Digital MarketingStrategiesAn Introduction to Dynamic Attitude Analysis, Part 1

An Introduction to Dynamic Attitude Analysis, Part 1

Monitoring online chatter is easier than you think. Part one of a series.

When I was in the agency world years ago, I worked on a project to use the Web to market a new superhero movie. It would be my first (really, only) Hollywood experience. It seemed to start on the right foot: We began the marketing campaign well before the movie was filmed. Actually, before the script was even written. All we had was a treatment, a handwritten page roughly describing what the movie would sort of be about.

I very clearly remember being told by the studio creative director he wanted to make sure this would be the first interaction people would have with the movie experience. Well, guess what. He was already too late. Fans knew the movie was being made. They already knew all the decisions that had been made. And they were very clearly expressing their opinions about those decisions.

We created something we called the “Faith Index.” Essentially, we’d troll all the online discussion boards and capture postings related to a given subject. The choice of a director was a critical, early issue. Then, we’d score the number of fans who thought the idea was good. We called it the Faith Index because it became clear the early challenge was to make sure core fans, the influencers, had faith in the studio to interpret a character they’d been following for many, many years.

We did this all manually back then. It became clear through this experience that brands must understand what’s being said about them. The problem back then was gathering all this info was a royal pain in the neck. Oddly, it made us think this must be what it feels like to be bitten by a radioactive spider.

Not for any particular reason, of course.

Introducing Dynamic Attitude Analysis

Fast-forward to today: A few companies must have experienced the same problem, or at least perceived the same need. There are now several new options that allow marketers to get an amazingly close, up-to-date view of precisely who’s talking about their products and brands and what’s being said. Here at Jupiter, we call these tools and this practice dynamic attitude analysis (DAA). Every marketer should engage in DAA at some level — and there are several levels to it.

DAA is built on the premise that discussions online reflect discussions offline; what people say in discussion groups, blogs, and opinion sites is analogous to what they say (or don’t say) to their friends when sitting around in the break room. This may be a bit of stretch. If so, I’d hazard to say it’s a stretch in volume. People online may speak more freely and in greater depth than they would offline.

All DAA tools, to some degree, capture, analyze, and notify marketers (really, anyone who’s interested) when particular keywords and keyword phrases are mentioned. In a way, these tools and services are outgrowths of the search technology explosion. But they take things a step further, filtering down the information that’s specifically appropriate for the user, and, at the most advanced level, they make determinations about the mindset of an individual poster.

DAA Level 1: Capturing Mentions From Consumers

Perhaps the primary reason marketers should adopt at least some DAA practices is doing so is cheap and easy.

Let’s start with the cheap part. Services you can use to begin monitoring discussions for brand mentions are currently offered at no cost. There are several out there; look particularly at PubSub and Bloglines.

Think of these services as inverse search engines (in fact, that’s how PubSub sometimes describes itself). A search engine is a database of content, into which queries are entered. These sites are databases of queries, into which content is entered.

When setting up an account at either of these sites, you’ll enter a query: a string of keywords. You’ll then be notified when new content containing that query is run through the engine. Content is pretty rich but tends to focus on blogs and discussion groups. Bloglines has an interesting functionality that allows you to subscribe to a newsletter or listserv and to have that content analyzed as well. Other feeds might include things such as SEC filings or news stories.

These are the sorts of queries you should load in:

  • Your brand, company, and product names
  • Your competitors’ brand, company, and product names
  • Words that describe your category (e.g., “plumbing parts” or “auto accessories”)
  • Words that describe brand qualities you’re trying to establish via advertising or marketing campaigns (e.g., “finicky eater” for a cat food)
  • Names of your executives or others associated with your company (e.g., a well-known programmer at a tech company or a corporate spokesperson)

Because these services tend to work much like search engines, your queries can combine several elements into a single query, using Boolean operators such as “or” and “not.”

The result is a specialized collection of content, generally delivered either via email or RSS feed. The RSS feed may be best, particularly if you’re working in a team or at an agency and want to ensure both your client and you are looking at the same information set.

What to Do With Your New Data

Using these tools, you’re plugged into discussions that are occurring and that may be relevant to you. It’s still just raw data, though, and you must do something along the lines of the Faith Index to translate it into something meaningful. You must categorize the data, watching it for latent needs or problems that exist within your audience. Opening this new data spigot is fairly easy; using the data isn’t.

Before engaging in this practice, make sure it’s very clear to everyone who will see the data what expectations are and how the data will be used. For example, since you’ll be getting Usenet posts, you may discover someone flame-baiting, generating inflammatory posts specifically designed to raise the ire of other group members. The poster doesn’t necessarily feel one way or another. Rather, he enjoys upsetting people. Those posts must be taken with a bit of caution and interpreted appropriately. Marketers should view DAA as a new method of learning about consumers, mixing it with the qualitative and quantitative research they’re already conducting.

I’ll follow up with a discussion of the next level of DAA — tools that actually provide interpretation functionality, dynamically determining if a given post is good or bad.

Until then, set up feeds for your brands. Feel free to email me about your experience.

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