How to interpret these results from poly() in binary logistic regression with GLM?

Question

I am currently studying the effects of intercontact time in a customer's purchase journey related to his/her purchase probability, as well as the influence of the number of contacts. Based on extensive literature I expected an inverted U-shaped relationship between intercontact time and purchase probability, which is why I developed the following model:

mylogit_poly <- glm(purchase_any ~ poly(intercontact_time, 2)  + number_contacts_mc, data = Subs1, family = "binomial"(link="logit"))
summary(mylogit_poly)

Which results in:

[

Now, based on feedback I received from my supervisor, I tried different levels of the polynomial term. When I got to the model below (with poly being 4), I suddely get all significant results.

mylogit_poly4 <- glm(purchase_any ~ poly(intercontact_time, 4)  + number_contacts_mc, data = Subs1, family = "binomial"(link="logit"))
summary(mylogit_poly)

[

My question now is what to make of this? Does this mean that the relationship can be considered fully non-linear? Can I infer something from this?

PS: My full model exists of an additional 15 control variables that I left out for the sake of complexity. The output shown above comes from the full model.