What is GeoTrap?
While you are running local or even national campaign, it’s quite easy to cope with geo issues. You can just use ‘region’ breakdown and see what happened in particular State, region or district in detail. However, when it comes to worldwide level, the situation changes drastically.
Take a look at this screenshot:
In our client’s case, ‘Region’ breakdown produced 1862 entries. They spent $30,385 on this campaign during ten days. The biggest amount of money spent on one region was $2,078.28. The average sum spent in one region was $36.13.
Obviously, analysis of such amount of data is a time-consuming and complicated task, especially using Facebook analytical interfaces. That’s why marketers usually decide to enlarge the scale and use ‘Country’ breakdown. And this is the GeoTrap!
GeoTrap means making decisions regarding global campaigns based on ‘Country’ scale and ignores region specificity.
Each region in every country all over the world is unique. Remember your homeland. Probably, you know the difference between East and West, North and South. Some regions are more wealthy than others. Other areas are more focused on tourism, and some are more concentrated on business or manufacture. People may speak even different languages as, for example, in Switzerland, Spain or Canada.
If a marketer does not take into account all these specific, it becomes so easy to set wrong targeting, approach with an inappropriate message and lose a budget.
Is it possible to understand all regions all over the world?
Probably, not. At least, I did not succeed in it. However, data can lead you to right decisions.
How to avoid GeoTrap?
- Start your campaign ignoring region specific and make tests
First of all, you should collect some data about customers and how they respond to your products. Even low-level metrics like CTR and CPC will help you to figure out where the offer has proper demand and where not.
- Export FB stats to a spreadsheet to work with it in Excel or another tool
You will get tables broken down by region. They will also have valuable additional columns, which include adsets, ads, promoted object, optimization goals and call-to-action types.
- Try to find bad performers among these segments
It’s time to reveal outliers. There are quite a lot of techniques how to define what is abnormal for your data set. The easiest one is to determine a threshold and consider as outlier every segment beyond it. Advanced marketers with math knowledge usually apply more effective approaches, for example, standard deviation.
- Go from top to bottom
You should start with top-level metrics like ROAS and CPA. Work with segments that are below the threshold first. Then, move to lower level metrics, like CTR, CPC, CPM. Do it on a daily basis.
- Make a decision and try to figure out what caused such deviation
You will find quite a lot of bad performers. However, why these particular regions perform worse than others? If you are going to work with these countries and areas, you should dive deeper and define reasons for such difference. I usually start from Wikipedia and international reports about economic and culture in the target country.
To be honest, finding such insights is a complicated process for lazy marketers like me. That’s why we have developed Captain Growth 🙂
How to deal with GeoTrap automatically?
Our AI finds such insights (and many others) effortlessly, every day. You can just explore essential information as easy as watching funny videos with kittens on the Facebook feed.
Let’s consider how Captain Growth resolved our case.
Captain founded 63 insights about different regions during these ten days. Total losses on bad-performing insights were apparent $13,000. Take a look at one of them.
According to insights, client changed the campaign.
- The worst-performing regions were excluded from the campaign. The campaign moved from 1862 regions to only 476.
- The budget for suspicious regions was reduced and strictly limited.
- Budget was allocated to good-perfoming regions. Reach in these regions was scaled.
In result, these deep analysis-based interventions allowed saving more than $20,000 during next 20 days!
Now you know what is GeoTrap and how to avoid it. The moral of the story is that you should not be afraid of low-level details — they are the source of power and wealth in analytics-scaffolded hands.