The objective of this article is to enable you to use TARGET with the best performances possible.
When you create a filter or a campaign, there are always several ways to reach your goal. We will present you in this guide the fastest and the slowest ways in terms of population calculation. These best practices are even more important if you have a big database in terms of contacts or transactions, and if you are working on big campaigns, such as Black Friday.
Applying those rules will decrease calculation time considerably when Splio is computing your populations.
Openings: In this article, we are showing examples of filters using email openings. Please note that this metric will be impacted by the Apple Mail Privacy protection.
Filters within filters
Use case: I want to exclude my female population in my weekly newsletter aimed at my male population. I have two options.
Option #1 (possible but not preferable): when possible, avoid including the condition ‘presence in filters’ as this condition will mean that several filters will need to be calculated when sending your campaign.
Option #2 (preferable): including directly the sub-condition in your main filter.
We recommend, when it is possible, to use a condition in a filter rather than ‘presence in a filter’ to ensure optimum filter calculation.
Exclusions in filters
Use case: I want to target my Italian speakers contacts.
Option #1: excluding all languages which are not Italian. This option implies a longer filter calculation time.
Option #2 (preferable):
If possible, it is always best to include the condition rather than excluding multiple ones.
Use case: creating my active /inactive filters. What are the best practices?
The conditions that are fast to calculate:
For the condition ‘number of openings over X months’ and ‘number clicks over X months’ the months calculated are calendar, and not relative.
The slow conditions:
The conditions with a campaign category (even if you don't use it) will mean a longer calculation time for your filters and should only be used if you are trying to re-target from specific campaigns. Here is the list of these conditions:
- Number of clicks in emails
- Number of emails openings
- Number of emails received
- Number of SMS received
- Number of X channel messages sent
Setting a time limit to filters
Use case: I want to target my population who has bought at least two items.
Avoiding unlimited time periods for filters will mean your filter calculation is quicker. If no time limit is set, you could create filters that will calculate as far back as we have data in the platform and may slow down calculation.
Use case: I want to exclude my inactive users from my campaigns.
Option #1: Excluding the inactive population in the filter.
It is of course possible to use this option, however, the calculation of the filter will be slower than if you use option #2.
Option #2: exclude the inactive population directly by selecting the inactive filter and excluding it in your campaign scenario.
If you are doing always the same population exclusions (i.e for inactive users) it is best to do it in your campaign scenario.
Gap between the expected and final target
A gap can be found between the expected versus final target. The main reasons behind are the following ones:
- suppression lists: on every sending, Splio won't consider contacts who don't want to receive emails anymore (contacts in suppression list).
- natural unsub: contacts may choose to unsub themselves from your lists using the unsub link. Therefore, unsubscribed contacts may be removed from the final target.
- deletion of duplicate entries: if the target is being updated with several temporary files, Splio will remove the duplicates entries to avoid hitting the same contacts several times.
In most cases, natural unsubscription is the main explanation of this unexpected result and this is why we strongly advice following up the subscription trend evolution of your database.