Building systems and strategies for Social Business intelligence

Social Media monitoringAs our means for collecting data, and the sources of data increase, we have to get smarter about putting all that good data to use. Social Psychology research shows us that oftentimes more choices, options, and inputs lead to slower and “worse” decisions. How do we avoid the human trap, and design our systems and strategies in an effective way to make use of the world of big data?

Creating a Mental Model

The Daschis group has a penchant for putting out complicated but useful visual representations of their work and thoughts on social business/Enterprise 2.0. This time around, Dion nixed the complicated and provided an excellent model for building strategy around using data from social media.

Social Business Intelligence

(image credit: Dion Hinchcliffe)

What I particularly enjoy about this diagram is the vertical flow on the right side of the diagram with concrete concepts: fast data, big analytics, and actionable insight.

The Heart of Multiplicity

The concept of multiplicity is best explained by Avinash Kaushik, and at its core lies three tenets: multiple inputs of data (qualitative and quantitative), micro and macro analysis, and business outcome/goal oriented. From that mindset we can better understand how the model above works toward driving actionable insights and results:

  • Fast data – I believe we like to think we know more about what to do with real-time social data than we do. Said another way, unless you and your industry have enough proof points to make spending time on this bucket, skip it. Others, like Beth Kanter, might argue that real time social media data is valuable, but I haven’t seen enough proof of this, and Gatorade’s hyped “command station” feels like just that – expensive hype.
  • Big analytics – On the other hand, pay deep attention to what sources are driving conversions on your website. This is more relevant than ever for two reasons. First, Google Analytics has recently tweaked their algorithm to better capture what sources are driving both goal assists (e.g. what originally brought users to your website) and goal completions (in the same visit, what was the final source that led to the desirable outcome). Second, instead of following real time ‘trends,’ a deep dive into understanding what you did that drove social media action (e.g. a blog post, news story, conference showing, etc.) that led to conversions is the best way to know where to double down or equally important, what to stop doing.
  • Actionable insight – As mentioned above, actionable insights come not from immediacy, but correlating trends, analysis, your strategies, and desired business outcomes. Perhaps the most important insights gained through social analytics is informing ways that you can better inform your segmentation when you’re reviewing your marketing strategies and tweaking website for higher conversion/better results.

What this means you should be doing

First, if you have limited resources, spend it on the actionable insights part. If you only have one analyst, they’ll be tempted to play with the sexiest real-time social media toys. Just say no.

Second, make sure you are measuring social media data both as an input to conversions on your website, but also to identify trending topics, stories, etc. I really don’t know enough about this to say more, but just google “social media analytics” and you’re likely to get good advice.

Third, spend time building a social media strategy. Have a lot of ‘likes’ and ‘retweets’ actually isn’t very interesting unless it’s driving toward outcomes. Marketers still like to talk about impressions, and having “2 million likes” is impressive at first, but if it cost a company 500k to get the ad campaigns to get those likes, and didn’t see any increase in revenue for it, what does that tell us? Point is, build a strategy so that when you start getting ‘likes’, you have a specific call to action or plan to put those ‘likes’ to action, not just talk about them in Board meetings.

Call for stories

While it’s still early, I would love to hear more about your first hand experiences using real-time social data to drive meaningful strategic decisions. I’ll continue prowling in the meantime.
  • http://blog.evoapp.com Sergei Dolukhanov

    “ I believe we like to think we know more about what to do with real-time social data than we do. ”

    I would argue that “social media monitoring” has lead to this confusion… but there is hope. 

    People are on a craze right now of monitoring their brand, listening to mentions online, and charting all this information in fancy graphs and pretty dashboards. However, as companies continue doing this, they are finding the same patterns in every social monitoring software in the market; they can’t seem to correlate all this data to their actual business processes. 

    Why? Because social media monitoring only scratches the surface when it comes to actual business results. 

    To get to the next level, people need to keep the bottom line in mind; how much money is my company making from all of these social mentions? How do I correlate all of this social (or any type of unstructured) data to my business? 

    This is where social media business intelligence comes in. 

    You take social data and overlay it with key business performance metrics, the result being trends and patterns that you can base real decisions on. 

    For example, take the popular TV block “Snick at night”. We overlayed sentiment and volume metrics of each of their shows during that block of time with the Nielsen ratings for the same data variable. Because of the recent revival of some of the old 90′s shows, Snick at Nights Nielsen ratings were higher than any other TV block. When we dove in to the social data, the sentiment and volume counts reflected this positive surge, and noticed there was a great deal of positive hype surrounding the show. We were able to figure out which shows generated the most buzz, and suggest the best possible show lineup to elevate Nielsen ratings to their highest possible levels. 

    Social data is mysterious and sometimes irrelevant by itself, but when you correlate it with key business performance metrics, you can find very interesting patterns beneficial to any business. This is just an example, but you can do this with any type of unstructured data. 

    Thanks for the post!

    - Sergei Dolukhanov
    Director of Marketing @EvoApp:twitter 
    @sdolukhanov:twitter 

    • Anonymous

      Hi Sergei - 
      Thanks for such an insightful and thoughtful response! There was one sentence that really struck me: “We were able to figure out which shows generated the most buzz, and suggest the best possible show lineup to elevate Nielsen ratings to their highest possible levels.”

      I’m curious about your thoughts on how you can use that data and insight to do two things:
      1) Identify causation – did social media have anything to do with the buzz, or did the buzz actually help drive the viewing and ratings? (e.g. peer influence)
      2) Identify further targeting and segmentation opportunities – with the data you had and the particular programming, how could you better market the shows via social and traditional media?

      • http://blog.evoapp.com Sergei Dolukhanov

        Josh,

        Thanks for the response. 
        1) I view social media as a medium – similar to the job our mouths do in relation to the ideas in our heads. People watch the shows, and express their views online. Nick at night would’ve had to run the shows first in a certain lineup, and people simply talked about it online. However, I think the answer would actually be both. Some people express their own views based on watching the shows, but at the same time other people look at other peoples statements online and and get excited to watch the shows because of it. The only thing we do know is that the buzz was there, and we were able to isolate patterns in the data between the sentiment online and the shows actual Nielsen ratings. What would begin as a medium for expressing opinion would end as an actual buzz generator because of the exponential scaling that occurs in places like Facebook and Twitter. 2) As far as targeting is concerned, you can actually see which shows had the best ratings, the most hype / buzz surrounding them, and at which times the most hype was generated. Its nothing new that you can see at which blocks of time the most people watch the shows, but you can use social media data to isolate the shows and times that generate the highest volume of positive mentions. In looking at Nielsen ratings and social media data in tandem, you can decide which shows would hypothetically generate the most hype in certain time slots. This would help you better market the shows through social media. In addition, social media allows you to actively engage with the audiences who watch your shows, so you can use Facebook polls or surveys through twitter and ask people what their favorite lineups and shows are. 
        Advertising in traditional media is expensive, and would only be worth it if you could isolate a specific genre of viewers that you think might be interested in watching your programs (in Nick at night’s case, you wouldn’t advertise earlier in the day because children wouldn’t be watching your late night shows.) You could use the data you pull from social media to understand the demographics of your audience better, and target that specific demographic on other networks. For Nick at night viewers, many of the shows they revived were from the 90′s favorites, so a good demographic to target would be people in the 20-30 year old range as they grew up watching those shows. For traditional media, I would just suggest sticking with whats worked for the last 30 years in television. However, social media adds a new dimension to marketing, so it only enhances the ability to target certain audiences and promote certain programs. I hope this somewhat clarifies things. Anyway, thanks for the comments and I look forward to reading more of your material.

        Cheers, 

        - Sergei

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