It’s not just a generational thing, there are so many more places to go than any of us have time or interest. Oh, I just meant on-line. Whether you do or don’t bother with Facebook, or seek wisdom browsing social sites, every place on-line data mining traps lie in wait. Search tools once brought great excitement. At the tip of your fingers, one box combined the knowledge of encyclopedia, yellow pages and the shopkeepers of the world. Today, I wonder, search and its promise of the information revolution resembles a trap and less a portal to discovery. Personalization and customizing the experience takes priority over serendipity and pure exploration. That’s the clarion bell signaling opportunity and let me share my own cursory take.
The trend lines all show mobile apps displacing Google search. By the way, so do my browser’s add-on buttons. Still, old habits are hard to break. Bing and Yahoo, sure they keep trying, but Google’s continuous upgrades still deliver personally satisfying search results faster. Their under the hood tracking of my past choices and search history increase their predictive precision. I’m a statistical geek at heart and so I miss the old Google, that shared the probability score on every result. Today, for kicks I typed “ ” into my browser’s Google search box. and these numbers appeared above the search: 1,200 personal results. 4,430,000,000 other results.
Ironic, to discover 4.4 trillion requests made to Google to find Yahoo doesn’t it?
These numbers illustrate Google’s additional value–their search provide me an opportunity to put my search into a broader context. In this case the numbers led to an insight, how I can quantify and size a search word’s popularity. I can measure a meme or popular thought’s magnitude. I was on a roll, what other meanings or associations might these search placed terms hold?
My browser (Firefox) search box dynamically lists offers suggestions to help me refine my search. As I begin to type, additional search terms appear ranked by popular preferences. The phrases anticipate and nudge me to narrow down my intention, refine the Yahoo search from 4.4Trillion results. The drop down menu offers 10 suggestions: mail, my.yahoo.mail (the address that I’ve used to get my mail), finance, news, sports, answers etc.
Hmm.. imagine using the old yellow pages. Organized by category, the listings appear alphabetically. Google and the rest of the search engines never worked that way. If I try, using the category Restaurant, I get
1,250 personal results. 364,000,000 other results
What do these numbers mean? Looking over the first few results in the search output and accompanying map, I’m shown a neighborhood where I used to live 30 years ago and visit occasionally. Before wondering why that neighborhood popped up I compared Google’s competitors.
Bing returned 50,100,000 results and Yahoo reported 48,500,000 results. These are not small discrepancies and may explain why Google remains #1 not just in my preference stack but apparently for the world too. But there’s more.
Ambiguity the new opportunity
The habits Google encourages and its customized learning of personal preferences revolutionized how people spend their holidays, shop, work and play. For the last several years Venture Beat reported in January that Google grew its “semantic network” to at least 570 million objects and 18 billion facts .
In the hands of marketers, the more numerous, diverse associations attached to an idea or phrase makes good business. The variation allows room to play off nuanced differences and at the same time drill down to find the universal or shared meanings that bring different community segments together. Once established, shared meaning offers a foundation upon which new experiences and associations can be built, It can also segregate individuals into subgroups based on mutual understandings. Consider the difference between word slang and its normal usage as in Bad. What associations come to mind, reflects all the nuances of your interests and the company you keep. The same word carries multiple definitions and its usage varies within different populations. Google ad words and SEO allows marketers placement based on the nuanced choice of their target market.
So how did Bing challenge the space or more importantly what inherently does its value proposition offer? Bing displays results in three distinct columns: the traditional search , a second column of paid advertisers and the third Facebook’s search results. It may be nice to differential but I’m not sure I understand the benefits. Or if I did, I’d use Bing more.
Historically, the search service results listed in order of frequency of association or popularity. The resulting match returns ranked sites whose keywords had greater influence in that particular domain. To effectively compete, I’d need to use the same key words to get my ideas on the inside of information sharing circle. This is the old Buzz game, I want people to talk up a particular topic or a brand, the idea needs to insert itself into the conversation, right? Google still helps you see the trend lines behind the scenes, which words are trending over time, when and where.
Extrapolation from the past ain’t foresight
Which approach develops foresight? How can someone track the spread of ideas or get a true bead on what’s coming next?
Yes foresight not insight, as in opportunity creation and positioning for advantage. Tools to find and understand emerging customer trends requires something else. It requires context. Let me introduce Crawdad Software. Their process follows a patented system called Centering Resonance Analysis (CRA; Corman, Kuhn, McPhee, & Dooley, 2002). It differs from traditional inquiry methods that deliver results based on word frequency. CRA’s Latent semantic analysis,”uses computational linguistics to model a text as a network of words…its grammatical rules understand how words take meaning from context. Whereas word frequency methods create insight based on a “pile of words”, CRA creates insight through applying network analysis.” The following is an example of Aesop’s fables courtesy of Kevin Dooley, CEO of Crawdad.
Which matters, who does the sharing or what exactly they share and why. Which words they use turn out to be useful to understanding and predicting reactions. That’s the association that triggers action. The map above illustrates how naturally Aesop comes to mind when the word fable appears. But notice how many additional words appear too.
Imagine a leading manufacturer’s #1 product, also leads in its category, suddenly loses ground to another competitor. It failed to notice an opportunity and others stepped in to their space. Foresight capability, like radar, offers early warning signs of a new attraction or distraction drawing interest in your field of operation.
Once people talk about an idea, understanding who does the talking is as important as why there’s talk at all. Semantic analysis techniques assess the context, meaning and relational significance of this new idea. Whether outside or inside, social media listening posts require additional intelligence to be useful. Sure, there’s added value to Google’s intelligent semantics.
Example of search term semantics
|bear with me||276,000,000|
Its methods borrow from Crawdad, but its output sure doesn’t. I assembled the table above manually. In March, I discovered IBM Data Analytics offers semantic sentiment analysis along the lines illustrated above. They plan to put the results in the cloud this summer and make it more widely available, or at least platform neutral for subscribers. So I am not able to create a comparison for you,or even an illustration.
All of these tools offer greater opportunities. Numerous data-mining tools, and increasing integration of social media into knowledge management functions, dashboards and automated evaluation systems makes trivial the opening description of what’s going on with search. Getting a bead on what’s next however won’t matter unless you are capable, resilient and flexible enough to adapt and make use of that knowledge ahead and better than others.
The understanding of diverse associations makes sense to marketers who understand what to do with them. The challenge is to help more people within your organization understand what to do with this information. IBM shared these competencies at their Analytics summit last week.
|Social Media Analytics is about Business|
|Marketing||Human Resources||Risk Management|
|1. Brand Reputation||1.Company Reputation||1.Partner Reputation|
|2.Messaging||2.Attracting key professional talent||2.Union members wants|
|3.Campaign Management||3.Attracting College talent||3.Identify key managers online conversations|
|4.Competitive Positioning||4.Identifying key reasons for attrition||4.Reputational risk|
|5.Identification of key Influencers||5.Identification of key Influencers||5.Impact of my customer’s reputation|
The world doesn’t really get any more complicated, just more diverse. We keep adding new words, new phrases and at the same time, adding new meanings to old words and phrases. Survival depends on proper interpretation, how well we understand others and how well they understand us. I suggest there’s great value that we leave on the table when we let ambiguity get the best of us. Take time to verify others understanding, don’t merely respond based on assumption that is unless you plan to seek forgiveness every time. Start listening fully first, learn what others understand, not just what but why their expectations exist and then choose whether to adapt or to share back.