{"id":71485,"date":"2021-06-03T10:37:49","date_gmt":"2021-06-03T09:37:49","guid":{"rendered":"https:\/\/www.bullhorn.com\/eu\/?p=71485"},"modified":"2021-09-20T14:08:04","modified_gmt":"2021-09-20T13:08:04","slug":"is-your-data-breaking-your-automations","status":"publish","type":"post","link":"https:\/\/www.bullhorn.com\/eu\/blog\/is-your-data-breaking-your-automations\/","title":{"rendered":"Is Your Data \u2018Breaking\u2019 Your Automations?"},"content":{"rendered":"

As automation continues to be \u2018the\u2019 thing in the world of recruitment tech, users are reporting huge amounts of time saved and efficiencies created. If automation is the new trend everyone wants to be involved in, data is its less cool sidekick\u2026 but it\u2019s also the key to making those automations work. If you\u2019re using automations, or thinking about incorporating this into your processes, then trust us \u2013 you NEED to be including data cleansing as part of your plan!<\/span><\/p>\n

The impact of bad data on recruiting automation<\/b><\/h3>\n

Dirty data is inevitable, and it often happens without you even noticing – 62% of organisations rely on data that\u2019s up to 40% inaccurate. Here are some key examples of how this can affect your recruiting automations\u2026<\/span><\/p>\n

Example 1: Checking in with clients<\/b><\/h4>\n

The data challenge: <\/b>Duplicate records<\/span>
\n<\/span>The detail: <\/b>The same client has entered your CRM in three different ways, meaning there are duplicate records. This results in your automation sending the same email three separate times.<\/span>
\n<\/span>The fix: <\/b>Merge duplicate records to ensure you\u2019re starting with only one clean record.<\/span><\/p>\n

Example 2: Candidate survey<\/b><\/h4>\n

The data challenge:<\/b> Outdated \/ inactive records<\/span>
\n<\/span>The detail: <\/b>You run a marketing-led survey on the candidates in your database. There are hundreds of thousands of records in there, so you are expecting a good reply rate. But the responses you get end up being far fewer than anticipated.<\/span>
\n<\/span>The fix: <\/b>Search for candidates that you haven\u2019t spoken to within a specific timeframe and set them to \u2018Archived\u2019 so that you can start excluding them from your automations.<\/span><\/p>\n

\u00a0<\/span>Example 3: Targeting candidates with specific skills<\/b><\/h4>\n

The data challenge:<\/strong> Out of date skills
\n<\/span>The detail: <\/b>You have a developer role you need to fill so you\u2019ve set up an automation to target candidates with specific skills. After pushing the automation live you realise it\u2019s reaching a lot fewer candidates than you had expected. You then realise that the primary and secondary skills on your records are out of date and don\u2019t match what you included on the automation.<\/span>
\n<\/span>The fix:<\/b> Create a hotlist containing candidates with both the primary and secondary skills and mass update them for the records on that list.<\/span><\/p>\n

How you can action these fixes and much more<\/b><\/h3>\n

Kyloe DataTools gives you everything you need to dedupe, identify stale records, and make mass updates to your data with ease:<\/span><\/p>\n