Nate-Silvering Small Data Leads to Internet Service Provider (ISP) industry insights

There is much talk of Big Data and how it is changing/impacting how businesses improve the customer experience. In this week’s post, I want to illustrate the value of Small Data.

Internet Service Providers (ISPs) receive the lowest customer satisfaction ratings among the industry sectors measured by the American Customer Satisfaction Index (ACSI). As an industry, then, the ISP industry has much room for improvement, some more than others. This week, I will use several data sets to help determine ISP intra-industry rankings and how to improve  their inter-industry ranking.

Table 1. Internet Service Provider Ratings
Table 1. Internet Service Provider Ratings

I took to the Web to find several publicly available and relevant data sets regarding ISPs. In all, I found 12 metrics from seven different sources for 27 ISPs. I combined the data sets by ISP. By merging the different data sources, we will be able to uncover greater insights about these different ISPs and what they need to do to increase customer loyalty. The final data set appears in Table 1. The description of each metric appears below:

  • Broadband type: The types of broadband were from PCMag article.
  • Actual ISP Speed: Average speed for Netflix streams from November 2012: Measured in megabits per second (Mbps).
  • American Customer Satisfaction Index (ACSI): an overall measure of customer satisfaction from 2013. Ratings can vary from 0 to 100.
  • Temkin Loyalty Ratings: Based on three likelihood questions (repurchase, switch and recommend) from 2012. Questions are combined and reported as a “net score,” similar to the NPS methodology. Net scores can range from -100 to 100.
  • JD Power: A 5-star rating system for overall satisfaction from 2012. 5 Star = Among the best; 4 Star = Better than most; 3 Star = About average; 2 Star = The rest.
  • PCMag Ratings (6 metrics: Recommend to Fees): Ratings based on customer survey that measured different CX areas in 2012. Ratings are based on a 10-point scale.
  • DSL Reports: The average customer rating across five areas. These five areas are: 1) Pre-Sales Information, 2) Install Coordination,  3) Connection reliability, 4) Tech Support and 5) Value for money. Data were pulled from the site on 6/30/2013. Ratings are based on a 5-point scale.

As you can see in Table 1, there is much missing data for some of the 27 ISPs. The missing data do not necessarily reflect the quality of the data that appear in the table. These sources simply did not collect data to provide reliable ratings for each ISP or simply did not attempt to collect data for each ISP. The descriptive statistics for and correlations among the study variables appear in Table 2.

Table 2. Descriptive Statistics of and Correlations among Study Variables
Table 2. Descriptive Statistics of and Correlations among Study Variables

It’s all about Speed

Customer experience management research tells us that one way of improving satisfaction is to improve the customer experience. We see that actual speed of the ISP is positively related to most customer ratings, suggesting that ISPs that have faster speed also have customers who are more satisfied with them compared to ISPs who have slower speeds. The only exception with this is for satisfaction with Fees; ISPs with faster actual speed tend to have customers who are less satisfied with Fees compared to ISPs with slower actual speed.

Nate-Silvering the Data

Table 3. Rescaled Values of Customer Loyalty Metrics for Internet Service Providers
Table 3. Rescaled Values of Customer Loyalty Metrics for Internet Service Providers

Recall that Nate Silver aggregated several polls to make accurate predictions about the results of the 2012 presidential elections. Even though different polls, due to sampling error, had different outcomes (sometimes Obama won, sometimes Romney won), the aggregation of different polls resulted in a clearer picture of who was really likely to win.

In the current study, we have five different survey vendors (ASCI, Temkin, JD Power, PCMag and assessing customer satisfaction with ISPs. Depending on what survey vendor you use, the ranking of ISPs differ. We can get a clearer picture of the ranking by combining the different data sources because a single study is less reliable than the combination of many different studies. While the outcome of aggregating customer surveys may not be as interesting as aggregating presidential polls, the general approach that Silver used to aggregate different results can be applied to the current data (I call it Nate-Silvering the data).

Given that the average correlations among the loyalty-related metrics in Table 2 are rather high (average r = .77; median r = .87), aggregating each metric to form an Overall Advocacy Loyalty metric makes mathematical sense. This overall score would be a much more reliable indicator of the quality of an ISP than any single rating by itself.

To facilitate the aggregation process, I first transformed the customer ratings to a common scale, a 100 -point scale using the following methods. I transformed the Temkin Ratings (a net score) into mean scores based on a mathematical model developed for this purpose (see: The Best Likelihood to Recommend Metric: Mean Score or Net Promoter Score?). This value was then multiplied by 10. The remaining metrics were transformed into a 100-point scale by using a multiplicative function of 20 (JD Power, DSLREPORTS) and 10 (PCMag Sat, PCMag Rec). These rescaled values are located in Table 3. While the transformation altered the average of each metric, these transformations did not appreciably alter the correlations among the metrics (average r = .75, median r = .82).

Table 4. Rankings of Internet Service Providers based on the average loyalty ratings.
Table 4. Rankings of Internet Service Providers based on the average loyalty ratings.

The transformed values were averaged for each of the ISPs. These results appear in Table 4. As seen in this table, the top 5 rated ISPs (overall advocacy ratings) are:

  1. WOW!
  2. Verizon FiOS
  3. Cablevision
  4. Earthlink
  5. Bright House

The bottom 5 rated ISPs (overall advocacy ratings) are:

  1. Windstream
  2. CenturyLink
  3. Frontier
  4. WildBlue
  5. HughesNet


Small Data, like its big brother, can provide good insight (with the help of right analytics, of course) about a given topic. By combining small data sets about ISPs, I was able to show that:

  1. Actual ISP speed is related to customer satisfaction with speed of ISP. ISPs that have objectively faster speed receive higher ratings on satisfaction with speed.
  2. Different survey vendors provide reliable and valid results about customer satisfaction with ISPs (there was a high correlation among different survey vendors).
  3. Improving customer loyalty with ISPs is a function of actual ISP speed.

The bottom line is that you shouldn’t forget the value of small data.

Source: Nate-Silvering Small Data Leads to Internet Service Provider (ISP) industry insights

#Compliance and #Privacy in #Health #Informatics by @BesaBauta

#Compliance and #Privacy in #Health #Informatics by @BesaBauta

In this podcast @BesaBauta from MeryFirst talks about the compliance and privacy challenges faced in hyper regulated industry. With her experience in health informatics, Besa shared some best practices and challenges that are faced by data science groups in health informatics and other similar groups in regulated space. This podcast is great for anyone looking to learn about data science compliance and privacy challenges.

Besa’s Recommended Read:
The Art Of War by Sun Tzu and Lionel Giles

Podcast Link:

Besa’s BIO:
Dr. Besa Bauta is the Chief Data Officer and Chief Compliance Officer for MercyFirst, a social service organization providing health and mental health services to children and adolescents in New York City. She oversees the Research, Evaluation, Analytics, and Compliance for Health (REACH) division, including data governance and security measures, analytics, risk mitigation, and policy initiatives.
She is also an Adjunct Assistant Professor at NYU, and previously worked as a Research Director for a USAID project in Afghanistan, and as the Senior Director of Research and Evaluation at the Center for Evidence-Based Implementation and Research (CEBIR). She holds a Ph.D. in implementation science with a focus on health services, an MPH in Global Health and an MSW. Her research has focused on health systems, mental health, and integration of technology to improve population-level outcomes.

About #Podcast:
#FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Want to sponsor?
Email us @

#FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Source: #Compliance and #Privacy in #Health #Informatics by @BesaBauta by v1shal

Big Data in China Is a Big Deal

Big data means different things in different regions – in China retailers are finding ways to make it useful.

One thing Western brands have learned from expansion into the East is that Chinese shoppers are a discerning consumer group. They want genuine quality – fake items are no longer acceptable, value (demonstrated by Single’s Days’ record-shattering sales levels), and VIP treatment.

They also spend a lot of money, with around 250 million of them parting with approximately $275 billioneach year for Internet purchases alone. That’s a massive 60 percent of all online purchases in Asia.

It’s a hugely significant retail market, and key to leveraging its potential is the intelligent use of the wealth of data gathered each time a shopper researches a product, visits a store, or makes a purchase.

“Big data” is often a loaded term – it can mean different things to different people, depending on the industry. But retailers have gone some way toward pinning it down and making it useful.

Targeting With Precision 

Perhaps the most important – and profitable – use of consumer data is extracting preferences and patterns of purchase and using the information to offer highly targeted value-added services and products.

For example, Alibaba’s Open Data Processing Service (ODPS), has allowed it to analyze millions of transactions and set up a highly effective loan service for small online businesses. Data from Alipay and Alibaba’s shopping sites, including purchases, reviews, and credit ratings, assesses a borrower’s ability to repay a loan.

The use of more than 100 computing models and around 80 billion data entries has allowed Alibaba to reduce the cost of lending to a fraction of the cost of a traditional bank loan.

Of course, this kind of accuracy in consumer targeting opens the door to clienteling and the personalized service customers in China are looking for – the ability to identify with precision the needs and wants of people looking for a superior service.

For a small fee, retailers can use the might of the ODPS’ processing power to identify trends, pinpoint key demographics, and plan future ranges and campaigns aimed to meet the exact requirements of their customers.

Tracking Rogue Traders

Concern over counterfeit products means that consumers are prepared to pay a premium for genuine Western items, and will choose online stores such as TMall, which have a reputation for trading in authentic brands. However, the proliferation of fake goods is still a problem, and businesses are turning to big data to help tackle the issue. Following a report by the Chinese government, an e-commerce union comprising key online firms has been established, designed to pool vendor data in an attempt to identify rogue traders through their online shops, transactions, and other sales activity.

The amount of detailed information available to sales platforms should, in theory, mean that there is simply nowhere left to hide for sellers with less than scrupulous product standards.

Transfer of Intelligence

According to the Chinese University of Hong Kong, the three biggest players in China’s online industry, known as “BAT” (Baidu, Alibaba, Tencent) are “sitting on a goldmine of big data.”

The potential for cross-industry application is huge – data integration and mining between retail and financial institutions, for example, will drive the future of both online and physical commerce.

Tapping into the skills and experience of BAT – Baidu alone has thousands of analysts assessing data every day – will give retailers and other industries unprecedented accuracy in profiling the people buying their products and using their services, rich in both detail and opportunity.

The bottom line is that, especially for retailers, big data is a big deal. Expect to see even more sophisticated targeting models and customer-centric business operations coming from China over the next year, thanks to the intelligent use of information.

Originally via “Big Data in China Is a Big Deal”

Originally Posted at: Big Data in China Is a Big Deal

Unraveling the Mystery of Big Data

Curious about the Big Data hype? Want to find out just how big, BIG is? Who’s using Big Data for what, and what can you use it for? How about the architecture underpinnings and technology stacks? Where might you fit in the stack? Maybe some gotchas to avoid? Lionel Silberman, a seasoned Data Architect spreads some light on it. A good and wholesome refresher into Big Data and what all it can do.
Our guest speaker:

Lionel Silberman,
Senior Data Architect, Compuware
Lionel Silberman has over thirty years of experience in big data product development. He has expert knowledge of relational databases, both internals and applications, performance tuning, modeling, and programming. His product and development experience encompasses the major RDBMS vendors, object-oriented, time-series, OLAP, transaction-driven, MPP, distributed and federated database applications, data appliances, NoSQL systems Hadoop and Cassandra, as well as data parallel and mathematical algorithm development. He is currently employed at Compuware, integrating enterprise products at the data level. All are welcome to join us.



Source: Unraveling the Mystery of Big Data by v1shal

Voices in AI – Episode 80: A Conversation with Charlie Burgoyne

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About this Episode

Episode 80 of Voices in AI features host Byron Reese and Charlie Burgoyne discussing the difficulty of defining AI and how computer intelligence and human intelligence intersect and differ.

Listen to this one-hour episode or read the full transcript at

Transcript Excerpt

Byron Reese: This is Voices in AI brought you by GigaOm and I’m Byron Reese. Today my guest is Charlie Burgoyne. He is the founder and CEO of Valkyrie Intelligence, a consulting firm with domain expertise in applied science and strategy. He’s also a general partner for Valkyrie Signals, an AI-driven hedge fund based in Austin, as well as the managing partner for Valkyrie labs, an AI credit company. Charlie holds a master’s degree in theoretical physics from Georgetown University and a bachelor’s in nuclear physics from George Washington University.

I had the occasion to meet Charlie when we shared a stage when we were talking about AI and about 30 seconds into my conversation with him I said we gotta get this guy on the show. And so I think ‘strap in’ it should be a fun episode. Welcome to the show Charlie.

Charlie Burgoyne: Thanks so much Byron for having me, excited to talk to you today.

Let’s start with [this]: maybe re-enact a little bit of our conversation when we first met. Tell me how you think of artificial intelligence, like what is it? What is artificial about it and what is intelligent about it?

Sure, so the further I get down in this field, I start thinking about AI with two different definitions. It’s a servant with two masters. It has its private sector, applied narrowband applications where AI is really all about understanding patterns that we perform and that we capitalize on every day and automating those — things like approving time cards and making selections within a retail environment. And that’s really where the real value of AI is right now in the market and [there’s] a lot of people in that space who are developing really cool algorithms that capitalize on the potential patterns that exist and largely lay dormant in data. In that definition, intelligence is really about the cycles that we use within a cognitive capability to instrument our life and it’s artificial in that we don’t need an organic brain to do it.

Now the AI that I’m obsessed with from a research standpoint (a lot of academics are and I know you are as well Byron) — that AI definition is actually much more around the nature of intelligence itself, because in order to artificially create something, we must first understand it in its primitive state and its in its unadulterated state. And I think that’s where the bulk of the really fascinating research in this domain is going, is just understanding what intelligence is, in and of itself.

Now I’ll come kind of straight to the interesting part of this conversation, which is I’ve had not quite a hundred guests on the show. I can count on one hand the number who think it may not be possible to build a general intelligence. According to our conversation, you are convinced that we can do it. Is that true? And if so why?

Yes… The short answer is I am not convinced we can create a generalized intelligence, and that’s become more and more solidified the deeper and deeper I go into research and familiarity with the field. If you really unpack intelligent decision making, it’s actually much more complicated than a simple collection of gates, a simple collection of empirically driven singular decisions, right? A lot of the neural network scientists would have us believe that all decisions are really the right permutation of weighted neurons interacting with other layers of weighted neurons.

From what I’ve been able to tell so far with our research, either that is not getting us towards the goal of creating a truly intelligent entity or it’s doing the best within the confines of the mechanics we have at our disposal now. In other words, I’m not sure whether or not the lack of progress towards a true generalized intelligence is due to the fact that (a) the digital environment that we have tried to create said artificial intelligence in is unamenable to that objective or (b) the nuances that are inherent to intelligence… I’m not positive yet those are things through which we have an understanding of modeling, nor would we ever be able to create a way of modeling that.

I’ll give you a quick example: If we think of any science fiction movie that encapsulates the nature of what AI will eventually be, whether it’s Her, or Ex Machina or Skynet or you name it. There are a couple of big leaps that get glossed over in all science fiction literature and film, and those leaps are really around things like motivation. What motivates an AI, like what truly at its core motivates AI like the one in Ex Machina to leave her creator and to enter into the world and explore? How is that intelligence derived from innate creativity? How are they designing things? How are they thinking about drawings and how are they identifying clothing that they need to put on? All these different nuances that are intelligently derived from that behavior. We really don’t have a good understanding of that, and we’re not really making progress towards an understanding of that, because we’ve been distracted for the last 20 years with research in fields of computer science that aren’t really that closely related to understanding those core drivers.

So when you say a sentence like ‘I don’t know if we’ll ever be able to make a general intelligence,’ ever is a long time. So do you mean that literally? Tell me a scenario in which it is literally impossible — like it can’t be done, even if you came across a genie that could grant your wish. It just can’t be done. Like maybe time travel, you know — back in time, it just may not be possible. Do you mean that ‘may not’ be possible? Or do you just mean on a time horizon that is meaningful to humans?

I think it’s on the spectrum between the two. But I think it leans closer towards ‘not ever possible under any condition.’ I was at a conference recently and I made this claim which admittedly as any claim with this particular question would be based off of intuition and experience which are totally fungible assets. But I made this claim that I didn’t think it was ever possible, and something the audience asked me, well, have you considered meditating to create a synthetic AI? And the audience laughed and I stopped and I said: “You know that’s actually not the worst idea I’ve been exposed to.” That’s not the worst potential solution for understanding intelligence to try and reverse engineer my own brain with as little distractions from its normal working mechanics as possible. That may very easily be a credible aid to understanding how the brain works.

If we think about gravity, gravity is not a bad analog. Gravity is this force that everybody and their mother who’s older than, you know who’s past fifth grade understands how it works, you drop an apple you know which direction it’s going to go. Not only that but as you get experienced you can have a prediction of how fast it will fall, right? If you were to see a simulation drop an apple and it takes twelve seconds to hit the ground, you’d know that that was wrong, even if the rest of the vector was correct, the scaler is off a little bit. Right?

The reality is that we can’t create an artificial gravity environment, right? We can create forces that simulate gravity. Centrifugal force is not a bad way of replicating gravity but we don’t actually know enough about the underlying mechanics that guide gravity such that we could create an artificial gravity using the same techniques, relatively the same mechanics that are used in organic gravity. In fact it was only a year and a half ago or so closer to two years now where the Nobel Prize for Physics was awarded to the individuals who identified that it was gravitational waves that permeate gravity (actually that’s how they do gravitons), putting to rest an argument that’s been going on since Einstein truly.

So I guess my point is that we haven’t really made progress in understanding the underlying mechanics, and every step we’ve taken has proven to be extremely valuable in the industrial sector but actually opened up more and more unknowns in the actual inner workings of intelligence. If I had to bet today, not only is the time horizon on a true artificial intelligence extremely long-tailed but I actually think that it’s not impossible that it’s completely impossible altogether.

Listen to this one-hour episode or read the full transcript at

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Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

Source by analyticsweekpick