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

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

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

The convoluted world of data scientist

The convoluted world of data scientist.
The convoluted world of data scientist.

Data scientists are not dime a dozen and they are not in abundance as well. Buzz around bigdata has produced a job category that is not only confusing but has been costing companies a lot in their stride to look through the talent pool to dig for a so called data scientist. So, what exactly is the problem and why are we suddenly seeing a lot of data scientist emerging from nowhere with very different skill sets? To understand this we need to understand the bigdata phenomena.

With emergence of big data user companies like Google, Facebook, yahoo etc. and their amazing contribution to open source, new platforms have been developed to process too much data using commodity hardware in fast and yet, cost efficient ways. Now with that phenomenon, every company wants to get savvier when it comes to managing data to gain insights and ultimately building competitive edge over their competitors. But companies are used to understanding small pieces of data using their business analysts. But talk about more data and more tools. Who will fit in? So, they started on lookout for special breed of professional that have the capability to deal with big data and it’s hidden insights.

So, where is the problem here? The problem lies in the fact that only one job title emerged from this phenomenon- data scientist. The professionals who are currently practicing some data science via business analysis, data warehousing or data designing jumped on the bandwagon grabbing the title of the data scientist. What is interesting here is that data scientist job as explained above does not deserve a single job description so it should be handled accordingly. It was never a magical job title that has all the answers for any data curious organization, to be able to understand, develop and manage a data project.

Before we go into what companies should do, let’s reiterate what is a data scientist. As the name suggest, it is something to do with data and scientist. Which means it should include job description that has done some data engineering, data automation, and scientific computing with a hint of business capabilities. If we extrapolate, we are looking at a professional with computer science degree, doctorate in statistical computing and MBA in business. What would be luck in finding that candidate and by-the-way, they should have some industry domain expertise as well. What is the likelihood that such a talent exists? Rare. But, even if they are in abundance, companies should tackle this problem at much granular and sustainable scale. And one more thing to note here is that no two data scientist job requirements are the same. This means that your data scientist requirement could be extremely different from what anyone else is looking for in a data scientist. So, why should we have one title to cater to such a diverse category?

So, what should companies do? First it is important to understand that companies are building data scientists’ capabilities and should not be hiring the herd of data scientists. This means that companies/ hiring managers should understand that they are not looking for a particular individual but a team as a solution. It is important for businesses to clearly articulate those magic skillsets that their so-called data scientist should carry. Following this drill, companies should split the skillset into categories, Data analytics, Business analyst, data warehousing professionals, software developer, and data engineers to name a few. Finding a common island where business analysts, statistical computing modelers and data engineers work in harmony to address a system that handles big data is a great start. Think of it as putting together a central data office. Huh! another buzz word. Don’t worry; I will go into more details in the follow-up blogs. Think of it as a department where business, engineering and statistician work together on a common objective. Data science is nothing but an art to find value in lots of data. So, big-data is to build capability to parse/analyze lots of data. So, business should work through their laundry list of skillset. First identify internal resources that could accommodate that list. Following this, companies should form a hard matrix structure to prove the idea of set of people working together as a data scientist. BTW I am not saying that you need one individual from each category, but, together the team should have all the skills mentioned above

One important take away for companies is to understand that the moment they came across a so called data scientist, it is important to understand which side of data scientist the talent represents. Placing that talent in their respective silo will help provide a clearer vision when it comes to understanding the talent and understanding the void that could stay intact if the resources are not filled accordingly. So, living in this convoluted world of data scientist is hard and tricky. Having some chops into understanding data science as a talent, companies could really play the big data talent game to their advantage and lure some cutting edge people and grow sustainably.

Source

Why Is Big Data an Advantage for Your Business

Big Data is a large amount of raw data. The organizations collect, store and analyze data by using numerous ways in order to increase their productivity, adeptness and take better decisions. It can be classified into two major forms- structured and unstructured. Structured Data is easy to manage- analysis and organization is quite simple. On the other hand, unstructured data is a bit tough to handle- analysis takes time as it uses a variety of formats and cannot be easily interpreted using traditional data models and processes.

Unstructured Data is much harder to analyze and uses a variety of formats. It’s not just the big brands that can use big data to make data-driven decisions for their organization. Even the small businesses can reap the benefits too.

“Big data is defined as very large datasets that can be analyzed computationally to reveal patterns, trends, and associations – especially in connection with human behavior and interactions. A big data revolution has arrived with the growth of the Internet, wireless networks, smartphones, social media and other technology.”

Big Data Business Benefits

When the data volume continues to grow, its potential for business grows too. The reason behind is when Big Data management solutions evolve, it allows the businesses to convert raw data into relevant trends, predictions and projections with unprecedented accuracy. The businesses that use Big Data analytic solutions greatly benefit. It helps businesses gain more insights which help in driving smart decision-making.

Here are a few benefits of using Big Data- 

  1. Boosts business efficiency– Using Big Data analytics will give a boost to your business efficiency. It will help enhance the productivity and help you make right decisions which will improve your business efficiency. By using tools such as Google Earth, Google Maps, and social media, you can complete a number of tasks right at your desk without spending on travel. Moreover, these tools are relatively easy to use and do not need much of technical knowledge.
  2. Cost-saving– In a recent article published by Tech Cocktail- “it was illustrated how Twiddy& Company Realtors cut their operating costs by 15 percent. The company compared maintenance charges for contractors against the average of its other vendors. Through this process, the company identified and eliminated invoice-processing errors and automated service schedules.”
  3. Improves pricing– With the help of business intelligence tools that have been tried and tested, a business can evaluate its finances and get a clear picture of where it stands in the market. This will help a business strategize new policies and techniques to expand its operations and improve pricing.
  4. Reduces time– High speed tools such as Hadoop and in-memory analytics will help identify the new sources of data. Once you are able to analyze the data in a quick time, it will save your time and lets you make quick decisions, the decisions that are right for your business.
  5. Understand market value– Big data helps you get a clearer picture of the market conditions. For instance- By analyzing purchasing behavior of the customers’ like which product sells the most and is liked by customers. This will give you an idea on which products meet customers’ satisfying needs’ and the products that need an area of improvement. Using this information will help in improving the product and lets you stay ahead of the competitors.
  6. Hire right employees– Using Big Data, the recruitment companies will scan candidate’s resumes and LinkedIn profiles that will help businesses find the right employees. The hiring process is no longer based on what candidate looks like on paper. So, using Big Data will help you find the right employees.

Here are a few facts that will help you understand why you need Big Data-

  1. 94% of marketing professionals said personalization of customer experienceis verycrucial.
  2. $30 million in annual savings by influencing social media data in claims andfraud
  3. By 2020, 66% of banks will have blockchainin commercial production and at scale.
  4. Organizations will rely on smart datamore than big data.
  5. Machine-to-Human (M2H)enterprise interactions will be humanized by up to 85% by 2020.
  6. Businesses are investing 300% more in Artificial Intelligence (AI)in 2017 than they did in 2016.
  7. 25% growth rate in the emergence of speech as a relevant source of unstructured data.  
  8. Right to be Forgotten (R2BF)will be in-focus globally regardless of data source.
  9. The 43% of customer service teams that don’t have real-time analyticswill continue to shrink.  
  10. By 2020, the Augmented Reality (AR)market will reach $90 billion compared to Virtual Reality’s $30 billion.

Final Thoughts

The use of Big Data in business operations is vital- it will not only help in business growth but allows the companies to stand above their competitors. Big Data provides an enormous amount of information about different products, services, consumers, suppliers and more which help a business understand the process and optimize its operations to accomplish business objectives.

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Source: Why Is Big Data an Advantage for Your Business

Best Practices For Building Talent In Analytics

Best practice pinned on noticeboard

Companies across all industries depend more and more on analytics and insights to run their businesses profitably. But, attracting, managing and retaining talented personnel to execute on those strategies remains a challenge. This is not the case for consumer products heavyweight The Procter & Gamble Company (P&G), which has been at the top of its analytics game for 50 years now.

During the 2014 Retail/Consumer Goods Analytics Summit, Glenn Wegryn, retired associate director of analytics for P&G, shared best practices for building the talent capabilities required to ensure success. A leadership council is in charge of sharing analytics best practices across P&G — breaking down silos to make sure the very best talent is being leveraged to solve the company’s most pressing business issues.

So, what are the characteristics of a great data analyst and where can you find them?

“I always look for people with solid quantitative backgrounds because that is the hardest thing to learn on the job,” said Wegryn.

Combine that with mature communication skills and a talent for business acumen and you’ve got the perfect formula for a great data analyst.

When it comes to sourcing analytics, Wegryn says companies have an important strategic decision to make: Do you build it internally, leveraging resources like consultants and universities? Do you buy it from a growing community of technology solution providers? Or, do you adopt a hybrid model?

“Given the explosion of business analytics programs across the country, your organization should find ample opportunities to tap into those resources,” advised Wegryn.

To retain and nurture your organization’s business analysts, Wegryn recommended creating a career path that grows and the importance of encouraging talented personnel internally until they reach a trusted CEO advisory role.

Wegryn also shared key questions an organization should ask to unleash the value of analytics, and suggested that analytics should always start and end with a decision.

“You make a decision in business that leads to action that gleans insights that leads to another decision,” he said. “While the business moves one way, the business analyst works backward in a focused, disciplined and controlled manner.”

Perhaps most importantly, the key to building the talent capability to ensure analytics success came from P&G’s retired chairman, president and CEO Bob McDonald: “… having motivation from the top helps.”

Wegryn agreed: “It really helps when the person at the top of the chain is driven on data.”

The inaugural Retail & Consumer Goods Analytics Summit event was held September 11-12, 2014 at the W Hotel in San Diego, California. The conference featured keynotes from retail and consumer goods leaders, peer-to-peer exchanges and relationship building.

Article originally appeared HERE.

Originally Posted at: Best Practices For Building Talent In Analytics by analyticsweekpick

Avoiding a Data Science Hype Bubble

In this post, Josh Poduska, Chief Data Scientist at Domino Data Lab, advocates for a common taxonomy of terms within the data science industry. The proposed definitions enable data science professionals to cut through the hype and increase the speed of data science innovation. 

Introduction

The noise around AI, data science, machine learning, and deep learning is reaching a fever pitch. As this noise has grown, our industry has experienced a divergence in what people mean when they say “AI”, “machine learning”, or “data science”. It can be argued that our industry lacks a common taxonomy. If there is a taxonomy, then we, as data science professionals, have not done a very good job of adhering to it. This has consequences. Two consequences include the creation of a hype-bubble that leads to unrealistic expectations and an increasing inability to communicate, especially with non-data science colleagues. In this post, I’ll cover concise definitions and then argue how it is vital to our industry that we be consistent with how we define terms like “AI”.

Concise Definitions

  • Data Science: A discipline that uses code and data to build models that are put into production to generate predictions and explanations.
  • Machine Learning: A class of algorithms or techniques for automatically capturing complex data patterns in the form of a model.
  • Deep Learning: A class of machine learning algorithms that uses neural networks with more than one hidden layer.
  • AI: A category of systems that operate in a way that is comparable to humans in the degree of autonomy and scope.

Hype

Our terms have a lot of star power. They inspire people to dream and imagine a better world which leads to their overuse. More buzz around our industry raises the tide that lifts all boats, right? Sure, we all hope the tide will continue to rise. But, we should work for a sustainable rise and avoid a hype bubble that will create widespread disillusionment if it bursts.

I recently attended Domino’s rev conference, a summit for data science leaders and practitioners. I heard multiple leaders seeking advice on how to help executives, mid-level managers, and even new data scientists have proper expectations of data science projects without sacrificing enthusiasm for data science. Unrealistic expectations slow down progress by deflating the enthusiasm when projects yield less than utopian results. They also make it harder than it should be to agree on project success metrics and ROI goals.

The frequent overuse of “AI” when referring to any solution that makes any kind of prediction has been a major cause of this hype. Because of frequent overuse, people instinctively associate data science projects with near perfect human-like autonomous solutions. Or, at a minimum, people perceive that data science can easily solve their specific predictive need, without any regard to whether their organizational data will support such a model.

Communication

Incorrect use of terms also gums up conversations. This can be especially damaging in the early planning phases of a data science project when a cross-functional team assembles to articulate goals and design the end solution. I know a data science manager that requires his team of data scientists to be literally locked in a room for an our hour with business leaders before he will approve any new data science project. Okay, the door is not literally locked, but it is shut, and he does require them to discuss the project for a full hour. They’ve seen a reduction in project rework as they’ve focused on early alignment with business stakeholders. The challenge of explaining data science concepts is hard enough as it is. We only make this harder when we can’t define our own terms.

I’ve been practicing data science for a long time now. I’ve worked with hundreds of analytical leaders and practitioners from all over the world. Since AI and deep learning came on the scene, I’ve increasingly had to pause conversations and ask questions to discover what people really mean when they use certain terms. For example, how would you interpret these statements which are based on conversations I’ve had?

  • “Our goal is to make our solution AI-driven within 5 years.”
  • “We need to get better at machine learning before we invest in deep learning.”
  • “We use AI to predict fraud so our customers can spend with confidence.”
  • “Our study found that organizations investing in AI realize a 10% revenue boost.”

Confusing, right?

One has to ask a series of questions to be able to understand what is really going on.

The most common term-confusion I hear is when someone talks about AI solutions, or doing AI, when they really should be talking about building a deep learning or machine learning model. It seems that far too often the interchange of terms is on purpose, with the speaker hoping to get a hype-boost by saying “AI”. Let’s dive into each of the definitions and see if we can come to an agreement on a taxonomy.

Data Science

First of all, I view data science as a scientific discipline, like any other scientific discipline. Take biology, for example. Biology encompasses a set of ideas, theories, methods, and tools. Experimentation is common. The biological research community is continually adding to the discipline’s knowledge base. Data science is no different. Practitioners do data science. Researchers advance the field with new theory, concepts, and tools.

The practice of data science involves marrying code (usually some statistical programming language) with data to build models. This includes the important and dominant initial steps of data acquisition, cleansing, and preparation. Data science models usually make predictions (e.g., predict loan risk, predict disease diagnosis, predict how to respond to a chat, predict what objects are in an image). Data science models can also explain or describe the world for us (e.g., which combination of factors are most influential in making a disease diagnosis, which customers are most similar to each other and how). Finally, these models are put into production to make predictions and explanations when applied to new data. Data science is a discipline that uses code and data to build models that are put into production to generate predictions and explanations.

It can be difficult to craft a definition for data science while, at the same time, distinguishing it from statistical analysis. I came to the data science profession via educational training in math and statistics as well as professional experience as a statistician. Like many of you, I was doing data science before it was a thing.

Statistical analysis is based on samples, controlled experiments, probabilities, and distributions. It usually answers questions about likelihood of events or the validity of statements. It uses different algorithms like t-test, chi-square, ANOVA, DOE, response surface designs, etc. These algorithms sometimes build models too. For example, response surface designs are techniques to estimate the polynomial model of a physical system based on observed explanatory factors and how they relate to the response factor.

One key point in my definition is that data science models are applied to new data to make future predictions and descriptions, or “put into production”. While it is true that response surface models can be used on new data to predict a response, it is usually a hypothetical prediction about what might happen if the inputs were changed. The engineers then change the inputs and observe the responses that are generated by the physical system in its new state. The response surface model is not put into production. It does not take new input settings by the thousands, over time, in batches or streams, and predict responses.

My data science definition is by no means fool-proof, but I believe putting predictive and descriptive models into production starts to capture the essence of data science.

Machine Learning

Machine learning as a term goes back to the 1950s. Today, it is viewed by data scientists as a set of techniques that are used within data science. It is a toolset or a class of techniques for building the models mentioned above. Instead of a human explicitly articulating the logic for a model, machine learning enables computers to generate (or learn) models on their own. This is done by processing an initial set of data, discovering complex hidden patterns in that data, and capturing those patterns in a model so they can be applied later to new data in order to make predictions or explanations. The magic behind this process of automatically discovering patterns lies in the algorithms. Algorithms are the workhorses of machine learning. Common machine learning algorithms include the various neural network approaches, clustering techniques, gradient boosting machines, random forests, and many more. If data science is a discipline like biology, then machine learning is like microscopy or genetic engineering. It is a class of tools and techniques with which the discipline is practiced.

Deep Learning

Deep learning is the easiest of these terms to define. Deep learning is a class of machine learning algorithms that uses neural networks with more than one hidden layer. Neural networks themselves date back to the 1950s. Deep learning algorithms have recently become very popular starting in the 1980s, with a lull in the 1990s and 2000s, followed by a revival in our decade due to relatively small tweaks in the way the deep networks were constructed that proved to have astonishing effects. Deep learning can be applied to a variety of use cases including image recognition, chat assistants, and recommender systems. For example, Google Speech, Google Photos, and Google Search are some of the original solutions built using deep learning.

AI

AI has been around for a long time. Long before the recent hype storm that has co-opted it with buzzwords. How do we, as data scientists, define it? When and how should we use it? What is AI to us? Honestly, I’m not sure anyone really knows. This might be our “emperor has no clothes” moment. We have the ambiguity and the resulting hype that comes from the promise of something new and unknown. The CEO of a well known data science company was recently talking with our team at Domino when he mentioned “AI”. He immediately caught himself and said, “I know that doesn’t really mean anything. I just had to start using it because everyone is talking about it. I resisted for a long time but finally gave in.”

That said, I’ll take a stab at it: AI is a category of systems that people hope to create which have the defining characteristic that they will be comparable to humans in the degree of autonomy and scope of operation.

To extend our analogy, if data science is like biology and machine learning is like genetic engineering, then AI is like disease resistance. It’s the end result, a set of solutions or systems that we are striving to create through the application of machine learning (often deep learning) and other techniques.

Here’s the bottom line. I believe that we need to draw a distinction between techniques that are part of AI solutions, AI-like solutions, and true AI solutions. This includes AI building blocks, solutions with AI-ish qualities, and solutions that approach human autonomy and scope. These are three separate things. People just say “AI” for all three far too often.

For example,

  • Deep learning is not AI. It is a technique that can be used as part of an AI solution.
  • Most data science projects are not AI solutions. A customer churn model is not an AI solution, no matter if it used deep learning or logistic regression.
  • A self driving car is an AI solution. It is a solution that operates with complexity and autonomy that approaches what humans are capable of doing.

Remember those cryptic statements from above? In each case I asked questions to figure out exactly what was going on under the hood. Here is what I found.

  • An executive said: “Our goal is to make our solution AI-driven within 5 years.”
    The executive meant: “We want to have a couple machine learning models in production within 5 years.”
  • A manager said: “We need to get better at machine learning before we invest in deep learning.”
    The manager meant: “We need to train our analysts in basic data science principles before we are ready to try deep learning approaches.”
  • A marketer said: “We use AI to predict fraud so our customers can spend with confidence.”
    The marketer meant: “Our fraud score is based on a logistic regression model that has been working well for years.”
  • An industry analyst said: “Our study found that organizations investing in AI realize a 10% revenue boost.”
    The industry analyst meant: “Organizations that have any kind of predictive model in production realize a 10% revenue boost.”

The Ask

Whether you 100% agree with my definitions or not, I think we can all agree that there is too much hype in our industry today, especially around AI. Each of us has seen how this hype limits real progress. I argue that a lot of the hype is from misuse of the terms of data science. My ask is that, as data science professionals, we try harder to be conscious of how we use these key terms, and that we politely help others who work with us learn to use these terms in the right way. I believe that the quicker we can iterate to an agreed-upon taxonomy and insist on adherence to it, the quicker we can cut through hype and increase our speed of innovation as we build the solutions of today and tomorrow.

The post Avoiding a Data Science Hype Bubble appeared first on Data Science Blog by Domino.

Source: Avoiding a Data Science Hype Bubble by analyticsweek

Investment banks recruit for rise of big data analytics

Big data, or the large pools of data that can be captured, processed and then analysed, is now reaching into every sector and function of the global economy.

Financial services businesses, including the investment banks, generate and store more data than any other business in any other sector – broadly because it is such a transaction-heavy industry, often driven by models and algorithms.

Despite accumulating a wealth of information on capital market transactions, trades, financial markets, and other client and market data, the investment banks have been slower to embrace today’s definition of big data analytics than many consumer retail businesses, technology businesses, and even retail banking.

Organisations such as Amazon, Google, eBay and the UK’s big four supermarkets have been using big data analytics for many years, tracking consumer behaviour to suggest potential new products to consumers and develop customer loyalty schemes. Where investment banks have used big data, it has often been restricted to tracking individual sub-categories of asset classes.

The UK’s high-street banks have also been increasingly active in this area, using data analytics to study purchasing patterns, social media and location data, in order to tailor products and associated marketing material to individual customers’ needs.

Using big data analytics to increase profitability

The investment banks are now looking at how they can use big data to do what they do better, faster and more efficiently.

Senior executives at the banks want to enhance how they use data to raise profitability, map out markets and company-wide exposures, and ultimately win more deals.

While banks have, for many years, used data and value at risk modelling to measure and quantify the level of financial risk in a portfolio of assets, the fundamental difference with big data is that it has become an established standalone functional department rather than a series of small subsets of internal business units.

Big-data teams are now taking on the role of an influential internal consultancy, communicating to senior executives key insights on how to improve profitability.

Another key difference is that the banks are now not only analysing structured data, such as market or trading data, but also unstructured data, which can include sources such as tweets, blogs, Facebook posts and marketing material. This is now collected and recorded from a bank’s customers or clients – a significant shift from how data used to be captured.

Using large amounts of both structured and unstructured data and market data, the investment banks are now accurately modelling the outcome of investment decisions, and getting real-time insights into client demand.

Big data is also a fundamental element of risk-profiling for the banks, enabling data analysts to immediately assess the impact of the escalation in geopolitical risk on portfolios and their exposure to specific markets and asset classes. Specifically, banks have now built systems that will map out market-shaping past events in order to identify future patterns.

We are also seeing the banks using big data to analyse the effectiveness of their deals, looking for insights into which trades they did or did not win on a client-by-client basis.

But despite the recent growth in the use of big data by the banks, key challenges remain.

Unlike retail and technology giants such as Google, Facebook and Amazon, or any new startup or fintech company, the IT and data systems at most banks were not originally constructed to analyse structured and unstructured data. Updating and remodelling entire IT and data systems to accommodate the systems needed to generate a deep analysis of a bank’s data is time-consuming and costly.

Banks that have merged or acquired other banks or financial services businesses are likely to face even more complex issues when incorporating and updating legacy IT systems.

Surge in hiring big data analytics specialists

The competition between banks and fund managers to hire big data specialists is heating up.

The banks are actively recruiting big data and analytics specialists to fill two main, but significantly different roles: big data engineers and data scientists/analytics/insights.

Big data engineers will typically come from a strong IT development or coding background and are responsible for designing data platforms and applications. A big data engineer can typically command £55,000 a year and may also be known as a software engineer – big data, big data software architect or Hadoop developer.

Data scientists, in contrast, are responsible for bridging the gap between data analytics and business decision-making, capable of translating complex data into key strategy insight.

Data scientists – also known as analytics and insights manager or director of data science – are expected to have sharp technical and quantitative skills. Data scientists are in highest demand and this is where the biggest skill shortage exists.

Data scientists are responsible for examining the data, identifying key trends, and writing the complex algorithms that will see the raw data transformed into a piece of analysis or insight that the business can use to gain a competitive advantage.

Such is the shortage of individuals with this skillset that a data scientist can command between £75,000 and £110,000 a year, straight from graduating from university.

Big data teams will often be competing to hire from the same pool of mathematics and physics PhDs from which other areas of the investment bank will be hiring.


Christopher Adeyeri is associate director – head of technology at recruitment firm Astbury Marsden

Originally posted via “Investment banks recruit for rise of big data analytics”

Source by analyticsweekpick