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The Future of Social Data: Social Intelligence - A presentation from #SMWChicago
With the proliferation of data expected to grow 50x in the next ten years, companies need to not just listen to what’s happening now, but use data to predict what’s going to happen next, and be able to take actions on what they learn. Moreover, the rise in multimedia content like photos and videos means that listening and analytics need to include those data sources as well in order to stay at the bleeding edge of the social web. Plus, the companies leveraging these technologies need people, process and purpose as well to ensure that social intelligence isn’t lost in a corner of the marketing department but is positioned to be a key source of business intelligence for the entire organization.
But this has been an amazing shift, because the very nature of what makes this stuff hard to measure makes it incredibly powerful in terms of influence and driving people to decide or act.
Can you believe that despite our outward loathing of advertising, our trust in it is actually up thanks to social? The truth is that we don’t hate advertising, we hate irrelevant advertising.
Which means that we have a network of conversations and information that is unstructured, or lacking easily categorized information that falls into neat buckets and cells on a spreadsheet.
Conversations move quickly, and they’re driven by humans with fickle opinions and dynamic lives so it’s much harder to put their conversations in a bucket.
So as companies we figured out several years ago that these swaths of conversation probably held a lot of amazing information inside them, and we should pay attention to that and seek out the insights that it can provide.
But we quickly grew frustrated, because just having list of the things people were saying wasn’t enough to really give us answers.
It’s important to listen, to track what people are saying, to keep tabs on the online conversation.
But the truth is it’s 2015, and we need to do more than just do what we’ve always done for the last 7 or 8 years.
Not just because we need better information - because we do - but because there’s something happening as a result of social’s proliferation that we cannot ignore.
One of the key challenges marketers face is the proliferation of data.
Improving the performance of marketing means being able to ingest, process and analyze incredible amounts of data, and that problem is only going to get bigger. In fact, within 10 years the total digital data available in the world will increase by 50 times.
Today, Sysomos processes and analyzes nearly 4 terabytes of data each and every day, which totals over 2 petabytes annually, and we expect that to multiply several times over in the next two to five years alone.
The truth is that we don’t need more data. We need more of the right data, parsed in a way that can focus on the business challenges we face. More isn’t better if it doesn’t align with your needs and goals.
Only data science can solve against these kinds of problems, and we need common formulas that can be applied to data attributes over time.
Where we have to refocus our energy is on the intersection of data and our customers, and what patterns and behaviors we can discern from that.
Because customers each take personalized journeys across the web and digital data, We believe that product families must grow to capture those behaviors, patterns, preferences and actions so that we can create learning systems through smart iterations in data.
One of the challenges, of course, is the constant balance of personalized and relevant, and the desire of individuals to have both personalization AND privacy.
Providers like Facebook are trying to strike that balance with offerings like their Topic Data. We’ve actually built a product on top of this known as Sysomos Scout, which visualizes a lot of the topic-level data to help extract patterns and insights that could help inform business decisions.
It’s pretty interesting, and you can actually find a lot of insightful tidbits from it. But it’s only one piece of the puzzle. Pattern recognition and data science are where it’s at.
Data science isn’t just taking data and creating analytics from it. Data science is extracting knowledge from data through statistics, predictive analytics, and things like machine learning.
As we analyze the inputs from a myriad of digital interactions with customers, we will be able to not only understand what they need and want today, but learn and predict what they’ll need and want tomorrow. We can relate them to others, cross-reference them across communities and affinity groups, and even derive correlations across seemingly unrelated product families to uncover new opportunities for engagement and personalization of experiences.
But remember what we talked about, all that data? Those learnings don’t stop at social or the web.
Imagine a world that is continually populated with data from mobile interactions, media consumption, and the rapidly-growing world of sensor data like personal fitness wearables and smart homes. Now we’re talking about a perpetual network of information - a broad-reaching data layer that touches our lives in hundreds if not thousands of ways - and creates patterns in math and science that we can learn from.
As we build data into our worlds, we create more opportunities for complex and interesting databases of information that help us live smarter, do better business, and bridge the gap between individual and personal preferences and the organizations, products and people we interact with every day.
Today, most of our data analysis - especially in social analytics - has been descriptive, meaning it helps us look in hindsight at historical activity and report on it. The essence of social data at first only really helped us capture activity streams and try to make meaning of it.
We are moving toward a a more associative environment, layering on useful insights against that reporting to correlate past activity with possible information that can help drive business decisions. That’s analytics at work.
To date, of course, that insight has been largely human-driven, requiring the brain power of a person to draw conclusions, derive the actionable information from a set of conclusions, and decide how to act on it.
We need to insist that social data not only anticipates trends but can, for example, identify them in disparate, emerging data, overlay data sets, identify velocity points in data and pre-emptively configure actions and engagements in response. The smarter our systems and more diverse their data inputs, the closer we get to helping marketers deliver relentless customer relevance.
Think of social intelligence customer connection engine that analyzes information, surfaces interesting patterns and behaviors in order to provide potential action moments for marketers, and provides capabilities to target and publish content that is tailored to the needs, preferences and moments of choice and purchase of the community.
- Content & Engagement:
People: Ensuring that there are not just bodies but minds devoted to understanding and contextualizing data for customer intelligence
Purpose: Understanding the role that social intelligence plays in your overall business intelligence strategy and what grey areas it can fill or augment
Platform: having the right technologies integrated into your workflow and organization so you have consistent and broad data access and the ability to take data from the practitioner level to the c-suite
Process: We don’t get a prize for creating a report. In fact, measurement is the beginning of understanding. We need to take what we analyze and actually use it to make better business decisions.
Measurement can do something brilliant in business that is never irrelevant, never outdated, never not useful:
It can help us ask better questions of how we do things, why we do them, and what we can improve as a result.
Ultimately, the quest for data and intelligence should be a quest for better answers, and to change our business - maybe even the world - with what we learn.
The Future of Social Data: Social Intelligence - A presentation from #SMWChicago
The Future of Social Data:
Amber Naslund, SVP Marketing
This proliferation of social media has completely changed
the way people make decisions about where they spend
their time and money.
of worldwide consumers
say they trust earned
friends, communities and
of people rely on social
media - especially reviews
and recommendations from
friends and family - to guide
their purchase decisions.
of online consumers trust
advertising on social
networks, which is up from
26% in 2007 and
increasing. Trust in TV ads
is down by 25%.
For the first time ever, social
media has provided businesses
access to conversations and
communities online that are:
• Motivated to share
and ROI must be
more than a
Social Intelligence can be the…
• Early warning system for business threats and opportunities
• Trendwatcher to identify what communities are starting to care about
• Engine for customer advocacy inside the business
• Hub for dynamic social graph data and community behaviour
• Conduit to the “silent majority” of passive content consumers
• Preservation system for adaptation and customer relevance
Just a few examples of social intelligence in action:
• Drive the content creation lifecycle, like a major consumer
• Determine programming based on the voice of the audience,
• Align the product development lifecycle with real-time
feedback, like a major technology company
• Rework your brand positioning, like a global beverage
• Inform supply chain, like a global CPG company
• Adjust merchandising in real time like a clothing retailer
Are you prepared for social
6 key readiness questions
1. Do you know why you’re pursuing social listening at a strategic level?
2. Do your social metrics tie to overall business metrics?
3. Are you supplementing social data with other data sources?
4. Do you have human resources to contextualize your analysis?
5. Are you prepared to mature business process to include social data?
6. Can your existing technology stack support your objectives?
“We hear only those questions for
which we are in a position to find
- Friedrich Nietszche
Grab the Forrester report on Social