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30 April 2007
Wi-Fi (Wireless Fidelity) - Description

Wi-fi is a wireless data networking protocol, which allows for PCs and laptops to access the internet, within a given area or "hotspot", via a high frequency wireless local area network (WLAN). The term Wi-Fi was coined by the Wireless Ethernet Compatibility Alliance (WECA) as another name for IEEE 802.11b networking standard. WECA is still involved in certifying new wireless modems in order to verify that they are fully compatible with the standard.



My mind's unweaving/ 9:02 AM

28 April 2007
Anticipating Customer Behavior With Analytics

“Know your customers and give them what they want” is the fundamental principle of marketing.
This principle is simple in theory, but increasingly challenging to put into practice. Short of being a mind reader or having a crystal ball, it’s difficult for marketers to know what’s on a customer’s mind today, or anticipate what the customer may need or want tomorrow.
The challenge doesn’t stem from lack of customer data. The fact is, customers and prospects are giving us information about themselves all the time. Through every response, customer contact, event, transaction and Web site hit, they reveal something about themselves.

Databases are chock full of these useful tidbits, and call centers and other customer management systems are overflowing with details about customers and contacts. The challenge is that raw data does not have value per se; it needs to be turned into useful information.
That is where analytical technology comes into play. A philosopher once wrote that finding the patterns in the randomness of life is the way we create beauty and make art. A similar statement could be made about analytics, which find patterns in the randomness of data so that you can discover valuable information and gain insight.
An array of analytical products is available for desktop and enterprise systems and for pros and novices alike.
Generally, analytics fall into four categories:
1. Statistical analysis
2. On-line analytical processing (OLAP)
3. Data mining
4. Text mining
Statistical analysis refers to a collection of methods used to process large amounts of data to uncover key facts, patterns and trends.
Numerous statistical analysis procedures can be applied, but the two most commonly used by direct marketers are classification and segmentation. Classification uses predictor fields to predict a categorical target field, such as which groups of people will respond to a mailing. Segmentation divides subjects, objects or variables into various relatively homogeneous groups (e.g., segmenting customers into usage-pattern groups).
Popular statistical software can handle the entire analytical process—planning, data collection, data access, data management and preparation, data analysis, reporting and deployment.
For example, Rural Cellular Corporation (RCC), which provides wireless service to subscribers in 14 states covering a population of 5.9 million, uses statistical analysis for market research. This research includes customer satisfaction and branding studies to determine positioning for its products and service features. Before investing money in any new feature, RCC surveys its customers to determine exactly what features they want, what they want each of the features to do and how much they are willing to pay for them.
Online Analytical Processing enables users to easily and selectively extract data and then view it from different perspectives. For example, a user can request that data be analyzed and presented in a format that shows all of a company’s widgets sold in Wyoming in the month of August, compares revenue figures with those for the same products in October, and then compares other product sales in Wyoming for the same time period.
To facilitate this kind of analysis, OLAP data is stored in a multidimensional database, which considers each data attribute (such as product, geographic sales region and time period) as a separate “dimension.” This management tool allows marketers to quickly review history and trends to take advantage of emerging opportunities, and take corrective action on developing problems.
For example, Johnsonville Sausage Inc., a manufacturer and marketer of fresh, smoked and cooked sausage products, uses OLAP to access operational and financial data. Johnsonville can compare sales by customer, region and brand. With this information, it develops more accurate sales forecasts for production and manufacturing scheduling.
Data mining discovers the meaningful patterns and relationships in data—separating signals from noise—and provides decision-making information about the future. Data mining procedures include the following:
• Association: looking for patterns where one event is connected to another event
• Sequence or path analysis: looking for patterns where one event leads to a later event
• Classification: looking for new patterns
• Clustering: finding and visually documenting groups of facts not previously known
• Forecasting: discovering patterns in data that can lead to reasonable predictions about the future
Data mining provides a clear picture of what is going to happen—in time to change it—such as which customers might be most valuable, which customers are likely to defect, or, if the right data is gathered, which carry the risk of adverse reactions to marketing offers.
For example, Standard Life, a global mutual financial services company, needed to expand its share of the increasingly competitive mortgage market. A major part of its efforts was to develop models that could identify customer characteristics relevant to any mortgage product. Data mining enabled Standard Life to better understand the characteristics of its mortgage customers so that it could more accurately search for potential new clients. As a result, the company achieved a nine-times greater response to offers and has secured approximately $50 million worth of mortgage application revenue.
Text mining analyzes unstructured textual data by finding and discovering the patterns and relationships within thousands of documents, such as emails, call reports, Web sites and other information sources.
Text mining extracts terms and phrases and then classifies the terms into related groups, such as products, organizations or people, using the meaning and context of the text. This distilled information can be combined with other data sources and used with traditional data mining techniques such as clustering, classification and predictive modeling.
Questions to explore include… Which concepts occur together? What else are they linked to? What do they predict? With answers to such questions, the marketer is better able to identify potential customer defection, head it off and then maximize consumer satisfaction.
For example, a major online retailer combines data mining with text mining to analyze customer calls, emails, Web surveys and other customer communications to better understand what offers and recommendations are appropriate for each customer. As a result, the retailer has tripled its profits from the previous year.
With the massive amounts of customer data being generated every moment of every day, and the absolute necessity of carefully managing the customer relationship, analytics are no longer a nice thing to have; they are essential. The backlash against spam marketing, and new privacy legislation put into place as a result of this backlash, is forcing a more scientific approach to the art of marketing.
It will no longer be a matter of just throwing out a hook and seeing who bites; it will be about taking the time and using the right tools to truly understand customers, satisfy their needs and wants, and anticipate what they may want tomorrow.

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My mind's unweaving/ 12:50 PM

Amazon dot com, Inc. is an American electronic commerce company based in Seattle, Washington. It was one of the first major companies to sell goods over the Internet and was one of the iconic stocks of the late 1990s dot-com bubble. After the bubble burst Amazon faced skepticism about its business model, but it made its first annual profit in 2003.

Founded by Jeff Bezos in 1994, and launched in 1995, began as an online bookstore, though it soon diversified its product lines, adding DVDs, music CDs, computer software, video games, electronics, apparel, furniture, food, toys and more.

Amazon has established separate websites in Canada, the United Kingdom, Germany, Austria, France, China, and Japan. It ships globally on selected products.

History and Business Model

Amazon was founded in 1994, spurred by what Bezos refers to as his "regret minimization framework," i.e. his effort to fend off late-in-life regret for not staking a claim in the Internet gold rush. It is common lore that Bezos wrote its business plan while he and his wife drove a 1988 Chevrolet Blazer from Fort Worth, Texas to Bellevue, Washington.

The company began operating as an online bookstore under the name (as in abracadabra), a name that Bezos quickly abandoned due to its sounding like cadaver.While the largest brick-and-mortar bookstores and mail-order catalogs for books might offer 200,000 titles, an online bookstore could offer many times more. Bezos renamed his company "Amazon" after the world's most voluminous river. The company was incorporated in 1994 in the state of Washington, began service in July 1995, and was reincorporated in 1996 in Delaware. had its initial public offering on May 15, 1997, trading on the NASDAQ stock exchange under the symbol AMZN at an IPO price of $18.00 per share (equivalent to $1.50 after three stock splits during the late 1990s).

Amazon's initial business plan was unusual: the company did not expect to turn a profit for four to five years. In retrospect, the strategy was effective. Amazon grew at a steady pace in the late 1990s while many other Internet companies grew at a blindingly fast pace. Amazon's "slow" growth caused a number of its stockholders to complain, saying that the company was not reaching profitability fast enough. When the Dot-com bubble burst and many e-companies went out of business, Amazon persevered and finally turned its first profit in the fourth quarter of 2002: a meager $5 million, just 1 cent per share, on revenues of over $1 billion, but it was important symbolically. The firm has since remained profitable: net income was $35.3 million in 2003, $588.5 million in 2004, $359 million in 2005, and $190 million in 2006 (including a $662 million charge on R&D in 2006). Nevertheless, the firm's cumulative profits remain negative, since the positive performance of recent years is not yet sufficient to wipe out the losses of the past, as of 2005 the accumulated deficit stood at $2.03 billion.

Revenue continued to grow thanks to product diversification and international presence: $3.9 billion in 2002, $5.3 billion in 2003, $6.9 billion in 2004, $8.5 billion in 2005, and $10.7 billion in 2006. On November 21, 2005, Amazon entered the S&amp;P 500 index, replacing the venerable AT&T after it merged with SBC Communications.

Time Magazine named Bezos its 1999 Man of the Year in recognition of the company's success in popularizing online shopping.

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My mind's unweaving/ 12:09 PM

12 Laws of Customer Loyalty

by Jill Griffin
May 11, 2004
1. Build staff loyalty
It’s a fact: firms with high levels of customer loyalty have also earned high levels of staff loyalty. It’s darn near impossible to build strong customer loyalty with a staff that is in constant turnover.
Why? Because customers buy relationships and familiarity. They want to buy from people who know them and their preferences. Key rule of loyalty: serve your employees first so that they, in turn, can serve your customer.

2. Practice the 80/20 rule
In building customer loyalty, the 80/20 rule is alive and well. Roughly speaking, 80% of your revenue is being generated by 20% of your customers.
All customers are not created equal. Some represent more long-term value to your firm than others. A smart company segments customers by value and monitors activities closely to ensure high-value customers get their fair share of special offers and promotions. Unlike many firms that simply measure overall redemption, these savvy loyalty builders pay close attention to who redeems.
3. Know your loyalty stages and ensure that your customers are moving through them
Customers become loyal to a company and its products and service one step at a time. By understanding the customer’s current loyalty stage, you can better determine what’s necessary to move that customer to the next level of loyalty. There are six stages of customer loyalty: suspect, prospect, first-time customer, repeat customer, client and advocate.
If your customer relationship processes and programs aren’t moving customers forward, rethink them.
4. Serve first, sell second
Today’s customers are smarter, better informed and more intolerant of “being sold” than ever before. They expect doing business with you to be as hassle-free and gratifying for them as possible.
When they experience good service elsewhere, they bring a if-they-can-do-it-why-can’t–you attitude to their next transaction with you. They believe that you earn their business with service that is pleasant, productive and personalized; and if you don’t deliver, they’ll leave.
5. Aggressively seek out customer complaints
For most companies, only 10% of complaints get articulated by customers. The other 90% are unarticulated and manifest themselves in many negative ways: unpaid invoices, lack of courtesy to your frontline service reps and, above all, negative word of mouth.
With the Internet, an unhappy customer can now reach thousands of your would-be customers in a few keystrokes. Head off bad press before it happens. Make it easy for customers to complain, and treat complaints seriously. Establish firm guidelines regarding customer response time, reporting and trend analysis. Make employee complaint monitoring a key tool for executive decision making.
6. Stay responsive
Research shows that responsiveness is closely tied to a customer’s perception of good service. The advent of the Internet has changed the customer’s perception of responsiveness. More and more, customers are coming to expect round-the-clock customer service.
Moreover, customers now arrive at Web sites time-starved and eager to locate answers. Technology tools such as customer self-service, email management and live chat/Web callback are proving increasingly critical for companies as they address the demanding customer’s responsiveness needs.
7. Know your customer’s definition of value
The loyalty password is “value.” Knowing how your customers experience value and then delivering on those terms is critical to building strong customer loyalty.
But knowing your customer’s true definition of value is not easy, because your customers’ value definitions are constantly changing. Invest in customer loyalty research that enables you to understand, through the eyes of the customer, how well you deliver value.
8. Win back lost customers
Research shows that a business is twice as likely to successfully sell to a lost customer as to a new prospect. Yet, winning back lost customers is frequently the most overlooked source for incremental revenue in many firms.
Why? Because most firms consider a lost customer a lost cause. With the average company losing 20-40% of its customers every year, it’s imperative that firms create hard-working strategies not only for acquisition and retention but also for win-back. Since no customer retention program can be 100% foolproof, it follows that every company needs a process for recapturing those high-value customers who depart. Think of it as loyalty insurance.
9. Use multiple channels to serve the same customers well
Research suggests that customers who engage with a firm through multiple channels exhibit deeper loyalty than single-channel customers. But take note: this finding assumes that customers get the same consistent service whether coming into the store, logging on the Web site or calling the service center.
To achieve consistency, your firm must internally coordinate sales and service across multiple channels so that customer preferences are accessible no matter how the customer chooses to interact. Today’s customers expect to hop from channel to channel, and they expect good service to follow.
10. Give your frontline the skills to perform
Increasingly, for many companies, the employee “frontline” is a call center where agents interact with customers. These agents will be the “loyalty warriors” of the future. Converged call centers that bring together multi-channel access points (phone, fax, email, Web) are on the rise.
Gartner Group estimates that 70% of North America’s call centers will migrate to multi-channel contact centers by 2005. This means that those agents need to be as equipped to write a well-written email reply and navigate the company Web site as they are in being helpful and friendly on a phone call.
11. Collaborate with your channel partners
In today’s complex marketplace, a firm is often dependent on many suppliers to help serve its customers. Embracing these supply chain relationships for the greater good of the ultimate customer creates customer value that is hard for competitors to match.
For example, a European auto manufacturer converted its customer data base program into a system that could be shared by all channel partners. By refusing to hoard the information, the manufacturer helped create a blended channel strategy that built greater customer loyalty throughout the distribution chain.
12. Store your data in a centralized database
Most firms lack a 360-degree view of their customer because they have no centralized database. Billing departments, sales divisions and customer service centers might all have their own databases, with no effective means for creating a complete customer-information composite.
To effectively implement a sound customer loyalty strategy, data from all customer touchpoints must be combined into a centralized customer database. Without it, the firm is greatly handicapped in its efforts to serve the customer.

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My mind's unweaving/ 11:59 AM

25 April 2007
Metrodata Targets 13 Percent Revenue Increase

PT Metrodata Electronics has targeted an increase in this year's revenues of 13 percent.

According to President Director of Metrodata Lesan Lemanardja, last year the company's revenues reached US$175 million.

“This year, at least it will reach US$200 million,” said Lesan yesterday (04/24) in Jakarta.

Lesan is optimistic about the target because, according to International Data Center, Indonesia's expenditure on information technology in 2006 reached US$1.8 billion.

“This year, it is predicted to rise by 15 percent,” he said.

Lesan also explained that half of Metrodata's revenues were obtained through its role as the distributor of information technology products such as Dell, Epson, Fujitsu, Hewlett-Packard, IBM, Lenovo and Huawei-3Com.

Yesterday (04/24), through its subsidiary PT Metrodata e Business, Metrodata was appointed a distributor for the products of Emerson Network Power.

Emerson is a US-based company that provides information technology solutions such as power instruments, as well as connectivity and cooling solutions for computer systems.

According to President of Asia Pacific Emerson Ajeet Singh, the selling of Emerson products last year amounted to US$1 billion with 5.1 percent growth.

He said he hoped that market in Indonesia would contribute much for the turnover of Emerson products.

Emerson will aim at small- and medium-sized enterprises (SMEs).

“SMEs in Indonesia have more rapid development in their information technology penetration compared to medium- and large-sized enterprises,” said Lesan.

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My mind's unweaving/ 5:29 PM

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