Jun 29, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Insights  Source

[ NEWS BYTES]

>>
 Defense contractor stored intelligence data in Amazon cloud unprotected – Ars Technica Under  Cloud

>>
 Qatar firms can meet hybrid cloud challenges – Peninsula On-line Under  Hybrid Cloud

>>
 The tricky, personal politics of cloud security | Network World – Network World Under  Cloud Security

More NEWS ? Click Here

[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

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Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

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In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Mast… more

[ TIPS & TRICKS OF THE WEEK]

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

[ DATA SCIENCE Q&A]

Q:How frequently an algorithm must be updated?
A: You want to update an algorithm when:
– You want the model to evolve as data streams through infrastructure
– The underlying data source is changing
– Example: a retail store model that remains accurate as the business grows
– Dealing with non-stationarity

Some options:
– Incremental algorithms: the model is updated every time it sees a new training example
Note: simple, you always have an up-to-date model but you can’t incorporate data to different degrees.
Sometimes mandatory: when data must be discarded once seen (privacy)
– Periodic re-training in “batch” mode: simply buffer the relevant data and update the model every-so-often
Note: more decisions and more complex implementations

How frequently?
– Is the sacrifice worth it?
– Data horizon: how quickly do you need the most recent training example to be part of your model?
– Data obsolescence: how long does it take before data is irrelevant to the model? Are some older instances
more relevant than the newer ones?
Economics: generally, newer instances are more relevant than older ones. However, data from the same month, quarter or year of the last year can be more relevant than the same periods of the current year. In a recession period: data from previous recessions can be more relevant than newer data from different economic cycles.

Source

[ VIDEO OF THE WEEK]

Decision-Making: The Last Mile of Analytics and Visualization

 Decision-Making: The Last Mile of Analytics and Visualization

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

#FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

 #FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

More than 200bn HD movies – which would take a person 47m years to watch.

Sourced from: Analytics.CLUB #WEB Newsletter

8 Data Security Tips For Small Businesses

In 2015, more than 169 million personal records were exposed, ranging from financial records, trade secrets, and important files from education, government, and healthcare sector. Though big organizations are the usual victims of data breach, there is an ongoing trend which shows that small businesses are rapidly becoming a much-favored victim by hackers nowadays. 2017 should be high on charts for businesses to fix their security loopholes.

Here’s a great cheat sheet on 8 data security tips that will come handy in case one needs to revisit their data security strategy.

8 Pointers are:
Designate Computer Access Levels
Enable Two-Factor Authentication
Secure Wireless Network Connection
Use SSL for exchanging Sensitive Data
Use Trusted Resources for storage
Store Encrypted Data Backups
Make your staff aware

8 Data Security Tips For Small Businesses
8 Data Security Tips For Small Businesses

Originally Posted at: 8 Data Security Tips For Small Businesses

Jun 22, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Extrapolating  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Understanding Customer Buying Journey with Big Data by v1shal

>> The Differences Between a Business Analyst & a Data Analyst by anum

>> What Are the 3 Critical Keys to Healthcare Big Data Analytics? by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Traders are loading up on bets against China Evergrande Group … – Business Insider Under  Financial Analytics

>>
 Machine Learning Techniques for Predictive Maintenance – InfoQ.com Under  Machine Learning

>>
 Accepting What You Don’t Know Is Crucial to Detecting Risk – American Banker Under  Risk Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

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This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applicati… more

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their … more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:What is the maximal margin classifier? How this margin can be achieved?
A: * When the data can be perfectly separated using a hyperplane, there actually exists an infinite number of these hyperplanes
* Intuition: a hyperplane can usually be shifted a tiny bit up, or down, or rotated, without coming into contact with any of the observations
* Large margin classifier: choosing the hyperplance that is farthest from the training observations
* This margin can be achieved using support vectors

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

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[ QUOTE OF THE WEEK]

If we have data, let’s look at data. If all we have are opinions, let’s go with mine. – Jim Barksdale

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA

Subscribe 

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[ FACT OF THE WEEK]

As recently as 2009 there were only a handful of big data projects and total industry revenues were under $100 million. By the end of 2012 more than 90 percent of the Fortune 500 will likely have at least some big data initiatives under way.

Sourced from: Analytics.CLUB #WEB Newsletter

Three Big Data Trends Analysts Can Use in 2016 and Beyond

One of the byproducts of technology’s continued expansion is a high volume of data generated by the web, mobile devices, cloud computing and the Internet of Things (IoT). Converting this “big data” into usable information has created its own side industry, one that businesses can use to drive strategy and better understand customer behavior.

The big data industry requires analysts to stay up to date with the machinery, tools and concepts associated with big data, and how each can be used to grow the field. Let’s explore three trends currently shaping the future of the big data industry:

Big Data Analytics Degrees

Mostly due to lack of know-how, businesses aren’t tapping into the full potential of big data. In fact, most companies only analyze about 12 percent of the emails, text messages, social media, documents or other data-collecting channels available to them (Forrester). Many universities now offer programs for big data analytics degrees to directly acknowledge this skills gap. The programs are designed to administer analytical talent, train and teach the skillsets – such as programming language proficiency, quantitative analysis tool expertise and statistical knowledge – needed to interpret big data. Analysts predict the demand for industry education will only grow, making it essential for universities to adopt analytics-based degree programs.

Predicting Consumer Behaviors

Big data allows businesses to access and extract key insights about their consumer’s behavior. Predictive analytics challenges businesses to take data interpretation a step further by not only looking for patterns and trends, but using them to predict future purchasing habits or actions. In essence, predictive analytics, which is a branch of big data and data mining, allows businesses to make more data-based predictions, optimize processes for better business outcomes and anticipate potential risk.

Another benefit of predictive analytics is the impact it will have on industries such as health informatics. Health informatics uses electronic health record (EHR) systems to solve problems in healthcare such as effectively tracking a patient’s medical history. By documenting records in electronic format, doctors can easily track and assess a patient’s medical history from any certified access port. This allows doctors to make assumptions about a patient’s health using predictive analytics based on documented results.

Cognitive Machine Improvements

A key trend evolving in 2016 is cognitive improvement in machinery. As humans, we crave relationship and identify with brands, ideas and concepts that are relatable and easy to use. We expect technology will adapt to this need by “humanizing” the way machines retain memories and interpret and process information.

Cognitive improvement aims to solve computing errors, yet still predict and improve outcomes as humans would. It also looks to solve human mistakes, such as medical errors or miscalculated analytics reports. A great example of cognitive improvement is IBM’s Watson supercomputer. It’s classified as the leading cognitive machine to answer complex questions using natural language.

The rise of big data mirrors the rise of tech. In 2016, we will start to see trends in big data education, as wells as a shift in data prediction patterns and error solutions. The future is bright for business and analytic intelligence, and it all starts with big data.

Dr. Athanasios Gentimis

Dr. Athanasios (Thanos) Gentimis is an Assistant Professor of Math and Analytics at Florida Polytechnic University. Dr. Gentimis received a Ph.D. in Theoretical Mathematics from the University of Florida, and is knowledgeable in several computer programming/technical languages that include C++, FORTRAN, Python and MATLAB.

Source: Three Big Data Trends Analysts Can Use in 2016 and Beyond

Four Things You Need to Know about Your Customer Metrics

Customer Metrics
What Customer Metrics Do You Use?

A successful customer experience management (CEM) program requires the collection, synthesis, analysis and dissemination of different types of business metrics, including operational, financial, constituency and customer metrics (see Figure 1).  The quality of customer metrics necessarily impacts your understanding of how to best manage customer relationships to improve the customer experience, increase customer loyalty and grow your business. Using the wrong customer metrics could lead to sub-optimal decisions while using the right customer metrics can lead to good decisions that give you a competitive edge.  How do you know if you are using the right customer metrics in your CEM program? This post will help formalize a set of standards you can use to evaluate your customer metrics.

Customer Experience Management is EFM & CRM
Figure 1. Customer experience management is about collection, synthesis, analysis and dissemination of business metrics.

Customer Metrics

Customer metrics are numerical scores or indices that summarize customer feedback results. They can be based on either customer ratings (e.g., average satisfaction rating with product quality) or open-ended customer comments (via sentiment analysis). Additionally, customer ratings can be based on a single item or an aggregated set of items (averaging over a set of items to get a single score/metric).

Meaning of Customer Metrics

Customer metrics represent more than just numerical scores. Customer metrics have a deeper meaning, representing some underlying characteristic/mental processes about your customers: their opinions and attitudes about and intentions toward your company or brand. Figure 2 depicts this relationship between the feedback tool (questions) and the this overall score that we label as something.  Gallup claims to measure customer engagement (CE11) using 11 survey questions. Other practitioners have developed their unique metrics that assess underlying customer attitudes/intentions. The SERVQUAL method assesses several dimensions of service quality; the RAPID Loyalty approach measures three types of customer loyalty: retention, advocacy and purchasing. The Net Promoter Score® measures likelihood to recommend.

Figure 2. Advocacy Loyalty Index (customer metric) measures extent to which customers will advocate/ feel positively toward your company (underlying construct) using three items/questions.

Customer Metrics are Necessary for Effective CEM Programs but not Frequently Used

Loyalty leading companies compared to their loyalty lagging counterparts, adopt specific customer feedback practices that require the use of customer metrics: sharing customer results throughout the company, including customer feedback in company/executive dashboards, compensating employees based on customer feedback, linking customer feedback to operational metrics, and identify improvement opportunities that maximize ROI.

Despite the usefulness of customer metrics, few businesses gather them. In a study examining the use of customer experience (CX) metrics, Bruce Temkin found that only about half (52%) of businesses collect and communicate customer experience (CX) metrics. Even fewer of them review CX metrics with cross-functional teams (39%), tie compensation to CX metrics (28%) or make trade-offs between financial and CX metrics (19%).

Evaluating Your Customer Metrics

As companies continue to grow their CEM programs and adopt best practices, they will rely more and more on the use of customer metrics. Whether you are developing your own in-house customer metric or using a proprietary customer metric, you need to be able to critically evaluate them to ensure they are meeting the needs of your CEM program. Here are four questions to ask about your customer metrics.

1. What is the definition of the customer metric?

Customer metrics need to be supported by a clear description of what it is measuring. Basically, the customer metric is defined the way that words are defined in the dictionary. They are non-ambiguous and straightforward. The definition, referred to as the constitutive definition, not only tells you what the customer metric is measuring, it also tells you what the customer metric is not measuring.

The complexity of the definition will match the complexity of the customer metric itself. Depending on the customer metric, definitions can reflect a narrow concept or a more complex concept. For single-item metrics, definitions are fairly narrow. For example, a customer metric based on the satisfaction rating of a single overall product quality question would have the following definition: “Satisfaction with product quality”. For customer metrics that are made up of several items, a well-articulated definition is especially important. These customer metrics measure something more nuanced than single-item customer metrics. Try to capture the essence of the commonality shared across the different items. For example, if the ratings of five items about the call center experience (e.g., technical knowledge of rep, professionalism of rep, resolution) are combined into an overall metric, then the definition of the overall metric would be: “Overall satisfaction with call center experience.”

2. How is the customer metric calculated?

Figure 3. Two Measurement Criteria: Reliability is about precision; Validity is about meaning

Closely related to question 1, you need to convey precisely how the customer metric is calculated. Understanding how the customer metric is calculated requires understanding two things: 1) the specific items/questions in the customer metric; 2) how items/questions were combined to get to the final score. Knowing the specific items and how they are combined help define what the customer metric is measuring (operational definition). Any survey instructions and information about the rating scale (numerical and verbal anchors) need to be included.

3. What are the measurement properties of the customer metric?

Measurement properties refer to a scientifically-derived indices that describe the quality of a customer metric. Applying the field of psychometrics and scientific measurement standards (Standards for Educational and Psychological Testing), you can evaluate the quality of customer metrics. Analyzing existing customer feedback data, you are able to evaluate customer metrics along two criteria: 1) Reliability and 2) Validity. Reliability refers to measurement precision/consistency. Validity is concerned with what is being measured. Providing evidence of reliability and validity of your customer metrics is essential towards establishing a solid set of customer metrics for your CEM program. The relationship between these two measurement criteria is depicted in Figure 3. Your goal is to develop/select customer metrics that are both reliable and valid (top right quadrant).

Four Types of Reliability
Figure 4. Four Types of Reliability

While there are different kinds of reliability (see Figure 4), one in particular is especially important when the customer metric is made up of multiple items (e.g., most commonly, items are averaged to get one overall metric). Internal consistency reliability is a great summary index that tells you if the items should combined together. Higher internal consistency (above .80 is good; 1.0 is the maximum possible) tells you that the items measure one underlying construct; aggregating them makes sense. Low internal consistency tells you that the items are likely measuring different things and should not be aggregated together.

There are three different lines of validity evidence that help show that the customer metric actually measures what you think it is measuring. To establish that a customer metric assesses something real, you can look at the content of the items to determine how well they represent your variable of interest (establishing evidence of content validity), you can calculate how well the customer metric correlates with some external criteria (establishing evidence of criterion validity) and you can understand, through statistical relationships among different metrics, how your customer metric fits into a theoretical framework that distinguishes your customer metric from other customer metrics (e.g., How is the customer engagement metric different than the customer advocacy metric? - construct validity).

Figure 5. Evidence of criterion-related validity: Identifying which operational metrics are related to customer satisfaction with the service request (SR)

These three different lines of validity evidence demonstrate that the customer metric measures what it is intended to measure. Criterion-related validity evidence often involves linking customer metrics to other data sources (operational metrics, financial metrics, constituency metrics).

Exploring the reliability and validity of your current customer metrics has a couple of extra benefits. First, these types of analyses can improve the measurement properties of your current customer metrics by identifying unnecessary questions. Second, reliability and validity analysis can improve the overall customer survey by identifying CX questions that do not help explain customer loyalty differences. Removal of specific CX questions can significantly reduce survey length without loss of information.

4. How useful is the customer metric?

While customer metrics can be used for many types of analyses (e.g., driver, segmentation), their usefulness is demonstrated by the number and types of insights they provide. Your validation efforts to understand the quality of the customer metrics create a practical framework for making real organizational changes. Specifically, by understanding the causes and consequences of the customer metric, you can identify/create customer-centric operational metrics (See Figure 5) to help manage call center performance, understand how changes in the customer metric correspond to changes in revenue (See Figure 6) and identify customer-focused training needs and standards for employees (See Figure 7).

Figure 6. A useful customer metric (satisfaction with TAM) reveals real differences in business metrics (revenue)

Examples

Below are two articles on the development and validation of four customer metrics. One article focuses on three related customer metrics. The other article focuses on an employee metric. Even though this present blog post talked primarily about customer metrics, the same criteria can be applied to employee metrics.

In each article, I present the necessary information needed to critically evaluate each customer metric: 1) Clear definition of the customer metrics, 2) description of how metrics are calculated, 3) measurement properties (reliability/validity), 4) show that metrics are related to important outcomes (e.g., revenue, employee satisfaction). The articles are:

  • Hayes, B.E.  (2011). Lessons in loyalty. Quality Progress, March, 24-31. Paper discusses the development and validation of the RAPID Loyalty approach. Three reliable customer loyalty metrics are predictive of different types of business growth. Read entire article.
  • Hayes, B. E. (1994). How to measure empowerment. Quality Progress, 27(2), 41-46. Paper discusses need to define and measure empowerment. Researcher develops reliable measure of employee perceptions of empowerment, the Employee Empowerment Questionnaire (EEQ). The EEQ was related to important employee attitudes (job satisfaction). Read entire article.
Figure 7. Evidence of Criterion-Related Validity: Satisfaction with TAM Performance (customer metric) is related to TAM training.

Summary

A customer metric is good when: 1) it is supported with a clear definition of what it measures and what is does not measure; 2) there is a clear method of how the metric is calculated, including all items and how they are combined; 3) there is good reliability and validity evidence regarding how well the customer metric measures what it is supposed to measure; 4) they are useful in helping drive real internal changes (e.g., improved marketing, sales, service) that lead to measurable business growth (e.g., increased revenue, decreased churn).

Using customer metrics that meet these criteria will ensure your CEM program is effective in improving how your manage the customer relationship. Clear definitions of the metrics and accompanying descriptions of how they are calculated help improve communications regarding customer feedback. Different employees, across job levels or roles, can now speak a common language about feedback results. Establishing the reliability and validity of the metrics gives senior executives the confidence they need to use customer feedback as part of their decision-making process.

The bottom line: a good customer metric provides information that is reliable, valid and useful.

Source

Jun 15, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data Accuracy  Source

[ AnalyticsWeek BYTES]

>> 5 Steps Required to Building a Best Practice Digital Analytics Function by analyticsweekpick

>> 100 Greatest Quotes On Leadership by v1shal

>> For the airline industry, big data is cleared for take-off by anum

Wanna write? Click Here

[ NEWS BYTES]

>>
 The Rise of Network Functions Virtualization – Virtualization Review Under  Virtualization

>>
 Data Science Up and Down the Ladder of Abstraction – InfoQ.com Under  Data Science

>>
 Wildly inaccurate election forecasts highlight Big Data challenges – ZDNet Under  Big Data Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

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Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be ap… more

[ FEATURED READ]

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

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

[ TIPS & TRICKS OF THE WEEK]

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

[ DATA SCIENCE Q&A]

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

Source

[ VIDEO OF THE WEEK]

Surviving Internet of Things

 Surviving Internet of Things

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[ QUOTE OF THE WEEK]

In God we trust. All others must bring data. – W. Edwards Deming

[ PODCAST OF THE WEEK]

#FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

 #FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

Subscribe 

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[ FACT OF THE WEEK]

The largest AT&T database boasts titles including the largest volume of data in one unique database (312 terabytes) and the second largest number of rows in a unique database (1.9 trillion), which comprises AT&T’s extensive calling records.

Sourced from: Analytics.CLUB #WEB Newsletter

Why Focus Groups Don’t Work And Cost Millions

030120.focusgroup
We all know what “focus group” is and what it is used for. What we don’t admit quickly is that it has little use and that we all deal with it acting old school. With changing consumer ecosystem, we should think of some other more quantitative technique that is more relevant to the current stage. With ever evolving technology and sophisticated tools, there is no reason to feel otherwise. Focus group was never an efficient way to measure product-market fit. But, considering it was the only thing that was easily available that could provide a decent start; industry went with it. We are now at a point where we could change and upgrade ourselves to harness better ways to measure potential product need and adoption.

Few of the downsides of using focus group

Unnatural settings for participants
Consider a situation where a bunch of strangers come together and discuss about some product that they have not seen before. When in real life would such an incident occur? Why would someone speak honestly without any trust between moderator and the participant? This is not a natural setting where anyone experiences a real product. So why should we use this template to make decisions?

Not in accord of how a real decision process works
Calling people and having them sit in a group and vouch for product is not how we should decide on the attractiveness/adoption of a product. There are several other things that work in tandem to influence our decision making process spend on a product and those are almost impossible to replicate in focus group sessions. For example – In real life, most of the people depend on word of mouth and suggestions from friends and family to try and adopt a new product. Such a flaw induces greater margin of error in data gathered from such groups.

Motivation for the participants is different
This is another area which makes focus group less reliable area to focus on. Consider why someone will ever detach from their day-to-day lives to come to a focus group. The reasons could be many, namely – Money, early adopter, ability to meet / network with people etc. Such variation in experience and motivation for participants induces more noise than signals.

Not a right framework for asking for snap judgment on products
Another interesting point against focus group template is its framework to gather people out of the blue, have them experience product for the first time and ask for their opinion. Everyone brings their own speed to the table when it comes to understanding the product. So, how can it be not flawed when everyone is asked at same short interval to share their opinion? This also induces error in findings.

Little is useless and more is expensive
We all know that the background for the participants is highly variable, and it is almost impossible to carve a niche out of the participants. If few participants are invited, it is extremely hard to pin-point the needs of participants, and if we invite too many, it will be an expensive model and with all the error and flaws in it. This makes focus group model useless and costly.

It is not about the product but the experience
A product never alone work on its own, it often works in conjunction with experience that is delivered by other dependent areas. And cumulative interactions deliver the product experience. In focus group, it is extremely difficult to deliver an exact experience as it has not been built into the mix yet. Experience comes after numerous product iterations with customers. So, in initial stages, it is extremely difficult to suggest anything by just quick hands on with product and no experience build around it.

Innovation suppressant
Consider a case where iTunes is pitched to focus group. “iTunes is a place where you could buy individual songs and not the whole album, yes online and no, No CDs”. Have you ever wondered how that will fly? Focus group is great in suggesting something right in the ally of what is already present today. If there is a groundbreaking product whose market has not yet been explored, it could induce some uneasiness and could easily meet with huge rejection. So, focus groups are pretty much innovation killers.

People might not be honest unintentionally
Consider a case where you are asked about your true feelings for a product in a room full with people who think highly about it. Wouldn’t it skew your observation as well? We all have a strong tendency to bend towards political correctness causing us to skew actual findings. There are other such biases caused by group think, dominating personality in the room etc. that have been identified to invalidate the findings of the focus group sessions. This introduces error in judgment and makes collected data erroneous.

Above stated reasons are few of many that make a focus group obsolete, erroneous and unreliable. So, we should avoid using them and we should substitute it with other more effective ways.

So, what’s next? What should companies do? Let’s leave it to another day, and another blog. Catch you all soon.

Source: Why Focus Groups Don’t Work And Cost Millions by d3eksha

Jun 08, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Fake data  Source

[ AnalyticsWeek BYTES]

>> IBM and Hadoop Challenge You to Use Big Data for Good by bobehayes

>> AtScale opens Hadoop’s big-data vaults to nonexpert business users by anum

>> Hacking the Data Science by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

Machine Learning

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

[ FEATURED READ]

The Future of the Professions: How Technology Will Transform the Work of Human Experts

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This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:What is a decision tree?
A: 1. Take the entire data set as input
2. Search for a split that maximizes the ‘separation” of the classes. A split is any test that divides the data in two (e.g. if variable2>10)
3. Apply the split to the input data (divide step)
4. Re-apply steps 1 to 2 to the divided data
5. Stop when you meet some stopping criteria
6. (Optional) Clean up the tree when you went too far doing splits (called pruning)

Finding a split: methods vary, from greedy search (e.g. C4.5) to randomly selecting attributes and split points (random forests)

Purity measure: information gain, Gini coefficient, Chi Squared values

Stopping criteria: methods vary from minimum size, particular confidence in prediction, purity criteria threshold

Pruning: reduced error pruning, out of bag error pruning (ensemble methods)

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Big Data Analytics

 @AnalyticsWeek Panel Discussion: Big Data Analytics

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[ QUOTE OF THE WEEK]

In God we trust. All others must bring data. – W. Edwards Deming

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with David Rose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with David Rose, @DittoLabs

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Data production will be 44 times greater in 2020 than it was in 2009.

Sourced from: Analytics.CLUB #WEB Newsletter

What is the Value of International Polls about the US Presidential Candidates?

I saw the results of a recent opinion poll about the US presidential election that amazed me. While many recent polls of US voters reveal a virtual tie in presidential race between Barack Obama and Mitt Romney, a BBC poll surveying citizens from other countries about the US president found overwhelming support for Barack Obama over Mitt Romney. In this late summer/early fall study by GlobeScan and PIPA of over 20,000 people across 21 countries, 50% favored Obama and 9% favored Mr Romney.

Global Businesses Needs Global Feedback

Companies conducting international business regularly poll their customers and prospects across the different countries they serve in hopes to get better insights about how to run their business. They use this feedback to help them understand where to enter new markets, guide product development, and improve service quality, just to name a few. The end goal is to create a loyal customer base (e.g., customers come back, recommend and expand relationship).

The US government’s policies impact international relations on many levels (e.g., economically, financially and socially). Could there be some value from this international poll for the candidates themselves and their constituencies?

Looking at the results of the poll, there are few implications that stand out to me:

  1. The Romney brand has little international support. Mitt Romney has touted that his business experience has prepared him to be an effective president. How can he use these results to improve his image abroad?
  2. Many international citizens do not care about the US presidency (in about half of the countries, fewer than 50% of respondents did not express an opinion for either Obama or Romney).
  3. After four years of an Obama presidency, the international community continues to support the re-election of Obama. Obama received comparable results in 2008.

I like to use data whenever possible to help me guide my decisions. However, I will be the first to admit that I am no expert on international relations. So, I am seeking help from my readers. Here are three questions:

  1. Are these survey results useful to help guide US constituencies’ voting decision?
  2. Is international citizenry survey results about the US presidential candidates analogous to international customer survey results about US companies?
  3. If you owned a company and where selling the Obama and Romney brand, how would you use these survey results (barring simply ignoring them) to improve international customer satisfaction?

I would love to hear your opinions.

Source: What is the Value of International Polls about the US Presidential Candidates?