Perhaps you are new to the world of social media monitoring at work, or are embarking on your studies at university in this area, and you are wondering just exactly "what is in it for me ?"
Maybe your background is in traditional marketing or perhaps you work in a technical or customer services function and have heard that this new area is indeed something worth looking into further . How can monitoring social media help you make decisions and deal with problems and opportunities?
Well it is of course e-Marketing and web masters are the most interested and are sitting glued to the analytics and consumer opinions. Of course it is not just for the geeks: communications and customer services who are aslo getting engaged with SM. The benefits reach wider and deeper into the organisation though. Everything from new-product-introduction & tracking, or competitor pricing, to issue containment is amenable in almost real time : the feedback loop from action-consumer reaction is drastically shorter.
One burning question is how can information in SM be used to make conclusions about the wider consumer population? Well speaking mathematically, usually we cannot make inferences to the general population or produce actual statistics - yet.
The day may come in the near future though, when the numbers of consumers engaging in discussion will be so high that we can draw inferences to the wider population, such as intent-to-purchase or brand awareness, and put some hard numbers behind this with SM a quantitative source for extrapolation to consumer behaviour in the market as a whole.
Even then it will most likely be from a definable cross section of different geographical- or network-societies: age and education related. It would be dangerous to draw inferences on anything but the first three standard deviations from one of those sub populations who are engaged with the internet and SM. However we will be able to utilise statistical probability based sample methods within any accessable or stored data set to produce smaller data sets which are make analysis more efficient within given parameters of accuracy and inferential significance.
Social Media Monitor Should Be Qualitative
For the moment though, reporting is focuses rather on the valuable qualitative insights to be found, "straight from the horses mouth". These are the root causes of issues, the actual verbatim opinions, the dissatisfactions, the real point-of-touch customer experiences : I could go on! These help illustrate findings from a company's quantitative reports and data-sources, as well as pointing to new insights which uncover consumer opinion hidden or distorted by the very interactive nature of surveys, depth interviews or focus groups. Also they can uncover uncomfortable truths which are hidden by line managers, front line operators or re-sellers.
Everything is Relative
Despite the qualitative output of reporting, descriptive statistics can be used within the domain of social media to illustrate the relative prominence these qualitative observations This includes the relative prevalence (or you could say "share-of-voice" ) of brand names, consumer opinions and for instance problems with newly launched products.
In combination with ever-more-accurate sentiment algortyms running with AI (artificial intelligent) systems, this area of descriptive statistics will be used more and more to give a picture of the cross-section of society using SM to discuss your brands and customer support. The value will increase if it can be shown that the "listen-learn-decide-react" loop on the web connecting to SM, is functioning.
Then our little world of SM becomes a market in it's own right, and this is already happening with companies engaging in different campaigns and communications which are built around information from social-media-reportage. Some companies in future may only interact with consumers through this interface and connections form SM to their web-services.
Numbers and graphs are all very fine and nice to present and talk about, even with the provisio that this is a little and twisted version of the world at large. But even a very low number of "hits" within the latter, for example, can reveal invaluable insight into potential challenges in production lines or further back in the supply chain which are creating problems not detected earlier in the testing and launch programme.
Tracking New Product Introduction
This qualitative approach has been of particular value in tracking new devices launched on the market, which have a plethora of features and most likely diverse internal software. However, this is equally valuable in tracking a new service, or immaterial product from a financial institution or a mobile network operator. Or in defining an unmet need or latent demand out there in the market place.
Within the world of gadgets- consumer electronics like mobile phones, PDAs, laptops or digital cameras - it is in fact often the lead consumers who are the real experts: they can be tracked individually, from maybe a sample of 10, as they try and buy diverse gadgets and report their experiences on the web. Often they seem very informed on how the technical features, like processor, touch screen, GUI, actually deliver benefits in use and how much better this performance is to earlier products or competitors offerings.
In fact these lead consumers seem to have a more wholistic view of the product's perfromance than the head of R&D and most likely the CEO at the manufacturer! Worth listening to SM?