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Monitoring Social Media – Analytics and Web 2.0 Data

17th Mar, 2011 by Chris Geddes

SOCIAL MEDIA IS EVERYWHERE

Social Media has yet to have a definition truly nailed down. Wikipedia, an apt source for this information, states that “Social Media are media for social interaction, using highly accessible and scalable communication techniques”… which is interesting. It means that ANY medium through which a two-way conversation can take place counts as a Social Medium which, in turn, means that the pub counts.

This is an interesting (and I believe accurate) perspective as the logical conclusion we can arrive at is that not much has changed between pre and post Social Media: We are still, effectively, talking about “word of mouth” except, this time, it is on steroids. Whereas in the past a conversation in a pub might influence a handful of people, it is now possible to influence tens of millions of people. Herein lies the power of Web 2.0 Social Media: The voice of the customer is now broadcast, linked and amplified to a far greater extent than ever before.

Its not just 140 character “tweets” that the comments are made through. Facebook updates (and updates on other social networks), YouTube videos and Forum comments also add to the noisy conversations that are happening 24/7.

When I ask clients whether or not they use Social Media, they often respond along the lines of “Do I look like I use Social Media?” but upon further probing, most do indeed use Social Media. Most people think that Social Media are the same thing as Social Networks… not so. Most of the clients accept that they use Social Media when I point out that Amazon, Ebay and Google all have social elements and if they have used any of the reviews (or other User Generated Content), then they are, indeed, users.

The reason I have spent so long in explaining what Social Media really are - is so that it is understood just how much UGC there is “out there”. No single corporate entity can afford to store all of the Social data that is produced by users -there is just too much – so how do companies make it manageable and leverage the reams of data?

SOCIAL MEDIA MONITORING

Social Media Monitoring tools have been around for some years now, but are only just starting to really get to grips with the volume and diversity of content. Essentially, they all work by trawling the internet for specific triggers and then pulling back the information, rather than storing it all in an enormous database and then running queries. The main difference to the user of one of these tools is that it is easier to start a search and allow it to run into the future than it is to try to go “back in time” through searching stored information, unless the information is stored elsewhere. Sites such as Twitter, however, don’t keep the “tweets” for long, though, so running a search to measure the effectiveness of a new product launch, for example, has to be done right first time… there are no “re-dos”.

Its not just keywords, though, that the budding Social Media monitoring employee should think about. Software is now advanced enough as to understand sentiment to a useful degree of accuracy, and even sarcasm… which is soooooo easy for us humans to pick up, but much harder for machines. The mechanism for flagging remarks as sarcastic is long, though, and complicated and a subject for another blog post sometime, but the point is that these tools are well established and can whittle the billions of comments made every day down to those that are relevant to you or your brand at which point, I would suggest, a human being deals with them.

If you decide not to use any of these tools, you could watch Twitter, for example, 24 hours a day, but what about the other 500+ major social media channels? Its going to be expensive to hire the manpower necessary.

Fig. 1: Social Media Monitoring Tool classes

Genre of Social Media Monitoring Tool                What it does                                       
Keyword monitoring Monitors the (public) internet for keywords or phrases
Buzz measuring Monitors the volume of comments concerning a topic (collection of keywords/phrases) or keyword
Sentiment monitoring Measures the feeling associated to a topic or keyword
Association monitoring Monitors a keyword for frequent associations with other keywords and their associated topics
Influence monitoring Measures influence of online or offline individuals in the online space

So, what vendors are the best ones for you? That’s a tricky question. When I last counted how many Social Media monitoring tools there were “out there”, I got to 168, each of which has its own strengths and weaknesses to evaluate, which is not the purpose of this blog post. The purpose is to address how best to use Social Media monitoring tools to analyse Web 2.0 data (or so it says in the title).


USING SOCIAL MEDIA MONITORING TOOLS TO ANALYSE WEB 2.0 DATA

There are two ways that these tools can be used to enhance a company’s insight on customers, both current and prospective.

1) Monitor the public activity of known individuals and this, in most cases, requires some kind of opt-in from them and is thus hard to achieve (but immensely valuable).

2) Address customers and prospective customers as one entity and see how people are reacting, in real-time, to various stimuli en mass.

This approach could, for example, give a good indicator as to the impact a new advertising campaign is having on discussion volume and sentiment and provide feedback as to how to improve it … or whether to remove it.  If taken to the extreme, this data can be mapped and segmented into very precise, small segments which, with the aid of a representative sample of the population, be mapped back to your own customer data… giving new levels of customer understanding to the data analysts. Let me give an example: Customer data indicates that I am a male, I am 27 years old and I earn £200k a year (I wish!). It also shows that I live in west London and drive a motorbike. Finally, it states that I spend £100 a month with your company. With the online profiling just mentioned, it is possible to see what online behaviours others that share these characteristics have, such as hobbies, holiday destinations, favourite cartoon, least liked food takeaway type, political leanings and anything else you might want to know.

It really is that powerful if handled right.

Why would you want that kind of modelled information on a customer?

Knowledge is power - power to stop bombarding customers with irrelevant marketing (and thus power to market more effectively), power to design products and services around what your customers are likely to want, not what you think they will want, power to engage with customers about things they care about, not what you think they care about but overall, it’s the power to listen and let your customers know that you are listening.

Since customers that feel a company cares about their opinion sell around 50% more effectively than companies that don’t, I think the argument for Social Media Monitoring pretty much sells itself, don’t you?

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Integrating online behavioural data with offline CRM data using Google Analytics

9th Mar, 2011 by Saran Subram

Saran SubramThe online marketing channels such as Email, Pay per Click (PPC), Video, Social Media and Mobile are growing more rapidly than traditional offline marketing channels such as Direct Mail, Newspaper and TV Ads. Marketing professionals need to integrate both online behavioural data and offline CRM data together, in order to create a sales and marketing process that takes advantage of the best of both worlds.

The main reasons behind the growth of online CRM include more accurate targeting, more predictable ROI and the availability of free web analytics reporting tools such as Google Analytics (GA). With GA, you can track and measure many types of online campaigns including social media, and conduct A/B and multivariate testing. GA is also capable of tracking and reporting offline campaigns such as phone and TV campaigns, creating an affordable platform for integrated marketing.

But GA metrics are available only at an aggregated level, which means you cannot readily integrate it with offline CRM systems. Based on our recent investigation, this blog post outlines the steps to break down the aggregated metrics into individual visitor and session level data and how to export and integrate it with the offline CRM systems.

There are three steps involved:-

  1. Extracting the visitor and session level data from the GA tracking process
  2. Exporting the web metrics at visitor and session level
  3. Pass data to the offline CRM databases

1. Extracting the visitor and session level data – you can modify the existing GA javascript tracking code to include a new custom variable that collects the visitor and session level data from first party visitor cookies that is written/read by GA on the customer’s PC. The data is stored in GA servers and you will need to access it through GA data export API (explained in the next step).

2. Exporting the web metrics – the visitor or session level data collected in the above step can be accessed via the GA data export API after authentication through the main GA account. You can export the data as a text file which will enable it to be integrated into most offline CRM systems.

3. Pass data to the offline CRM databases – Once you have the data as a text file, the online data can be matched to the offline data with identifiable information such as surname and postcode. This information can be either collected from the order transactions or when the visitor submits some type of form or engages in some mechanism that includes identifiable information.

Steps to break down the aggregated metrics and integrate with the offline CRM systems.

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Achieving critical mass in social networks – Part Three

24th Feb, 2011 by Chris Geddes

How do you fix the results to achieve critical mass?

Critical Mass can be achieved in several different ways.

These include the following:

Leverage existing networks and relationships

The closer the relationships that individuals have with others, the more frequent the communication becomes because there is more in common, more shared time together, more shared experiences and so on. Thus starting a social network amongst an already-strong network, where conversations are already happening regularly and relationships are strong, is an important factor in achieving the frequency of interaction (F) that is required to get to critical mass.

Furthermore, the closer the relationships, the greater the probability that an individual has a high level of influence (I) over the audience. The influence of the user is not directly related to the content itself, however, but it is important to the success of the network.  

This is because those with influence have a stronger “pull” on peers. Influence is based on trust that is built up over numerous interactions. In essence, influence is based on the reputation of the individual making the comment.

Restrict the topics of conversation

By decreasing the scope of conversation that can be held through the social network, you ensure that a higher proportion of the comments are relevant to the individuals that are using it. Plumbers may not, for example, be interested in hairdressing tips, but would be more likely to be interested in plumbing knowhow.

Demonstrate the value that users achieve

By showing off the value that is provided by the social network as publicly as possible, you ensure that the message is able to be passed on to others, thus increasing the saturation of a community and progressing towards critical mass. Showing photos, leads, connections, friends, “likes” or any other statistic is a reasonably public way that encourages others to join to reap the same rewards. This also triggers competitiveness amongst some users, further increasing the rate of adoption.

In order to be a big fish, shrink the size of the pool

Critical mass is essential to the success of a social network, but it  is not dependant on the size of the target community. Facebook reached saturation within a single university before it spread to a second, a third and, eventually, opened up to the rest of the university world. Only after Facebook had reached saturation within the student communities did it open its doors to the world.

The principle behind this is that at each enlargement of the community, the saturation never dropped below the critical mass.

Think of a bucket that has been filled with water. This equates to the Harvard University Facebook saturation. The bucket is full and so cannot hold any more water – every student that will use Facebook now does. If, however, the bucket is doubled in size, by adding another university, for example, there is now room to pour in more water once again.

If the increase in the size of the bucket (community population) does not result in the new bucket being less that 15% full, this bucket can now fill up until it, too, is full. This process can, and did, continue, bucket by bucket, until Facebook became the phenomenon it is today.

If, however, the population saturation (amount of water in the bucket) had fallen below 15%, the Facebook we know may have collapsed in its infancy. [1]

Filling the first, small bucket

The equation for good content is complex because N (Community population size) is actually comprised of Users and Employees – those that are independent of the company and those that are subject to direction – such that:

N = U + E

N = Community population size, U = Users (non employees), E = Employees

NB: U and E are mutually exclusive

The way to ensure that the seed network that is started reaches critical mass, is to increase E as much as possible by mandating participation from employees. By controlling the majority of the population on the network, you can fix the frequency, the value and the relevance, not to mention the decreased distance of relationship between co-workers compared to strangers.

This approach takes careful planning, strategy, training, policies etc so as to ensure that the communications are transparent, legally compliant, relevant and of value, but can help an entity achieve critical mass.

Although difficult to overcome, it is absolutely possible to grow a social network from nothing to critical mass organically. Success is, however, absolutely reliant on getting it right first time, as users are fickle and are significantly less likely to log in a second time if burned, for whatever reason, the first time.

The trick is to ensure that each of the communities reaches a high enough saturation that the inclusion of new communities doesn’t dilute the content to below the critical mass required.

Influencing the equation

Letter Desired Effect Example Strategy
 P  Increase Increase Perceived competitive positioning, perceived associations, customer need or perceived proposition delivery
 I  Increase Leverage close, existing networks with established influencers. Mine existing conversations and identify existing super-influencers for targeting
 F  Increase Leverage existing strong relationships in existing networks, as they have more frequent conversations
 R  Increase Decrease the scope of conversation on the network, so as to ensure that more conversations are on topic
 U  Increase Allow users to reward or thank users that produce great content, thus encouraging new, unique content
 V  Increase Clearly demonstrate the value that the network offers whilst also displaying the value that other users get from it
 D  Decrease Leverage close, existing relationships initially
 N  Decrease Reduce the target community size so as to ensure that the saturation increases faster

 

P = Perceived User Value I = Influence  F = Frequency   R = Relevance  U = Uniqueness  V = Value  D = Distance of relationship  N = Community population size.

The approach should be carefully planned in advance and should involve deep thinking in the following areas: Overall Strategy, People and Training, Process and Policy, Technology and Tools and Data and Reporting.

[1] Note: Over expansion does not necessarily result in collapse of a social network, as humans have an inherent ability to ghetto-ise themselves into smaller compartments within the larger community, thus maintaining saturation within their sub-community. However, in cases where this does not occur, the probability of collapse is significantly increased.

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Achieving critical mass in social networks: Part Two

23rd Feb, 2011 by Chris Geddes

How does a fledgling social network develop the content needed to achieve critical mass?

Consumers will make repeat visits to a website because they find that it consistently delivers value to the user in one of the four areas, outlined below in Fig.1. However, the reason that people visit social networks on a repeat basis is slightly more complex.

 

Fig. 1 The four pillars of online value (V) delivery channels

Information Capital Provides user with information that they find useful E.g. “Plastic windows are more insulating”
Emotional Capital Provides content that triggers an emotion in the user E.g. “Knock knock….” or “Bankers are fat-cats”
Temporal Capital Provides a function that saves the user time or effort E.g. “I can find a plumber when I’m on the train”
Financial Capital Provides a financial incentive to use the site E.g. “20% off plumbing service this week”

 

The true value (V) of the site is the sum of the four forms of capital, outlined in the table above, but it is only one element of the considerations that are made when users evaluate a social network:

Perceived User Value (P) for a user is a factor of the content itself as well as the frequency (F) at which the content refreshes. The strength (distance) of relationship - (R) that the contributor has with the user is also important, along with the actual value of the site.

It can be defined as:

 P = I(F x R x U x V)/(D x N)

P = Perceived User Value, I = Influence,  F = Frequency,   R = Relevance, U = Uniqueness,  V = Value,  D = Distance of relationship,  N = Community population size

It should be noted that Perceived User Value (P) itself is comprised of four components:

  • Perceived competitive positioning – “I think this is the best”
  • Perceived associations – “Others have perceptions of this, and I do/do not want to be associated with these things”
  • Customer need – “This does what I need it to do”
  • Perceived proposition delivery – “The exchange works for me”

Recommendations:

NOTE: In response to specific questioning regarding where recommendation fits within this equation: Recommendation or “anti-recommendation” is captured within the Four Pillars as the recommendation will either save time/effort or result in an increase in financial capital or knowledge capital for the recipient. Within the equation for Perceived User Value, P, the power of a recommendation IS the Perceived User Value.

In order to ensure that a site achieves the 15% opt in from a community needed to achieve critical mass, as discussed in the previous blog, the content must be good, but, initially, at least, frequency (F) will be low.

This is because there are few contributors, and distance of relationship is likely to be high as the probability of any user chosen at random throughout a population being closely affiliated to another randomly chosen individual is slim.

There are, however, techniques that can be used to sway the equation in your favour. Critical Mass is achieved by increasing the numerator in the above equation and decreasing the denominator. Check out our next blog to find out ways to do this effectively.

The full white paper on Achieving Critial Mass in Social Networks can be downloaded here.

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