Who Are My Advocates?

Identifying social media brand advocates has long been among a marketer’s aspirations. We all know that nothing carries cred like a happy, independent believer. And if that believer happens to have, say, a few hundred thousand social media followers, the boost to the brand can be enormous.

The corollary, sadly, is also true: make an enemy out of a highly-influential blogger, and you have a major PR issue on your hands.

Identifying both of them follows about the same course, so let’s stick with advocates. How can you figure out who is advocating on your behalf?

The answer lies in the combination of two “signals” that we identify in a text (in fact, the addition of one more can be a real refinement; more on that later).

The first, and obvious, signal is polarity (a.k.a. sentiment). It’s hard to consider someone an advocate if they don’t like you. So step one is to isolate posts where a positive, direct reference to your brand has been made. Essencient provides that information directly and with an industry-high level of accuracy.

Positive sentiment is not, on its own, a definite marker for advocacy. This is because advocates have more skin in the game than just having an opinion: they want you to have their opinion, too. So, they offer advice about what they advocate: they tell you to buy it, or consider it, or take some other (presumably constructive) action about it.

That’s as distinct from them just intending to do something themselves. “I like that new Beamer and I’m going to buy it” is a good thing, but not as strong an advocacy as “I like that new Beamer and you should buy it”. The offering of advice is a clear marker, with positive sentiment, of advocacy. Essencient, and only Essencient, provides the so-called Guidance signal that can identify advocacy.

There’s a good refinement available: Flamboyancy, another signal that Essencient (alone) provides. Flamboyancy measures how “flowery” the language is in a post. “I took a long, exciting test drive in that beautiful new Beamer.  You should buy it immediately!” is a far stronger endorsement and call to action than the last example above. Factoring Flamboyancy into the identification of advocates can only improve your results.

As I mentioned, the corollary to advocacy is detraction. Knowing who vocally dislikes your brand is pretty important, as well. And, of course, knowing who your competitor’s detractors are is a powerful insight as well. Identifying detractors differs from identifying advocates only in the sentiment being expressed (obviously, it will be negative for detractors).

So there it is: advocates and detractors identified. If you layer on reach or Klout scores, you can pinpoint who your greatest reward and risk bearers are and, through your engagement team, influence them appropriately.

Chasing 80%: Peripheral Sentiment

I recall that academic research performed in the past has demonstrated that determination of polarity in text (what we have come, perhaps incorrectly, to call “sentiment”) has an accuracy ceiling of about 80%. That is, given a body of statements, a team of human beings will not agree on the sentiment in those statements more than 80% of the time. That’s because, well, people have opinions.

So, the best we can hope to achieve in automated sentiment analysis would be that 80%. A lofty goal, indeed.

In an earlier post, I discussed the importance of World Knowledge in making good decisions: that knowledge we’ve gained from lining in our world that biases, hopefully correctly, the calls we make. Computers are not good at world knowledge, although we’re always trying to get better.

At Essencient, we have recently developed a concept that we call Peripheral Sentiment. Since our objective is not to make decisions for you, but to make sure that the texts you see are worthy of human evaluation, PS is a very useful thing.

PS says that for a given topic, if there is no directly measurable sentiment for or against it, but there is sentiment of some kind at the level of the entire text, some of that latter sentiment might be ascribed to the topic. Therefore, the text is probably important enough for human evaluation.

Here’s an example: “I stayed at Hotel Roger last night. The food was terrible.” World knowledge tells us that a hotel can be judged by its food. Therefore, this text could be considered negative about Hotel Roger, although nothing directly critical of that topic was said. Peripheral sentiment score: negative. Human, please take a look.

It’s not perfect. Consider this one: “Drove the Benz to dinner last night. The food was terrible.” You get it, of course: this says nothing bad about the car, since food and cars have no world knowledge relationship. In this case, we’d put this in front of a human, who would probably consider it unimportant (about Benz, that is).

Still, even with imperfections, it does make it more likely that texts with only indirect sentiment towards your brand will at least end up getting reviewed. That’s why PS takes us a step closer to the 80% mark. Expect to see more about PS going forward.

Your Team is Smarter Than Our Tech

One of the cornerstone beliefs reflected in Essencient product design is the notion of your team’s competence: you invest in their training and the systems that support them, because you know that all they need to excel is the best conditions to do so.

Put another way, you’re not asking us to decide whether a social media post is a lead or a case, or anything else other than significant enough to warrant attention. Removing the hay so the needles are visible is the best way we can empower your team to make the best decisions. So we concentrate upon doing that.

One of the main reasons we emphasize hay removal is that your people are smarter than our tech. That will always be true. And one of the main reasons for that is what we call “world knowledge”. Sometimes people call that “context”. World knowledge is what we employ to make quality decisions about everything, and why we are capable of making decisions based upon more than just the case that’s in front of us.

A great example of the need for world knowledge is sarcasm. If I were to say, “Wow, isn’t it great that taxes are going up again?”, the odds are overwhelmingly good that I’m being sarcastic. Why? Because very few people actually like to pay more tax (I don’t know any, that’s for sure).

One of the greatest challenges to NLP tech like ours is a computer’s lack of world knowledge. Sure, we can try to develop constructs and methods to approximate world knowledge (and we do try…), but they will always be error-prone.

Your people, though, are far more likely to make the right call. So, our logic goes, why ask you to depend upon a substitute for something people do better? Precisely because we can dependably eliminate the noisy, useless conversations at low cost, we can deliver the much smaller number of highly useful conversations to your team for high-value decisions.  And that, we believe, is what you really want us to do.

Do you agree? We’d appreciate knowing your opinion.