Take a current page such as What is Cuban Cuisine which has keywords which aren’t bad (as there is content for them) but clearly isn’t as good as it gets and there are way too many keywords to be effective (sort of a “hit or miss” approach which doesn’t work that well – like optimizing everything on a page by listing all the keywords on it – not very effective because it dilutes the quality of what is on the page).
What I would have done is created the same page but with a paragraph on Cuban Culture and a definition of Cuban Cuisine while mentioning the importance of Cuban Kitchens in the scheme of things (show some pictures too). Point out the differences between American Cuban Cuisine and Chinese Cuban Cuisine.
In fact, I could have gone much further and created a whole section of pages on Havana Central’s site on Cuban Cuisine and used the Wonder Wheel to break up the basic keyword sets for each page in the series.
I constructed 8 pages that gave me the skeleton of what to write about on each page; once I have that, I can fill in content while building in semantic relevance. True, this approach won’t work everywhere or for everything, but what approach ever does?
If your stumped for keywords try Wonder Wheel, then when your done playing with the Wheel, turn Instant Search back on (which turns the Wonder Wheel back off).
So many things going on for me that it’s sometimes hard to focus – often I look “outside” to see what is going on in Social Media that’s interesting ( my Web Journals); other times, like this one, I feel the need to touch the data personally in a different way – that’s the substance of this blog – touching the data and look at it differently (hopefully, than anyone else does).
If you read this post all the way through I promise you’ll get methods you can use to improve the information your gathering from Social Media Monitoring and Organic Search (but you don’t have to stop there, Site Search would work just as well under this model, etc).
Note: I haven’t worked close enough with Text Analytics platforms like Lexalytics and Clarabridge - it’s possible they might supply aspects of this functionality – just to be sure – check them out in case they do offer it (though I doubt they offer it in the way I’m presenting it here).
I started with my own Organic Search Traffic from Google this month (you can download the entire spreadsheet here). My Search traffic now comprises 40% of all the traffic to WebMetricsGuru.com and seems to be going up more and more – almost all of it is “long tail” – and how does one make sense of it?
Along with the Search Query I sorted by City as the secondary dimension and the report became a lot more usable to me all of a sudden. It’s as if, by sorting by city, I could not see a pattern in the information that wasn’t as clear before – and it became the takeoff for this long, and I hope, rewarding post for you to read.
Here’s a chart I created around two subjects I found in my search logs – Restaurants and Social Media. I could have picked far more – could have spent days on this – but that’s not the point – the point is to evolve a way of working with data – if you want to spend days working your data – or mine, be my guest.
As I looked at my own search logs for this month – I saw patterns (the artist in me) – it’s as if the data was speaking to me – people were asking about weather there was a net promoter score for restaurants, or if I was doing any more comparisons between social monitoring platforms, or even if I had a quick and easy Social Media Scorecard to share.
I saw patterns, such as Social Media + “how to” and Social Media + ROI, etc. Sure, there were only a few people asking those questions (in some cases, they spent hours on my blog looking for the answers, just look at the excel spreadsheet in full and you’ll see the most engaged reader was looking for (based ib time spent in a single session).
As I pulled this data it looked strangely familiar – no I’m not speaking about Radian6 Word Maps (they are limited by a single word and are not capable of what I did in my first slide – grouping by concept – at least, not with considerable amount of tagging – something most people don’t want to deal with). Rather, I’m speaking of Google’s own Wonder Wheel.
Speaking for myself only, the Wonder Wheel is a “Wonderful Experiment” or Tool created by Google Engineers to create some sort of order out of searches that people are making on Google – it’s also there to help give you ideas when you want to come up with keywords for ads you might want to run in AdWords. But the Wonder Wheel doesn’t work with your own data -unless Google, or someone else, were to adapt it and use it to process Search Query Referral Traffic, Site Search Queries (when site search is being used on a site) and SOCIAL MEDIA MONITORING platforms like Radian6, Sysomos, BrandWatch, etc, at el.
The Wonder Wheel makes sense, in other words if you want to operate on other peoples data - but not much help if you want to understand your own – if your monitoring your own logs, if your running your own query in Radian6, if your pulling your own data – if your a brand and you want to know what people said about you when they visited your properties – nothing like the Wonder Wheel exists at the very moment, that I know off, offhand, for your own data.
That’s the data you care about the most – your own – not Google’s.
I suppose Google could have used the Wonder Wheel with Google Analytics and Google WebMasterTools – after all – it’s all their platforms – we absolutely know Google haven’t used the Wonder Wheel technology at all for Google Analytics – but did they use it for WebMasterTools? No, they didn’t – but there is useful information – just not the semantic breakdown I’m talking about today.
Undoubtedly , the Wonder Wheel would be very useful in Google Analytics but it’s also fair to point out that such analysis is left out of every web analytics tools I’ve ever used – none of them have that information nor were they built for it. So while I could see the benefit of Google adding Wonder Wheel to Google Analytics, I’m not sure they ever would – and as far as Web Master Tools – they certainly could add it – if they wanted to – and it would make a lot of sense if they did.
Using a “Wonder Wheel” for Social Media Monitoring
What if he had something like the Wonder Wheel for Social Media Monitoring? – How much better do you think the intelligence would be? Again, you might think Lexalytics or Clarabridge, if you upload your monitoring data to those platforms – but I don’t think they’re going exactly the same place I’m going with this).
Besides, many of the monitoring platforms already use Lexalytics on their backend for sentiment analysis scoring and aren’t providing anything like what I’m focusing on today - so why would Lexalytics provide it on their own? Wouldn’t these platforms have done it already if it was so easy for them?
…though the action we take from alerts is open to debate - while we should monitor competitors – I know we have to be very careful how we actually engage with them – Brian Solis has that pegged down better than I do and perhaps I ought to read his book so I understand the full implications of monitoring being set up and how you’d use it to engage with your customers – or just listen, if that’s all want to do.
The first thing I’d do is sort by content alphabetically – to try to simulate the Wonder Wheel type of map that Radian6 can’t today provide (see below- but who knows, in the future – maybe they’ll read this post and add it as a feature next year sometime, along with additional interesting technologies I know they’re working on merging).
You can certainly click on the words you care about like “Havana”- but there’s no real semantic grouping – nothing that does what I put out as my first slide in this post. Wonder Wheel could eat up Radian6’s logs, digest them and give you something that actually helps you answer what people are looking for or saying.
Could have gone a bit deeper but there wasn’t that much really to summarize here – and honestly – the messaging is more uni-directional than bi-directional – which is what you need it to be for effective Social Media Outreach.
If the Wonder Wheel type technology had a crack at these logs you could click on them and it would keep bringing up more information – except the information would be coming from your data – not what Google wants you to create ads for, or what others on Google searched for – but instead, what you need to understand about the people who are coming to you asking questions – questions we all should be answering.
And with that – my next post will attempt to answer a few questions my readers have asked me – based on this post and my own data from Google Analytics.
Marshall Sponder is an independent Web Analytics and SEO/SEM specialist working in the field of market research, social media, networking and PR. He provides digital data convergence generating ROI and develops data metrics, KPI’s and dashboards that drive businesses by setting, evaluating benchmarks and teaches Analytics at UCI Extension and Social Media for The Arts at Rutgers University.