In the past decade and more, social media has morphed at a dizzying pace. It has changed avatars, reinvented itself and defined a space for itself in society. Once termed a ‘fad’, social media today has come a long way and certainly seems here to stay. Testament to this is the fact that organisations now take it very seriously, with the social media function within businesses maturing and getting the necessary eyeballs at the decision making table.
Enterprises are beginning to appreciate the potential of social media and have begun developing social media strategies that further their specific business objectives. Setting up dedicated social listening teams and empowering them with full-feature social analytics tools is only a beginning though.
Getting familiar with and adept at using features such as sentiment analysis, influencer identification and customer profiling will soon become necessary skills for every marketer. However, the true competitive edge for businesses will come from devoting time to comprehending data coming out of the system and converting these into real, actionable business insights.
The field is so dynamic that predicting the trends for the next year is an accomplishment in itself. Having worked with multiple Fortune 500 clients, solving their high-impact business problems, we distinctly see some key trends emerging over the next year that will catapult this social media analytics to new heights.
These trends emanate from a strong need for enterprises to create a sustainable niche for themselves. The main motivation for companies will be to employ multi-perspective data, which empowers them to make better business decisions. Besides, accessing and processing customer signals from various sources and synthesising these data into actionable business decisions can give businesses a competitive edge. Here are the trends:
Integrated tools
New listening tools will emerge that would help enterprises intelligently interpret multiple large datasets together. These tools will go beyond isolated social media analytics and offer connected digital intelligence – allowing enterprises to have a combined view of their structured (CRM, call centre, survey data, etc.) and external (social media and web) data.
Vendors will shift towards offering not only data collection from multiple touch points but also flexible data warehouses with built-in analytical APIs as a service. This will potentially be bundled with further customised service offerings, which will enable customers to integrate social intelligence into their analytics and reporting ecosystems.
Clearly, this will enable enterprises to synthesise a more holistic picture of consumers by combining various data sets and unleash valuable insights to make better decisions across departments. For instance, analysing consumer product feedback data from social platforms, call centre logs and local store CRM to get a comprehensive view of how users experience products; or analysing video and CRM data for retail stores to study consumer preferences; or combining solicited surveys with social data to offer an improved customer service experience.
Internet of Things and Social Media
Enterprises will start exploring the business value of integrating data from connected devices with social media research. The first generation of connected ‘Internet of Things’ (IoT) devices like Samsung Smartgear and Google Glass are already influencing people’s behaviour. Data from these devices can be used to generate fresh insights. At the same time, social media offers another set of completely new data around human behaviour, with conversations happening on open forums that often go beyond traditional, physical boundaries. Combining data from such varied sources is bound to make Big Data even Bigger and better. This will enable enterprises to generate a deeper understanding of their consumers, their transactional, social, attitudinal and behavioural patterns. This understanding can empower enterprises to strategise, plan and make better-informed, consumer-centric decisions. Consider this: a consumer wearing an Apple iWatch walks into a large retail store and checks into her Facebook and mentions that she is on the lookout for a tablet. Here, the sales teams from the tablet manufacturer end as well as the retailer end can benefit greatly from being able to harness her post in real time. They can send customised offers with a discount or deal tailored specifically to the prospect customer in near real time.
Social Media from source to solution
Over the last few years the priority for enterprises has been to monitor social media for their brand and online presence. However, businesses are now increasingly looking at social data to also provide solutions to their problems. So, instead of just manually monitoring consumer buzz about their brands, enterprises now want to categorise these into various buckets like positive, severe, product complaint, software complaint, etc. and based on these categories, trigger downstream business events to involve multiple departments that could in-turn generate customised responses to engage the right customers in the right manner.
Solutions that go beyond mere social listening and enable such use-cases of providing end-to-end customer support for enterprises are the next logical step.
Another such adoption of social media as solution could be the use of data to know what consumers are looking at in a product offering, before even developing it and continuously engaging with customers throughout the development process. The shift is mainly driven by the realisation of the fact that Big Data could offer analysis of huge volume of data compared to the traditional data sources like surveys or call centre logs. Also, social media conversations and interactions are based on instant response factors and emotions, and are not necessarily premeditated, as is often the case with survey responses.
This real-time and real-life information makes it a more factual and relevant data for enterprises to consider leveraging.
Rachana Khanzode is the product marketing manager for Mu Sigma
