John Lovett and Jeremiah Owyang has written (with others) a white paper on Social Marketing Analytics. I’ll be referencing the report throughout this posting, so go check it out.

This response is divided into three parts. It starts with a ‘I see where you’re coming from’, then ‘a few questions and inquiries’ and then ‘a few caveats and ways I’d improve it’.

First, I see where John is coming from.

John states, clearly, that “The objectives and metrics defined….in this report are a starting point for the infrastructure of social media measurement.” (p. 6). The whole document then goes into a very transparent goal alignment strategy – where four business objectives are lined out based on a goal, then KPI’s are identified out. He uses a circle-bulls-eye sort of device to describe that process (which I like, because it’s a lot more accessible than a formal hierarchy, while retaining the relationships you have in a goal architecture framework). He then defines 12 KPI’s and how they align back.

I’ll say that I see where he’s coming from. I’ve been practicing goal alignment strategy for the better part of my career – and this is a very disciplined one. The approach is excellent, and as analytics strategists, we’d all be better off if you used this general methodology.

Next, this quote: “By making learning and continuous improvement a primary goal, your social marketing activity will develop in a positive direction” (p. 6). It’s one of the nicer ways of saying ‘evidence based marketing should result in sustainable competitive advantage’. I enjoyed that passage too. It’s a less jargony way of saying it.

John outlines four broad business objectives associated with social media marketing: foster dialog, promote advocacy, facilitate support, and spur innovation.

These are not the only objectives possible in social media marketing. He never said that they were. In fact, “Not all objectives and metrics will resonate with each audience nor will our foundational framework give you all the elements necessary for success.” (p. 7).

It’s at this point, again, that I accept where he’s coming from. We set that aside, and we start to dig in.

Secondly, a few questions and inquiries:

The initial list of questions was around 100 deep, at which point I realized that there was little utility in going that far. Instead, I’ll focus on just three points of inquiry:

The third metric, conversation reach, is defined as total people participating divided by total audience exposure. Are unique visitors people? Are unique visitor figures traceable? “Conversation reach can be evaluated in both volume and location across social media channels”. (p. 13). Is this indeed the case? Can they? How are creators accounted for – in terms of actual conversing, as opposed to lurkers – or people who are observing the conversation (the exposure)? Is conversation reach better understood as the total number of people who have actually been exposed to the conversation, as opposed to the ratio between the participation and the exposure?

The fourth metric, active advocates, is a marketing one. I applaud John for using the word advocate over influencer (which I think blurs a fundamental marketing line). Could somebody be considered an advocate if they are constructively critical of the product and yet refer people to the product? Indeed, this is very common among innovators at the beginning of the product lifecycle. The devil remains in the term ‘positive’ and ‘negative’, and what an advocate is. The recency aspect is particularly excellent.

Which leads to the eleventh metric: sentiment ratio. First, is the positive/neutral/negative paradigm really indicative of innovation? Ie. Does it measure ‘innovation’? As applied to a topic area, indeed, raw general sentiment scores have been used – but it’s only done well if rigid topic-object hierarchy is identified. NextStage Sentiment Analysis (NSSA) is the closest that I’ve seen that takes into account additional dimensions over and above the straight positive/neutral/negative paradigm.

Finally – ‘a few caveats and how I would improve it’.

A confluence of three thoughts. The first is Claude C. Hopkins who, eighty years removed, implored me to think of analytics and scientific advertising as a profit center, not a cost center. The second is Jim Novo (of course) who has been imploring us to link up with the CFO. The third is a baptism at Syncapse – which is the closest thing to a phd in management science that I could hope for and is responsible for reinforcing an underlining bias about innovation.

There should be three central goals with social media: to make money, to offset cost, and to realize sustainable competitive advantage.

I would improve the framework by calling that out: to make money.

There are many products that are high consideration and where word of mouth / social influence play a huge role. Try ordering a cheap malt liquor at Bier Markt on a Thursday night and watch the reaction from your developer friends. (What? No Delierium?). There is real money to be made in social marketing because the consumption of certain products is indeed a social exercise. Always has been. It’s now, increasingly, in a medium where we can observe and quantify it (The actioning of that intelligence continues to be a sore point). I think that’s what has really changed: the observable WOM.

Some of these metrics can be worked into a cause-effect model of that. Earned Media Value (EMV) might very well be an excellent metric as part of that cause-effect model. There will be no one-size-fits-all attribution model for sales driven by social. (At least, not within the next 2 years).

To offset cost is another one. And that’s attractive with the current state of the economy. Cost offsets may very well be realized through the ‘facilitate support’ business objective.

Sustainable competitive advantage can be realized through learning and spurring innovation. The accumulation and actioning of intelligence and real insight is a huge key. To John’s credit, he uses the term ‘spur’ innovation, not ‘do innovation’ or ‘action innovative ideas’, which is an organizational KPI best left to the mythical balanced scorecard.

There are other dimensions from a different paradigm, for a different time.

In general –

I applaud and thank John Lovett and Jeremiah Owyang for coming out with this. The approach is solid. You can make it your own. It’s an excellent document for what it does.

While this is termed a ‘response to John Lovett’, I’d like to carry on this discussion through cross-blogs, in comments, and at eMetrics London in May with anybody who is interested in this area. There is so much to discuss.

9 thoughts on “Social Marketing Analytics, a response to John Lovett

  1. John says:

    Hi Christopher,

    Let me start off by saying thanks for the thorough and well considered post on our report. One of our sneaky goals for this research was to start a dialog and get people thinking about ways to measure social in ways that extend beyond our own microspheres. You win for falling prey to our goal 😉

    Regarding your questions:

    1) We deliberated over Conversation Reach for some time to determine if people could effectively be tracked within social channels. It’s our opinion that unique visitors is a slippery metric in web analytics that isn’t usually aligned with precision directly to people. Yet, in social we do have an opportunity to identify actual people who comment, post, participate in a conversation. Total exposure can also be measured, but we still can’t always reconcile individuals. But remember here that our Conversation Reach KPI falls under the business objective of Foster Dialog – so while gauging total exposure may be valuable for some insight – we wanted to go beyond the isolated metric to determine who was actually getting involved in the conversation.

    2) I think you answered your own question here. This blog post is constructively critical of our report, yet you refer people to it and actually encourage them to read it (btw Thanks for that 😉 I consider you an advocate who we could benefit from tremendously by soliciting your input for future research to get your POV. The KPI can be used according to an individual company’s advocate program, and mine consists of sharing with and learning from advocates that opine both positive and negative.

    3) I agree that sentiment is tricky and three shades doesn’t always cover the spectrum. Further, sentiment can be misinterpreted as most machines I know have trouble detecting sarcasm. That’s why I like tools such as Scout Labs who offer varying degrees of sentiment that can be fine tuned by the user. At this stage in the game, human intervention is still required. But tools that can point in the right direction, or send up flags that indicate undeterminable sentiment are better than not measuring.

    Lastly, I challenge you to think differently about your three central social media goals. While making money and offsetting costs are certainly very valid aspirations, many social efforts take hold way earlier in the decisioning process. We acknowledge and severely encourage all social marketers to measure the fiscal gains their programs produce, but these are typically easier to measure because there is a transaction. With this research we attempted to look at many of the softer metrics within social analytics that take place in the widest part of the funnel. I wholeheartedly agree that measuring all the way through this funnel to the conversion event is an imperative. Yet, most practitioners that are coming at measuring from a web analytics background have that aspect covered.

    I’ll close out this very long comment by saying that there’s lots more to discuss. The concept of Earned Media Value is a great one and it deserves a white paper of its own. Let’s keep this conversation going and collectively we can help companies measure their social initiatives more effectively.

    John Lovett

  2. Eh John,

    Thank you for the detailed response. I love it. Thank you for reading my response for what it was: constructive conversation. 🙂

    I’ll take on your challenge of thinking beyond the big-2. You make very good points.

    Let’s talk in London.

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