Over the last 10 years, the social data market sector has enabled a multitude of ways to understand how audiences interact with brands, organizations, political candidates, governments, and more. Social data platforms have expanded in functionality and complexity through investment and industry consolidation, while simultaneously adjusting to new and evolving data sources. In the case of Facebook and Twitter, the availability or restricted use of existing data sources has required platforms to divert from their original product roadmaps. Even with the changing data access landscape, social data platforms have access to a staggering amount of consumer and media content — data that needs to be collected, filtered, and processed into a usable format.
From an innovation perspective, and as a response to the amount of data available, much attention has been paid to enhancing and simplifying the user experience of these platforms with the goal of attracting and maintaining the widest possible audience of analysts, researchers, brand managers, subject matter experts, and others.
Attention has also been given to automating, as much as possible, the results delivered by these platforms once configured for an entity or use case. Fulfilling the ‘ease of use’ benefit that many platforms tout as differentiators, users have come to expect that producing and consuming useful insights should require no more than one or two clicks of a mouse.
At the same time, users of social data platforms continue to face headwinds when it comes to answering key value-oriented questions: What should we be measuring? What are the right KPIs? What is the expected outcome of the data we collect? Do reports generated by our chosen platform align to business goals? Are these insights actionable?
Access to massive amounts of data, the pressure users have placed on platform developers to simplify user experience, the expectation of automation, as well as the near real-time need for actionable intelligence, is driving the market to an inflection point — an inflection point that will change how these platforms are used to justify their investment.
Today, new questions are emerging that focus more on topical context and relevancy rather than vanity metrics such as audience growth and engagement rates. Yes, users of these platforms continue to measure, with good reason, how many shares and retweets their owned content generates. They continue to count earned media placements. They continue to plan and generate content with an expectation of virality.
But increasingly, brands, organizations, and governments are realizing that the definition of insights is achieved through a granular, contextual understanding of how audiences respond to a campaign or topic. Users need to be able to quickly and efficiently digitally curate massive amounts of data in a very short time to be able to extract truly relevant and actionable insights from the data.
Digital curation begins by configuring and tuning social data platforms to listen not only for a brand, organization, candidate, etc., but to categorize media and consumer conversations on a campaign-by-campaign or topic-by-topic basis. The output of these categorizations enables an analyst or researcher to make a baseline comparison against the total conversation as well as understand the overall sentiment of the topic.
The real value of digital curation comes from leveraging software to enable humans to quickly analyze and process a subset of the categorized data to determine the tone, narrative, and impact of the campaign or topic as a whole. The software offers access to the data, while humans extract unique, contextual elements of the data to make it useful and actionable. Through digital curation, the reporting of insights becomes more than just raw performance numbers on a campaign or topic. Results can be presented in a more persuasive way by presenting stakeholders with what consumers, media, and competitors are actually saying within the context of a topic — similar to a comment card.
By integrating digital curation tools and processes into today’s highly advanced social data platforms, users can more quickly define what should be measured and what should be ignored. They can settle in on a concise, realistic set of KPIs. They can align social data more succinctly to business goals. And, most importantly, they can justify the investment in social data by finding unique ‘needles in the haystack’ that often cannot be found via any other type of business intelligence or research platforms.