#WeakSignals

I admit I am prone to fall in love with expressions. “Weak Signals” is my latest favorite, so let me explain why I am boring everyone around me with it.

What is a Weak Signal?

It is particularly fascinating to use the expression Weak Signal in the context of Digital Communications which, at the end of the day, it’s all about Strong Signals – 0s and 1s, unequivocal expressions that are supposed to reduce reality to a measurable quantity. Either the bit is ON or is OFF.

A WS instead captures evidence that reality is more complex than this and does not necessarily fall into any of the two pigeonholes: the bit may be ON at some times and then OFF at some others, and can flip back and forth frequently. If we were talking physics we would be arguing whether electromagnetic radiation is a wave or a particle to find that it is both. Or neither.

Weak Signals are therefore needed to capture the nuances of reality and models have a lot to gain when they can accommodate them.

Weak Signals at work

So much for the intellectual reason for my love. But there is also a professional one.

Whenever we work on the Insight phase of a client project, we essentially hunt for Weak Signals. (The strong ones they know already, no need for high-pay consultants to tell them what they know already).

For a client I visited today, the key insight was that the majority of topical content around their area of interest is negatively influencing purchasing decisions and therefore damaging their business: they need to rebalance the mix so that people can form their opinions unbiased. This insight comes as the archetypal Weak Signal, which you can only distinguish when you remove all the background noise; perhaps unconsciously, our social brains are evolving the ability to filter out thousands of irrelevant mentions and conversations to focus on the few items that will help shape opinions.

How do you recognize a Weak Signal?

Tautologically, a WS carries information. So the easiest way to deal with WS is to focus on their informative content, regardless of the carrier. Once information is recognized, we can abstract the waveform of the carrier and use it to look for other bits of information elsewhere.

This sounds very complicated, but it’s not.

Let’s assume I am seeking insight on the level of satisfaction associated with the “X” brand of Widgets. Doing my research, I discover that some users have started using Twitter to complain about the life of the X Widgets, using the hashtag #XWidgetSuck. The information is represented by the fact that X Widgets break down, the waveform is the Twitter hashtag.

First conclusion (because there may be others I don’t see right now)

While in old Marketing & Communications strong signals (reach and frequency) were the name of the game, in the new, social world opinions are formed by Weak Signals.

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3 thoughts on “#WeakSignals

  1. Great post once again Gianni. I can totally relate this to what we have been talking lately.

    From my point of view, you can only make a difference (and by “difference” we mean the variation from “life” to “death” in the world ) by:

    1. Monitoring the environment correctly
    2. Track down “WS”
    3. Use you judgment in order to pick up the ones that apply to your (brand) (name) (service) etc
    4. Treat them with the right “medicine” …

    Before they become STRONG SIGNALS and they require additional time, effort, money etc …

    D.

  2. Gianni
    Perhaps, in a world that’s moving from big/centralized to small/networks across the board, the value of information is in small signals across the network ;-). i.e: if, below the noise, you find that x people say something similar, you’ve got to pay attention to it. In monitoring words, it means that it’s not just the #mention but who mentions…i.e: ‘enough of the right people’ who says something new and unique should trigger an alarm.
    L

  3. It is interesting how this half-baked concept attracted the interest of several people. Jack also said he’s not fully understood where am headed, and to be honest I do not have a good answer.

    The time dimension raised is a good one, kinda catch them while they’re young, and also the people dimension, a post is a post, but some posts are more equal than others.

    But I was thinking more along the lines of what happens when you have the oscilloscope set to the wrong scale: the signal looks like a flat line, simply because you’re not measuring variations on the right scale, and hence you’re not catching any information (or rather the wrong information that no variation is happening…).

    I definitely must think of a real life analogy.

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