Social Media

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6
May 12

There is No Magic Bullet in Social Media Measurement

Recently a post from Avanish Kaushik was put before me for consideration. The post was “Best Social Media Metrics: Conversation, Amplification, Applause, Economic Value” and in reading it I personally found some flaws with his overall approach which I wanted to address.

Measurement Isn’t So Simple:

Knowing how complex websites and search are, Avinash should know that good measurement cannot be simplified into a few key metrics across everything and everyone. Despite this he tries to do just that with his proposed 4 metrics for measuring social media.

Different companies have different goals for what they want to achieve in Social media and assigning metrics that don’t take this into account can potentially side track these initiatives. For example in the case of say Avinash’s Conversation rate it doesn’t speak to the quality of the conversations that a firm may want to see or the overall goal of the content they put out. With such a focused scope these metrics don’t take into account the many other variables that can be tracked on corporate social media channels.

The size of the fan base does matter:

Another downside I see with the type of measures that Avinash suggests is that it does not take into account the audience that the company taps into. Without taking into account a firm’s fan base an even grounding is not created to ensure a comparable measure across fan sizes. Instead what more often than not will happen is that these measures will present a constantly increasing ratio because a larger fan base often results in more engagement.

Outside of large spikes when there is a fan base drive or large campaign, a measure that does not take fan base into account will most likely continually go up in a linear fashion. The downside of this is that it’s not easy to discern what proportion of a fan base is engaged. As a result there is no clear way to determine whether an initiative was successful.

Measuring Awareness Still Matters:

Much like with television or radio advertising, simple exposure to social media messaging still has an effect on consumers whether they act on this immediately or if this changes their habits further down the road. While this makes sales attribution difficult, measuring how far your content reaches is still important. Unfortunately for Avinsash’s framework it does not take content exposure into account. By knowing your content reach you are a better able to compare and contrast initiatives to see if there was a discernible change in a firms end outcome as a result of marketing messaging.

With developing field like Social Media to make broad statements like “the 4 best social media metrics” in my opinion is harmful. What this does is it has the potential to close people’s minds to other alternatives and more meaningful approaches. That’s why beyond the fact of perhaps not being too public with my work (for confidentiality reasons of course…) I won’t say that one measure stands above all the rest. Instead my answer will continually be that measures need to be formed based on the end user’s goals and what they are trying to better understand.

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29
Apr 12

Determining A Brand’s Active Facebook Fan Base

A topic that isn’t discussed very often is the idea of active vs. inactive fan page users. When we are looking at a Facebook fan page’s user base this isn’t necessarily a true representation of who actually has an interest or may even see this page’s content (in recent months the metric of user unsubscribes has been removed from Facebook Insights). Compared to Twitter where someone who doesn’t like your content can immediately unfollow, on Facebook the function of removing yourself from a page is much less obvious and often not the first action a user will take to remove content from their stream.

So how should this be measured? First of all it’s in my opinion that Facebook should be providing this data as ultimately they offer the function of fan page unsubscribes. As this is unfortunately not the case then the only way of going about this is determining a proxy measure. On the top of my head the only solution (I’m open to others if anyone has an idea!) is developing a tab hosted survey with an email opt in for allowing a follow up survey to be sent.

My idea regarding this type of system would be that a first initial survey would be administered through the fan page with a request to be able to email the participant in the future with a follow up survey. In a selected time period (of say 3-6 months) the follow up email would be sent where fans would be asked if they are still subscribed or recall any content from this page. I admit that there are some weaknesses to this type of approach:

Low initial user response rate: Most people dislike surveys. Without a substantial incentive only the most active of your fans will likely participate meaning that you won’t get a fully representative sample.
Low follow up rate: It is very likely that a large amount of survey participants will not respond to your follow up survey meaning that you won’tt get a fully accurate representation of your initial sample.

After getting some sort of idea how many users from a page are falling off I would go about developing a fan degrade rate. With that I mean along with measuring how many users become fans of your page you would also have a stand in variable for calculating approximately how many of these fans become inactive over a period of time. An example of this sort of calculation would be ( current fans + new fans) – (current fans *fan degrade rate)= current active fan base. A note about taking this approach is that the survey data needs to be renewed on a consistent basis to ensure that the degrade rate is kept up to date.

Strategic Implications:

By understanding the rate of which your fans are becoming inactive a brand can understand when it needs to put into place initiatives to increase user involvement (such as user appreciation giveaways etc.) but they can also have a better idea of the overall growth of their fan base. If it’s being calculated that there are more fans unsubscribing than fanning (a sign of a mature fan base) the brand needs to take a look at what steps (if any) they need to take moving forward.

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11
Apr 12

Analysing The Replublican Leadership Race Online (PART 2)

So perhaps this is a little less relevant with Mitt Romney now essentially the winner of the Republican leadership race but in accumulating the data (all 669 posts for February and March) there were some interesting findings that I felt were worth writing down. So here goes part 2 of my GOP Facebook analysis.

In this analysis I looked at only per post engagement counts and the content of the post. I didn’t take into account the size of the respective fan bases in evening out engagement as a rate (that may come later…).

To analyse basics we’ll have to look at what content came out on top. Looking at the top 10 posts out of the data set it broke out as follows: 5 posts from Ron Paul, 3 from Mitt Romney and 2 from Newt Gingrich. I suspect that a small fan base and high posting frequency were reasons why Rick Santorum fell off this list.

What was the highest scoring piece of content overall? Well it wasn’t hard hitting political rhetoric or an announcement of a big win. It was something that showed a softer side of the candidate that the general public general doesn’t see.

My initial count had this post at more than 80K engagements ( likes,comments, shares) and the highest of each individual engagement category. Interesting enough anniversary content also rounded out the top three posts with #2 announcing the anniversary of Mitt Romney and #3 as a follow up post to Ron Paul’s picture. Looking through the remainder of this content I think candidates missed an opportunity to differentiate themselves as more of a person rather than an icon. This may have opened up more people to their messaging.

There were also some interesting findings on a per candidate level that I felt were useful enough to share:

Mitt Romney: The common thinking among those who want to use edge rank for the highest reach in achieving the highest post reach is to use photo content as it generally receives more engagement. Well looking at Mitt Romney’s content that may not be the case in all situations. In looking at Mitt Romney’s top 20 posts only three of these were photo content. Instead fans of this page were most affected by brief but resonating status posts positioning Mitt Romney as someone with strong leadership skills.

Ron Paul: Outside of his 1^st spot post it was interesting to see Ron Paul’s tone. Out of the 4 candidates the tone of this page was much more first person like the candidate was writing it rather than someone on his campaign. In terms of data trends it looked like “Like” and “share” engagements were more common with “comment” engagements occurring less often. Perhaps some conversational content would have been useful to spur on response and increase post engagement.

Newt Gingrich: Out of all of the candidates Newt Gingrich made the most use out of photo content including vivid imagry and strong messaging within the photo itself. Placed along with strong calls to action such as “ Can we get 5,000 likes for an UNHAPPY Obamacare birthday?” Newt Gingrich was able to boost his engagement despite having a comparably small fan base (296K vs 925K for Ron paul and 1.5 million + for Mitt Romney).

Rick Santorum: Ignoring the data itself the sheer amount of posting was what really surprised me about the Rick Santorum page. Of a total of 669 posts recorded, the Rick Santorum page came in with 316 (47%) posts. To put this on a per day perspective that’s approximately 5 posts a day. In looking at this data on a by fan base perspective it will be important to see whether what seems to be over posting had an effect on engagement or not.

Looking at the data, Rick Santorum did not seem to be in the running when it came to the volume of engagements his posts received. Looking down the list his first post doesn’t register until 107^th position. Arguably he did have the smallest fan base ( 188K) but it’s still interesting to see that none of his content particularly stood out especially as there seemed to be so much effort put towards it.

Part 2 of this set of posts looked at the bulk count of metrics but didn’t take into account the relative fan bases and only touched upon the different content variables such as content type. In the next post I hope to dig a bit deeper in seeing where the Candidates stood in terms of how engaged their fans were and who in the end of it all was most engaging. Stay Tuned!

ADDITIONALLY: As the data I used turned out to be a giant data set I’m hoping to make it accessible for download via a Google doc for any interested party to check it out. Once I’ve added fan base estimates and I’ve cleaned up the set I hope to have this going.

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4
Apr 12

Analysing The Online Race For The Republican Nomination (Part 1)

This post is part one of a multi-post series that I’m currently putting together. While not exactly a relevant news story for most Canadians I came across the Facebook pages of the American Republican party leadership candidates and after some digging saw some trends that I wanted to investigate and see if there were potentially some learning’s that can be applied elsewhere.

This first post is a simple one to start things off. Looking at the publicly available fan and demographic data from each candidate I came across some interesting observations:

Internet Popularity ≠ Real Life Action:

Currently in the delegate count the standings from first to last are Romney, Santorum, Gingrich and Paul. Organized by Facebook fan base this is dramatically different and goes as follows: Romney, Paul, Gingrich, and Santorum. Romney simply by being the front runner in the race dominates in terms of fan membership but whats more interesting is Ron Paul is beating both Gingrich and Santorum while being last in terms of delegates earned. This points towards Paul having a strong presence online but a weak ability in converting this following to actual votes.

Fan Base Demographics Are Split Amongst The Candidates:

Another interesting finding I had was that fan base demographics were spread across the GOP candidates. Perhaps as expected Ron Paul attracts more fans aged 25-34 lending to the idea of his internet fame. On the other hand Romney and Gingrich are more popular among users 45-54 leaning more on the older side of the spectrum. Comparatively Santorum is able to bridge a bit more of a gap with his popularity among the 35-54 demographic. If Santorum uses this demographic spread to his advantage he may have an edge over the other candidates in terms of building awareness for his platform.

Growth Is Starting To Decline And One Candidate is Losing Steam:

My last bit of observation to share is of the slowing fan growth amongst the candidates. Over the month of March fan acquisition has sees a progressive drop off among all candidates with Romney then Paul leading in most fans gained. A stand out in this slump has been Gingrich who has started to receive nearly no new fans. With talk suggesting that he drop out of the race perhaps his momentum has come to a halt.

Like I had mentioned this is the first post in a sequence. As I begin to get my full data set together and get deeper into the results I’ll begin sharing more. Stay tuned!

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29
Feb 12

Brand Impressions vs. Brand Experiences

At the recent Facebook Marketing conference Facebook presented what most likely will be how the firm will operate its site for the next few months. While the most visible change to the general public will be the inclusion of the Facebook Timeline on branded pages, Facebook also placed an emphasis on expanding its capabilities for paid ad space on its online and mobile platforms.

Impressions vs. Engagement:

A key point of focus for digital marketers has been encouraging consumer engagement with brands rather than blasting out messaging. With the recent changes in its insights platform and expansion into how brands can promote itself through the site, Facebook seems to be disregarding this idea by placing brand impressions at the forefront.

Potential for Backfire:

As even brand page content has now become potential ad space, consumers will become continually exposed to branded content but also may become dis-engaged. As brands begin to fight their way onto user’s newsfeeds marketers may see a potential backlash where consumers decide to unlike pages to decrease the onslaught of content aimed towards them. This will be because while the content will have higher visibility it may have little relevance to the audience it hits.

Getting back to basics:

My thought on this is that it’s a move backwards towards pay and spray advertising and away from the relational marketing that I personally hold in higher regard. As a consumer a brand creates little to no interest to me by continually exposing itself through ads and promotional activities. Instead I have higher affinity for a brand and its product when I feel a sense of value from the relationship it has built and the worth that I place in using the product. While through advertising I may see the brand more often, it probably won’t change my opinion when this advertising provides me with no demonstrable value.

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4
Dec 11

What Facebook Should Do With Gowalla

With the rumour out that Facebook is looking to purchase location based social network Gowalla, discussion is beginning about what the social network could use this new asset for. While Facebook’s location based services haven’t yet caught on, for myself and I’m sure many other data focused marketers the potential of having a successful location service which is tied in with Facebook has been the subject matter of maybe a few dreams!

The Potential For Location Based Data

In a recent update of its systems Facebook has improved the insights dashboard that fan page owners can use to analyse their efforts. Companies can now have a more in depth look at how and how much of their fan base and Facebook users in general interact with the brand generated messaging being put out. Adding data from a location based service could add an even deeper (and more meaningful) additional layer to these new features.

There is a large list of potential benifits to measurement and analysis that location based data could create (I’ve had some time to think about it) but in regards to Facebook and fan pages I can think of particularly two scenarios of how location based data could be used when thinking about brands with retail locations:

Analysis As To What Drives Fans To Purchase: Businesses could potentially see the correlation between the traffic to their stores and the content they had posted during a particular time frame. In the case of for example a product announcement, a company could potentially measure check ins to the company’s stores to see if there was an increase in visits and linking it back to the firms sales data they could determine if against past product launches there was an increase of decrease in sales.

Determining Where Your Fans Shop: For retail chains knowing the makeup of your customer base on a store by store level is another potential use. By taking a look at the demographic data of users who have checked in, the firm can see if a particular type of customer is more likely to shop at a certain location. From this data the firm can also have a look if there any particular purchasing trends among this sub set. Looking at this data in a time series could additionally provide insight as to if particular promotions or products should be added or augmented to fit the particular grouping of customers.

In the next few weeks we’ll see if the rumours of Gowalla’s acquisition by Facebook are true or if a few news sources have to maybe review who they get their info from . None the less, for companies on Facebook and otherwise tapping into the databases of location based services still hold a lot of potential for improving their results. As brands continue to look deeper into the data that’s available through social media and online sources cwe’ll have to see if these location based services will come into play.

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24
Nov 11

Why I Sometimes Don’t ‘Like’ The Like

At times I have a bit of a love /hate relationship with the ‘like’ on Facebook posts and spread across the web. I think we can all agree that it is an action, it’s a sign that someone’s awake or at least somewhat conscious of what they are clicking. What it isn’t? A sale of product or a sign that the user has just done about face and has professed their love of your brand. Overall though there seems to be a sort of fixation on this simple engagement.

Like this if you like Puppies!

Asking users to like something does have its tactical uses. Those who are a bit educated about the ins and outs of the Facebook news feed know that a user engaging with a source’s content frequently equals a higher relevancy score to the content source. Throwing up a piece of content that is generally enjoyable and asking users to like it means that the relevancy score is bumped up a few spots and fans are just a bit more likely to see your content. Does this mean you should flood your feed with “LIKE THIS!” content? Well if that’s the sort of depth you want your brand to have then go for it! What this won’t do is sell your product or improve a users thoughts on certain attributes of your product unfortunately.

Time to throw away the ‘like’?

Am I asking people to ignore the ‘like’ completely? Absolutely not! Should the like engagement be considered along with other factors? Yes! Compare it to consumer response (what are people saying in the comments) , the propensity of users to like your key messaging compared to other content and I could go on…. Measuring channel success in my opinion means measuring how you are changing people’s perception of your brand and product. Are people more likely to talk positively about your brand and as a result increasing sales because of WOM? Has your brand gone from being seen as stale to something that people have feelings for? And lastly at the end of it all have you been able to track increased dollars going into your firms bank account? At the end of the day changing minds rather than encouraging clicks should be the end goal, not the afterthought.

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6
Nov 11

Can I Get Dirty With Your Data?

The initial steps of data analysis; Collecting data from various sources, ensuring the accuracy of the data and then putting it all into a legible format. Doing the grunt work of social media analytics (or really any data analysis) is not the most fun or glamorous part of the process but through my experience I’ve learned that having this level of intimacy with your data is an important part of in ensuring the delivery of solid insight and analysis .

Getting In Close With The Numbers

Having to compile the different parts of your data puts you at a level of closeness with it that is not as easy to get otherwise. Entering in each source and dealing with all the tools you see almost instantly when there is a gap or significant change within your metrics because you see all the data points in front of you. Compare this with just getting summary data and you just get a snap shot of the entire picture of what is happening. Let’s just say this is like taking your data out for the day compared to having a quick phone call with them. One is much more effective in getting your desired result than the other.

Dressing up your data. Picture C/O Exey Panteleev

Having Your Way with The Data:

When all of the grunt work is done and your data set is put together and prettied up you tend to already have a sense of what you want to explore when you actually go and do your analysis. You’ll know where all the sweet spots are and try to determine why things happened the way they did. Experiencing all the parts of the data as a whole rather than in separation there’s a better understanding of the linkages and causes of change in the data. You are better able to see where something went up as a result of X or Y action. By having your hands in everything means you are able to get in there and push the right buttons to get the best end result.

Growing Old Together:

In my work there have been some data sets that I’ve dealt with for a LONG time, sometimes since their inception going months back. Much like a long term relationship with a person you can get a read on when things are going as per usual or even great but you can also see when there is a significant drop off or unexpected event. In this sense your mental set is more finely tuned to see the trends and linkages within your data store. When your data set is mad at you, you know almost immediately to do something to fix it!

The 'sexy' end result. Picture C/O @BenLucier

Setting Yourself Up For The “Money Shot”

When all is said and done, the data is all in and you’re fully into writing the report, by knowing your data you are able to tell the right story and enter in the best summary metrics to share with your client. In the end, the job of an analyst is to see the data and make sense of it in the best way possible for your specific audience. Your “money shot” in all of this is when your client is sent off with the best insight and recommendations that they can take action on. When they are happy that they are seeing improvement in their operations or learning something useful that they can apply to their business this means increased results and profit for your organization. Essentially something people will pay to see .

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24
Sep 11

Is Sharing the New ‘Like’ on Facebook?

Getting users to ‘like’ their content has been a strategy of many Facebook fan page owners in recent weeks because it quickly amasses simple engagements and creates awareness of the page. But with users given better ability to curate the content on their news feed and profiles, being able to focus more so on app and ‘life period’ content, this mode of user engagement may not be as useful moving forward.

Engagement above the fold:

With the new OpenGraph optimized news feed, a separation has been made between content more useful to the user which is the first thing they see along with everything else further down the page. As this feature allows us to grab just quick information on the go we are less likely to scroll down the page and view everything else. This includes that post to ‘like’ carrots from the food fan page you liked a few weeks ago!

Can you ‘share’ this?

Without the full changes to Facebook completely implemented it’s hard to make an exact prediction to how corporate content will operate (sponsored posts anyone?) but with demos from the recent F8 and some functionality already in place some predictions can be made.

Prediction ONE: Posts of major announcements and big campaigns will reach higher on the news feed. Why? Much like with Twitter, people want to be the first ones to share breaking news and content with their friends. As a result this may lead to multiple content shares (increasing volume relevancy) and friend discussion on the topic ultimately driving this content higher up the news feed.

Prediction TWO: Branded apps will become a stronger way to engage with fans. Why? The creation of brand relevant apps where users can either create content or where users are encouraged to return to on a regular basis is something that will either build the opportunity for a news feed impression (ex: creating a Spotify playlist) or because of usability reasons will create constant brand exposures as a result of using the app. With the new Timeline taking more of focus on life activities instead of user engagements apps are a way for users to become more active with a brand and integrate it into their online ’life file’.

Is it Time to Rethink Brands on Facebook?

All in all Facebook made some big changes to how the site will operate. Without looking into the future too much I think it’s going to be interesting to see how brands will need to adapt to continue to stay relevant online with consumers. I don’t think it’s going to be as simple anymore as just putting out rounds and rounds of content for users to ‘like’ and comment on. I feel brands will need to create deeper relevance to continue to drive their messaging to consumers.

KevRichardTechnology and Digital Communications

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