android - New to AdMob/Firebase: how to choose local business adverts? -


i developing android app, , approaching ads part of development.

i using firebase app, , need know best approaches implementing following advertising requirements respective scenarios (i have never done before , wealth of information , approaches little overwhelming) :

scenario 1: initial release
initially, app used students of single university, , such want adverts arranged local businesses, adverts of interest students.

  • what have in firebase/admob provide this?
  • what businesses have utilize after have made agreement them?

scenario 2: progressed release
end-goal have app, in personalized form, every university interested in using students. each university's app users receive ads relevant them (ie. user @ mit wouldn't receive ads 20%-off restaurant deal in south african town) again:

  • what have in firebase/admob provide this?
  • what businesses have utilize after have made agreement them?
  • and how make users ads?

thanks!

sounds there 2 concerns here.

for first, if app uses location information intended functions (and has location permission turned on), mobile ads sdk use info fetch geographically relevant ads. happens automatically. not every ad local business though--if national advertiser highest bidder impression, that's ad served.

for second concern (how direct deals local businesses), i'd recommend admob's direct-sold campaigns. once have regular admob ads serving, can mix in own campaigns directly negotiate businesses (e.g. "we'll give 100,000 impressions xxx dollars" or 10% of traffic, or whatever). business in charge of entering campaign details , uploading creatives sent whom you're advertising.


-

Comments

Post a Comment

Popular posts from this blog

serialization - Convert Any type in scala to Array[Byte] and back -

i have following question: i have variable value in program declared value. i want convert value byte array.. how can serialize byte array , back? found examples related other types such double or int, not any. this should need. it's pretty similar how 1 in java. import java.io.{bytearrayinputstream, bytearrayoutputstream, objectinputstream, objectoutputstream} object serialization extends app { def serialise(value: any): array[byte] = { val stream: bytearrayoutputstream = new bytearrayoutputstream() val oos = new objectoutputstream(stream) oos.writeobject(value) oos.close stream.tobytearray } def deserialise(bytes: array[byte]): = { val ois = new objectinputstream(new bytearrayinputstream(bytes)) val value = ois.readobject ois.close value } println(deserialise(serialise("my test"))) println(deserialise(serialise(list(1)))) println(deserialise(serialise(map(1 -> 2)))) println(deserialise(seri
Read more

matplotlib support failed in PyCharm on OSX -

any idea how fix ? how can debug might problem ? not seem : https://youtrack.jetbrains.com/issue/py-15520 i in console: python 2.6.9 (unknown, jul 14 2015, 19:46:31) [gcc 4.2.1 compatible apple llvm 6.0 (clang-600.0.39)] on darwin >>> import matplotlib.pyplot plt matplotlib support failed the backend : >>> import matplotlib >>> print(matplotlib.rcparams['backend']) macosx
Read more

python - Matplotlib: TypeError: 'AxesSubplot' object is not callable -

i new programming please pardon me if rookie mistake. i writing python script have defined functions create subplots of distributions. i have slider widget end user change value of variables(mu , sigma) , see effect on distribution. here's code import numpy np print np.__version__ import scipy print scipy.__version__ scipy.stats import norm, lognorm, uniform import matplotlib print matplotlib.__version__ import matplotlib.pyplot plt matplotlib.widgets import slider, button, radiobuttons, checkbuttons #####calculating mean , standard deviation##### global mu_a1 mu_a1 = 1 global mu_b1 mu_b1 = 10 global mu_c1 mu_c1 = -13 global sigma_a1 sigma_a1 = 0.14 global sigma_b1 sigma_b1 = 1.16 global sigma_c1 sigma_c1 = 2.87 mu_x01 = -11 sigma_x01 = 1.9 #####_____##### #####generating random data##### a1 = 0.75*mu_a1 + (1.25 - 0.75)*sigma_a1*np.random.sample(10000) b1 = 8*mu_b1 + (12 - 8)*sigma_b1*np.random.sample(10000) c1 = -12*mu_c1 + 2*sigma_c1*np.random.sample(10000)
Read more