Social media monitoring trends: context, trend alerts, and integration
Up to now most professional social media monitoring platforms have done a good job of acquiring and classifying social media content.
They are able to use complex keyword expressions to find targeted content and to a reasonable extent classify the sentiment of that content – in English at least. They are able to configure alerts based upon the occurrence of keywords and sentiment e.g. send an email to a nominated person whenever negative brand mentions occur.
What’s been missing is the addition of context to the current content-based social media monitoring software.
Adding context simply means ‘making use of other things we know about the content that we are filtering out of the streams of social data‘. You may know that every tweet has 65 pieces of metadata — context — associated with it. For example, for each tweet we know a lot about the person tweeting such as their influence, followers, following, and self-description.
More interestingly, we are also seeing tools which can produce personality profiles of people tweeting and the personality profile provides rich context which helps us decide on the value of the content.
The value of knowing context is that we will be able to make much more effective decisions about what actions to take with respect to the content we find in social media.
For example, take a positive brand mention. If this is made by someone with low influence, low number of followers, and an extroverted personality then we would take “action A”. In contrast if it is made by someone who has “CEO” in their profile description, high influence, a large following, and conservative personality then we would take “action B”. The two different actions would potentially employ two different forms of engagement, with different content, and the building of different dynamic lists for future reference.
The type of context-based decision making described above is a step up from today’s content-only decision making, and will be generally available by the end of 2015.
At the same time, another context-based feature which advanced social media monitoring tools will be releasing is “trend awareness” and trend alerts.
This will allow alerts and actions to be triggered based upon such context as negative mentions e.g. if negative brand mentions are increasing rapidly then take certain action. Or more simply, if the volume of any specified set of keywords increases or decreases quickly then take action e.g. notify someone or queue an action in a workflow.
This trend awareness will provide additional value for a wide range of social media monitoring roles such as crisis monitoring, event monitoring, and campaign monitoring.
Integration of social data into business applications
Finally, another important trend is quite new. It is the ability to identify mentions in social media using the methods above, and then to pass on this data to the enterprise systems which people are using for the day-to-day operational work. For example, details of mentions in social media identified within the monitoring system will be passed on to an organisation’s CRM system, or to their call centre customer support software for example.
This type of data integration is a very significant development in the potential use of social data to improve operational performance. While it is possible now to export social data from social listening tools and to import that into operational systems, and to reporting dashboards for example, this is inefficient. Because it is inefficient, it is often an inflexible process and only used when the resource investment can be justified. With the forthcoming “drag and drop” integration features such transfers of social data into enterprise operational and reporting systems will be able to be done extremely flexibly and at essentially zero development cost.
This will spur the use of social data for better lead generation, customer support, product development, ad targeting, marketing automation, and social selling. It will also provide very tangible evidence to the perennial doubters and laggards in business of the value of social data to aid productivity and operational outcomes.
This, in turn, will stimulate an unprecedented wave of interest in how social data and social data analytics can add value to all parts of an enterprise – but that’s the subject of a future post.
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