Why and how to perform A/B testing with ProspectIn?
When you prospect on LinkedIn, defining the right approach or the right sequence is essential. A/B testing is a “scientific” approach that allows you to determine the optimal message for your campaigns. Let’s dive into it.
What is A/B testing?
A/B testing is a fairly simple technique, “scientific” because it is based on quantified results. It consists of elaborating two different messages (message “A” and message “B”) and then send these 2 messages to a representative sample of your prospect base to determine which message is the most effective.
Once you’ve found the message that works best, you can use it on the rest of your prospect base.
Why performing A/B tests?
When prospecting, it is important to test on the best approaches and to iterate on the techniques. When prospecting on LinkedIn, the prospect base is almost infinite, so you will probably send a very large volume of message. Under these circumstances, a variation as small as 5-10% in the response or acceptance rate can be huge in the end.
It is therefore important to carry out a certain number of tests and to be rigorous in measuring performance before launching yourself into a frantic prospecting.
How to implement A/B tests with ProspectIn?
The absolute rule of A/B testing is rigor. The differences between the approaches are, in most cases, quite subtle, and the differences in performance between one approach and the other are often (but not always!) Small. It is therefore essential to apply a great discipline in the execution of these tests so that they can be significant and that they can positively impact your prospecting.
1st step: target and export your prospects in ProspectIn
For A/B testing to be valid, an approach must be linked to a specific prospect (or persona) segment. If my message A works very well for my prospect segment “Marketing director in the luxury industry”, it may not be as effective for my prospect segment “Sales freelance”.
When setting up an A/B test, the 2 messages (“A” message and “B” message) are therefore sent to the same and specific target.
Now that you’ve targeted and exported your prospects to ProspectIn, let’s go to the next step.
2nd step: definition and distribution of messages
Once again, for A/B testing to be valid, it is necessary to dispatch a minimum number of sendings, in general, we recommend a minimum of 100 sendings per message, keep in mind that it is a minimum; the higher the number of sendings, the more significant the results.
The two messages must also be sent exactly the same number of times.
Take the example of an A/B testing on the connection request, here we want to measure the acceptance rate of our invitation requests.
Start by defining your 2 messages “A” and “B” in ProspectIn, be careful, the message associated with the connection request (the “note”) is limited to 300 characters. See: What is the difference between “message” and “connection” on LinkedIn?
Once your 2 messages are ready, simply send your first message to your first 100 prospects by selecting all the prospects on the 1st page by clicking once on the checkmark.
Then send your 2nd message to the next 100 prospects by selecting the 2nd page.
The actions are now in the queue and will be sent gradually, provided you have a LinkedIn tab open.
After 3 days maximum (quotas limit connection requests between 80 and 100 requests per day), all your requests will be sent.
3rd step: analysis of the results
In order for the results to be valid, it is necessary to wait a minimum of 10 days. Indeed, you have to give your prospects time to connect to LinkedIn to see your connection request, not everyone connects to LinkedIn every day 😉.
Once this waiting period has passed, you only have to consult the results provided by ProspectIn for each of the notes.
These results will help you determine which approach works best when it comes to acceptance rates.
Note that if you have a large number of prospects on which to perform A/B tests, you can iterate your tests as many times as you want.
A/B testing on messages
We have given here the example of an A/B test on a connection request, but it is also possible to set up A/B tests on messages. The principle remains exactly the same, the difference is that you will have to be already connected with the prospects and that we will measure here the response rate, not the acceptance rate.