Listening 2.0: Leveraging Social Intelligence to Drive Business Results, a white paper from Converseon describes the evolution of social media monitoring and how companies can find business value and drive success by infusing social intelligence into their operations.
It describes how basic social media monitoring is giving way to more sophisticated analytics and the changing role of listening in the brand enterprise. Additionally, it outlines actions companies can take to achieve business outcomes through social media monitoring and engagement.
Describing the social media monitoring challenges facing brands, the paper states:
Social media is becoming a core component of business strategy. As such, we are witnessing a rapid evolution from ad hoc and sponsored exploration to a desire for enterprise enablement, whereby social media and social intelligence become competitive advantages that enable critical business performance. For organizations who will participate in this evolution, four important areas must be addressed:
- Determining how and where listening can signiï¬cantly impact business outcomes and objectives.
- Understanding how to manage the vast rivers of data, ï¬nd meaningful insights, and support business processes and use cases – for today and tomorrow.
- Determining what should be automated and the role that people need to play; and determining the balance of internal versus external resources and capabilities.
- Creating frameworks to infuse social intelligence into the far reaches of the organization and ensuring timely action with a systematic, best-practice approach that includes performance measurement based on impact to the business.
- Converseon provides an overview of a new generation of listening solutions – it calls them Listening 2.0 – that is evolving to help meet these challenges and supersede current monitoring solutions. The company sees these new solutions providing deeper intelligence that aligns with business goals, addresses the challenges above and helps social media and social intelligence flourish across organizations.
The white paper describes the differences between existing and evolving social media monitoring (SMM) across ten dimensions. While some social media professionals may take exception to some of these observations, as a whole they offer insight into the rapid evolution of the science of our industry.
1. Transitioning from insufï¬cient analytics to deep intelligence
Existing SMM: The entire process relies on automated, and often unreliable, analytics. Automated sentiment analysis, geo-location, inï¬‚uence and more are notoriously and demonstrably inaccurate.
Evolving SMM: Machines do what they do best, and humans do what they do best. In Listening 2.0, machines scale human intelligence rather than replace human intelligence. While natural language processing and machine learning are getting better every day, machines simply can not clearly and effectively understand human language, and that will remain true for quite some time. New approaches utilize human intelligence, supported by advanced technologies, to capture the nuances and insights required for meaningful understanding and action. These types of solutions include the application of social sciences such as cultural anthropology, sociology and linguistics to understand both the explicit and implicit meanings in the conversation.
2. Transitioning from one size ï¬ts all to custom configuration
Existing SMM: Off-the-shelf tools often provide an overwhelming quantity of features and metrics, but little business insight. For example, one may be able to see how conversation volumes changed over time, but automated solutions cannot show why the change is happening. In addition, every user sees the same features, regardless of their use case or functional area.
Evolving SMM: Where existing SMM approaches often focused on PR and customer care, 2.0 approaches satiate the needs of multiple (and proliferating) use cases across the enterprise. They do so via custom conï¬guration, advanced analytics and integrated workï¬‚ows. These advanced solutions do a far better job of supporting R&D, campaign measurement, product lifecycle management, risk management, compliance and more.
3. Transitioning from unreliable sentiment to reliable sentiment:
Existing SMM: Existing automated sentiment solutions yield 60% sentiment accuracy at best, despite vendor claims to the contrary. And that’s for conversations they can ï¬nd and analyze, which also is quite limited. Automated solutions are also incapable of understanding sarcasm and slang. The simple reality is that automated tools that claim greater than 60% accuracy almost always calculate that number based only upon their analysis of the most obvious records, and they simply do not include the very signiï¬cant portion of records which their tool scores as “neutral” where it was unable to determine sentiment. And anyone who has sat in front of a river of records in an automated tool can tell you, those neutral records are usually a very large portion of the records.
Greater accuracy: Advanced approaches address the limitations of machine intelligence by integrating human analysis. Machine/human hybrid solutions enable greater accuracy in analytics while also providing tremendous scalability in the solution. While human coding of sentiment, topics and voices is far more accurate than automated scoring, human analysts also help to discover unexpected ï¬ndings that the brand did not explicitly seek in the data, and which pure algorithmic approaches simply cannot provide. Where present tools let you “only ï¬nd what you look for”, 2.0 solutions let you ï¬nd whatever is relevant in the conversation – whether you already knew about it, or not.
4. Transitioning from generic metrics to custom metrics
Existing SMM: Off-the-shelf listening platforms have to provide every user the same set of metrics, which means you are only able to measure what the software provider decides you can measure. They are not tailored to speciï¬c industries or business models. The most basic metrics cannot provide the level of granularity and insight required to make meaningful business decisions.
Evolving SMM: Basic metrics give way to new advanced metrics and insights that monitoring tools simply cannot provide. In fact, they align to a brand’s unique business requirements because the metrics are determined based on the needs of the individual business. As a result, the social media metrics and analytics can integrate into existing business KPIs and performance reporting. Such metrics can measure “voices” in the conversation based on your existing customer segmentation, or differentiate between what your employees are saying versus your competitor’s employees. The advanced solutions can model intent and more. In addition, the advanced solutions measure sentiment against the topics that matter to you: your pricing, packing, naming, locations, features – whatever you want to know. And you can know all of that for each of your customer segments. The simple reality is that it’s no longer enough to know your general sentiment because overall sentiment simply is not actionable.
In the second part of this post, we’ll look at the other six elements of in the evolution of social media monitoring, as well as a list of recommended practices for your brand to best take advantage of this new paradigm.