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  • json parse error in ruby - unexpected token at

    - by RahTha
    Hi, I get errors at a lot of places when trying to retrieve ticker symbols for US companies from http://d.yimg.com/autoc.finance.yahoo.com/autoc?callback=YAHOO.Finance.SymbolSuggest.ssCallback&query=Wal-Mart I have tried to: resp = Net::HTTP.get_response(URI.parse(url)) data = resp.body qwe = data.split("symbol") p qwe[1] arr1 = data.split("(") arr2 = arr1[1].split(")") fnl = arr2[0].gsub(/-/, '') fnl = fnl.gsub(/\(/, '') fnl = fnl.gsub(/\)/, '') fnl = fnl.gsub(/\./, '') fnl = fnl.gsub('\'', '"') fnl = fnl.gsub(/([\{|\,}])\s*([a-zA-Z]+):/, '\1 "\2":') But this doesnt help as i see: /Library/Ruby/Gems/1.8/gems/json-1.2.0/lib/json/common.rb:123:in `parse': 353: unexpected token at '{"symbol":"BEEV","name": "BENCHMARK ENERGY CORP ' (JSON::ParserError) Any clues as to what i might be doing wrong?

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  • WPF bind to a static double var

    - by devin250
    hi, i have these double values: public partial class Window1 : Window { public Window1() { InitializeComponent(); double num1 = 50.0; double num2 = 90.0; double num3 = 120.0; double num4 = 20.0; double num5 = 80.0; } } how do i bind them to xaml? <vc:Chart.Series> <vc:DataSeries RenderAs="Column" AxisYType="Primary" > <vc:DataSeries.DataPoints> <vc:DataPoint AxisXLabel="Wall-Mart" YValue="{Binding Source={StaticResource num1}}" /> .... help please

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  • How do I get around Inconsistant accessibilty error in C # ?

    - by xarzu
    How do I get around Inconsistant accessibilty error in C # ? I need to pass a pointer to a node in a linked list to a method. When I do, I get a "Compiler Error CS0051" Example The following sample generates CS0051: Copy Code // CS0051.cs public class A { // Try making B public since F is public // B is implicitly private here class B { } public static void F(B b) // CS0051 { } public static void Main() { } } That is a simple example. The actual program is a bit more complicated. I am actually using a node in a linked list to pass to the method LinkedListNode node The method uses recursion because the node is mart of a huge linked list structure of linked lists that outputs an xml file. Either I have to find a way to use recursion without using methods or I need to find a way to pass pointers nodes or actual nodes.

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  • Walmart's Mobile Self-Checkout

    - by David Dorf
    Reuters recently reported that Walmart was testing an iPhone-based self-checkout at a store near its headquarters.  Consumers scan items as they're placed in the physical basket, then the virtual basket is transferred to an existing self-checkout station where payment is tendered.  A very solid solution, but not exactly original. Before we go further, let's look at the possible cost savings for Walmart.  According to the article: Pushing more shoppers to scan their own items and make payments without the help of a cashier could save Wal-Mart millions of dollars, Chief Financial Officer Charles Holley said on March 7. The company spends about $12 million in cashier wages every second at its Walmart U.S. stores. Um, yeah. Using back-of-the-napkin math, I calculated Walmart's cashiers are making $157k per hour.  A more accurate statement would be saving $12M per year for each second saved on the average transaction time.  So if this self-checkout approach saves 2 seconds per transaction on average, Walmart would save $24M per year on labor.  Maybe.  Sometimes that savings will be used to do other tasks in the store, so it may not directly translate to less employees. When I saw this approach demonstrated in Sweden, there were a few differences, which may or may not be in Walmart's plans.  First, the consumers were identified based on their loyalty card.  In order to offset the inevitable shrink, retailers need to save on labor but also increase basket size, typically via in-aisle promotions.  As they scan items, retailers should target promos, and that's easier to do if you know some shopping history.  Last I checked, Walmart had no loyalty program. Second, at the self-checkout station consumers were randomly selected for an audit in which they must re-scan all the items just like you do at a typical self-checkout.  If you were found to be stealing, your ability to use the system can be revoked.  That's a tough one in the US, especially when the system goes wrong, either by mistake or by lying.  At least in my view, the Swedes are bit more trustworthy than the people of Walmart. So while I think the idea of mobile self-checkout has merit, perhaps its not right for Walmart.

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  • The most dangerous SQL Script in the world!

    - by DrJohn
    In my last blog entry, I outlined how to automate SQL Server database builds from concatenated SQL Scripts. However, I did not mention how I ensure the database is clean before I rebuild it. Clearly a simple DROP/CREATE DATABASE command would suffice; but you may not have permission to execute such commands, especially in a corporate environment controlled by a centralised DBA team. However, you should at least have database owner permissions on the development database so you can actually do your job! Then you can employ my universal "drop all" script which will clear down your database before you run your SQL Scripts to rebuild all the database objects. Why start with a clean database? During the development process, it is all too easy to leave old objects hanging around in the database which can have unforeseen consequences. For example, when you rename a table you may forget to delete the old table and change all the related views to use the new table. Clearly this will mean an end-user querying the views will get the wrong data and your reputation will take a nose dive as a result! Starting with a clean, empty database and then building all your database objects using SQL Scripts using the technique outlined in my previous blog means you know exactly what you have in your database. The database can then be repopulated using SSIS and bingo; you have a data mart "to go". My universal "drop all" SQL Script To ensure you start with a clean database run my universal "drop all" script which you can download from here: 100_drop_all.zip By using the database catalog views, the script finds and drops all of the following database objects: Foreign key relationships Stored procedures Triggers Database triggers Views Tables Functions Partition schemes Partition functions XML Schema Collections Schemas Types Service broker services Service broker queues Service broker contracts Service broker message types SQLCLR assemblies There are two optional sections to the script: drop users and drop roles. You may use these at your peril, particularly as you may well remove your own permissions! Note that the script has a verbose mode which displays the SQL commands it is executing. This can be switched on by setting @debug=1. Running this script against one of the system databases is certainly not recommended! So I advise you to keep a USE database statement at the top of the file. Good luck and be careful!!

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  • Multidimensional Thinking–24 Hours of Pass: Celebrating Women in Technology

    - by smisner
    It’s Day 1 of #24HOP and it’s been great to participate in this event with so many women from all over the world in one long training-fest. The SQL community has been abuzz on Twitter with running commentary which is fun to watch while listening to the current speaker. If you missed the fun today because you’re busy with all that work you’ve got to do – don’t despair. All sessions are recorded and will be available soon. Keep an eye on the 24 Hours of Pass page for details. And the fun’s not over today. Rather than run 24 hours consecutively, #24HOP is now broken down into 12-hours over two days, so check out the schedule to see if there’s a session that interests you and fits your schedule. I’m pleased to announce that my business colleague Erika Bakse ( Blog | Twitter) will be presenting on Day 2 – her debut presentation for a PASS event. (And I’m also pleased to say she’s my daughter!) Multidimensional Thinking: The Presentation My contribution to this lineup of terrific speakers was Multidimensional Thinking. Here’s the abstract: “Whether you’re developing Analysis Services cubes or creating PowerPivot workbooks, you need to get into a multidimensional frame of mind to produce a model that best enables users to answer their business questions on their own. Many database professionals struggle initially with multidimensional models because the data modeling process is much different than the one they use to produce traditional, third normal form databases. In this session, I’ll introduce you to the terminology of multidimensional modeling and step through the process of translating business requirements into a viable model.” If you watched the presentation and want a copy of the slides, you can download a copy here. And you’re welcome to download the slides even if you didn’t watch the presentation, but they’ll make more sense if you did! Kimball All the Way There’s only so much I can cover in the time allotted, but I hope that I succeeded in my attempt to build a foundation that prepares you for starting out in business intelligence. One of my favorite resources that will get into much more detail about all kinds of scenarios (well beyond the basics!) is The Data Warehouse Toolkit (Second Edition) by Ralph Kimball. Anything from Kimball or the Kimball Group is worth reading. Kimball material might take reading and re-reading a few times before it makes sense. From my own experience, I found that I actually had to just build my first data warehouse using dimensional modeling on faith that I was going the right direction because it just didn’t click with me initially. I’ve had years of practice since then and I can say it does get easier with practice. The most important thing, in my opinion, is that you simply must prototype a lot and solicit user feedback, because ultimately the model needs to make sense to them. They will definitely make sure you get it right! Schema Generation One question came up after the presentation about whether we use SQL Server Management Studio or Business Intelligence Development Studio (BIDS) to build the tables for the dimensional model. My answer? It really doesn’t matter how you create the tables. Use whatever method that you’re comfortable with. But just so happens that it IS possible to set up your design in BIDS as part of an Analysis Services project and to have BIDS generate the relational schema for you. I did a Webcast last year called Building a Data Mart with Integration Services that demonstrated how to do this. Yes, the subject was Integration Services, but as part of that presentation, I showed how to leverage Analysis Services to build the tables, and then I showed how to use Integration Services to load those tables. I blogged about this presentation in September 2010 and included downloads of the project that I used. In the blog post, I explained that I missed a step in the demonstration. Oops. Just as an FYI, there were two more Webcasts to finish the story begun with the data – Accelerating Answers with Analysis Services and Delivering Information with Reporting Services. If you want to just cut to the chase and learn how to use Analysis Services to build the tables, you can see the Using the Schema Generation Wizard topic in Books Online.

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  • codeigniter & cjax framework, fatal error class 'CI_Controller' not found

    - by Martin
    I'm having this weird error with Codeigniter 2.1.3 and latest cjax for codeigniter. Weird thing is, when I download the latest codeigniter, and latest cjax framework for codeitniger and copy to my friends server, and call: domain.com/ajax.php?test/test2 to show the test ajax examples ... it works like a breeze, but when I do this on my server, I get server error (even tho, we both have same php version and such). Server then throws in error log file this error: PHP Fatal error: Class 'CI_Controller' not found in /hosting/www/domain.com/www/application/response/test.php on line 3 Now, I've read thru stackoverflow with people having this problem and solving by changing the construct and calling CI_Controller instead of Controller. But I already do that ... - I mean it's in the basic example that is suppose to work without touching the code, and it does, just not on my domain for some crappy reason. Ajax.php from cjax framework for codeingter should load controller from folder response, named test and call function test2, which looks like this (the actual file named test.php): class Test extends CI_Controller { function __construct() { parent::__construct(); } /** * * ajax.php?test/test/a/b/c * * @param unknown_type $a * @param unknown_type $b * @param unknown_type $c */ function test($a = null,$b = null, $c = null) { $this->load->view('test', array('data' => $a .' '.$b.' '.$c)); } /** * ajax.php?test/test2 * * Here we are testing out the javascript library. * * Note: the library it is not meant to be included in ajax controllers - but in front-controllers, * it is being used here for the sake of simplicity in testing. */ function test2() { $ajax = ajax(); $ajax->update('response','Cjax Works'); $ajax->append('#response','<br /><br />version: '.$ajax->version); $ajax->success('Cjax was successfully installed.', 5); //see application/views/test2.php $this->load->view('test2'); } I was hoping someone could bring some light into this problem - or maybe someone has already experienced it? Thanks for your time! Mart

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  • Importing a WebService:

    - by Pierre
    Hi all, I'm trying to import the following web service: http://www.biomart.org/biomart/martwsdl Using curl for the service getResistry() : everything is OK: curl --header 'Content-Type: text/xml' --data '<?xml version="1.0"?><soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/" xmlns:mar="http://www.biomart.org:80/MartServiceSoap"> <soapenv:Header/> <soapenv:Body> <mar:getRegistry/> </soapenv:Body> </soapenv:Envelope>' http://www.biomart.org:80/biomart/martsoap it returns: <?xml version="1.0" encoding="UTF-8"?> <soap:Envelope xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:soapenc="http://schemas.xmlsoap.o rg/soap/encoding/" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:soap="http://schemas.xmlsoap.org/soap/ envelope/" soap:encodingStyle="http://schemas.xmlsoap.org/soap/encoding/"> <soap:Body> <getRegistryResponse xmlns="http://www.biomart.org:80/MartServiceSoap"> <mart> <name xsi:type="xsd:string">ensembl</name> <displayName xsi:type="xsd:string">ENSEMBL GENES 57 (SANGER UK)</displayName> <database xsi:type="xsd:string">ensembl_mart_57</database> (...) OK. But when this service is generated using CXF/wsdl2java ( or even wsimport) mkdir src wsdl2java -keep -d src -client "http://www.biomart.org/biomart/martwsdl" javac -g -d src -sourcepath src src/org/biomart/_80/martservicesoap/MartServiceSoap_BioMartSoapPort_Client.java java -cp src org.biomart._80.martservicesoap.MartServiceSoap_BioMartSoapPort_Client the generated client returns an empty list for getRegistry(): Invoking getRegistry... getRegistry.result=[] why ? what should I do, to make this code work ? Many thanks Pierre

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  • SQL Server database change workflow best practices

    - by kubi
    The Background My group has 4 SQL Server Databases: Production UAT Test Dev I work in the Dev environment. When the time comes to promote the objects I've been working on (tables, views, functions, stored procs) I make a request of my manager, who promotes to Test. After testing, she submits a request to an Admin who promotes to UAT. After successful user testing, the same Admin promotes to Production. The Problem The entire process is awkward for a few reasons. Each person must manually track their changes. If I update, add, remove any objects I need to track them so that my promotion request contains everything I've done. In theory, if I miss something testing or UAT should catch it, but this isn't certain and it's a waste of the tester's time, anyway. Lots of changes I make are iterative and done in a GUI, which means there's no record of what changes I made, only the end result (at least as far as I know). We're in the fairly early stages of building out a data mart, so the majority of the changes made, at least count-wise, are minor things: changing the data type for a column, altering the names of tables as we crystallize what they'll be used for, tweaking functions and stored procs, etc. The Question People have been doing this kind of work for decades, so I imagine there have got to be a much better way to manage the process. What I would love is if I could run a diff between two databases to see how the structure was different, use that diff to generate a change script, use that change script as my promotion request. Is this possible? If not, are there any other ways to organize this process? For the record, we're a 100% Microsoft shop, just now updating everything to SQL Server 2008, so any tools available in that package would be fair game.

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  • lapply slower than for-loop when used for a BiomaRt query. Is that expected?

    - by ptocquin
    I would like to query a database using BiomaRt package. I have loci and want to retrieve some related information, let say description. I first try to use lapply but was surprise by the time needed for the task to be performed. I thus tried a more basic for-loop and get a faster result. Is that expected or is something wrong with my code or with my understanding of apply ? I read other posts dealing with *apply vs for-loop performance (Here, for example) and I was aware that improved performance should not be expected but I don't understand why performance here is actually lower. Here is a reproducible example. 1) Loading the library and selecting the database : library("biomaRt") athaliana <- useMart("plants_mart_14") athaliana <- useDataset("athaliana_eg_gene",mart=athaliana) 2) Querying the database : loci <- c("at1g01300", "at1g01800", "at1g01900", "at1g02335", "at1g02790", "at1g03220", "at1g03230", "at1g04040", "at1g04110", "at1g05240" ) I create a function for the use in lapply : foo <- function(loci) { getBM("description","tair_locus",loci,athaliana) } When I use this function on the first element : > system.time(foo(cwp_loci[1])) utilisateur système écoulé 0.020 0.004 1.599 When I use lapply to retrieve the data for all values : > system.time(lapply(loci, foo)) utilisateur système écoulé 0.220 0.000 16.376 I then created a new function, adding a for-loop : foo2 <- function(loci) { for (i in loci) { getBM("description","tair_locus",loci[i],athaliana) } } Here is the result : > system.time(foo2(loci)) utilisateur système écoulé 0.204 0.004 10.919 Of course, this will be applied to a big list of loci, so the best performing option is needed. I thank you for assistance. EDIT Following recommendation of @MartinMorgan Simply passing the vector loci to getBM greatly improves the query efficiency. Simpler is better. > system.time(lapply(loci, foo)) utilisateur système écoulé 0.236 0.024 110.512 > system.time(foo2(loci)) utilisateur système écoulé 0.208 0.040 116.099 > system.time(foo(loci)) utilisateur système écoulé 0.028 0.000 6.193

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  • Effecient organization of spare cables and hardware

    - by Jake Wharton
    As many of you also likely do, I have a growing collection of cables, hardware, and spare parts (screws, connectors, etc.). I'm looking to find a good system of organization so that everything isn't a tangled mess, mismatched, and potentially able to be damaged. Since the the three things listed above are all have varying sizes and degrees of delicacy this poises an interesting problem. Presently I have those cheap plastic storage bins you find at Wal-mart for everything. Cables that were once wrapped neatly have become tangled due to numerous "I know I have a cable for this" moments. Hardware is mixed in other bins with odds and ends with no protection from each other. NICs, CPUs, and HDDs are all interacting and likely causing damage. Finally there are stray parts sprinkled amongst these two both in plastic bags and loose. I'm looking to unify this storage into a controlled chaos. Here are my thoughts: Odds and ends are the easiest. Screws, connectors, and small electronic parts lend themselves perfectly to tackle boxes and jewelry boxes. Since these are usually dynamically compartmentalized I can adjust for the contents and label them on the outside or inside of the lid. Cables are easily wrangled with short velcro strips but that doesn't stop them from being all mixed in together. Hardware is the worst offender. Size, shape, and degree of delicacy changes with nearly every piece. I'm willing to sacrifice a bit of organization for a somewhat efficient manner. What are all your thoughts? What is the best type of tackle or jewelry box to use? Most of them are cheap and flimsy. Is there a better alternative? How can I organize cables to know exactly (within reason) where one is? What about associating cables with hardware (Wall adapter to router, etc.)? What kind of storage unit lends itself to all shapes of hardware? Do I need to separate by size or degree of delicacy for better organization?

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  • Mark Hurd on the Customer Revolution: Oracle's Top 10 Insights

    - by Richard Lefebvre
    Reprint of an article from Forbes Businesses that fail to focus on customer experience will hear a giant sucking sound from their vanishing profitability. Because in today’s dynamic global marketplace, consumers now hold the power in the buyer-seller equation, and sellers need to revamp their strategy for this new world order. The ability to relentlessly deliver connected, personalized and rewarding customer experiences is rapidly becoming one of the primary sources of competitive advantage in today’s dynamic global marketplace. And the inability or unwillingness to realize that the customer is a company’s most important asset will lead, inevitably, to decline and failure. Welcome to the lifecycle of customer experience, in which consumers explore, engage, shop, buy, ask, compare, complain, socialize, exchange, and more across multiple channels with the unconditional expectation that each of those interactions will be completed in an efficient and personalized manner however, wherever, and whenever the customer wants. While many niche companies are offering point solutions within that sprawling and complex spectrum of needs and requirements, businesses looking to deliver superb customer experiences are still left having to do multiple product evaluations, multiple contract negotiations, multiple test projects, multiple deployments, and–perhaps most annoying of all–multiple and never-ending integration projects to string together all those niche products from all those niche vendors. With its new suite of customer-experience solutions, Oracle believes it can help companies unravel these challenges and move at the speed of their customers, anticipating their needs and desires and creating enduring and profitable relationships. Those solutions span the full range of marketing, selling, commerce, service, listening/insights, and social and collaboration tools for employees. When Oracle launched its suite of Customer Experience solutions at a recent event in New York City, president Mark Hurd analyzed the customer experience revolution taking place and presented Oracle’s strategy for empowering companies to capitalize on this important market shift. From Hurd’s presentation and related materials, I’ve extracted a list of Hurd’s Top 10 Insights into the Customer Revolution. 1. Please Don’t Feed the Competitor’s Pipeline!After enduring a poor experience, 89% of consumers say they would immediately take their business to your competitor. (Except where noted, the source for these findings is the 2011 Customer Experience Impact (CEI) Report including a survey commissioned by RightNow (acquired by Oracle in March 2012) and conducted by Harris Interactive.) 2. The Addressable Market Is Massive. Only 1% of consumers say their expectations were always met by their actual experiences. 3. They’re Willing to Pay More! In return for a great experience, 86% of consumers say they’ll pay up to 25% more. 4. The Social Media Microphone Is Always Live. After suffering through a poor experience, more than 25% of consumers said they posted a negative comment on Twitter or Facebook or other social media sites. Conversely, of those consumers who got a response after complaining, 22% posted positive comments about the company. 5.  The New Deal Is Never Done: Embrace the Entire Customer Lifecycle. An appropriately active and engaged relationship, says Hurd, extends across every step of the entire processs: need, research, select, purchase, receive, use, maintain, and recommend. 6. The 360-Degree Commitment. Customers want to do business with companies that actively and openly demonstrate the desire to establish strong and seamless connections across employees, the company, and the customer, says research firm Temkin Group in its report called “The CX Competencies.” 7. Understand the Emotional Drivers Behind Brand Love. What makes consumers fall in love with a brand? Among the top factors are friendly employees and customer reps (73%), easy access to information and support (55%), and personalized experiences, such as when companies know precisely what products or services customers have purchased in the past and what issues those customers have raised (36%). 8.  The Importance of Immediate Action. You’ve got one week to respond–and then the opportunity’s lost. If your company needs more than a week to answer a prospect’s question or request, most of those prospects will terminate the relationship. 9.  Want More Revenue, Less Churn, and More Referrals? Then improve the overall customer experience: Forrester’s research says that approach put an extra $900 million in the pockets of wireless service providers, $800 million for hotels, and $400 million for airlines. 10. The Formula for CX Success.  Hurd says it includes three elegantly interlaced factors: Connected Engagement, to personalize the experience; Actionable Insight, to maximize the engagement; and Optimized Execution, to deliver on the promise of value. RECOMMENDED READING: The Top 10 Strategic CIO Issues For 2013 Wal-Mart, Amazon, eBay: Who’s the Speed King of Retail? Career Suicide and the CIO: 4 Deadly New Threats Memo to Marc Benioff: Social Is a Tool, Not an App

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  • How to remove music/videos DRM protection and convert to Mobile Devices such as iPod, iPhone, PSP, Z

    - by tonywesley
    The music/video files you purchased from online music stores like iTunes, Yahoo Music or Wal-Mart are under DRM protection. So you can't convert them to the formats supported by your own mobile devices such as Nokia phone, Creative Zen palyer, iPod, PSP, Walkman, Zune… You also can't share your purchased music/videos with your friends. The following step by step tutorial is dedicated to instructing music lovers to how to convert your DRM protected music/videos to mobile devices. Method 1: If you only want to remove DRM protection from your protected music, this method will not spend your money. Step 1: Burn your protected music files to CD-R/RW disc to make an audio CD Step 2: Find a free CD Ripper software to convert the audio CD track back to MP3, WAV, WMA, M4A, AAC, RA… Method 2: This guide will show you how to crack drm from protected wmv, wma, m4p, m4v, m4a, aac files and convert to unprotected WMV, MP4, MP3, WMA or any video and audio formats you like, such as AVI, MP4, Flv, MPEG, MOV, 3GP, m4a, aac, wmv, ogg, wav... I have been using Media Converter software, it is the quickest and easiest solution to remove drm from WMV, M4V, M4P, WMA, M4A, AAC, M4B, AA files by quick recording. It gets audio and video stream at the bottom of operating system, so the output quality is lossless and the conversion speed is fast . The process is as follows. Step 1: Download and install the software Step 2: Run the software and click "Add…" button to load WMA or M4A, M4B, AAC, WMV, M4P, M4V, ASF files Step 3: Choose output formats. If you want to convert protected audio files, please select "Convert audio to" list; If you want to convert protected video files, please select "Convert video to" list. Step 4: You can click "Settings" button to custom preference for output files. Click "Settings" button bellow "Convert audio to" list for protected audio files Click "Settings" button bellow "Convert video to" list for protected video files Step 5: Start remove DRM and convert your DRM protected music and videos by click on "Start" button. What is DRM? DRM, which is most commonly found in movies and music files, doesn't mean just basic copy-protection of video, audio and ebooks, but it basically means full protection for digital content, ranging from delivery to end user's ways to use the content. We can remove the Drm from video and audio files legally by quick recording.

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  • SQL SERVER – Guest Post – Architecting Data Warehouse – Niraj Bhatt

    - by pinaldave
    Niraj Bhatt works as an Enterprise Architect for a Fortune 500 company and has an innate passion for building / studying software systems. He is a top rated speaker at various technical forums including Tech·Ed, MCT Summit, Developer Summit, and Virtual Tech Days, among others. Having run a successful startup for four years Niraj enjoys working on – IT innovations that can impact an enterprise bottom line, streamlining IT budgets through IT consolidation, architecture and integration of systems, performance tuning, and review of enterprise applications. He has received Microsoft MVP award for ASP.NET, Connected Systems and most recently on Windows Azure. When he is away from his laptop, you will find him taking deep dives in automobiles, pottery, rafting, photography, cooking and financial statements though not necessarily in that order. He is also a manager/speaker at BDOTNET, Asia’s largest .NET user group. Here is the guest post by Niraj Bhatt. As data in your applications grows it’s the database that usually becomes a bottleneck. It’s hard to scale a relational DB and the preferred approach for large scale applications is to create separate databases for writes and reads. These databases are referred as transactional database and reporting database. Though there are tools / techniques which can allow you to create snapshot of your transactional database for reporting purpose, sometimes they don’t quite fit the reporting requirements of an enterprise. These requirements typically are data analytics, effective schema (for an Information worker to self-service herself), historical data, better performance (flat data, no joins) etc. This is where a need for data warehouse or an OLAP system arises. A Key point to remember is a data warehouse is mostly a relational database. It’s built on top of same concepts like Tables, Rows, Columns, Primary keys, Foreign Keys, etc. Before we talk about how data warehouses are typically structured let’s understand key components that can create a data flow between OLTP systems and OLAP systems. There are 3 major areas to it: a) OLTP system should be capable of tracking its changes as all these changes should go back to data warehouse for historical recording. For e.g. if an OLTP transaction moves a customer from silver to gold category, OLTP system needs to ensure that this change is tracked and send to data warehouse for reporting purpose. A report in context could be how many customers divided by geographies moved from sliver to gold category. In data warehouse terminology this process is called Change Data Capture. There are quite a few systems that leverage database triggers to move these changes to corresponding tracking tables. There are also out of box features provided by some databases e.g. SQL Server 2008 offers Change Data Capture and Change Tracking for addressing such requirements. b) After we make the OLTP system capable of tracking its changes we need to provision a batch process that can run periodically and takes these changes from OLTP system and dump them into data warehouse. There are many tools out there that can help you fill this gap – SQL Server Integration Services happens to be one of them. c) So we have an OLTP system that knows how to track its changes, we have jobs that run periodically to move these changes to warehouse. The question though remains is how warehouse will record these changes? This structural change in data warehouse arena is often covered under something called Slowly Changing Dimension (SCD). While we will talk about dimensions in a while, SCD can be applied to pure relational tables too. SCD enables a database structure to capture historical data. This would create multiple records for a given entity in relational database and data warehouses prefer having their own primary key, often known as surrogate key. As I mentioned a data warehouse is just a relational database but industry often attributes a specific schema style to data warehouses. These styles are Star Schema or Snowflake Schema. The motivation behind these styles is to create a flat database structure (as opposed to normalized one), which is easy to understand / use, easy to query and easy to slice / dice. Star schema is a database structure made up of dimensions and facts. Facts are generally the numbers (sales, quantity, etc.) that you want to slice and dice. Fact tables have these numbers and have references (foreign keys) to set of tables that provide context around those facts. E.g. if you have recorded 10,000 USD as sales that number would go in a sales fact table and could have foreign keys attached to it that refers to the sales agent responsible for sale and to time table which contains the dates between which that sale was made. These agent and time tables are called dimensions which provide context to the numbers stored in fact tables. This schema structure of fact being at center surrounded by dimensions is called Star schema. A similar structure with difference of dimension tables being normalized is called a Snowflake schema. This relational structure of facts and dimensions serves as an input for another analysis structure called Cube. Though physically Cube is a special structure supported by commercial databases like SQL Server Analysis Services, logically it’s a multidimensional structure where dimensions define the sides of cube and facts define the content. Facts are often called as Measures inside a cube. Dimensions often tend to form a hierarchy. E.g. Product may be broken into categories and categories in turn to individual items. Category and Items are often referred as Levels and their constituents as Members with their overall structure called as Hierarchy. Measures are rolled up as per dimensional hierarchy. These rolled up measures are called Aggregates. Now this may seem like an overwhelming vocabulary to deal with but don’t worry it will sink in as you start working with Cubes and others. Let’s see few other terms that we would run into while talking about data warehouses. ODS or an Operational Data Store is a frequently misused term. There would be few users in your organization that want to report on most current data and can’t afford to miss a single transaction for their report. Then there is another set of users that typically don’t care how current the data is. Mostly senior level executives who are interesting in trending, mining, forecasting, strategizing, etc. don’t care for that one specific transaction. This is where an ODS can come in handy. ODS can use the same star schema and the OLAP cubes we saw earlier. The only difference is that the data inside an ODS would be short lived, i.e. for few months and ODS would sync with OLTP system every few minutes. Data warehouse can periodically sync with ODS either daily or weekly depending on business drivers. Data marts are another frequently talked about topic in data warehousing. They are subject-specific data warehouse. Data warehouses that try to span over an enterprise are normally too big to scope, build, manage, track, etc. Hence they are often scaled down to something called Data mart that supports a specific segment of business like sales, marketing, or support. Data marts too, are often designed using star schema model discussed earlier. Industry is divided when it comes to use of data marts. Some experts prefer having data marts along with a central data warehouse. Data warehouse here acts as information staging and distribution hub with spokes being data marts connected via data feeds serving summarized data. Others eliminate the need for a centralized data warehouse citing that most users want to report on detailed data. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Business Intelligence, Data Warehousing, Database, Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • SQL SERVER – Weekly Series – Memory Lane – #039

    - by Pinal Dave
    Here is the list of selected articles of SQLAuthority.com across all these years. Instead of just listing all the articles I have selected a few of my most favorite articles and have listed them here with additional notes below it. Let me know which one of the following is your favorite article from memory lane. 2007 FQL – Facebook Query Language Facebook list following advantages of FQL: Condensed XML reduces bandwidth and parsing costs. More complex requests can reduce the number of requests necessary. Provides a single consistent, unified interface for all of your data. It’s fun! UDF – Get the Day of the Week Function The day of the week can be retrieved in SQL Server by using the DatePart function. The value returned by the function is between 1 (Sunday) and 7 (Saturday). To convert this to a string representing the day of the week, use a CASE statement. UDF – Function to Get Previous And Next Work Day – Exclude Saturday and Sunday While reading ColdFusion blog of Ben Nadel Getting the Previous Day In ColdFusion, Excluding Saturday And Sunday, I realize that I use similar function on my SQL Server Database. This function excludes the Weekends (Saturday and Sunday), and it gets previous as well as next work day. Complete Series of SQL Server Interview Questions and Answers Data Warehousing Interview Questions and Answers – Introduction Data Warehousing Interview Questions and Answers – Part 1 Data Warehousing Interview Questions and Answers – Part 2 Data Warehousing Interview Questions and Answers – Part 3 Data Warehousing Interview Questions and Answers Complete List Download 2008 Introduction to Log Viewer In SQL Server all the windows event logs can be seen along with SQL Server logs. Interface for all the logs is same and can be launched from the same place. This log can be exported and filtered as well. DBCC SHRINKFILE Takes Long Time to Run If you are DBA who are involved with Database Maintenance and file group maintenance, you must have experience that many times DBCC SHRINKFILE operations takes a long time but any other operations with Database are relatively quicker. mssqlsystemresource – Resource Database The purpose of resource database is to facilitates upgrading to the new version of SQL Server without any hassle. In previous versions whenever version of SQL Server was upgraded all the previous version system objects needs to be dropped and new version system objects to be created. 2009 Puzzle – Write Script to Generate Primary Key and Foreign Key In SQL Server Management Studio (SSMS), there is no option to script all the keys. If one is required to script keys they will have to manually script each key one at a time. If database has many tables, generating one key at a time can be a very intricate task. I want to throw a question to all of you if any of you have scripts for the same purpose. Maximizing View of SQL Server Management Studio – Full Screen – New Screen I had explained the following two different methods: 1) Open Results in Separate Tab - This is a very interesting method as result pan shows up in a different tab instead of the splitting screen horizontally. 2) Open SSMS in Full Screen - This works always and to its best. Not many people are aware of this method; hence, very few people use it to enhance performance. 2010 Find Queries using Parallelism from Cached Plan T-SQL script gets all the queries and their execution plan where parallelism operations are kicked up. Pay attention there is TOP 10 is used, if you have lots of transactional operations, I suggest that you change TOP 10 to TOP 50 This is the list of the all the articles in the series of computed columns. SQL SERVER – Computed Column – PERSISTED and Storage This article talks about how computed columns are created and why they take more storage space than before. SQL SERVER – Computed Column – PERSISTED and Performance This article talks about how PERSISTED columns give better performance than non-persisted columns. SQL SERVER – Computed Column – PERSISTED and Performance – Part 2 This article talks about how non-persisted columns give better performance than PERSISTED columns. SQL SERVER – Computed Column and Performance – Part 3 This article talks about how Index improves the performance of Computed Columns. SQL SERVER – Computed Column – PERSISTED and Storage – Part 2 This article talks about how creating index on computed column does not grow the row length of table. SQL SERVER – Computed Columns – Index and Performance This article summarized all the articles related to computed columns. 2011 SQL SERVER – Interview Questions and Answers – Frequently Asked Questions – Data Warehousing Concepts – Day 21 of 31 What is Data Warehousing? What is Business Intelligence (BI)? What is a Dimension Table? What is Dimensional Modeling? What is a Fact Table? What are the Fundamental Stages of Data Warehousing? What are the Different Methods of Loading Dimension tables? Describes the Foreign Key Columns in Fact Table and Dimension Table? What is Data Mining? What is the Difference between a View and a Materialized View? SQL SERVER – Interview Questions and Answers – Frequently Asked Questions – Data Warehousing Concepts – Day 22 of 31 What is OLTP? What is OLAP? What is the Difference between OLTP and OLAP? What is ODS? What is ER Diagram? SQL SERVER – Interview Questions and Answers – Frequently Asked Questions – Data Warehousing Concepts – Day 23 of 31 What is ETL? What is VLDB? Is OLTP Database is Design Optimal for Data Warehouse? If denormalizing improves Data Warehouse Processes, then why is the Fact Table is in the Normal Form? What are Lookup Tables? What are Aggregate Tables? What is Real-Time Data-Warehousing? What are Conformed Dimensions? What is a Conformed Fact? How do you Load the Time Dimension? What is a Level of Granularity of a Fact Table? What are Non-Additive Facts? What is a Factless Facts Table? What are Slowly Changing Dimensions (SCD)? SQL SERVER – Interview Questions and Answers – Frequently Asked Questions – Data Warehousing Concepts – Day 24 of 31 What is Hybrid Slowly Changing Dimension? What is BUS Schema? What is a Star Schema? What Snow Flake Schema? Differences between the Star and Snowflake Schema? What is Difference between ER Modeling and Dimensional Modeling? What is Degenerate Dimension Table? Why is Data Modeling Important? What is a Surrogate Key? What is Junk Dimension? What is a Data Mart? What is the Difference between OLAP and Data Warehouse? What is a Cube and Linked Cube with Reference to Data Warehouse? What is Snapshot with Reference to Data Warehouse? What is Active Data Warehousing? What is the Difference between Data Warehousing and Business Intelligence? What is MDS? Explain the Paradigm of Bill Inmon and Ralph Kimball. SQL SERVER – Azure Interview Questions and Answers – Guest Post by Paras Doshi – Day 25 of 31 Paras Doshi has submitted 21 interesting question and answers for SQL Azure. 1.What is SQL Azure? 2.What is cloud computing? 3.How is SQL Azure different than SQL server? 4.How many replicas are maintained for each SQL Azure database? 5.How can we migrate from SQL server to SQL Azure? 6.Which tools are available to manage SQL Azure databases and servers? 7.Tell me something about security and SQL Azure. 8.What is SQL Azure Firewall? 9.What is the difference between web edition and business edition? 10.How do we synchronize On Premise SQL server with SQL Azure? 11.How do we Backup SQL Azure Data? 12.What is the current pricing model of SQL Azure? 13.What is the current limitation of the size of SQL Azure DB? 14.How do you handle datasets larger than 50 GB? 15.What happens when the SQL Azure database reaches Max Size? 16.How many databases can we create in a single server? 17.How many servers can we create in a single subscription? 18.How do you improve the performance of a SQL Azure Database? 19.What is code near application topology? 20.What were the latest updates to SQL Azure service? 21.When does a workload on SQL Azure get throttled? SQL SERVER – Interview Questions and Answers – Guest Post by Malathi Mahadevan – Day 26 of 31 Malachi had asked a simple question which has several answers. Each answer makes you think and ponder about the reality of the IT world. Look at the simple question – ‘What is the toughest challenge you have faced in your present job and how did you handle it’? and its various answers. Each answer has its own story. SQL SERVER – Interview Questions and Answers – Guest Post by Rick Morelan – Day 27 of 31 Rick Morelan of Joes2Pros has written an excellent blog post on the subject how to find top N values. Most people are fully aware of how the TOP keyword works with a SELECT statement. After years preparing so many students to pass the SQL Certification I noticed they were pretty well prepared for job interviews too. Yes, they would do well in the interview but not great. There seemed to be a few questions that would come up repeatedly for almost everyone. Rick addresses similar questions in his lucid writing skills. 2012 Observation of Top with Index and Order of Resultset SQL Server has lots of things to learn and share. It is amazing to see how people evaluate and understand different techniques and styles differently when implementing. The real reason may be absolutely different but we may blame something totally different for the incorrect results. Read the blog post to learn more. How do I Record Video and Webcast How to Convert Hex to Decimal or INT Earlier I asked regarding a question about how to convert Hex to Decimal. I promised that I will post an answer with Due Credit to the author but never got around to post a blog post around it. Read the original post over here SQL SERVER – Question – How to Convert Hex to Decimal. Query to Get Unique Distinct Data Based on Condition – Eliminate Duplicate Data from Resultset The natural reaction will be to suggest DISTINCT or GROUP BY. However, not all the questions can be solved by DISTINCT or GROUP BY. Let us see the following example, where a user wanted only latest records to be displayed. Let us see the example to understand further. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Memory Lane, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Right-Time Retail Part 3

    - by David Dorf
    This is part three of the three-part series.  Read Part 1 and Part 2 first. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Right-Time Marketing Real-time isn’t just about executing faster; it extends to interactions with customers as well. As an industry, we’ve spent many years analyzing all the data that’s been collected. Yes, that data has been invaluable in helping us make better decisions like where to open new stores, how to assort those stores, and how to price our products. But the recent advances in technology are now making it possible to analyze and deliver that data very quickly… fast enough to impact a potential sale in near real-time. Let me give you two examples. Salesmen in car dealerships get pretty good at sizing people up. When a potential customer walks in the door, it doesn’t take long for the salesman to figure out the revenue at stake. Is this person a real buyer, or just looking for a fun test drive? Will this person buy today or three months from now? Will this person opt for the expensive packages, or go bare bones? While the salesman certainly asks some leading questions, much of information is discerned through body language. But body language doesn’t translate very well over the web. Eloqua, which was acquired by Oracle earlier this year, reads internet body language. By tracking the behavior of the people visiting your web site, Eloqua categorizes visitors based on their propensity to buy. While Eloqua’s roots have been in B2B, we’ve been looking at leveraging the technology with ATG to target B2C. Knowing what sites were previously visited, how often the customer has been to your site recently, and how long they’ve spent searching can help understand where the customer is in their purchase journey. And knowing that bit of information may be enough to help close the deal with a real-time offer, follow-up email, or online customer service pop-up. This isn’t so different from the days gone by when the clerk behind the counter of the corner store noticed you were lingering in a particular aisle, so he walked over to help you compare two products and close the sale. You appreciated the personalized service, and he knew the value of the long-term relationship. Move that same concept into the digital world and you have Oracle’s CX Suite, a cloud-based offering of end-to-end customer experience tools, assembled primarily from acquisitions. Those tools are Oracle Marketing (Eloqua), Oracle Commerce (ATG, Endeca), Oracle Sales (Oracle CRM On Demand), Oracle Service (RightNow), Oracle Social (Collective Intellect, Vitrue, Involver), and Oracle Content (Fatwire). We are providing the glue that binds the CIO and CMO together to unleash synergies that drive the top-line higher, and by virtue of the cloud-approach, keep costs at bay. My second example of real-time marketing takes place in the store but leverages the concepts of Web marketing. In 1962 the decline of personalized service in retail began. Anyone know the significance of that year? That’s when Target, K-Mart, and Walmart each opened their first stores, and over the succeeding years the industry chose scale over personal service. No longer were you known as “Jane with the snotty kid so make sure we check her out fast,” but you suddenly became “time-starved female age 20-30 with kids.” I’m not saying that was a bad thing – it was the right thing for our industry at the time, and it enabled a huge amount of growth, cheaper prices, and more variety of products. But scale alone is no longer good enough. Today’s sophisticated consumer demands scale, experience, and personal attention. To some extent we’ve delivered that on websites via the magic of cookies, your willingness to log in, and sophisticated data analytics. What store manager wouldn’t love a report detailing all the visitors to his store, where they came from, and which products that examined? People trackers are getting more sophisticated, incorporating infrared, video analytics, and even face recognition. (Next time you walk in front on a mannequin, don’t be surprised if it’s looking back.) But the ultimate marketing conduit is the mobile phone. Since each mobile phone emits a unique number on WiFi networks, it becomes the cookie of the physical world. Assuming congress keeps privacy safeguards reasonable, we’ll have a win-win situation for both retailers and consumers. Retailers get to know more about the consumer’s purchase journey, and consumers get higher levels of service with the retailer. When I call my bank, a couple things happen before the call is connected. A reverse look-up on my phone number identifies me so my accounts can be retrieved from Siebel CRM. Then the system anticipates why I’m calling based on recent transactions. In this example, it sees that I was just charged a foreign currency fee, so it assumes that’s the reason I’m calling. It puts all the relevant information on the customer service rep’s screen as it connects the call. When I complain about the fee, the rep immediately sees I’m a great customer and I travel lots, so she suggests switching me to their traveler’s card that doesn’t have foreign transaction fees. That technology is powered by a product called Oracle Real-Time Decisions, a rules engine built to execute very quickly, basically in the time it takes the phone to ring once. So let’s combine the power of that product with our new-found mobile cookie and provide contextual customer interactions in real-time. Our first opportunity comes when a customer crosses a pre-defined geo-fence, typically a boundary around the store. Context is the key to our interaction: that’s the customer (known or anonymous), the time of day and day of week, and location. Thomas near the downtown store on a Wednesday at noon means he’s heading to lunch. If he were near the mall location on a Saturday morning, that’s a completely different context. But on his way to lunch, we’ll let Thomas know that we’ve got a new shipment of ASICS running shoes on display with a simple text message. We used the context to look-up Thomas’ past purchases and understood he was an avid runner. We used the fact that this was lunchtime to select the type of message, in this case an informational message instead of an offer. Thomas enters the store, phone in hand, and walks to the shoe department. He scans one of the new ASICS shoes using the convenient QR Codes we provided on the shelf-tags, but then he starts scanning low-end Nikes. Each scan is another opportunity to both learn from Thomas and potentially interact via another message. Since he historically buys low-end Nikes and keeps scanning them, he’s likely falling back into his old ways. Our marketing rules are currently set to move loyal customer to higher margin products. We could have set the dials to increase visit frequency, move overstocked items, increase basket size, or many other settings, but today we are trying to move Thomas to higher-margin products. We send Thomas another text message, this time it’s a personalized offer for 10% off ASICS good for 24 hours. Offering him a discount on Nikes would be throwing margin away since he buys those anyway. We are using our marketing dollars to change behavior that increases the long-term value of Thomas. He decides to buy the ASICS and scans the discount code on his phone at checkout. Checkout is yet another opportunity to interact with Thomas, so the transaction is sent back to Oracle RTD for evaluation. Since Thomas didn’t buy anything with the shoes, we’ll print a bounce-back coupon on the receipt offering 30% off ASICS socks if he returns within seven days. We have successfully started moving Thomas from low-margin to high-margin products. In both of these marketing scenarios, we are able to leverage data in near real-time to decide how best to interact with the customer and lead to an increase in the lifetime value of the customer. The key here is acting at the moment the customer shows interest using the context of the situation. We aren’t pushing random products at haphazard times. We are tailoring the marketing to be very specific to this customer, and it’s the technology that allows this to happen in near real-time. Conclusion As we enable more right-time integrations and interactions, retailers will begin to offer increased service to their customers. Localized and personalized service at scale will drive loyalty and lead to meaningful revenue growth for the retailers that execute well. Our industry needs to support Commerce Anywhere…and commerce anytime as well.

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  • Understanding the value of Customer Experience & Loyalty for the Telecommunications Industry

    - by raul.goycoolea
    Worried by economic woes and market forces, especially in mature markets, communications service providers (CSPs) increasingly focus on improving customer experience. In fact, it seems difficult to find a major message by a C-level executive in the developed world that does not include something on "meeting and exceeding customers' needs". Frequently in customer satisfaction studies by prominent firms, CSPs fall short of the leadership demonstrated by other industries that take customer-centric approaches to their bottom-line strategies. Consider the following:Despite the continued impact of global economic crisis, in July 2010, Apple Computer posted record revenue and net quarterly profit. Those who attribute the results primarily to the iPhone 4 launch should note that Apple also shipped around 30% more Macintosh computers than the same period the previous year. Even sales of the iPod line increased by 8% in a highly commoditized, shrinking media player market. Finally, Apple began selling iPads during the quarter, with total sales of more than 3 million units. What does Apple have that the others lack? Well, some great products (and services) to be sure, but it also excels at customer service and support, marketing, and distribution, and has one of the strongest brands globally. Its products are useful, simple to use, easy to acquire and augment, high quality, and considered very cool. They also evoke such an emotional response from many of Apple's customers, which they turn up their noses at competitive products.In other words, Apple appears to have mastered virtually every aspect of customer experience and the resultant loyalty of its customer base - even in difficult financial times. Through that unwavering customer focus, Apple continues to drive its revenues and profits to new heights. Other customer loyalty leaders like Wal-Mart, Google, Toyota and Honda are also doing well by focusing on customer experience as an essential driver of profitability. Service providers should note this performance and ask themselves how they might leverage the same principles to increase their own profitability. After all, that is what customer experience and loyalty are all about: profitability.To successfully manage all the critical touch points of customer experience, CSPs must shun the one-size-fits-all approach. They can no longer afford to view customer service fundamentally as an act of altruism - which mentality dates back to the industry's civil service days, when CSPs were typically government organizations that were critical to economic development and public safety.As regulators and public officials have pushed, and continue to push, service providers to new heights of reliability - using incentives and punishments - most CSPs already have some of the fundamental building blocks of customer service in place. Yet despite that history and experience, service providers still lag other industries in providing what is seen as good customer service.As we observed in the TMF's 2009 Insights Research report, Customer Experience Management: Driving Loyalty & Profitability there has been resurgence in interest by CSPs. More and more of them have stated ambitions to catch up other industries, and they are realizing that good customer service is a powerful strategy for increasing business performance and profitability, not an act of good will.CSPs are recognizing the connection between customer experience and profitability, as demonstrated in many studies. For example, according to research by Bain & Company, a 5 percent improvement in customer retention rates can yield as much as a 75 percent increase in profits for companies across a range of industries.After decades of customer experience strategy formulation, Bain partner and business author, Frederick Reichheld, considers "would you recommend us to a friend?" as the ultimate question for a customer. How many times have you or your friends recommended an iPod, iPhone or a Mac? What do your children recommend to their peers? Their peers to them?There are certain steps service providers have to take to create more personalized relationships with their customers, as well as reduce churn and increase profitability, all while becoming leaner and more agile. First, they have to define customer experience, we define it as the result of the sum of observations, perceptions, thoughts and feelings arising from interactions and relationships between customers and their service provider(s). Virtually every customer touch point - whether directly or indirectly linked to service providers and their partners - contributes to customer perception, satisfaction, loyalty, and ultimately profitability. Gaining leadership in customer experience and satisfaction will not be a simple task, as it is affected by virtually every customer-facing aspect of the service provider, and in turn impacts the service provider deeply - especially on the all-important bottom line. The scope of issues affecting customer experience is complex and dynamic.With new services, devices and applications extending the basis of customer experience to domains beyond the direct control of the service provider, it is likely to increase in complexity and dynamism.Customer loyalty = increased profitsAs stated earlier, customer experience programs are not fundamentally altruistic exercises, but a strategic means of improving competitiveness and profitability in the short and long term. Loyalty is essential to deriving long term profits from customers.Some of the earliest loyalty programs date back to the 1930s, when packaged goods companies offered embedded coupons for rewards to buyers, and eventually retail chains began offering reward programs to frequent shoppers. These programs continued for decades but were leapfrogged in the 1980s by more aggressive programs from the airlines.This movement was led by American Airlines, which launched the first full-scale loyalty marketing program of the modern era with the AAdvantage frequent flyer scheme. It was the first to reward frequent fliers with notional air miles that could be accumulated and later redeemed for free travel. Figure 1: Opportunities example of Customer loyalty driven profitOther airlines and travel providers were quick to grasp the incredible value of providing customers with an incentive to use their company exclusively. Within a few years, dozens of travel industry companies launched similar initiatives and now loyalty programs are achieving near-ubiquity in many service industries, especially those in which it is difficult to differentiate offerings by product attributes.The belief is that increased profitability will result from customer retention efforts because:•    The cost of acquisition occurs only at the beginning of a relationship: the longer the relationship, the lower the amortized cost;•    Account maintenance costs decline as a percentage of total costs, or as a percentage of revenue, over the lifetime of the relationship;•    Long term customers tend to be less inclined to switch and less price sensitive which can result in stable unit sales volume and increases in dollar-sales volume;•    Long term customers may initiate word-of-mouth promotions and referrals, which cost the company nothing and arguably are the most effective form of advertising;•    Long-term customers are more likely to buy ancillary products and higher margin supplemental products;•    Long term customers tend to be satisfied with their relationship with the company and are less likely to switch to competitors, making market entry or competitors gaining market share difficult;•    Regular customers tend to be less expensive to service, as they are familiar with the processes involved, require less 'education', and are consistent in their order placement;•    Increased customer retention and loyalty makes the employees' jobs easier and more satisfying. In turn, happy employees feed back into higher customer satisfaction in a virtuous circle. Figure 2: The virtuous circle of customer loyaltyFigure 2 represents a high-level example of a virtuous cycle driven by customer satisfaction and loyalty, depicting how superiority in product and service offerings, as well as strong customer support by competent employees, lead to higher sales and ultimately profitability. As stated above, this is not a new concept, but succeeding with it is difficult. It has eluded many a company driven to achieve profitability goals. Of course, for this circle to be virtuous, the customer relationship(s) must be profitable.Trying to maintain the loyalty of unprofitable customers is not a viable business strategy. It is, therefore, important that marketers can assess the profitability of each customer (or customer segment), and either improve or terminate relationships that are not profitable. This means each customer's 'relationship costs' must be understood and compared to their 'relationship revenue'. Customer lifetime value (CLV) is the most commonly used metric here, as it is generally accepted as a representation of exactly how much each customer is worth in monetary terms, and therefore a determinant of exactly how much a service provider should be willing to spend to acquire or retain that customer.CLV models make several simplifying assumptions and often involve the following inputs:•    Churn rate represents the percentage of customers who end their relationship with a company in a given period;•    Retention rate is calculated by subtracting the churn rate percentage from 100;•    Period/horizon equates to the units of time into which a customer relationship can be divided for analysis. A year is the most commonly used period for this purpose. Customer lifetime value is a multi-period calculation, often projecting three to seven years into the future. In practice, analysis beyond this point is viewed as too speculative to be reliable. The model horizon is the number of periods used in the calculation;•    Periodic revenue is the amount of revenue collected from a customer in a given period (though this is often extended across multiple periods into the future to understand lifetime value), such as usage revenue, revenues anticipated from cross and upselling, and often some weighting for referrals by a loyal customer to others; •    Retention cost describes the amount of money the service provider must spend, in a given period, to retain an existing customer. Again, this is often forecast across multiple periods. Retention costs include customer support, billing, promotional incentives and so on;•    Discount rate means the cost of capital used to discount future revenue from a customer. Discounting is an advanced method used in more sophisticated CLV calculations;•    Profit margin is the projected profit as a percentage of revenue for the period. This may be reflected as a percentage of gross or net profit. Again, this is generally projected across the model horizon to understand lifetime value.A strong focus on managing these inputs can help service providers realize stronger customer relationships and profits, but there are some obstacles to overcome in achieving accurate calculations of CLV, such as the complexity of allocating costs across the customer base. There are many costs that serve all customers which must be properly allocated across the base, and often a simple proportional allocation across the whole base or a segment may not accurately reflect the true cost of serving that customer;  This is made worse by the fragmentation of customer information, which is likely to be across a variety of product or operations groups, and may be difficult to aggregate due to different representations.In addition, there is the complexity of account relationships and structures to take into consideration. Complex account structures may not be understood or properly represented. For example, a profitable customer may have a separate account for a second home or another family member, which may appear to be unprofitable. If the service provider cannot relate the two accounts, CLV is not properly represented and any resultant cancellation of the apparently unprofitable account may result in the customer churning from the profitable one.In summary, if service providers are to realize strong customer relationships and their attendant profits, there must be a very strong focus on data management. This needs to be coupled with analytics that help business managers and those who work in customer-facing functions offer highly personalized solutions to customers, while maintaining profitability for the service provider. It's clear that acquiring new customers is expensive. Advertising costs, campaign management expenses, promotional service pricing and discounting, and equipment subsidies make a serious dent in a new customer's profitability. That is especially true given the rising subsidies for Smartphone users, which service providers hope will result in greater profits from profits from data services profitability in future.  The situation is made worse by falling prices and greater competition in mature markets.Customer acquisition through industry consolidation isn't cheap either. A North American service provider spent about $2,000 per subscriber in its acquisition of a smaller company earlier this year. While this has allowed it to leapfrog to become the largest mobile service provider in the country, it required a total investment of more than $28 billion (including assumption of the acquiree's debt).While many operating cost synergies clearly made this deal more attractive to the acquiring company, this is certainly an expensive way to acquire customers: the cost per subscriber in this case is not out of line with the prices others have paid for acquisitions.While growth by acquisition certainly increases overall revenues, it often creates tremendous challenges for profitability. Organic growth through increased customer loyalty and retention is a more effective driver of profit, as well as a stronger predictor of future profitability. Service providers, especially those in mature markets, are increasingly recognizing this and taking steps toward a creating a more personalized, flexible and satisfying experience for their customers.In summary, the clearest path to profitability for companies in virtually all industries is through customer retention and maximization of lifetime value. Service providers would do well to recognize this and focus attention on profitable customer relationships.

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