It's a wired wired world.

The Internet is turning out to be the most vital customer touch point and the major profit generator, even for businesses that are not first of mind.
And it is why an emerging field called Web Analytics is rapidly penetrating into the list of what companies nowadays must take into consideration.

And without the latter, my fellow data miners' (whose blog links are found left of this page) and my datamin class with Mr. Ramon Duremdes Jr. would not be as meaningful as it is. Indeed, it is a wired wired world.

The Web Analytics Association officially defines web analytics as the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing Web usage.

Figuratively speaking, web analytics is just a tot. The child has grown up since birth and now can somewhat nosh itself, however there is plenty of growth and development ahead of it. The web has indeed "grown up" as a conduit for most firms, and swiftly there is an unfathomable demand for the web channel.

A good number of people are introduced to web analytics by means of reports popping out of a web log parser, Google Analytics or perhaps one of the high-end tools. We come across massive amounts of reports and make an attempt to make sense of them. Web analytics, however, is relatively multifaceted, and it is at all times, most favorable to take a step back from the tools and reports and beforehand, comprehend the basics.

Critical components of a successful web analytics strategy, according to what Avinash Kaushik mentioned in Web Analytics: An Hour A Day, consist of the following:

Focus on customer centricity. Assessing how your website is bringing to your customers will facilitate focus on your web analytics program and allow you to thoroughly juggle around with the metrics needed to be calculated to rate performance of the website. This means new metrics, approaches, tools, and people. Simply to think about the existence of the website.

The Trinity Approach to web analytics is based in conveying customer centricity to your web analytics strategy. The Trinity puts a huge importance on measuring all faces of customer experience to totally comprehend why customers visit your website and how the website is doing in terms of meeting their needs.
Customer centricity is an approach that when implemented can provide a sustainable competitive advantage.

Solve for business questions. Commence the process of dealing with business questions early on –prior to having a web analytics tool, before knowing what the site is or what it does. Recognizing business questions is a journey. This development and evolution is a mark that you are in fact answering business questions and not just doing reporting, because business is at all times evolving and changing to you have to merely learn to change with it.

Follow the 10/90 rule. 10 percent of the budget should be used up on tools, and the remaining 90 percent on people who will be in charge of insights. This speaks to the apparent secret of web analytics’ success: it’s the people, not the tools and cool technology.

A successful web analytics strategy does not come in a box, with a one-time purchase cost. As a matter of fact, getting the adequate web analytics tools is still, only half the battle; the real focus of any web analytics program must be the people who work with it.

Hire great web analysts who

• Have used more than one web analytics tool extensively
• Frequent the Yahoo! Web Analytics group and the top web analytics blogs
• Before doing any important analysis, visit the website and look at the web pages
• Their core approach is customer centric
• Understand the technical differences between page tagging, log files, packet sniffing and beacons
• Are comfortable in the quantitative and qualitative worlds
• Are avid explorers
• Are effective communicators
• Are street smart
• Play offense and not just defense
• Bonus: are survivors

Identify optimal organizational structure and responsibilities. In nearly all companies, the IT team still possesses web analytics and is in the business of choosing vendors and even making available standard reports. Nonetheless, the world has drastically changed. The kinds of metrics and reports called for are dissimilar, the vendor/solution models, are fundamentally diverse and lastly, the utilization of web data is different.

And just like all things that are worth doing, this never-ending process takes patience as well as persistence. As Johann Wolfgang von Goethe said, Knowing is not enough, we must apply. Willing is not enough, we must do.

For every action, there is an equal and opposite.. reputation.

It was Abraham Lincoln who said Character is like a tree and reputation like a shadow. The shadow is what we think of it; the tree is the real thing.

Aside from our parents, it is in school wherein we are aided in developing a good character. Nevertheless, ways on how to monitor our reputation (and online) as well as manage stakeholder perceptions of our characters are typically not included in our education packages.
Fortunately, thanks to our datamin class with Mr. Ramon Duremdes Jr., my fellow data miners (whose blog links are found left of this page) and I now at least have ideas on ways how to build, monitor and repair our online reputations.

Reputation, according to Merriam-Webster, is 1) an overall quality or character as seen or judged by people in general, and 2) a place in public esteem or regard: a good name.

In wittier words and as Isaac Newton would have said, for every action, there is an equal and opposite.. reputation.

It's funny how it is in the nature of man to tend to underestimate the things he knows he possesses and nobody could take away. But it's even funnier when one believes that it's better to have a bad reputation than not to have a reputation at all.

Just like New York, the Internet is a place that doesn't sleep. Not only that, according to Radically Transparent: Monitoring and Managing Reputations Online by Andy Beal and Dr. Judy Strauss, the reality is, we live in a transparent, always-on, wired world where anyone can and will post pictures or write about you, your company or just about anybody else, 24/7.. online.

Reality check: ExecuNet found that 78% of executive recruiters consistently make use of search engines in order to know more about job candidates, and 35% have discarded candidates based on the information they came across with.

Joseph Hall once said, A reputation once broken may possibly be repaired, but the whole world will always keep their eyes on the spot where the crack was.

Worried yet? Well, it's good to be scared. It means you still have something to lose.

People are talking about personal and corporal reputations as you read, thanks to social media, which are online tools and platforms (blogs, wikis, photo and video sharing, forums, networks) that let Internet users to collaborate on content, share insights and experiences and connect, for business or pleasure.

The power to create or destroy reputation is now well-established in the average consumer due to the technologies that allow just about everyone to publish online, eventually leading consumer opinions to drive reputations.

Edelman's 2008 Trust Barometer found that some 58% of survey respondents find a person like yourself to make available the most credible information. Consumers strongly take into account the opinions of like minded people and have a vital influence on their purchasing behavior.

As a Chinese proverb says, No amount of money can make others speak well of you behind your back.

The Internet has steered in an epoch of corporate transparency. What was already available publicly became accessible anytime, anywhere, from any device with access to the Web.

With the best endeavors to create a positive online repute, we are still vulnerable to the criticism of our stakeholders. And so arises the need to continuously monitor conversations concerning us, that would provides us an opportunity to tell our side of the story and repair damages incurred to our name, should they be necessary.

Why monitor?
The Internet is a growing source of information and it is advantageous to be one step ahead of any possible reputation crisis. It is but obvious to to think that the earlier we know of stakeholder complaints, the quicker we can respond.

Reality check: research from John Tschohl found that a recipient of good customer service will tell only five of their acquaintances. On the other hand, a recipient of bad service will tell ten people, a hundred or even more, given a fact that he is a blogger or part of a social network.

What to monitor?

• Your own brands: there are different touch points (company, products and services, executives and spokespeople) for stakeholders to interact with your brands
  
• Your marketing campaigns: intended to share positive news about your brand and light passion in your stockholders
  
• Industry trends: can reveal areas of opportunity, or potential diseases, long before your brand is specifically mentioned
  
• Your competitors: understanding each competitor's strength, weaknesses, and reputation
  
• Your known-weaknesses: admit your mistakes before someone else exaggerates the story
  

Where to monitor?

• Cast the net wide by monitoring a broad range of activity
• Finding the centers of influence my monitoring where stakeholders hangout together online in special-interest group, social networks, and blogs (industry blogs, vertical communities, employee hangouts, business locations).
• A combination of the latter two techniques.
  

When to monitor?
NOW. As said previously, the earlier we start building our online reputation, we should also start monitoring it; because it also the sooner we are able to discover our strengths, weaknesses, opportunities and threats.


Need not panic, as tedious as all this may sound, again, thanks to the ever so revered technology, there exist tools (from low-cost to not so low-cost) that automate reputation monitoring. Tools such as these include:

• Real Simple Syndication readers: manages all of our subscribed RSS feeds and enables us to sort and sift the information delivered, based on certain and specific needs. Among the most popular readers are Google Reader, Bloglines, and NewsGator.
• E-mail updates: not all websites provide an RSS feed. These help keep track of important e-mails perhaps only accessible by subscribing to newsletters.
  
• Tracking software: not all websites want to be found and make it hard to be tracked. Software such as Copernic (with a onetime fee of around $50) allows users to keep track of just about any web content.

With first hand experiences, after being introduced and having experimented on Google Alerts and Google Analytics, and not utilizing these latter for either building, monitoring or repairing online reputations, would certainly be opportunity-wasting. Not to mention, the ease and simplicity of how these tools function, the automation of web content monitoring brings us relevant information knocking on each of our doors.

The relevance of all this to the banking industry is pretty much just like in any other industry. It's not only about opening a Facebook fan page or a Twitter account, but clinching in all facets of social media. Knowing that it would not be without its challenges, companies must understand that if they are not part of this yet, they simply are ignoring and passing over opportunities to connect with an entire generation.

Reading between the mines.

At some point in earlier times, mining was among the major, most-developing industries. Not surprisingly, history does repeat itself. But this time around, it's not the Au-kind of gold we're after and digging for.

One of the emerging and most powerful disruptive technologies in this day in age is termed data mining. As defined by Kurt Thearting in An Introduction To Data Mining, data mining is the extraction of hidden predictive information from large databases. In simpler words... reading between the mines.

Basically as the subject name indicates, this post is in accordance to our data mining class with Mr. Ramon Duremdes Jr.. Other insights on this are found on my co-data miners who's blog links are found left of this page.

Data mining aids companies focus on all the relevant information they can get hold of. Together with business intelligence, data mining tools, given databases of adequate size and quality, can create business opportunities by automating prediction of trends and behaviors and automating discovery of formerly unidentified patterns.
Data mining tools have the ability to answer business questions that traditionally were constrained with time. They quarry databases for veiled patterns, stumbling on upon information that perhaps human perception may overlook.

In order to survive in today's business ventures, many companies, aside from gathering and analyzing massive amounts of data, also see the need to shift and integrate data mining tools to their existing structures.
Data mining, along with artificial intelligence was listed by GartnerGroup Advanced Technology Note among the major technology areas that are and will keep on booming and in which companies of diverse industries will be investing in within the next couple of years. Well, with all the wonders of data mining, who wouldn't?

Companies nowadays are quite eager and enthusiastic of this rapdily-cultivating concept because of the fact that extensive processes of research and development has transformed data mining drastically as diverse techniques have surfaced along. Among these techniques, the most frequently used are artificial neural networks, decision trees, genetic algorithms, nearest neighbor method, rule induction, to name a few.

But before going into unfathomable concepts, how is it imaginable that data mining has the ability to tell us things we do not know or what would happen within the week, the month or year after, having such probabilistic and unstable economic conditions? Answer? Read on down.

The food chain begins with data and information, which of course are essential to creating business intelligence. Business intelligence, according to SAS, is the technology and practice of implementing information to make decisions. Business intelligence is considered to be obtained when information shows its true worth. And subsequent to this, not only a window, but an infinite number of doors are opened.

The cycle continues with knowledge workers utilizing the adequate Information Technology tools in order to define and analyze correlation and logic that lies behind the information. Examples of these tools are are databases, database management systems, data warehouses and data mining tools.

According to Haag, Phillip and Cummings on Management Information Systems for The Information Age, data mining tools are the software utilized to query information in a data warehouse supported by online analytical processing, which is the manipulation of information to support decision-making tasks.
Data mining tools include query-and-reporting tools, intelligent agents, multidimensional analysis tools and statistical tools.

• Query-and-reporting tools are similar to Query-By-Example tools, Structured-Query-Language and report generators in the typical database environment.

• Intelligent agents use several artificial intelligence tools as are neural networks and fuzzy logic in order to mold the basis of "information discovery" and building business intelligence.

• Multidimensional analysis tools are slick-and-dice techniques that permit the viewing of multidimensional information from different viewpoints.

• Statistical tools aid in applying various mathematical models to the information stored in a data warehouse to discover new information.

But the road is long and broad. Possessing the right data mining tools only bring us a step further and closer to home run. Obviously, there exist diverse industries, dissimilar organizational structures, business processes. A company must then adapt the adequate data mining technique/s.

Data mining techniques, according to Kurt Thearling in An Overview of Data Mining Techniques, are classified into classical techniques and next generation techniques.

The classical techniques are the techniques used 99.9% of the time on existing business problems, mainly because industries rely on techniques that consistent, understandable and explainable. Among the classifcal techniques are the following:

• Statistics: statistical techniques have long been used and are not data mining techniques. Nonetheless, statistical techniques are dependable on data and are utilized to discover trends and build forecasting models. Being familiar or knowledgeable of how statistical techniques work and how they can be used is a big factor in this technique.

• Neighborhoods: among the first used techniques in data mining, it is a prediction technique that is similar to clustering. Its nature is that to forecast a prediction value in one record, it looks for records with similar predictor values in the historical database and utilizes the prediction value from the record that is closes to the unclassified record.

• Clustering: method through which similar records are grouped together in order to give a birds eye view of the business of what is happening within the database.

Trees, networks and rules are among the classified under the next generation techniques. These techniques are the most frequently used over the past two decades of research and can be used for discovering fresh information within large databases or for bulding predictive models.

• Trees: decision tree algorithms tend to automate the whole process of reating hypothesis and validate them in a more complete and integrated manner compared to any other data mining techniques. Trees are proficient in handling raw data with few or no pre-processing and have been applied for problems related to credit card attrition prediction to time series of exchange rate currencies.

• Networks: networks are computer programs that implement the detection of trend and pattern detection learning and machine learning algorithms to build predictive models from large historical databases.

• Rules: one of the key forms of data mining and perhaps may be of the most common for of knowledge discovery. Rule induction bears a resemblance to the process that people think of when they think about data mining, namely "mining", digging for gold through an immense database.

The business world constantly faces situations in which business intelligence plays a key role. Auspiciously, not only IT experts can data mine. As a matter of fact, mining data is made possible with everyday applications such as Microsoft Excel and Microsoft Access to SAS, Cognos, Informatica, among many others.

An industry that is better off or is ought to make use of data mining tools is the banking industry.
Companies want to comprehend the risks they are facing, they want to understand and relate to customers before their competitors do, they want to know what drives their costs, profits; they want to be in the know. And all these can and are done with the right information, the right tools, the right people... in short, data mining.

More and more companies are making use of this booming concept and with reasonable grounds: it can make significant and ample benefits. But what must be kept in mind is that there is a lot more to it than simply purchasing a toolkit.