my trust was taken advantage of, I felt betrayed. Thus, because of this betrayal, consequently, I will also reduce all my spendings with this company, whether it is for food deliveries or with its rides.

Whoever did the data analytic and the marketing decision to charge exorbitant amount when the "condition" is right (at night, raining, etc), is making a big mistake. As by doing so, they are going to lose more than they've gained as customers like me who regularly used their services will reduce or stop their spendings. Compare that to the profit they've gained for that single service. I don't think it's a good marketing move. What do you think?