Push Follow Along - Sweeps

Why would't someone that clicks a push alert reach the URL -> your landing page ?

 

Thanks for sharing , trying to get understand push notification campaign optimization process , the question i got is since you're pushing on a segment of the same audience depending on the reach .

Can you exclude previously converted devices ID on propAds ?

 

^^clicks to where?
Whose LP yours or the offer?

1006 |310 means you have a 69% drop-off on your LP CTR to the offer?

if you look at the server logs for Your landing page and compare the traffic metrics you may see the reason for the lower CR.
Lower quality traffic maybe or something to do with the day or time of day.

You are only going to be able to possibly deduce this info from your own server logs. If your landing page uses Google-Analytics that can help group results but no tool is going to detail your traffic metrics like the raw data from a server access log that you have set up for the metrics you want.

You could go as far as adding a custom script to your landing page to record the variables that you want.

 

Hello Doubledouble it good result hope you scale it up.
I just want ask you how much you bid when you start your campaign.
And which affiliate network ?

 

hello i have couple of questions in my mind
1. What type of landing page do you use (ex. wheel, mutliple questions, etc)
2. do you use cloaker?
3. is the location you running english speaking country?

thank you

 

what do you mean by rrqrd?
are you using CPM or CPC?

i tried CPM and got 1 convertion, iam trying CPCnow but traffic is slow nad still 0 conversion

 

thank you i will test more heheh

 

If that 31% is your; CTR push=>Your (pre-lander) landing page => the offer's landing page (or sign up page) I think a 31 % pass through CTR is pretty good.

You could just use Google-Analytics on YOUR pages if SEO is not a concern on that domain.
To work custom analyze your logs you would want to use live commands over SSH or on a local terminal.

1. number of accesses by IP [by day and then by date range]
   
2. visitor GEO data
3. access time [if multiple pages; time between accesses]
4. visitor user-agent -- that can be broken down to o/s browser with version
5. I use a dummy javascript to attempt to detect who is a bot and what sort of bot they are [$ grep 'scriptame.js' access.log]
6. I can zgrep and zcat my compressed log archive and get the data from a time period lie 4 months ago or a time range. This depends in the size of the logs.

Then there is scripting;

1. screen size
2. cookie retention
3. many other factors

The point of all of this is to construct reasonable customer personas. Personas are aggregated profiles of customers that (might) convert. The problem is completing the loop with the actual data from the end seller i.e.; *the offer*
offer owners do not share this information so you cannot track the customer actions and the CR transaction on the offer owner's website. So with the affiliate model you can only do part of the job -- and shaving is always the 800 pound gorilla in the corner on the CR end.

That said, it is relatively easy to detect traffic padding or drop-off rates traffic source=> your pages using an access log. just use a server rewrite and take a count if you want to get the actual *hits [clicks]* delivered by a traffic source. page.html?s=5 will record on the log s[ource]=5[abc traffic network]

$ grep -c 's=5' access.log
1001
$ grep 's=5' access.log | do something with that list

$ grep 's=5' access.log | cut -d'"' -f1|sort -nr|uniq -c | less
Normally I would make a bash script .sh to do the following but this was just a simple test so I hacked it out

Code:

echo -n '"January,2019";' >>t.co-test && echo $(tac /home/user/x.___.com/logs/access.log |egrep   't.co/' |grep 'Jan/2018'|cut -d'-' -f1|uniq   |wc -l) >> t.co-test

Twitterbot growth pattern from a test feed ceased posting in September, 2018

"January,2019";179
"December,2018";200
"November,2018";213
"October,2018";185
"September,2018";170
"August,2018";135

unique v. raw hits over on month test
$ echo -n '"October,2018";' >>t.co-test && echo -n $(tac /home/user/x.___.com\
/logs/access.log |egrep   't.co/' |grep 'Oct/2018'|cut -d'-' -f1|uniq   |wc -l) \
>> t.co-test && echo  -n ';'$(tac /home/user/x.___.com/logs/access.log \
|egrep   't.co/' |grep 'Oct/2018'|cut -d'-' -f1   |wc -l) >> t.co-test

"October,2018";185;204

You could break this down by visitor IP and track IP changes of the same visitor by device possibly too
If someone arrives with a desktop and then a 4G you lose them cross-network usually but if the mobile is wireless the IP will be the same if they are using the same router public IP address routed by a NAT 192.168.1.100 as an example.

 

the final arbitrator has to be the EPC in any affiliate campaign -truth

 

This happened to me to be honest.

Day 1 14 conversions
Day 2 12 Conversions
Day 3 total flop
(along those lines)

I wondered if it was offer burnout, lander saturation or creative burnout but I guess it's to do with the quality of traffic you first receive on propeller to assess your ad performance. (for propeller, the better the ctr - the more they make from you from sent out notifications)

Did you select "all" traffic to start?

 

What about SUBID , even if the variable isn't listed near the target url params , it's working , i've seen a demo with someone using it on same traffic source for push notifs .

i'm setting same type of traffic there and now working on tracking mechanisms , i'll probably confirm that later if you don't before