Better late than never

Thanks for the read and the comment. I am very skeptical in saying that listening is the first step since a lot of people will be stuck there in their way to doing something with it. I preer to refer to listening as a necessary evil on the way to actionable insights. Listening can be very distracting if you don’t keep the end objective in mind.

Thanks
Esteban

Thanks for the shoutout to my SodaBowl post – hope you enjoyed it! Actually the company that created this analytic is not MarketIQ, it’s Biz360. We had called our blog MarketIQ, but we are changing that because it’s causing some confusion in the market.

I actually wrote a follow-up post to SodaBowl, attempting to examine which brand made a better decision (to use SM exclusively vs. SM + traditional superbowl advertising) – http://marketiq.biz360.com/2010/02/sodabowl-coke-pepsi-ads-social-media/

Also, wanted to underline the point you made about sentiment analytics just being a starting point for actionable analysis. Listening is first step, but it’s what you do with it that defines whether or not you will win.

Cheers!

Maria Ogneva, Biz360
@biz360 @themaria

Thanks for the read and a great comment. And for agreeing. As a long-time proponent of the value of integrated data analysis (been writing about it since 2001 and thinking about since before then), I cannot emphasize enough how much better things are when we aggregated data and analyze it altogether.

Ah, you know about the lack of analysts that can handle the engines then.

I believe there is a very marked difference between people who can manage the tools and the output (i.e. statisticians and computer scientist, people who understand the relationships between data and reality) and the business stakeholders that run the business functions.

The business stakeholders, people who understand the relationship between the specific functions and what the data means, are in short supply. Is it a question of training or supply? Yes. We need to take seasoned business people who understand the functions and the correlations between them and the rest of the organization and train them in data management and data analysis. This is going to be a critical need in the coming years. It cannot be done by outsiders, unfortunately, since the knowledge of the specific business function and the organization’s travails is essential. You can hire a statistician to draw relationships between datum, but without the knowledge of which data to correlate, they would just be bringing you cute and empty information (e.g. 70% of people who use twitter use your product — but how do they get to the product? via twitter? something else? and are those among my client base — or potentials? and what are we doing already for that demographic? is the current campaign working? those questions can only be answered by people that understand both the data and how the company works — and that is just a simple example).

As for responsibilities – they will still need to respond to the needs of their individual business units, so this is part of the job description. Will they have to have their results tied to their compensation? potentially, maybe not — these are questions best handled once they get going and we can see the results. Incentives always make for better performance, but they have to be the right incentives.

Anyway, this is a very long conversation and there aren’t that many data points to pull from, so this is all opinion right now on the people we need and what they should do.

Very interesting question, will definitely keep exploring it.

Thanks for the comment, sorry for the delay — long week.

I am certainly interested and working on what you are talking about in both your points, but as you probably now is the hardest part of aligning actions and metrics. I will continue to work on it, and as soon as I have an interesting case to share I will definitely do that.

As for your second point, I would imagine that you would have seen some examples of at least feedback management while in your previous roles (maybe even at your current one). Alas, I can imagine that the word scale means something completely different at the level you are using it.

However, one thing that I think is key to keep in mind is that segmentation plays a critical role in good community management. A community of 1,000,000 provides very limited value to anyone, versus a smaller and more manageable community that certainly provides a lot of value when properly segmented and targeted.

I think we are going to see derived value from communities increase as we see the size decrease and the focus sharpen. Not there yet, most companies cannot handle a single or a handful of communities — less along a large number of them.

If you have some time, would love to hear what you guys are doing at ATT in regards to community. Just send me an email if you have some time to chat, easiest way is through the link on top.

Many thanks for an exceptional comment — stay tuned as more on communities and analytics is coming!

Thank you for this interesting post.

I fully agree with you that “the role of analytical engines for communities is not to analyze the community as a stand-alone channel […] but to integrate the valuable data from the communities into the rest of the data the organization collects and produce insights from this superset of feedback.”

I see analytical engines as octopus-like tools (with 8 or more arms) to aggregate, monitor, structure, visualize and analyze data from a variety of internal and external communication channels and data sources. As with all tools, the value of analytical engines lies with the people using them. It requires human judgment and expertise to further analyze data, evaluate and refine insights, and turn these insights into concrete actions for improvement.

Given that we now have technology to assist us with monitoring, aggregating, structuring and analyzing data from various sources, who (or: what kind of team) should ‘own’ these tools? What skills are we looking for, and what kind of mandate and responsibilities should these people have to turn their insights into measurable business improvements? Alternatively: what kind of people/mandates/commitments are required to prevent analytical engines from becoming shelfware?

Thanks!

my apologies for the delay in replying, not sure how your comments got lost in my queue.

Thanks for the comment, I think that communities that further the sharing of information and the message (like Healthymaginations) are essential for people to feel like they get something out of collaboration. Of course, the collection of insights and the analysis of the feedback is what essentially proves the value of the community to GE — they probably won’t say that is their real objective, of course.

Thanks for the example, it is quite interesting indeed.