Office Depot Stitches Together the Customer Journey Across Multiple Touchpoints

In January 2017, the AURELIUS Group (Germany) acquired the European operations of Office Depot, creating Office Depot Europe. Today, Office Depot Europe is the leading reseller of workplace products and services with customers in 14 countries throughout Europe selling anything from paper, pens and flip charts, to office furniture and computer.

Centralizing Data to Respond to Retail Challenges

Traditionally, Office Depot’s European sales were primarily sourced through an offline, mail-order catalog model drive by telemarketing activities. The company has since moved to a hybrid retail model, combining offline and online shopping, which required a data consolidation strategy that optimized the different channels. Additionally, the company’s myriad of backend systems and disparate supply chain data collected from across Europe had become difficult to analyze.

Using Talend, Office Depot can now ingest data from its vast collection of operational systems. The architecture includes an on-premise Hadoop cluster using Hortonworks, Talend Data Integration, and Data Quality to perform checks and quality control on data before ingesting it into the Hub’s data lake.

Powering Use Cases from Supply Chain to Finance

Integrating online and offline data results in a unified, 360-degree view of the customer and a clear picture of the customer journey. Office Depot can now create more-specific audience segments based on how customers prefer to buy, and tailor strategies to reach the most valuable consumers whether they buy online or in-store. They can compare different offline customer experiences to see how they are influenced by digital ads. Customer service operators have complete information on a customer, so they can talk to them as they know their details.

Office Depot’s data hub approach also provides high-quality data to all back-office functions throughout the organization, including supply chain and finance. Office Depot can now integrate data from the range of supply chain back-end systems in use in various countries, and answer questions such as which distribution center has the most efficient pick-line and why; or which center is in the risky position of having the least amount of stock for the best-selling products.

The post Office Depot Stitches Together the Customer Journey Across Multiple Touchpoints appeared first on Talend Real-Time Open Source Data Integration Software.

Originally Posted at: Office Depot Stitches Together the Customer Journey Across Multiple Touchpoints

Feb 14, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Statistically Significant  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Oct 12, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Future of Public Sector and Jobs in #BigData World #FutureOfData #Podcast by v1shal

>> State of Data Warehouse: A GigaOm Market Landscape Report by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Cisco and Amazon partner on hybrid-cloud approach – MarketWatch Under  Hybrid Cloud

>>
 Lack of analytics skill leads to over 76000 empty positions – People Matters Under  Talent Analytics

>>
 How data science is shaping the modern NHS – New Statesman Under  Data Science

More NEWS ? Click Here

[ FEATURED COURSE]

CS109 Data Science

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Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data managem… more

[ FEATURED READ]

How to Create a Mind: The Secret of Human Thought Revealed

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Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse… more

[ TIPS & TRICKS OF THE WEEK]

Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.

[ DATA SCIENCE Q&A]

Q:Is it better to spend 5 days developing a 90% accurate solution, or 10 days for 100% accuracy? Depends on the context?
A: * “premature optimization is the root of all evils”
* At the beginning: quick-and-dirty model is better
* Optimization later
Other answer:
– Depends on the context
– Is error acceptable? Fraud detection, quality assurance

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom. – Clifford Stoll

[ PODCAST OF THE WEEK]

Want to fix #DataScience ? fix #governance by @StephenGatchell @Dell #FutureOfData #Podcast

 Want to fix #DataScience ? fix #governance by @StephenGatchell @Dell #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Every person in the US tweeting three tweets per minute for 26,976 years.

Sourced from: Analytics.CLUB #WEB Newsletter

The UX of Brokerage Websites

UX Brokerage Websites

UX Brokerage WebsitesBuying and selling stocks has dramatically changed since the advent of the web.

Online brokerages have made trades more accessible, faster, and dramatically cheaper for the retail investor.

Prior to the web, it was common for a full-service broker to charge 2.5% for a stock trade. Now transactions are low-cost commodities with many trades costing less than $10 (or even free).

But the ease of executing trades, access to account data, and the tsunami of financial information available online hasn’t taken away some common challenges for the typical investor.

To understand this experience, we benchmarked the user experience of six top US-based brokerage websites to understand how people are using these services and where the process can be improved.

We benchmarked the desktop and mobile user experiences of the following six brokerage websites:

  • Charles Schwab (schwab.com)
  • E*Trade (us.etrade.com)
  • Fidelity Investments (fidelity.com)
  • Merrill Lynch (ml.com)
  • TD Ameritrade (tdameritrade.com)
  • Vanguard (investor.vanguard.com)

We collected SUPR-Q data, including NPS data, and investigated reasons for using the website, users’ attitudes toward the website, and how well people understood key terms and features. We also supplemented the empirical data with a guideline review using the Calibrated Evaluator’s Guide (CEG) where evaluators score the websites using 107 standardized criteria that have been shown to impact the user experience.

Benchmark Study Details

We recruited 199 participants in August 2018 for a retrospective study where we asked current brokerage account holders to reflect on their most recent experiences using their brokerage company’s website.

Participants in the study answered the 8-item SUPR-Q (including the Net Promoter Score) and questions about their prior experiences.

Quality of the Brokerage Website User Experience: SUPR-Q

The SUPR-Q is a standardized measure of the quality of a website’s user experience and is a good way to gauge users’ attitudes. It’s based on a rolling database of around 150 websites across dozens of industries, including brokerages.

Scores are percentile ranks and tell you how a website experience ranks relative to the other websites. The SUPR-Q provides an overall score as well as detailed scores for subdimensions of trust, usability, appearance, and loyalty.

The scores for the six brokerage websites are well above average, which isn’t too surprising given the financial incentive to make transactions easy and frequent. The average SUPR-Q is at the 92nd percentile (scoring better than 92% of the websites in the database). Merrill Lynch has the lowest SUPR-Q of the group with a score at the 88th percentile. E*Trade and TD Ameritrade lead the group with scores at the 95th percentile. Brokerages as a group also scored higher than the banking websites we recently evaluated.

Usability Scores & Trust

We asked participants to reflect on how easy they thought it was to use and to navigate through their brokerage websites. Fidelity has the highest score in the group (at the 93rd percentile) and Vanguard has the lowest usability score at the 77th percentile.

Not surprisingly, trust scores for this group are also high as users are confident in the companies that handle their money. Participants rated their brokerage companies very high in trust and credibility, and all sites in our study scored between the 92nd and 99th percentiles. This is higher than our group of retail banking websites,  which had trust scores between the 76th and 95th percentiles.

Loyalty/Net Promoter Scores

The brokerage websites have an average NPS of about 41%. While we’ve seen that users are more reluctant to recommend their banking websites (with an average NPS of 16%), the same was not the case with other financial websites. This relatively high likelihood to recommend could also be from the effects of a long-term bull market and 401k retirement accounts are at all time highs[pdf]. U.S. equities are up about 40% in the prior two years leading up to the data collection period in August 2018. That is, people may be more interested in purchasing stocks, viewing mutual fund balances, and recommending to friends because of positive financial experiences (as opposed to actual differences in website changes).

Use of Brokerage Sites & Mobile Apps

As a part of this benchmark, we asked participants how they accessed their brokerage site and the activities they attempted on their last visit and in the last year. Not surprisingly, most participants use a desktop or laptop computer to access their brokerage site. However, on average, about 35% of participants reported using their mobile devices (about equally between the mobile website or mobile app).

The mobile app is most popular for checking portfolios while the desktop site is used for tasks such as investment research and transferring funds. The brokerage mobile app usage is lower than the banking app usage, which showed significantly more mobile app and mobile website usage (46% and 64%, respectively).

Overall, checking account balances is the top reason to visit the brokerage sites (27%), followed by looking at the recent performance of stocks and investments (24%). The top tasks were the same across desktop and mobile. More details on the mobile app experience are available in the report.

The Learning Curve & Understanding Jargon

Users on some of the brokerage sites (Charles Schwab, E*Trade, and Vanguard) mentioned a steep learning curve at the start of use, especially with complex jargon.

  • “To the uncertain investor they have a lot of information that is complex and it can be very overwhelming looking at all of the investing lingo they have on their website. Such as looking at their EFTS, bonds, stocks, 401k, and IRA options—there are a lot of things that you can choose from and getting started is complex.”—Vanguard user
  • “Honestly, my only problems were at the beginning, when I was still learning their site’s features and layout.”—Charles Schwab user
  • “I was a little lost and there wasn’t a lot of guidance. I had to do a lot of my own research to get started.”—E*Trade user

To further assess how participants understood common jargon, we asked participants to describe three common brokerage terms in their own words. A YTD return was the most widely known term (91%), whereas only 64% of participants knew what a prospectus was, and 73% knew what an ETF was. This comprehension was generally higher than the health insurance websites.

Calling Customer Service

As with banking websites, taking calls is expensive and time consuming. On average, 13% of brokerage website respondents reported contacting customer service in the last year (lower than the 20% on banking websites and 30% for health insurance websites). Being able to accomplish the task without calling customer service was one of the key drivers of UX quality; it explained 8% of the variation in SUPR-Q scores.

Also, like the banking websites, the top reason for brokerage respondents to call customer support is login troubles. However, this only accounted for 5% of participants calling in (compared to 29% for banking and 21% for health insurance). Other common reasons for calling in were researching particular products (stocks, bonds, ETFs) (5%), and transferring funds (3%).

Having an online chat system can help reduce the number of calls to customer support and it’s something our respondents were looking for.

“I wish they had a way to contact customer service other than by phone. I prefer using email or chat to contact customer service so that I can have text to refer back to if I’m trying to get help with something, but they only seem to allow phone contact.”—Vanguard user

In the guideline review (more info can be found in the report), Vanguard has the lowest scores on Help & Information partly because it doesn’t have an online chat option.

Product Pages Challenging

While we didn’t conduct a usability evaluation for this industry, to identify problem areas, we did conduct a detailed guideline review using the CEG. The top weakness identified by evaluators on both Merrill Lynch and Fidelity was the organization of the product pages.

For Merrill Lynch, the website lacks a clear path to finding investment products as the information is not organized effectively. The page seems to lack an element hierarchy or sidebar to narrow choices into products like ETFs, Retirement Accounts, Stocks, or Mutual Funds.

 “It’s a little hard to navigate unless you know exactly what you’re looking for. Otherwise, it takes time to navigate and look at all of their options.”—Merrill Lynch user

Image 1 Product pages form Merril Lynch

Image 1: Product pages form the Merrill Lynch website shows a lack of hierarchy on the page.

A similar challenge is on the Fidelity website. While it has a better visual arrangement, the vast number of products are hard to differentiate for the typical retail investor as Fidelity has no apparent overall product comparison page to understand and differentiate the financial products.

Image 2 fidelity investments products

Image 2: There’s no overall investment products page where users can look at products with descriptions from a high-level view.

Image 3 Users must navigate to each individual product page

Image 3: Users must navigate to each individual product page to get any information, which can seem tedious.

The websites that performed well in the product pages category, like E*Trade, have an overview of the investment products with short descriptions and an option to learn more on a detailed product page.

Image 4 E-trade different product options

Image 4: E*Trade makes it easy for users to quickly digest the different product options and see where they want to dive deeper.

Key Drivers

To better understand what’s affecting SUPR-Q scores, we asked respondents to agree/disagree to the following attributes of their brokerage website on a five-point scale from 1=strongly disagree to 5=strongly agree.

  1. My portfolio dashboard is clear and understandable.
  2. I can buy or sell stocks and bonds easily.
  3. The [Brokerage] site is consistent however I use it.
  4. I can find and understand my investment fees and taxes.
  5. I can accomplish what I want to do without calling customer support.
  6. The [Brokerage] site helps me to research and understand investment products.
  7. The [Brokerage] website keeps my information secure.
  8. I can find my account details quickly and securely.

Information Security & Dashboards

The top key driver for the brokerage website user experience is keeping user information safe and secure. “The brokerage website keeps my information secure” explained 16% of the variation in SUPR-Q scores.

The second biggest driver of the perception of the website UX quality is having a clear portfolio dashboard.

Clear dashboards explain 12% of the variation in SUPR-Q scores. Checking portfolio balances is also a top task for this group, so it’s not surprising this is found to be so important to the quality of the brokerage website experience.

The six sites we tested all scored high for the item “My portfolio dashboard is clear and understandable” (Item 1). The lowest score is on TD Ameritrade where only 88% agreed or strongly agreed compared to the highest for Vanguard and Charles Schwab (both 94%).

Summary

An analysis of the user experience of six brokerage websites found:

1. Brokerage websites offer above average UX across devices. Current users find their brokerage website experience well above average, with SUPR-Q scores falling at the 92nd percentile. Merrill Lynch scored the lowest but still above average at 88% while E*Trade and TD Ameritrade lead the pack at the 95th percentile. Mobile apps are widely used in this industry with 35% of respondents reporting using the app or website within the past year.

2. Users need to overcome a steep learning curve and overwhelming product pages. Respondents on half of the brokerage sites (Charles Schwab, E*Trade, and Vanguard) mentioned a learning curve when first using the website. Many said the brokerage sites didn’t lend themselves to novice investors and some jargon was confusing to learn at first. Improved on-boarding and more common language could improve the experience on these sites. The product pages, in particular, may be difficult for less experienced users to differentiate and select.

3. An understandable dashboard and security are key drivers of UX. The top two key drivers of the brokerage website user experience were an understandable portfolio dashboard (12%) and information security (16%). Users want a quick and easy view of their portfolio and they don’t want to worry about their personal information being compromised (they probably also want their portfolio balances to increase).

Full details are available in the downloadable report.

 

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Originally Posted at: The UX of Brokerage Websites

Hilarious 12 tweets from 2012 VP Debate

Hilarious 12 tweets from 2012 VP Debate

From Joe Biden’s smirks, smiles, laughs, sharp elbows and impolite interruptions to Paul Ryan’s focussed and serious attitude, 2012 VP debate was all fun and informative. Both the sides were trying to be reasonable and focussed. Along the way we got an ocean of hilarious tweets that emerged and made us laugh.

Here is my list of 12 tweets that stood out in yesterday’s debate:

Patton Oswalt — Ryan is a nervous Walmart manager. Biden is an irate customer with the receipt, the warranty & he’s friends w/ the store owner. #debate

Josh Branson — BREAKING: Post-debate poll has … Biden interrupting the results. #vpdebate

Bill Maher ‏‪— Hello 9 1 1? There s an old man beating a child on my tv

GuyEndoreKaiser — Tonight’s debate is scheduled for ninety minutes, but Paul Ryan is already claiming he can finish in fifty something.

Kelly Oxford — While Ryan speaks, Biden looks like he’s trying to order a drink at the bar and the bartender is ignoring him. #vpdebate

Jill Morris ‏‪– The VP candidates get to sit because they’re exhausted from standing up for our values. ‪#VPDebate

Paul Ryan Gosling ‏‪– Hey girl, I’m not taking nervous sips of water, I’m drinking every time Biden laughs at me. ‪#vpdebate

Jose Antonio Vargas ‏‪– Before this ‪#VPDebate, ‪@JoeBiden had a Venti macchiato and two cans of Red Bull.

Morgan Murphy ‏‪– Biden’s teeth are so white they’re voting for Romney. ‪#VPDebate

hilarious 12 tweets from 2012 VP debateIndecision ‏‪– Watered-down sanctions are the worst. You need four just to get tipsy. ‪#vpdebate

James Garrett ‏‪– I kind of feel like Joe Biden is Kanye and Paul Ryan is Taylor Swift. ‪#VPDebate

National Review ‏‪– Wait, is Biden yelling at Martha Raddatz right now? I thought he was debating Paul Ryan…‪#VPDebate

Jeffrey Wisenbaugh ‏‪– Biden is yelling less. I think it’s getting closer to his bed time. ‪#sleepy ‪#VPDebate

Chad Schomber ‏‪– To think, all this just to sway 4-6% of undecided voters. And those folks are not watching the ‪#VPdebate

Seth Masket ‏‪– Actually, this is like if Aaron Sorkin wrote an exchange between the Skipper and Gilligan. ‪#vpdebate

*image source RRStar

Originally Posted at: Hilarious 12 tweets from 2012 VP Debate by v1shal

Feb 07, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Correlation-Causation  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Why Is Big Data Is So Big In Health Care? by analyticsweek

>> Top 5 Lessons LEGO story teaches an entrepreneur by v1shal

>> Voices in Data Storage – Episode 3: A Conversation with Leo Leung of Oracle by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Smarter AI: Machine learning without negative data – Science Daily Under  Machine Learning

>>
 Data center security company A10 Networks could be eyeing a sale – DatacenterDynamics Under  Data Center

>>
 How tech and data can spot and stop the quitters – Raconteur Under  Sentiment Analysis

More NEWS ? Click Here

[ FEATURED COURSE]

Deep Learning Prerequisites: The Numpy Stack in Python

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The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence… more

[ FEATURED READ]

The Industries of the Future

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The New York Times bestseller, from leading innovation expert Alec Ross, a “fascinating vision” (Forbes) of what’s next for the world and how to navigate the changes the future will bring…. more

[ TIPS & TRICKS OF THE WEEK]

Data Have Meaning
We live in a Big Data world in which everything is quantified. While the emphasis of Big Data has been focused on distinguishing the three characteristics of data (the infamous three Vs), we need to be cognizant of the fact that data have meaning. That is, the numbers in your data represent something of interest, an outcome that is important to your business. The meaning of those numbers is about the veracity of your data.

[ DATA SCIENCE Q&A]

Q:What do you think about the idea of injecting noise in your data set to test the sensitivity of your models?
A: * Effect would be similar to regularization: avoid overfitting
* Used to increase robustness

Source

[ VIDEO OF THE WEEK]

@DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

 @DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The most valuable commodity I know of is information. – Gordon Gekko

[ PODCAST OF THE WEEK]

@SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

 @SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.

Sourced from: Analytics.CLUB #WEB Newsletter

10 Analytics Blog Posts to Read Before 2019

It should come as no surprise that 2018 was a crazy and explosive year for BI and analytics. As a market that changes on what feels like a daily basis, there’s always a new technology to learn about or a new way to up your analytics game. With such a fast-paced industry, there’s never a lack of things to write about and here at Sisense, we take it upon ourselves to be a one-stop shop for all the analytics content and learning you need.

With that in mind, it’s time for one of my favorite posts of the year! Here’s a wrap up of the top 10 blogs that you shared the most over the past 365 days (based on data, of course).

Let’s get down to it!

10.Gartner Grapevine 2018 Wrap-Up: Day One


This year Gartner Grapevine was jam-packed with impactful sessions and tons of analytics knowledge. In his day one wrap-up our VP Product, Boaz Farkash, talks about his key takeaways for BI leaders to address in order to scale value for their customers. Spoiler alert: it has to do with a fundamental shift in the market of BI turning to AI.

9. Data Science vs. Data Analytics – What’s the Difference?

data science vs. data analytics

Often mistaken for one another, it can be confusing to differentiate between data analytics and data science. Despite the two being interconnected, they provide different results and pursue different approaches. We break down the differences in this post.

8. How To Get More From Your Data With Embedded Analytics


Embedded Analytics, or analytics capabilities bundled into business applications, are making a dramatic foray. If you’re not already using embedded analytics in your organization, this post will help you understand what data you’re leaving on the table.

7. The Unexpected Connections Between Bitcoin and The Dow

bitcoin vs the dow

Part of our new series, GoFigure!, this post and the accompanying analysis report and interactive dashboard dig through data to see if there are any connections between Bitcoin and The Dow. Our findings might surprise you.

6. Going Embedded: The Pillar of Analytics Success

embedded analytics

Sure, you can build your own analytics solution. But, by the time it’s ready to launch your competitors will be way ahead of you. Crafted for the R&D professional, this post breaks down what functionalities you should consider when embedding analytics.

5. Retail Predictive Analytics – How to Use Predictive Analytics in Retail

Retail Analytics

Few fields may be as optimized for predictive analytics compared to retail. In a field where businesses succeed by effectively uncovering what customers will like next, predictive analytics can be the difference between a strong revenue stream and a dwindling sales pool.

4. It’s International Women’s Day…Here Are 17 Badass Women Working in Data

women in data

Although every day is a day to celebrate the women who are shaping the data world, on this year’s International Women’s day we celebrated 17 of the most awesome ladies out there championing the data and analytics cause. If you don’t know these women, you should!

3. The Best Big Data Applications for Financial Services


Today’s financial service providers operate almost entirely online, and every single transaction and penny transmitted creates hundreds of data points. This post details how financial service organizations can find the right data streams and KPIs.

2. Refugee migration: Where are people fleeing from and where are they going?

Refugee Data

Millions of people live as refugees and individuals seeking asylum. Where are they fleeing and where are they seeking sanctuary? In this edition of GoFigure!, our blog post, original analysis, and interactive dashboard take a look at data from the World Bank and reveal insights on the world refugee crisis.

1. The Next Generation of BI is Here… And So Is The 2018 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms


Every year BI vendors wait on pins and needles to see where they’ll be placed in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms. This year’s top blog post announces our exciting position (Visionaries!) as well as what our VP Strategic Growth and Innovation, Guy Levy Yurista, PHD, thinks the Quadrant means for the analytics market.

Thanks for being loyal readers. Happy 2019!

Source: 10 Analytics Blog Posts to Read Before 2019

Why Using the ‘Cloud’ Can Undermine Data Protections

By Jack Nicas

While the increasing use of encryption helps smartphone users protect their data, another sometime related technology, cloud computing, can undermine those protections.

The reason: encryption can keep certain smartphone data outside the reach of law enforcement. But once the data is uploaded to companies’ computers connected to the Internet–referred to as “the cloud”–it may be available to authorities with court orders.
“The safest place to keep your data is on a device that you have next to you,” said Marc Rotenberg, head of the Electronic Privacy Information Center. “You take a bit of a risk when you back up your device. Once you do that it’s on another server.”

Encryption and cloud computing “are two competing trends,” Mr. Rotenberg said. “The movement to the cloud has created new privacy risks for users and businesses. Encryption does offer the possibility of restoring those safeguards, but it has to be very strong and it has to be under the control of the user.”

Apple is fighting a government request that it help the Federal Bureau of Investigation unlock the iPhone of Syed Rizwan Farook, the shooter in the December terrorist attack in San Bernardino, Calif.

The FBI believes the phone could contain photos, videos and records of text messages that Mr. Farook generated in the final weeks of his life.

The data produced before then? Apple already provided it to investigators, under a court search warrant. Mr. Farook last backed up his phone to Apple’s cloud service, iCloud, on Oct. 19.

Encryption scrambles data to make it unreadable until accessed with the help of a unique key. The most recent iPhones and Android phones come encrypted by default, with a user’s passcode activating the unique encryption key stored on the device itself. That means a user’s contacts, photos, videos, calendars, notes and, in some cases, text messages are protected from anyone who doesn’t have the phone’s passcode. The list includes hackers, law enforcement and even the companies that make the phones’ software: Apple and Google.

However, Apple and Google software prompt users to back up their devices on the cloud. Doing so puts that data on the companies’ servers, where it is more accessible to law enforcement with court orders.

Apple says it encrypts data stored on its servers, though it holds the encryption key. The exception is so-called iCloud Keychain data that stores users’ passwords and credit-card information; Apple says it can’t access or read that data.

Officials appear to be asking for user data more often. Google said that it received nearly 35,000 government requests for data in 2014 and that it complies with the requests in about 65% of cases. Apple’s data doesn’t allow for a similar comparison since the company reported the number of requests from U.S. authorities in ranges in 2013.

Whether they back up their smartphones to the cloud, most users generate an enormous amount of data that is stored outside their devices, and thus more accessible to law enforcement.

“Your phone is an incredibly intricate surveillance device. It knows everyone you talk to, where you are, where you live and where you work,” said Bruce Schneier, chief technology officer at cybersecurity firm Resilient Systems Inc. “If you were required to carry one by law, you would rebel.”

Google, Yahoo Inc. and others store users’ emails on their servers. Telecom companies keep records of calls and some standard text messages.
Facebook
Inc. and Twitter Inc. store users’ posts, tweets and connections.

Even Snapchat Inc., the messaging service known for photo and video messages that quickly disappear, stores some messages. The company says in its privacy policy that “in many cases” it automatically deletes messages after they are viewed or expire. But it also says that “we may also retain certain information in backup for a limited period or as required by law” and that law enforcement sometimes requires it “to suspend our ordinary server-deletion practices for specific information.”

Snapchat didn’t respond to a request for comment.

Write to Jack Nicas at jack.nicas@wsj.com
(END) Dow Jones Newswires
02-18-161938ET
Copyright (c) 2016 Dow Jones & Company, Inc.

Originally Posted at: Why Using the ‘Cloud’ Can Undermine Data Protections

Jan 31, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Accuracy  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Startup Movement Vs Momentum, a Classic Dilemma by v1shal

>> Accelerating Discovery with a Unified Analytics Platform for Genomics by analyticsweek

>> Black Friday and Cyber Monday – Analyzing Your Holiday Numbers by analyticsweek

Wanna write? Click Here

[ NEWS BYTES]

>>
 Building a data security strategy – why the industry needs to work together – SC Magazine Under  Data Security

>>
 Global Prescriptive Analytics Market 2018 Growth, Production, Suppliers, Consumption, Region Forecast to 2023 – The West Chronicle (press release) (blog) Under  Prescriptive Analytics

>>
 New Master’s in Data Science Prepares Students for Fastest Growing Field in US – University of New Haven News Under  Data Science

More NEWS ? Click Here

[ FEATURED COURSE]

Artificial Intelligence

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This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances…. more

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

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A black swan is an event, positive or negative, that is deemed improbable yet causes massive consequences. In this groundbreaking and prophetic book, Taleb shows in a playful way that Black Swan events explain almost eve… more

[ TIPS & TRICKS OF THE WEEK]

Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.

[ DATA SCIENCE Q&A]

Q:Explain selection bias (with regard to a dataset, not variable selection). Why is it important? How can data management procedures such as missing data handling make it worse?
A: * Selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved
Types:
– Sampling bias: systematic error due to a non-random sample of a population causing some members to be less likely to be included than others
– Time interval: a trial may terminated early at an extreme value (ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all the variables have similar means
– Data: “cherry picking”, when specific subsets of the data are chosen to support a conclusion (citing examples of plane crashes as evidence of airline flight being unsafe, while the far more common example of flights that complete safely)
– Studies: performing experiments and reporting only the most favorable results
– Can lead to unaccurate or even erroneous conclusions
– Statistical methods can generally not overcome it

Why data handling make it worse?
– Example: individuals who know or suspect that they are HIV positive are less likely to participate in HIV surveys
– Missing data handling will increase this effect as it’s based on most HIV negative
-Prevalence estimates will be unaccurate

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

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[ QUOTE OF THE WEEK]

Data are becoming the new raw material of business. – Craig Mundie

[ PODCAST OF THE WEEK]

@DrewConway on fabric of an IOT Startup #FutureOfData #Podcast

 @DrewConway on fabric of an IOT Startup #FutureOfData #Podcast

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[ FACT OF THE WEEK]

The data volumes are exploding, more data has been created in the past two years than in the entire previous history of the human race.

Sourced from: Analytics.CLUB #WEB Newsletter

What is Customer Loyalty? Part 2: A Customer Loyalty Measurement Framework

True_Test_of_Loyalty_Article_Cover
Read about the development of the RAPID Loyalty Approach. Click image to download the article.

Last week, I reviewed several definitions of customer loyalty (see What is Customer Loyalty? Part 1) that are being used in business today. It appears that definitions fall into two broad categories of loyalty: emotional and behavioral. Emotional loyalty is about how customers generally feel about a company/brand (e.g., when somebody loves, trusts, willing to forgive the company/brand). Behavioral loyalty, on the other hand, is about the actions customers engage in when dealing with the brand (e.g., when somebody recommends, continues to buy, buys different products from the company/brand).  Generally speaking, then, we might think of customer loyalty in the following way:

Customer loyalty is the degree to which customers experience positive feelings for and engage in positive behaviors toward a company/brand.

This week, I will propose a customer loyalty measurement framework to help you understand how to conceptualize and measure customer loyalty. After all, to be of practical value to business, customer loyalty needs to be operationalized (e.g., bringing the concept of loyalty into the measurable world). Once created, these metrics can be used by businesses in a variety of ways to improve marketing, sales, human resources, service and support processes, to name a few. First, I will present two approaches to measuring customer loyalty.

Measurement Approaches

There are two general approaches to measuring customer loyalty: 1) objective approach and 2) subjective (self-reported) approach.

  1. Objective measurement approach include system-captured metrics that involve hard numbers regarding customer behaviors that are beneficial to the company.  Data can be obtained from historical records and other objective sources, including purchase records (captured in a CRM system) and other online behavior. Examples of objective loyalty data include computer generated records of “time spent on the Web site,” “number of products/services purchased” and “whether a customer renewed their service contract.”
  2. Subjective measurement approach involves “soft” numbers regarding customer loyalty. Subjective loyalty metrics include customers’ self-reports of their feelings about the company and behavior toward the company. Examples of subjective loyalty data include customers’ ratings on standardized survey questions like, “How likely are you to recommend <Company> to your friends/colleagues?”, “How likely are you to continue using <Company>?” and “Overall, how satisfied are you with <Company>?”
Figure 1. Companies with higher levels of customer loyalty experience accelerated business growth.
Figure 1. Companies with higher levels of customer loyalty experience accelerated business growth.

While I present two distinct customer loyalty measurement approaches, there are likely gradients of the subjective measurement approach. On one end of the subjective continuum, ratings are more perceptually based (what is typically used today) and, on the other end of the subjective continuum, ratings are more behaviorally based that more closely approximate the objective measurement approach. The objective/subjective dichotomy, however, provides a good framework for discussing measurement approaches.

Before continuing on the measurement of customer loyalty, it is useful to first put customer loyalty in context of how it impacts your business. Generally speaking, companies who have higher levels of customer loyalty also experience faster business growth (See Figure 1).  While I argue elsewhere that the customer loyalty metrics you use depend on your business needs and the types of behaviors in which you want your customers to engage, understanding how customer loyalty impacts business growth will help you determine the types of loyalty metrics you need.

Three Ways to Grow a Business: Retention, Advocacy, Purchasing

Figure 2. Business models illustrate that there are three ways to grow your business. Top Model is from Reichheld,1996; Bottom model is from Gupta, et al. 2006. Click image to enlarge.

Let us take a look at two business models that incorporate customer loyalty as a key element of business growth and company value (See Figure 2). The top graph is from Fred Reichheld and illustrates the components that drive company profit. Of the components that contribute to company profits, three of them reflect customer loyalty: retention (measured in years), advocacy (measured as referrals) and expanding purchasing (measured through increased purchases).

Similarly to Reichheld’s model, Gupta’s Customer Lifetime Value model focuses on customer loyalty as a mediator between what a company does (e.g., business programs) and the company value (see graph on the bottom of Figure 2). Again, customer loyalty plays a central role in understanding how to increase firm value. Improving 1) retention behaviors, 2) advocacy behaviors and 3) purchasing behaviors will increase company value.

Customer Loyalty Measurement Framework: Operationalizing Customer Loyalty

Our loyalty metrics need to reflect those attitudes and behaviors that will have a positive impact on company profit/value. Knowing that customer loyalty impacts company profits/value in three different ways, we can now begin to operationalize our customer loyalty measurement strategy. Whether we use an objective measurement approach or a subjective measurement approach, our customer loyalty metrics need to reflect retention loyalty, advocacy loyalty and purchasing loyalty.  Here are a few objective customer loyalty metrics businesses can use:

  • Churn rates
  • Service contract renewal rates
  • Number/Percent of new customers
  • Usage metrics – frequency of use/visits, page views
  • Sales records – number of products purchased

Here are a few subjective customer loyalty metrics businesses can use:

  • likelihood to renew service
  • likelihood to leave
  • overall satisfaction
  • likelihood to recommend
  • likelihood to buy different/additional products
  • likelihood to expand usage
Customer Loyalty Measurement Framework
Figure 3. Customer Loyalty Measurement Framework: You can measure emotional (e.g., advocacy) and behavioral loyalty (e.g., retention and purchasing) using different measurement approaches (e.g., subjective and objective).

Figure 3 illustrates how these loyalty metrics fit into the larger customer loyalty measurement framework of loyalty types and measurement approaches. Each of the customer loyalty metrics above falls into one of the four quadrants of Figure 3.

It is important to point out that the subjective measurement approach is not synonymous with emotional loyalty. Survey questions can be used to measure both emotional loyalty (e.g., overall satisfaction) as well as behavioral loyalty (e.g., likelihood to leave, likelihood to buy different products). In my prior research on measuring customer loyalty, I found that you can reliably and validly measure the different types of loyalty using survey questions.

Looking at the lower left quadrant of Figure 3, you see that there are different ways to measure advocacy loyalty. While you might question why “likelihood to recommend” and “likelihood to buy same product” are measuring advocacy loyalty, research shows that they are more closely associated with emotional rather than behavioral loyalty. Specifically, these questions are highly related to “overall satisfaction.” Also, factor analysis of several loyalty questions show that these three subjective metrics (sat, recommend, buy) loaded on the same factor. This pattern of results suggests that these questions really are simply measures of the customers’ emotional attachment to the company/brand.

I have include the metrics of “level of trust,” “willingness to consider” and “willingness to forgive” as emotional loyalty metrics due to their strong emotional nature. Based on what I know about how customers rate survey questions. I suspect these questions would essentially provide the same information as the other questions in the quadrant. That, however, is an empirical question that needs to be tested.

Subjective vs. Objective Measurement Approach

While companies have both objective and subjective measurement approaches at their disposal, surveys remain a popular approach to measuring customer loyalty. In fact, surveys remain the cornerstone of most customer experience management programs.

Companies use customer surveys to measure customer loyalty rather than solely relying on objective metrics of customer loyalty because: 1) Customer surveys allow companies to quickly and easily gauge levels of customer loyalty, 2) Customer surveys can provide rich information about the customer experience that can be used to more easily change organizational business process and 3) Customer surveys provide a forward look into customer loyalty.

RAPID Loyalty Approach

I have conducted research on the subjective approach to measuring customer loyalty over the past few years. Based on the results of this research, I developed the RAPID Loyalty approach that supports the three ways businesses can grow their business: Retention, Advocacy and Purchasing loyalty.  The RAPID loyalty approach includes three metrics, each assessing one of three components of customer loyalty:

  • Retention Loyalty Index (RLI): Degree to which customers will remain as customers or not leave to competitors; contains loyalty questions like: renew service contract, leave to competitor (reverse coded).
  • Advocacy Loyalty Index (ALI): Degree to which customers feel positively toward/will advocate your product/service/brand; contains loyalty questions like: overall satisfaction, recommend, buy again.
  • Purchasing Loyalty Index (PLI): Degree to which customers will increase their purchasing behavior; contains loyalty questions like: buy additional products, expand use of product throughout company.

Each of the RAPID loyalty indices has excellent measurement properties; that is, each index is a reliable, valid and useful indicator of customer loyalty and is predictive of future business growth. Specifically, in a nationwide study asking over 1000 customers (See Figure 4) about their current network operator, each loyalty index was predictive of different business growth metrics across several US network operators (Alltel, AT&T, Sprint/Nextel, T-Mobile, and Verizon):

Different customer loyalty metrics predict different types of business growth.
Figure 4. The RAPID Loyalty indices (ALI, PLI and RLI), each predict different types of business growth.
  • RLI was the best predictor of future churn rate
  • ALI was a good predictor of new customer growth
  • PLI was the best predictor of Average Revenue per User (ARPU) growth

The bottom line is that there are three general ways to grow your business: keep customers coming back (retention), recommending you to their friends/family (advocacy) and expanding their relationship with you by buying different products/services (purchasing). To increase company profits/firm value, it is imperative that you measure and optimize each type of customer loyalty. Falling short on one type of customer loyalty will have a deleterious effect on company profit/firm value.

State of Customer Loyalty Measurement

In an informal online poll taken during a talk, Asking the Right CX Questions (part of CustomerThink’s Customer Experience Summit 2011), I asked participants about their CEM program loyalty metrics. While a little over 75% of the respondents said their company uses advocacy loyalty measures, only a third of the respondents indicated that their company uses purchasing loyalty measures (33%) and retention loyalty measures (30%).

Benefits of Measuring Different Types of Customer Loyalty

It appears that most companies’ customer loyalty measurement approach is insufficient. Companies who measure and understand different types of customer loyalty and how they are impacted by the customer experience have several advantages over companies who narrowly measure customer loyalty:

  • Target solutions to optimize different types of customer loyalty. For example, including retention loyalty questions (e.g., “likelihood to quit”) and a purchasing loyalty questions (e.g., “likelihood to buy different”) can help companies understand why customers are leaving and identify ways to increase customers’ purchasing behavior, respectively.
  • Identify key performance indicators (KPIs) for each type of customer loyalty. Identification of different KPIs (key drivers of customer loyalty) helps companies ensure they are monitoring all important customer experience areas. Identifying and monitoring all KPIs helps ensure the entire company is focused on matters that are important to the customer and his/her loyalty.
  • Obtain more accurate estimates of the Return on Investment (ROI) of improvement initiatives. Because ROI is the ratio of additional revenue (from increased loyalty) to cost (of initiative), the ROI of a specific improvement opportunity will depend on how the company measures customer loyalty. If only advocacy loyalty is measured, the estimate of ROI is based on revenue from new customer growth. When companies measure advocacy, purchasing and retention loyalty, the estimate of ROI is based on revenue from new and existing customer growth.

The primary goal of CEM is to improve customer loyalty. Companies that define and measure customer loyalty narrowly are missing out on opportunities to fully understand the impact that their CEM program has on the company’s bottom line. Companies need to ensure they are comprehensively measuring all facets of customer loyalty. A poor customer loyalty measurement approach can lead to sub-optimal business decisions, missed opportunities for business growth and an incomplete picture of the health of the customer relationship.

Summary

Customer loyalty is a very fuzzy concept. With various definitions of customer loyalty floating around in the literature, it is difficult to know what one is talking about when one uses the term, “customer loyalty.” I tried to clarify the meaning of customer loyalty by consolidating different customer loyalty definitions into two general customer loyalty types: emotional loyalty and behavioral loyalty.

Additionally, I discussed two measurement approaches that companies can utilize to assess customer loyalty: objective measurement approach and subjective measurement approach.

Finally, I offered a customer loyalty measurement framework to help companies think about customer loyalty more broadly and help them identify customer loyalty metrics to help them better measure and manage different types of business growth: acquiring new customers (Advocacy), retaining existing customers (Retention) and expanding the relationship of existing customers (Purchasing).

One of the biggest limitations in the field of customer experience management is the lack of a coherent set of clearly defined variables with instruments to effectively measure those variables. When we talk about customer loyalty, we talk past each other rather than to each other. To advance our field and our understanding of what procedures and methods work, we need precision in ideas and words. One way to start is to clearly define and measure constructs like customer loyalty. While customer loyalty is one such vaguely defined and measured variable, our field is full of others (e.g., customer engagement, employee engagement). I hope I was able to provide some clarification on the notion of customer loyalty, both in its meaning and its measurement.

Originally Posted at: What is Customer Loyalty? Part 2: A Customer Loyalty Measurement Framework

Jan 24, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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 The Future of US Data Security in the Wake of GDPR – Innovation & Tech Today (satire) (press release) (blog) Under  Data Security

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Machine Learning

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6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending … more

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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

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Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored f… more

[ TIPS & TRICKS OF THE WEEK]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q:How do you handle missing data? What imputation techniques do you recommend?
A: * If data missing at random: deletion has no bias effect, but decreases the power of the analysis by decreasing the effective sample size
* Recommended: Knn imputation, Gaussian mixture imputation

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[ VIDEO OF THE WEEK]

Big Data Introduction to D3

 Big Data Introduction to D3

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[ QUOTE OF THE WEEK]

He uses statistics as a drunken man uses lamp posts—for support rather than for illumination. – Andrew Lang

[ PODCAST OF THE WEEK]

@EdwardBoudrot / @Optum on #DesignThinking & #DataDriven Products #FutureOfData #Podcast

 @EdwardBoudrot / @Optum on #DesignThinking & #DataDriven Products #FutureOfData #Podcast

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[ FACT OF THE WEEK]

100 terabytes of data uploaded daily to Facebook.

Sourced from: Analytics.CLUB #WEB Newsletter