Using Facebook as a Research Tool

Data capture is at the heart of Facebook advertisingIf you’ve been using Facebook ads for more than 24 hours you have a wealth of prospect data

Most people either don’t know about this or don’t bother with it.

That is a mistake.  

Instead of conducting surveys, or paying for expensive data, why not tap into what your customers can tell you about their needs and wants via actions on Facebook.

Getting Research Data from Facebook
The starting point for this type of data mining is to write adverts and launch them with the right settings. The most frequent mistake is to set up an advert and point it at everyone in a given geographic region.  Spend a few minutes at this stage and you can generate the data you require.

For example say you want to promote a Widgets course for school leavers.  Most providers, understandably in hurry, set up the ad and point it at everyone in the area or just at, say, 13-16 year olds.  They might select those that have identified their interest in widgets in their profile and they might split test a handful of different images in their ads.

Clearly this is a start but we all know that parents are involved in course selection for their children and the above prevents them from seeing your adverts.

A better strategy is to segment your adverts by age, gender, location, etc. and generate much more information than a basic ad will provide.  This does of course take a bit longer, but undertaken by someone that understands these nuances, isn’t nearly as time consuming as you might imagine.

Case Study: Provider Facebook Data Generation.
One of my clients wanted to generate recruitment in their Widgets course.  So step one was to drive traffic to a detailed course page on the provider’s website where data capture and/or a completed application form could be gathered.  To get them to the website we used Facebook adverts.

In doing this we wrote a series of adverts segmented by gender, six age groups and several towns.  We ended up with 72 adverts which we then ran for a short period on both the newsfeed and in the right hand column.

After the test period we then changed the image and ran all 72 ads again.

After running the adverts several more times we had a wealth of data.  Knowing the widget course take up was exclusively 16-17 year old you might wonder, as did our client, why we bothered with advertising to people in their 40s, 50s and 60s.  The reason was clear when we can report that the adverts were clicked on by more teenage females than males (9:1 ratio) and this represented the largest cohort.  However we were also able to report that a large number of females in their 30s and 40s also clicked on the adverts.

Based on this data we then asked the question why these older females clicked on the advert.  Was it for their offspring or for themselves?
The outcome was that a significant number of older females also wanted to take the widgets course.  Additional research indicated they didn’t want to study during “normal” college hours and a course was designed and run for them as a discrete group.

Conclusion
As can be seen, by setting up the adverts correctly a mass of data can be determined.  The secret is determining what data you are interested in, and then setting up the campaign settings correctly.

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