When social media first began gaining popularity with brands, the first thing they wanted to know was, “What are people saying about us?”
It’s understandable – whereas before brands would only get to find out what the public’s perception of them was when the cash registers slowed down, now they could get insights on what was being said before it became a problem, and the perception of their response to that conversation (positive or negative).
This led to a booming market in social monitoring platforms. Companies sprung up with technologies that could monitor millions of conversations, send alerts to brand managers, and define the changing sentiment around a brand and the subsequent buzz that went with it.
But the truth of the matter is social monitoring is flawed, and will continue to be flawed, while we still think in bits and bytes when it comes to human behaviour.
We Are Not Machines
The basic premise of a social monitoring platform is simple:
- Choose your industry;
- Choose keywords to monitor (brand, product, person);
- Set up your alerts;
- Define your goals (buzz, sentiment, volume, leads);
- Gather data, report, refine, rinse and repeat.
Sure, there are other areas of data that brands may set up, depending on their goals – competitor intelligence, for example. But even this basic set-up of goals and tactics highlights the flaw in social monitoring – we’re hoping humans behave like machines to tell us what we want to know.
For most social monitoring platforms, the technology is still fairly basic in that all it does is monitor online conversations for certain keywords (much like Google scans the web for your search terms and then gives you a series of results).
The problem with this approach is it requires linear thinking on behalf of the target. Instead of true natural conversations like the ones you have with friends on Facebook, monitoring tools are often looking for non-connected scripts. Take the following example.
I’m in my house, freezing my butt off in a typical Canadian winter. I go online to moan, and say something as simple as “Being cold sucks.” There’s nothing really there for monitoring software to pick up. Or is there?
If the software was advanced enough, there are multiple reasons for me being cold. Is there a hole in my window? Is my roof insulation not working properly? Is my furnace broken? Am I struggling to pay heating bills?
Immediately, there are now four opportunities for four vendors to take an interest in me – glazier, roof insulators, HVAC companies and maybe even my bank, to see if they can help financially.
All from three little words, instead of a monitoring solution looking for me going online and asking “Know any good HVAC companies in central Ontario?”
You can see why we still have a way to go when it comes to monitoring. But that’s just a part of it.
Social Monitoring and the Influence Effect
Let’s take monitoring one step further, when it’s being used as part of an influencer outreach campaign.
When brands use influencers, they need to know who’s making the buzz happen and who’s creating action from intent. Otherwise, they’re just shooting in the dark while paying thousands of dollars to social influencer sites for putting them in touch with the influencers in the first place.
So, as a campaign unfolds, brands use monitoring platforms to see where the conversation is stemming from (influence solution partners can offer this information but you should be monitoring for your own needs as well). They track the times an influencer speaks, and whether this causes a trickle or ripple effect.
And this sets up another problem with monitoring at that high level – it doesn’t take into effect all the disruptive factors that help a decision be made, positive or negative.
For example, I see an influencer talk about the new Ford F-150. I trust the influencer (he or she’s a car geek, just like me), and I like the mix of fuel economy and torque that the F-150 offers. I’m sold, and I mention as such to the influencer on their blog, so that goes down as a positive net.
But I’m not the decision maker when it comes to finances – my wife is.
So, as much as I love the truck and as much as I give off the vibe that I’m moving beyond intent to buy to actually buying, based on an influencer’s write-up, I don’t buy, because my wife has rightly said we need to go on vacation this year to unwind, and the money needs to go to that.
The effect of that decision isn’t felt, because the monitoring only stayed with me until I was a positive result for the brand and influencer. Had the monitoring or influencer program stuck with me for a week or two, they would have seen me jump online to say, “Vacation this year, truck next year.”
Instead, the brand wonders why there was a positive effect that didn’t correlate into a sale; the influence program is questioned for effectiveness; and the monitoring solution fails to follow up on my secondary conversation.
Take it one step further – let’s say the software really digs into who I’m talking with and can filter them into relevancy, as well as alerts if there’s a follow-up to our original conversation online. They would have picked up my wife speaking with her friends online and saying, “Danny wanted a truck, but we really need a vacation this year, so we’re doing that instead.”
The result would be immediate – the influencer program clearly worked, it’s just priorities that take precedence and, in this case, a vacation was a higher priority. But the message about the F-150 came through loud and clear and, had the vacation not been the disruptor in this case, the sale would have been completed.
We Need to Be Smarter with the Long Tail
Now, these are hypothetical examples, and there are companies that are trying to identify not just the main conversations, but the secondary and tertiary ones too. In our book, we highlight the ones we feel are making great inroads, and dedicate a chapter into using these platforms for your influence campaigns.
But as hypothetical as they are, they also clearly illustrate where we need to go, and that’s into the Long Tail of monitoring and/or influence. We can’t just stop at the result – we need to understand what made that result happen:
- What diverted an action (my wife being the logic to my emotional decision);
- Where the follow-up should be (in this case, reminders that I’m in the market for a truck in 12 months time);
- What language tipped the emotional purchase (prior to the vacation becoming a disruptor);
- Where the true result came from (in this case, a few weeks after the perceived success).
We’re not there yet, and while social influencers continues to be the lead when it comes to measuring influence online, we won’t get there anytime soon. The good news is, companies are moving away from scoring and really digging into all the data that’s available to us.
And it’s this human-powered AI that will really start driving the results your clients are looking for.