Sounding Off: Matias Alpert: What do “Likes” really mean and how much are they really worth?

Matias Alpert is CEO for EMEA & LATAM - Neo@Ogilvy.

 

 

 

 

Although social is booming, we often run into a challenge when we are faced with measuring its performance. It is common wisdom that you have to be present in social if you want to succeed. We also know that we can measure “everything” in the digital domain. So it should be easy and straightforward. It should simply and organically work better. That does not seem to be the case… just yet.

What do Likes really mean and how much are they really worth?

Probably nothing represents this more clearly than the question of the ’Value of Likes’. What do Likes really mean and how much are they really worth? The “Like-hunting” frenzy of the last years is missing at least one vital point. Likes should not be a goal to reach, but an organic outcome (and potentially an indicator) of a strong and meaningful relationship between the customers and the brand.

Measurement

Yet, if we can really measure everything in digital, why do we resort to such simplified and often not even meaningful measurements when we approach social? The answer could very easily be that we are searching in the wrong direction and asking the wrong questions: we may be looking for the same things as before, only better, when the whole game is different.

Maybe we should instead be exploring how measurement needs to be different in social in order to be meaningful and to take advantage of the opportunities in this new context. And in fact, that is what most cutting edge measurement experts and web analytics providers like Omniture or Webtrends are exploring.

One of the fundamental issues with respect to measuring performance in social is that in marketing we have become used to indicators that are by and large universal, time enduring and comparable: we have been measuring reach and frequency, share of voice, or even CPC, which promise all these. Simple, universal, comparable and straightforward. This is why we are so drawn to Likes as well.

You may, of course, look for such comparable and meaningful concepts of performance in social too, but they are far from simple and straightforward, such as the health of your network, or the level of engagement with relevant customers. And it will be a very different way of comparing things...

What most – and certainly the best – measurement tools and experts advocate is that you need a higher level of strategic involvement in planning what and why you want to measure than what you are used to:
a. Identify the right framework
b. Define Success: where does business value flow from?
c. Embrace Complexity and Design Unique Indicator Equations
d. Find the right tools and/or their combinations

  • Look at the Big Picture: across platforms, with all sorts of measurements as part of the same system, and probe deep to avoid and catch semantic misreads
  • Think Indicators: result oriented – activity oriented
  • Be Conscious of What You Need It For: performance focused – comparison focused
  • Very Young, Emerging, Consolidating Market with Lots of Players

e. You need to plan for and work with limitations (Data, Team, Resources).

When we look at the big picture, we experience a trend of multiple and ever growing data streams flowing in, and technologically we are getting closer and closer to measuring “everything”. In such a world, we may go astray in two ways: we may either end up with undertaking the enormous work of gathering all the digital breadcrumbs and keep wondering about the loaf, or – based on past patterns – be satisfied by being over simplistic and run the risk of blind confidence.

As it is a young and emerging industry out there, you may not find one or a few tools dominating all the others and best for most purposes. But you do find a whole host of interesting and good ones. So you need to take a more active part in measuring social just yet, at least until a market consolidation process will have brought its fruits. Till then, you may have to use a number of tools employed as a team, or work with a rawer dataset and design your own rule sets to find meaning.

 


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