Sometimes interesting things appear when you’re not even looking. And some lessons taught are applicable far beyond immediate challenges.
Case in point: The realization that Twitter advertising statistics can reveal brand-topic affinities.
Ad stats help you assess how well you’ve targeted promoted tweets — that’s their purpose — but you can use them for much more. You can use them to study competitive positioning and identify influencers around particular topics of interest. The trick is to craft tweets that don’t (only) promote a product or service, but also/instead help you evaluate the topic-engagement link. The insights revealed aren’t especially useful for me — I’m well-positioned in my text and sentiment analysis consulting specialization — but if your business depends on precision online targeting, you may find ads data to be a new, unique, inexpensive source of social intelligence.
Insights from Twitter Engagement
I’ll save you a long read: I ran two Twitter promoted tweet campaigns. One targeted a set of keywords. For the second, I entered a set of @handles to target people similar to those accounts’ followers. I promoted a single tweet, one associated with a well defined technology topic.
The targeted @handles: Each represents a brand, whether an organization, product, or person. IBM is a brand, and so are @IBMWatson, IBM Watson evangelist Fredrik @Tunvall, and Gartner analyst @Doug_Laney, whose coverage extends to Watson.
What I advertised isn’t important beyond that the ad content was single-topic and brand-neutral. Brand-neutrality reduces response bias, whether toward or against a brand. The single-topic focus eliminates ambiguity; it makes clear what prompted the response. Net is that we can associate engagement — retweets, replies, follows, and other clicks — with one, particular topic. Ad stats break out and rank engagement by targeted @handle and by keyword, giving us neat way to study affinities.
My @handle-targeted campaign achieved a 5.96% engagement rate, which I consider pretty good. Twelve of the @handles I targeted had over 10% engagement, out of 69 with at least 100 ad impressions, and seven were below 4%.
We learn from the variation, from the spread of response rates. We learn which brands are associated with a topic and which aren’t. The @handles for individuals: High engagement rates reveal or confirm influencers for the tweet’s topic. The uses of company and product @handle-topic associations is close to self-evident so I won’t elaborate on them.
Get a complete set of insights by running a parametric study, a series of ads with topics whose associations you wish to explore, for a fixed set of target @handles. You may find surprises. I did. In the end, you’ll gain solid, valuable social intelligence.
Finally: Cheap. Twitter Ads per-engagement costs are very, very reasonable, and because you pay by the engagement rather than by the impression, you’re not penalized for poorly composed or targeted advertising. (But please, let’s not waste anyone’s time.) I won’t tell you what I spent on my Twitter advertising, just that the amount was modest, with excellent return on investment.
My promoted tweet advertised a free report I recently published, delivering findings from my Text Analytics 2014 market study. The term “text analytics” describes a collection of technologies and processes that extract information from social, online, and enterprise text. My advertising aim was click-throughs to the download page, and secondarily, retweets and other forms of Twitter engagement.
(Twitter does offer additional advertising options, for instance lead generation cards and conversion tracking, useful for ad optimization but not for the affinity study I’m describing in this article.)
I chose to target 77 Twitter @handles, of solution providers that sell text analytics products or services, of industry analysts who cover text analytics or application areas, and of associations. Text analytics is commonly applied in customer experience management, market research, social intelligence, financial services, media and publishing, and public policy, so I included certain companies, analysts, and consultants who work in those domains.
(An ideal way to learn more is to check out a conference I’m organizing, LT-Accelerate, slated for December 4-5, 2014 in Brussels.)
As I’ve mentioned, results — ad engagement — varied widely.
Top scorer was @Confirmit, a survey research/insights firm, at 15.62%. Two in every thirteen promoted-tweet impressions led to a click, favorite, or retweet. I think that’s pretty good.
In the cellar, @The_ARF (the Advertising Research Foundation) at 0.57%.
The easy conclusion is that Confirmit and other top-scorers — @GateAcUk (GATE open-source text analytics) and @SAPAnalytics — have strong text analytics brand interest while only a small portion of the ARF’s audience has a text analytics affinity. SAP and Confirmit will want to play to the first point, while frankly, I may put less personal effort into working with the ARF.
I’ll paste in my full set of results below.
Finally, I’d be remiss if I didn’t discuss complications.
Secondary data use — analysis of data that was collected and reported for purposes other than your current ones — is rarely straightforward. The available data may not fit your preferred categories or characteristics — for instance, you might want hourly data, but daily is the best you can get — or you might be not have access to detailed metadata that fully describes the data and collection conditions.
There is a lot of follower overlap among the @handles I targeted. While I could cross-check follower lists, combinatorics suggest an intractable attribution task. If you need to account for ad engagements across a set of @handles (or ad-targeting keywords), I suggest running simultaneous, separate ad campaigns, one for each @handle, or choose yet another option, the one I chose: Don’t overthink your experiment, because you most likely don’t need highly precise results.
The following are my promoted-tweet campaign engagement results, for @handles with at least 75 impressions:
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