We want to make sure you are getting the most of your credits on the HR DataHub platform.
To do so, we have put this guide together to help you to understand exactly when you are spending them, and also share some tips and tricks to avoid spending more credits on searches than needed.
First, let's look at what costs a credit and what doesn't.
What will cost one credit?
Any alterations to the following 6 core fields will cost a search credit as they add an additional insight to your search;
✅ Job title (adding or removing)
✅ Location (adding, removing or excluding)
✅ Time frame (any changes)
✅ Keyword (adding or removing a keyword to any of the three options)
✅ Organisation (including or excluding any organisation(s))
✅ Industry (including or excluding any industry(s)).
There are some additional cases which will cost one credit, which include;
✅ Sharing a URL for a search (if a team member conducts a search and sends you the URL to view the results, this will cost an additional credit)
✅ No results found (if your search receives 0 results this will still cost a credit, so be sure to spell those job titles and locations correctly, and start wide!)
What won't cost an additional credit?
After you have started your search (with some combination of the core filters as listed above), you are able to make changes to the dataset without costing additional credits.
The core functions of searches that do not cost additional credits are;
❌ Switching between pay types (annual/daily/hourly/all (including ticking the 'show job posts with no pay information box)).
❌ Using the pay range filter (as many times as you like)
❌ Deleting individual job post(s) from the results table.
❌ Saving a search
❌ Opening a saved search
❌ Opening a recent search (that wasn't saved)
❌ Any editing to the results table (sorting by any column, removing jobs from results page, any pagination)
❌ Downloading results in any form (individual job posts / charts / table into excel)
❌ Refresh the page
How do I avoid spending credits unnecessarily?
There are some key principles to benchmarking smarter in our platform, to avoid spending credits without real need. And once you've got the hang of them, they'll help in more than one way.
Start broad, then refine 🔎
Every role, location and organisation is different.
However, we expect in almost all cases the approach should be to start broad and then refine our search.
Let's see everything that is going on in the market, and then pick which roles we want to keep in our dataset.
There are 3 key parts to 'starting broad';
Be flexible with job titles (include as many relevant variations as possible, using our 'also known as' drop down list, or by entering your own). Remember: You can always use the pay range to quickly eliminate outliers without spending an additional credit.
Start wider with your location (consider a Nationwide search by leaving the location field blank, or choose your city/town but with a wider radius of 20, 40 or 80 miles where applicable
Start with a larger date range (you can always filter by date in the results table and delete older results if you feel they aren't applicable for your search)
Use additional filters right away
A top tip to avoid spending credits unnecessarily is to open up the 'detailed view' of the platform for every benchmark, by clicking the small arrow to the right of the search bar, like this;
From here, you can enter job titles, a location, select a date range AND add all relevant keywords, organisations and use our industry filter in one search.
You are not charged per filter, but for each time you adjust a filter and then search again.
So let's add everything we think we'll need before pressing search.