A lot of the things that we do are interdisciplinary. We’re now expanding into different areas like global security, corporate strategy, and scaling a lot of the new technologies that we’ve brought into these areas. There are plenty of valuable new ways of looking at open-source intelligence and various other data streams.
CHANDLER WILSON, DIRECTOR OF ANALYTICS & INSIGHTS AT WALMART
There are still scores of marketing applications for social data, such as reputation tracking, campaign measurement and influencer marketing, but social’s Bright New Future has been celebrated in places as unlikely as legal and security departments.
Consider the case of one of the world’s largest media companies. Having paid an eight-figure sum for exclusive sports broadcast rights, the care-free, share-free ecosystem of the social web represents a potential dilution of those rights.
The publisher set up an intelligent system of alerts to keep track of those uploading sports clips to sites such as YouTube, Instagram and Vine, helping identify possible copyright violations and establishing a tight control on infringements, meaning the brand is able to maximize the value of the package exclusivity.
By locating these bits of content early, the brand is able to work with the networks to remove such posts before they get shared widely, and thus reducing the risk of being negatively perceived as Internet wardens by thousands.
This is not an isolated case either. It represents a wider trend; a movement that is seeing the commoditization of social data inside organizations – a commodity that’s increasingly in demand.
One American bank has its security team monitoring anonymous file sharing sites like PasteBin for potentially damaging cyber attacks. Conspiring fraudsters exchanging credit card information are keenly observed, and hackers’ plans are often foiled at the planning stage. This is the kind of application for social data not even considered in the days of social media monitoring.
It’s clear that brands are actually helping drive product direction because of the clever ways they are using them. Lots of those use-cases weren’t even in the engineers’ minds when they were building the these kinds of tools. The brands themselves have now become a catalyst for innovation.
GLENN WHITE, VP PRODUCT STRATEGY
Operating at the very fringes of the platforms’ capabilities, there are also businesses pushing social data into supply chain management, mapping conversations and insights across logistics networks to improve operations.
There are even businesses attempting to understand the relationship between social data and financial performance – analysts trawling through the numbers to see if there is some kind of meaning behind what people say online, and how the company’s public share price changes. We’re certainly not there yet, but it would be foolish to write the movement off altogether.
This 2015 research from Altimeter underscores this pattern.
More than fifteen departments are now investing in dedicated members of staff for social, or otherwise participating in social business efforts. Just like customer data before it, social data is something taken seriously across the business and with that has come a slew of new use-cases and applications.
And it’s not just the number of use-cases that has evolved in recent social intelligence history. Even the initial use-cases with their cringe-inducing names (think buzz monitoring) have morphed to become something far more nuanced and sophisticated than envisaged in the past.
Identifying and classifying those use-cases remains tricky, and there is yet to be a unifying consensus. Broadly, however, the primary purposes for social data can be bucketed into 13 distinct areas.
Influencer marketing |
Identifying and building relationships with influencers and brand advocates. |
Crisis detection and management |
The means of measuring the ongoing perception of a brand, and remaining instantly alert to any potentially negative developments |
Campaign intelligence |
Understanding the performance of a marketing initiative |
Consumer insights |
Gaining deeper knowledge of a brand’s existing and potential customers |
Reputation and brand management |
The means of measuring the ongoing perception of a brand, and remaining instantly alert to any potentially negative developments |
Competitor & market intelligence |
Gathering information on the activities of competitors and thoughts of their customers, as well as the wider marketplace |
Product strategy |
Using the comments of existing and potential consumers to inform future product development |
Social selling |
Taking advantage of segmentation and other means to identify and sell to potential customers on social media |
Content strategy |
Listening to audience behaviour to optimise content strategy, including SEO research and social media campaigns |
Social media command center |
Installation of visually engaging space to conduct campaigns and perform work in the presence of realtime data |
Customer service |
Monitoring for customer queries and complaints online, and intelligently responding to them (via partners) |
Employee recruitment, compliance and activation |
Allowing businesses to manage the processes around staff using social media internally and externally |
Clearly this list can by no means be exhaustive (indeed, it doesn’t include the earlier examples). It doesn’t come close to representing the full suite of applications for social data, but it does help frame the key ways in which businesses are moving towards enterprise-wide uses.
So, can organizations view this table as a kind of checklist for becoming a socially intelligent business? Well, yes and no. It would be crude to suggest that simply establishing a program against each of these 13 uses would equate to added business value, especially when seen against the context of how each use-case might be employed.
There is a raft of theory and best practice published on each one, and there are varying degrees of investment a firm can make as they approach, say, social customer service. Undoubtedly, It’s not a binary process.
What can be done then, to help provide enterprises with a framework for understanding their maturity with social data? It’s an important marker when viewed in the light of research by McKinsey that revealed for every initiative adopting marketing analytics technology, there is a meaningful 0.39% increase in profitability.
One model that’s gaining traction among marketers and other evangelists of social data is a more in-depth look at each use-case, mapped against four stages of sophistication.
A foundation level represents a brand’s early forays into a particular program, perhaps with a small amount of budget and direction.
Intermediate can be used to demonstrate a much more serious approach, meaning an increased amount of resource allocation with it.
Few brands stretch beyond this step, but some are confidently operating at the advanced level, particularly in North America and the UK. This level of maturity reflects an industry-leading implementation, with dedicated investment and backed by real business results.
Finally, the pioneering phase refers to those pushing the limits of what’s possible for each use-case, exploring the future of social data and genuinely innovating in the space.
These stages can be used to map any of the 13 core use-cases. The nuances in each area may differ from company to company, but the principles are universal.
Foundation |
Intermediate |
Advanced |
Pioneering |
|
|---|---|---|---|---|
Influencer marketing |
Paid endorsements and other primitive promotional activity around major influencers online | Isolation of industry specific influencers and tailored nurturing of key individuals | Cross-channel, continuous influencer programme spanning online and offline with measurement in place | CRM-enabled brand advocacy campaign integrated with online and offline influencer programme |
Crisis detection & management |
Active monitoring for negative mentions with no systems in place for responding to crises. | Implementation of smart & custom alerts to understand changes in data that indicate potential crises. Disaster communications protocol in place. | Intelligent monitoring for potential crises by product category, with releavant alerting. Cross-team disaster protocol in place. | Full tracking of sentiment and category data with sophisticated workflow of alerts and escalation protocols. Regular simulation of disasters. |
Campaign intelligence |
Tracking brand mention counts during and after campaigns, perhaps also in relation to specific hashtags and campaigns | Pre-launch research informing campaign material. Social data benchmarked against previous campaigns | Purchase intent or similar implemented into campaign measurement. Social data blended with web analytics and others to understand deeper campaign performance with some ROI comprehension | Comprehensive ROI of campaign understood, amalgamated from multiple datasets and real business objectives. Campaigns adjusted in realtime in response to data |
Organizations may wish to develop their own models to adjust to vertical-specific characteristics, but fundamentally firms should be able to reflect upon their own level of maturity in line with the wider marketplace.
It’s accurate to say then, that it’s not volume of use-cases adopted alone – and nor is it sophistication of use-cases already implemented – but rather a blend of both depth and breadth of social data maximization inside an organization.
Inherent within this model is the concept of a rising number of teams and departments accessing insights from social data; an artefact in line with many industry observers.
Successful social business requires more than a Facebook page and hiring a community manager; it requires a new model with an integrated approach across all functions of the company.
ANDREA COOK, SOCIAL MEDIA EDITOR AND CREATOR OF DIGITAL DASH
Mapping the whole enterprise against social data maturity therefore is also possible. Again, although each business may differ in the specific delivery and structure of social data programs, most will be able to chart their own progress against a common benchmark.
Speaking about the 2014 social business report, conducted by the Carroll School of Management at Boston College, associate professor Jerry Kane believes that the only worthwhile measure of the maturity of a social business project is understanding the complexity and adopted across the business, ‘requiring greater sophistication across multiple digital domains’.
While this report showed that businesses were beginning to derive value from social business initiatives, this value was directly tied to the company’s social business maturity. The single biggest driver of social business maturity, however, was whether and how the company used and analyzed data … in other words, the key to social business success was not necessarily something related to social business directly but involved how companies used data and analytics.
JERRY KANE, ASSOCIATE PROFESSOR OF INFORMATION SYSTEMS AT THE CARROLL SCHOOL OF MANAGEMENT AT BOSTON COLLEGE
Maturity Stage
| Informal | Formal | Integrated | Embedded | |
|---|---|---|---|---|
Access |
One department or multiple disconnected departments | 2-3 departments | 4 or more | Enterprise wide access |
Measurement |
Metrics are volume based, with no benchmarks | Social metrics mapped to business outcomes, with some internal benchmarks | Social metrics viewed in the context of wider business, and mostly benchmarked internally and externally | All social metrics mapped to business outcomes with all relevant benchmarking |
Data |
Social data only | Social data only | Social data and other marketing data viewed in tandem | Social data combined with enterprise data for business insights |
Social management |
Limited central controls. Social management possibly outsourced | Centralized control | Co-ordinated control with decentralized access | Managed control with enterprise wide empowerment, including training and code of conduct |
Use Cases |
1-2 foundation use-cases | 2-5 use cases, mostly intermediate | 5-10 use cases, mostly advanced | Wide variety of use cases with pioneering levels of maturity |
The model required for understanding maturity across the enterprise must rest upon the volume and complexity of use-case adoption, but other factors emerge as relevant to this concept.
How social data is understood, for example, depends upon how it is benchmarked and which other datasets it is combined with. How wide the reach of, and access to, social data extends across the business is also a worthwhile indicator of maturity.
The future is already here – it’s just not evenly distributed.
WILLIAM GIBSON, NOVELIST AND PROPHET NOIR
Like sport, it’s an even playing field. But like all competitive sports, some are much better than others. As some brands explore and flourish, particularly those in CPG, media, retail and financial service sectors, others are left behind.
As any other historical technology disruption will show, the rules of this game dictate that standing still and watching what happens is the most dangerous tactic possible.
The old techniques will deliver increasingly marginal returns, and the bold and the brilliant will continue to widen the gap as value from social data grows into a real and powerful competitive advantage.
