How To Use Social Networks To Create Laser-Targeted B2B Advertising Campaigns
The bane of most search marketers’ existence is search query ambiguity. Is a user searching for [one night stand] interested in an illicit affair or a piece of antique oak furniture for his bedroom? Does the query [internet security] reflect the needs of a consumer fed up with viruses or a CIO looking for a […]
The bane of most search marketers’ existence is search query ambiguity. Is a user searching for [one night stand] interested in an illicit affair or a piece of antique oak furniture for his bedroom? Does the query [internet security] reflect the needs of a consumer fed up with viruses or a CIO looking for a $10 million enterprise solution?
John Battelle has called a search engine a “database of intentions,” but that database is often fuzzy, to say the least.
In many cases, it would be much easier for marketers to know who the user doing the search was, as opposed to for what they were searching. Take the two examples above: if you knew that the searcher was a 19-year-old college student whose favorite brands were [Axe deodorant] and [Bud Light], you could probably determine quite quickly whether either query was right for your advertising budget.
But, with the exception of the limited use cases of Google’s RLSA (retargeting lists for search ads), the ability to fuse demographic or behavioral intent with query data does not exist on search engines.
In the last few months, however, social networks – led by Facebook and LinkedIn – have rolled out targeting features that enable advertisers to get very close to answering the “who” question. Importantly, these tools work especially well for B2B marketers, simply because they enable advertisers to exclude consumers (or just include businesses).
While still in their infancy, these new targeting tools are opening up big B2B opportunities online. If you’ve been frustrated by too many double-entendre keywords in your SEM campaigns, social media could be a better use of marketing dollars.
LinkedIn: Using Endorsements, Groups & Demographics For Fun & Profit
With over 200 million users, the odds are pretty high that if you are trying to reach a business professional, you can probably find him or her on LinkedIn. When LinkedIn’s self-service advertising platform was originally launched, advertisers could only target based on a LinkedIn user’s self-identified data. I say only because even this data is quite robust. Fields include:
- Languages spoken (I put “Old French – 842-ca.1400” on my profile, but no one has targeted me in that yet)
- Company or type of company
- School(s) attended
- Geography (down to a city)
LinkedIn then upped the ante by allowing advertisers to target users based on their LinkedIn group membership as well. Since many groups are narrowly focused around specific business issues and needs, targeting groups (in combination with demographics) is a great way to find users specifically looking for what you might be selling.
As example, I typed in [data warehouse] into the groups search box on LinkedIn and found more than 250 groups, some of which had over 20,000 members:
In the last few months, LinkedIn introduced “endorsements,” whereby your peers can endorse you for a set of skills. Shortly after rolling out endorsements, this, too, was added to the self-serve ad tool (described in the tool as “skills”), effectively creating a crowd-sourced targeting tool.
Put these three elements – profile demographics, group membership and endorsements – together and you can get pretty darn granular (though note: you must target at least 1,000 LinkedIn users per campaign).
To show you an example of what this looks like, I recently created a LinkedIn campaign targeting PPC experts at marketing agencies in the US at a manager to VP level (as a way to try to recruit people to my agency). I got a list of about 1,200 perfect candidates to market to.
Facebook: Demographics + First Party Data + Search?
Facebook has also been busy adding more refined targeting options to its original ad platform. The first iteration of Facebook targeting allowed advertisers to target based on self-reported data about users, including:
- Languages spoken
- Marital status
- Sexual orientation
- School(s) attended and current education status
- Interests on Facebook
- Connections on Facebook
Again, not too shabby! Since that initial launch, however, Facebook has added two new powerful tools: FBX – which allows you to retarget your Web visitors on Facebook – and Custom Audiences, which enables advertisers to upload a list of email addresses to Facebook and market to anyone on Facebook who has used one of these email addresses to sign up for Facebook.
While you can’t currently fuse Facebook demographic data with FBX retargeting; with custom audiences, you can! So, if you only wanted to target folks that had signed up for your newsletter *and* are married, went to the University of Iowa and are men, voila! – custom audiences combined with self-reported demographic information gets you that deep!
And, let’s not forget the elephant in the room – Facebook’s recent launch of “Graph Search.” Graph Search allows users to run searches on Facebook and get results based on their social graph. So, doing a search for “sushi in San Francisco” would not only give you a list of sushi restaurants, but also tell you which of your friends visited them recently.
While you can’t currently advertise on graph search (and indeed, most users don’t even have access to the feature yet, anyway), most advertisers assume that Facebook will soon roll out “search ads” on Graph Search. Combine the 1st-party data of Custom Audiences, the self-defined demographics of Facebook profiles, and the intent of a search query and, well, you just might have found the Holy Grail of online advertising!
And Don’t Forget Google
Of course, Google isn’t sitting idly by and letting LinkedIn and Facebook own “who-based” B2B marketing. Google Plus is already integrated into AdWords, RLSA allows for limited 1st-party data, and there’s no doubt there are many more plans at the Googleplex to fuse behavioral data and search intent.
Google already offers B2B behavioral targeting opportunities on the Google Display Network (GDN) and has really improved the ease-of-use and granularity of their remarketing (retargeting) on GDN, as well.
Compared to the social networks, Google has always taken a much more conservative approach to user privacy, which may limit the amount of behavioral data they will share with marketers; but at the end of the day, if Google is losing wallet share to other channels and the cause of this loss is better targeting, you can expect Google to respond with rival products on search as a result.
And don’t get me wrong, there are still plenty of highly-targeted B2B-only queries that get decent volume on AdWords, so this is definitely not a zero-sum game.
Assuming that all this data (and all of these channels) doesn’t either overwhelm marketers or freak out consumers, the combination of accurate behavioral and demographic data with search query intent is a pretty exciting opportunity for B2B marketers. Merging “who” with “what” and “where” on social networks enables advertisers to target business customers with incredible precision. It’s a very good time to be a B2B marketer!
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