5 Challenges Of Real-time Social Media Analytics

Hopefully you’ve adopted some social media analytics strategy to boost your ROI, but how effectively do those analytics translate into meaningful insights that guide your business tactics and how quickly do these insights percolate through your organization to affect strategic planning? Do you plan based on real-time social media analytics?

If you struggle to integrate real-time social media analytics and ROI, you’re in good company.

Just a few years ago, real-time social media analytics meant tracking #Fans/Followers, #shares, #website visitors. Today, those vanity metrics have faded as better tools arose to reduce big data into a more manageable group of metrics. Unfortunately, many firms struggle to produce actionable insights from these metrics and real-time social media analytics defy implementation.

So, why the disjoint between real-time social media analytics and ROI?

Disjoint between real-time social media analytics and ROI


Because social media is ON, 24/ 7, and literally MILLIONS of pieces of social media appear every second — just look at the infographic from Leverage below.

WHO can keep up with all that?

So, while the massive AMOUNT of data slows down the social media analytics process, other factors account for why insights are so slow to emerge.

1. Getting social media metrics to the right people

Often, social media is treated like the ugly stepchild within the marketing department and real-time social media analytics are either absent or ignored.

Real-time social media analytics creates serious challenges for many organizations. Often, organizations are married to an old paradigm — a vestige of by-gone days when data was hard to get, taking months of data gathering and analysis. These organizations didn’t integrate data gathering into tactical and strategic decision-making because they couldn’t. Incorporating real-time analytics just isn’t possible with their existing bureaucracy.

For one thing, real-time analytics requires moving analysts closer to decision-makers and enabling decision-makers with analytic skills for ad hoc data analysis. But, that’s not what most businesses look like. Many decision-makers lack analytics skills necessary for ad hoc analysis.

New arrival, Uber, which runs a ride-sharing program that competes with taxi companies and car services, uses real-time analytics to show how people move around a city at any give time, allowing Uber to optimize their customer service. Placing cars nearby reduces competition with local cab companies and real-time analytics provide insights necessary to do that. To do this, Uber uses real-time data to incentivize more drivers to provide services by raising the price of a ride.

Others such as Samsung and NASCAR do a great job of providing real-time social media analytics to guide decision-makers. Take a look at this command center NASCAR uses to monitor chatter surrounding their events.

2. Visualization

Visualizing real-time social media analytics is another key element involved in developing insights that matter.

Face it, human beings don’t do a great job of processing long tables of numbers. Notice on NASCAR’s command center, much of the date is displayed visually.

Simply displaying values graphically helps in making the kinds of fast interpretations necessary for making decisions with real-time data, but adding more complex algorithms and using models provides deeper insights, especially when visualized.

3. Unstructured data is challenging

Unlike the survey data firms are used to dealing with, most (IBM estimates 80%) is unstructured — meaning it consists of words rather than numbers. And, text analytics lags seriously behind numeric analysis.

While unstructured data tends to muck-up any kind of analysis, it’s especially challenging in the context of real-time analytics, because you want interpretations IN REAL TIME. Handling text in real time often means using computer-generated translations of the written word. However, no computer can effectively categorize much of what’s written in social media where “bad” might mean bad or it might mean good, depending on context, relationship, and other variables.

4. Increasing signal to noise

Social media data is inherently noisy. Reducing noise to even detect signal is challenging — especially in real time. Sure, with enough time, new analytics tools can ferret out the few meaningful comments across various social networks, but few can handle this in realtime.

5. A wait and see attitude

Again, businesses are used to a certain operational model that makes real-time social media analytics challenging. For instance, we listed to a presentation by an analyst from NPR. He showed complex A/B testing used to determine the effectiveness of headlines, even whole articles online. As a statistician, he’s concerned about achieving statistical significance in his testing before making decisions.

And, that’s great if your talking about putting $100 million into building and marketing a product, but doesn’t make much sense in the fast-paced world of social media. Real-time analytics require real-time decisions. Period.

If it’ll take you several days to gather enough data for statistical significance, forget it. Especially if you’re only trying to determine which headline does better, by the time you have a statistically significant answer, no one cares anymore. The news trend has moved to another topic.

Remember, the proverb:

a good plan today is better than a perfect plan tomorrow

and that couldn’t be more true than in social media analytics.

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Source: SAP Innovation