Category Archives: social-media-analytics

The roots of social business (short animated presentation)

How do social networks effect the way in which we conduct business?

I’ve had the opportunity to discuss this question with industry experts, academics and (thankfully) students over the last year or so. I’ve boiled the results down into this short presentation on social business:

Key points:

Scaling the conversational nature of business
In the pre-industrial era, business was localized, customized and highly conversational. Think of the way commerce happened in a small village. Stories would be shared over the sale of a loaf of bread at the bakery. Village folk would talk and trade recommendations.

Then came the industrial era and the imperative to realize economies of scales and produce goods for large global markets. The transactional process was optimized for efficiency (think supermarket checkout lines).

Social networking has brought back the potential for business processes to become more conversational, albeit on a larger scale to what was previously possible.

More customer-focused business
In the field of marketing and communications, we’re seeing chinks in the traditional broadcast model that was ushered in by the rise of mass media. The idea that it is the role of the company to create messages and broadcast those out with the intent of creating demand is being challenged.

Social media are creating forums for discussion with open dialogue occurring between companies (theoretically any employee), prospects and customers.

Increased workforce productivity
Social networking within the enterprise allows for the freer flow of knowledge across teams and departments leading to less information silos and more efficient internal processes for instance by allowing expertise to be more effectively sourced.

The pivotal role of analytics
Social analytics is necessary to uncover the business value of using social networking. This applies at many different levels across the whole enterprise. For instance, monitoring customer feedback following a product launch, determining which employees are the most effective networkers or helping key influencers extend their reach.

Look for further videos in this series which will look more specifically at how social networking is transforming different areas of business.

More introductory information on the nature of social business:

Resources from the MIT Sloan School of Business

Forrester blog posts on social business

IBM on social business

Dachis Group on social business

 

 

Measuring social business ROI: results from an IBM Jam

In February this year, IBM hosted a Jam (a 72-hour online forum with participation from IBM and beyond) on the topic of Social Business.

In case you’re not familiar with the term Social Business, here is definition I hear a lot in IBM corridors:

1: a business that embraces networks of people to create business value 
2: a business that is engaged, transparent, and nimble

This goes beyond social media, which is largely the domain of marketing/comms departments to touch on the very fabric of the enterprise, including internal collaboration and social networking with partners and suppliers.

As is the case with just about any discussion around Social Business, the thorny issue of ROI came up. How do you measure the value of this undertaking?

These are some of the metrics Jam participants suggested:

  • How often the brand is mentioned in social media (marketing/support/product management)
  • How engaged customers are by how often they comment on or share information about the brand (account management/customer support)
  • How many customers are being exposed to messaging (marketing/support)
  • How many customers are active advocates for the brand (support/account management)
  • How the efforts of these advocates are resulting in new customers or increased traffic (account management/marketing)
  • How many issues are being successfully resolved—and how quickly (support)
  • How satisfied customers are and what kind of feedback they are providing (support/account management)

I’ve added in parentheses the departments that have a major stake in those metrics. As you can see, this goes way beyond customer acquisition and the normal domain of marketing/communications. Bottom line: social business monitoring goes way beyond tracking Facebook Likes and Twitter Followers.

Are there key metrics you think should be added to the list? I’d love to hear!

Read more on the Social Business Jam (pdf)

Read more about Social Business

‘Measurement, analysis and learning’ key bottleneck for marketers

In Unica’s recently published ‘State of Marketing 2011’ study, measurement and analytics was identified as the top bottleneck for the 279 marketers polled:

This is the first year that this has registered as the top pain point. I’d suggest one reason for this, could be the maturity of web marketing and the emphasis this discipline puts on measurement.

Interestingly, the survey finds that the key issue marketers face is turning data into actions. There’s a problem with obtaining data, and there’s a problem with converting that data into valuable outcomes.

This relates to an earlier IBM study which showed that businesses who can act on business insights generally perform better. Looks like many marketers realize this but are struggling to turn this into reality themselves.

Peruse the report in its entirety:

Can Twitter sentiment analysis predict outcomes (like the Irish election)?

When I was growing up, election coverage was characterized by an exuberant political pundit leaping around large cardboard charts of the UK with the kind of coloring normally reserved for the weather report. The ‘exit polls’ we were familiar with only updated about every four hours and only included those people who were prepared to be cornered by the political researchers hanging around near polling stations.

Fast forward to 2011.

We currently have a general election unfolding in Ireland. The Irish online news site The Journal has been crawling over Twitter, that political social network du jour, using the conversations that happen there to predict which way the election will sway. And so far the headline graphic looks like this:

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It’s a great case study in the current status of analytics and throws up some wonderful points that have relevance beyond the Irish political scene.

Data is everywhere

Researchers no longer need to go in search of data. Whilst I don’t deny the added color and in-depth insight from questionnaires, focus groups and other tools used by human researchers (whether in the political or commercial realm), there is rich data out there that you don’t have to force out of people. Social networks like Twitter and Facebook give us access to voluntarily-provided information on social groups. We no longer have to bug people to provide us with data.

Growing importance of social media analysis

Let’s face it, we’ve seen a huge growth in the use of social networks over the last two years (not sure why I pick that time frame, maybe tied up with when Twitter/Facebook buttons first starting appearing in ads and on TV). We’ve taken our social lives online. And the beauty of being online is that everything can be tracked. We leave traces. and when you aggregate all of these, patterns start to appear. Is this level of analysis creepy? The privacy issue definitely has to be considered, however I’d contend that the information is so much more valuable in aggregate (effectively anonymized) than it is at the individual level.

Sentiment analysis can throw a curve-ball

Here is what the volume of conversations around the Irish election shows us:

image

Now look at the sentiment:

image

Fine Gael have by far the most conversations. However, much of this conversation is not positive. I’d say from a marketing perspective this is something we need to pay more attention to. Far too often we’re still using raw numbers as a determinant of campaign success. We need to add the sentiment layer on top to understand more of the nature of the conversations we ignite.

Presentation is everything

The first image I highlight in this post is so immediately descriptive. Newspapers have been producing wonderful infographics for decades. In the business world we still end up with reports that look more like this:

(not meaning to pick on anyone, this is just an image that came up in a search)

How much further our story goes if we take time to package it up. Business analytics will only move further into the mainstream if the findings are presented in an easily-consumable fashion.

So, having stuck my neck out in favor of The Journal’s Twitter Tracker, I’ll have to come back next week with some post-election analysis. In the meantime, back to Twitter to watch this election unfold.

What would YouTube want with a recommendation engine?

Techcrunch recently reported that Google (as the owner of YouTube) is looking at the purchase of Twitter-based movie recommendation site Fflick. Judging by the Fflick site today, this is more than just idle rumor:

image

What are the implications for YouTube?

On the one hand it signals a more concerted effort from the beefy video sharing site to play nicer with the other social networks in the playground (or at least hold hands with Twitter whilst working out a relationship with arch-rival Facebook). It could mean we see the kind of functionality in YouTube present on other video networks like Livestream: a display of all the Twitter backchannel related to a piece of content. For instance:

image

See the running commentary down the right? This is more ‘chat’ than ‘comments’ with tight integration with Facebook/Twitter. 

On the other hand, it also opens up the possibility of YouTube to start mining user data to offer recommendations. How useful is this on a video site? Just look at the Netflix story. The popular US video rental service has made a big deal of its ability to guess what movie you want to add to your rental wishlist. It bases its recommendations on what you’ve seen in the past, how you rated it, what others like you have seen (and a bunch of other variables even including the day of the week on which you’re viewing the site!) Netflix prizes this technology enough to have made it a central part of the site navigation and even paid a team from AT&T $1 M for coming up with a winnning algorithm in 2009.

YouTube has a much bigger collection of content, a wealth of behavioural data through its huge viewing figures. It generally knows less about its visitors than Netflix does as the site doesn’t require you to login to engage. Potentially, that’s where the Twitter piece comes in to play: you give up some of this information about yourself each time you tweet. Fflick provides the service to tie the tweet back to the video. Fflick also provides the service to pick through your Tweets and use these to determine what content you might like to see next.

This kind of application of predictive analytics is hot right now in the social media space. Foursquare is believed to be using predictive analytics to keep Facebook at bay in the location-based-services sector.

Social media is making us increasingly impatient and we are starting to demand more from our interfaces. Add to that the growing market for hand-held devices that offer precious little space for content, let alone navigation, and you have a compelling case for services using whatever technology they can to pinpoint what you probably want to do next, and serve that up. If they don’t engage, the next video-sharing site is only a short URL away.

More on the Fflick acquisition

Foursquare to use predictive analytics to beat Facebook?

There’s a growing battle in the location-based services business between Foursquare and Facebook. Foursquare, with its past emphasis on gaming and status building (who wants to be the mayor of the local laundromat?) is now focusing on a more functional aspect: helping people decide where they should go next. According to a report in Brandweek (backed up by this article on a recent job ad), Foursquare sees offering recommendations as its chance to avoid being squeezed out of existence by Facebook, who, with over 500 million users, is the ostensible gorilla in the room.

How does it plan to do this? Brandweek suggests it will adopt predictive services which are common on sites like Amazon and Netflix:

"Those services crunch behavior data—what movies you watch and books you read—to suggest new products. Foursquare wants to do the same, only with recommendations of real-world activities."

For instance, let’s say you are a sushi freak living in Chicago who’s been active on Foursquare for the last year. You’ve been using Foursquare to capture badges for most of the top local Japanese eateries. Foursquare can see your penchant for fine sushi in the windy city and look across its network for others in your area who share the same passion. It realizes that there is a new joint downtown and can suggest you check this out.

How does this crunching work? The data is mined along a process which runs something like this for each individual visitor:

  • What are the past actions you have recorded
  • What patterns can be determined from your actions
  • Who else in the network is like you
  • Where are the gaps between your actions and their actions?
  • Offer as predictions these actions that people like you have performed

Note, this obviates the need for a user to fill in a vast registration form listing all their likes and interests. The system can figure this out by looking at past behavior.

In terms of making predictions, systems need to be smart enough to factor in elements that can cause shifts in our patterns of behavior:

  • Seasonality (no taste for raw fish when snowing)
  • Change in tastes (eg. pregnancy pushes sushi off the menu)
  • Removing system bias (eg. not only favoring well-established popular places, but allowing new entrants a chance to prove themselves)

Whether Foursquare makes a concerted move in this direction remains to be seen, but as web and mobile applications creep further into every aspect of our existence (with their inherent ability to track behavior), expect to see an increasing use of business intelligence and predictive analytics to create smarter systems offering us more relevant information.