Setting up sourcing strategies

A sourcing strategy tells ama how and where to look for talent — which shapes the profiles that come back into your Talent Market Map. You can set up several strategies and run them alongside each other to cover different angles on the same role. The video walks through creating and configuring them.

Show transcript

Now that we understand how to set up a company list, we can go to the actual sourcing strategies. If you missed the last couple of videos, we’re here in the ‘Find new talents’ tab, and we’re now looking at sourcing strategies. Basically, this is a way of querying profiles directly from our database.

You have fields here you might be familiar with — current location, possible titles, and we’ve already filled some of them out for you. We also have ranking keywords, which we use to rank the profiles but not to filter for them.

But we have some very useful parameters as well. You can set a past location — let’s say you want to look at profiles all over Germany that in the past have been in Berlin. We can also use a company list, which we set up in the last video, so let’s look only at B2C startups.

We can also look not only for direct matches of these titles but for title combinations. We have functions like back-end, software engineer, full stack, and then some seniorities, because this is a more senior-level position. What happens is we do a combination of all of these functions with all of these seniorities — so we get ‘senior back-end’, ‘back-end senior’, ‘lead back-end’, ‘lead full stack’. This allows us to be very comprehensive with the keyword search. You can always add more keywords if you think it’s useful — front-end, for example. In this case it isn’t, so let’s remove it.

You can also add exclusion keywords like ‘intern’ or ‘working student’. You can filter for past experience — previously worked at a target company you think is an interesting background, for example consultancies like BCG or McKinsey. You can look for people with previous titles: for example, I only want to see back-end engineers who previously worked as data engineers. You can set must-have keywords, without which profiles won’t be shown. And as I said, we have the ranking keywords here.

You can also filter for education: the field of study, the university name, or even by ranking of the university. Let’s say we only want people who studied at one of the top 20 universities in Germany. You have years since graduation and years of experience, but you don’t have to fill in all of those.

In fact, let’s just run it like this and see how many profiles we get. The more fields we fill in, the longer this can take, so I’ll pause the video and come back once it’s ready.

There you go — we found 94 talents. Out of those, eight were already imported into the talent map, so we have 86 new talents. You can look at the profiles by clicking here and checking their LinkedIn. These profiles are ranked by the number of keyword hits they had.

You can simply import them and start the whole AI assessment, but what I’d recommend first is running this light pre-assessment, just to check they’re in the right function, skill and seniority. Let’s run it for all 86 new talents and see the results.

The assessment is almost done — basically done. What you can do now is apply this filter and only see those who score 50 or 75 or higher in function, skill and seniority. Now we have only 32 out of those 94 talents, and you can see the reasoning here. We now have a much more curated list of profiles before starting the whole assessment. What you can do now is simply import those 32, and they’ll appear in your Talent Market Map.

Auto-generated from the video.