Last week I wrote about the various derivative measures in social media – “metrics” that have dubious (or at least unquantified) ties to the measures that matter for your business. When I wrote that, I had some concern that I would be seen as a bit of a curmudgeon about the value of social media, when nothing could be further from the truth – I am a strong believer in the power of social media to transform business in tangible and measurable ways. In fact, as I wrote a couple of months ago, I think the current raft of clickstream-based metrics shortchanges social media somewhat by failing to measure adequately how social engagement influences consumers throughout their decision-making process.
Chris Brogan posted a helpful list of social media metrics today that illustrates this rather neatly, I think. Chris has said in the past that the only metrics that matter to him are the ones that ring the till, and on the surface that’s so eminently practical-sounding that it’s difficult to argue the point. Among the metrics that ring the register, according to Brogan: sales, leads and possibly members. Again, hard to quarrel with the value of those. But what about the other metrics that social media throws off, such as comments, retweets, shares and likes?
In my last article in this series, I pointed out that any given metric is meaningless until it isn’t – in other words, until you link it to a business measure. If you’re selling an information product with a short sales cycle (either directly, or as an affiliate), then maybe sales and leads is all there is. As your offering and sales process becomes more complex, the metrics you use to track the process must expand to meet the challenge. What this means for your business is that likes, shares and retweets actually might matter in tangible ways, but you have to do the work to get there.
Focusing only on the metrics that tie to leads and sales has the potential to devalue social media as little more than a direct response channel. I believe social media is far more powerful for branding efforts than for direct sales, and it may be that some of the various clickstream metrics we can easily track (retweets, “likes”) might correlate strongly with some aspect of consumer behavior that is a vital part of your particular sales model.
Think about the direct-to-consumer advertising we see for pharmaceutical products, such as Requip. Before I can sell you on the features and benefits of Requip, I first have to know if you recognize that “restless leg syndrome” is even a real problem. The path from recognizing that your legs don’t feel right to actually filling a prescription for Requip is long, convoluted and hardly a direct sale for the pharma companies – but it is a clearly defined model, with its own set of relevant business metrics. Will mining for tweets “sell” Requip? No. Will monitoring social media give me some sense about whether the public recognizes that “restless legs” might actually be a treatable syndrome? Well – maybe, if you trend it over time and can correlate these data to other sources.
Another example: I am starting to think about buying a new car. That phase – “starting to think about buying” – is a valid and established part of the consumer behavior model of new car buyers. It isn’t “buying a new car” (ringing the till), or even “thinking about what car to buy” – it is exactly what it sounds like: starting to think about even making the decision. When I am in this mode, there are certain communications and brand messages that will resonate with me – the messages I am most receptive to in that phase of the process – that won’t work at any other time.
As consumers move from problem recognition, to information seeking, to evaluating options and eventually to a sale (and to post-sale behaviors, loyalty, evangelism, etc.), there are key measures and metrics that help businesses understand exactly where consumers are in that process so that they can provide contextually-appropriate messages. If I am starting to think about buying a car, a smart auto brand will tailor their messages to encourage me to start gathering more information to help me get off the fence and learn why I might be dissatisfied with my current car before trying to ram a specific new car down my throat. Later, they’ll want to understand my consideration set, and I will be more receptive to messaging around a given class of car or truck. Eventually, I’ll make a decision, and go plunk my cash down at a dealer.
That cash-plunking will not take place via URL. There will be no handy shortened-link tracker that will tie my car purchase to a tweet. If car companies limited themselves to the direct metrics – what rang the till – then most of those metrics would be tied to local dealer activities. If I buy an Audi, however, I didn’t buy it because my dealer was having a Labor Day sale. That might have had some influence on the timing of my purchase, but there was a long, artful dance between brand and prospect prior to that day. What a smart business will realize is that social media has a role throughout that dance, and it may therefore be throwing off clues – indicators that consumers are being helped along from problem recognition to brand awareness and so on. It may be, for your specific business, that retweets, likes or shares actually correlate with your customers’ movement along that behavior continuum. Knowing that, to quote G.I Joe, is half the battle.
You’ll never figure this out by mining social media data alone – you have to integrate this data with survey data, retail traffic, and other offline metrics to get there. But, as I’ve said repeatedly in this space, it isn’t a black box. It’s just constructing a model, and finding what correlates. Living in Hootsuite won’t get you there, but if you do the work offline to see what social behaviors correlate to the metrics that matter for your business, then you may find that some of these “useless” metrics actually aren’t so useless.
So, your mileage may vary, but the extent to which your mileage may vary is knowable and measurable with a combination of online and offline metrics. You just have to do the work. The key is one of the central messages of this very site – don’t make assumptions. Don’t assume that a given social media metric has any value – but don’t assume it doesn’t, either.