Apr 02, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Human resource  Source

[ AnalyticsWeek BYTES]

>> Can Big Data Tell Us What Clinical Trials Don’t? by analyticsweekpick

>> Five Reasons to Create Custom Variables in Adobe Analytics by administrator

>> Introducing MLflow: an Open Source Machine Learning Platform by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

A Course in Machine Learning

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Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need… 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]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:What is statistical power?
A: * sensitivity of a binary hypothesis test
* Probability that the test correctly rejects the null hypothesis H0H0 when the alternative is true H1H1
* Ability of a test to detect an effect, if the effect actually exists
* Power=P(reject H0|H1istrue)
* As power increases, chances of Type II error (false negative) decrease
* Used in the design of experiments, to calculate the minimum sample size required so that one can reasonably detects an effect. i.e: ‘how many times do I need to flip a coin to conclude it is biased?’
* Used to compare tests. Example: between a parametric and a non-parametric test of the same hypothesis

Source

[ VIDEO OF THE WEEK]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data beats emotions. – Sean Rad, founder of Ad.ly

[ PODCAST OF THE WEEK]

@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

 @JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

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

Social Media Analytics – What to Measure for Success?

If you are using social media for business or promotional purposes, then you should know how to measure it. However, when getting on to measure social media, it is not just for the sake of having some metrics, but to measure the effectiveness of the social media campaigns and further streamline it for better results. With proper analytics, you will be able to understand what was successful, what wasn’t, what your target audience expects, and how you can improve.

Two types of measurements

We may find that there are two major social media measurements to consider as:

  • Ongoing analytics – Tracking activities over time and monitoring performance.
  • Campaign-centric analytics – Getting analytics related to each event or campaign to assess its success.

Ongoing analytics will help to track the pulse of your social media communications regarding your business and brand in general. Once you set up the elements of brand tracking, you may just let is run by default and frequently fetch the data to see how it is going.

On the other hand, the campaign-specific metrics will help to understand the actual impact of your targeted content, which may be different from campaign to campaign. An ideal social media analytics program will take both of these measurements in a fine balance.

Monitoring social media analytics

Monitoring your social media analytics may surely distinguish between success and failure of your social media activities. In this excerpt, we are trying to outline what all things you need to monitor on social media and what tools to be used to do proper analysis and reporting or social media campaigns.

Social media analytics compass

It is almost impossible to monitor and measure everything related to social media on every channel at a time. So, we need to determine what is essential for your business and how to do monitoring of it well. For a better understanding of beginners, we will discuss the most critical generic areas of the so-called social media compass, i.e., the most important analytical measures.

  1. Size of the target audience

Many are confused about the matter whether the size of your audience really matters? Of course, it does matter if you are trying to promote a brand or service and building a relevant audience. It is essential to continuously build a relevant audience if you want to take your message to the right people on time.

Your audience will grow gradually through organic methods as well as on investing in paid ads or so. In fact, there is nothing wrong if you plan to invest in audience building tactics if you have a scope of converting the audience into business overtime. You should compare your rate of audience growth over a week or month with that of your competitors. Along with building the audience rate, also keep track of the unfollowers too on the go.

  1. Audience profile

As you slowly grow your audience, it is also essential to ensure that you are building the right type of audience, especially when you are paying for it, deciding whether you are making a worthy investment. Say, for example, if you try to build it through Twitter, this platform will allow you to access reports stating what types of profiles like marketers, entrepreneurs, or musicians, etc. are a part of your audience group.

You can do the same on Facebook also by setting up an ad which is targeted to a specific category of your Facebook target audience. This is also possible on Instagram to get some real Instagram likes. Say, for example; you can do this filtering for a specific interest of people, and see how many of your followers fall in that category to plan relevant campaigns for them. With a smart approach to it, you may perform this profile analysis across all social media platforms. You may use a traditional approach like surveys to most advanced premium tools offered by social media platforms to accomplish this.

  1. Reach and engagement

The campaigners also should monitor the social reach of your content and also see how much actually pay keen attention to it, even if not responding. Lack of responses doesn’t necessarily be non-interest. Engagement is another key aspect of monitoring as some of them with keen interest may engage with your content. If you find no engagement at all, then it may be either be the wrong content or the wrong audience you hit. You may typically split your audience under the following categories.

  • Lurkers – those who simply watch your content, but not interacting.
  • Influencers – They are connected to a large audience and can make an influence among them.
  • Engagers – People who are largely active in your target community and people will start recognizing their names.
  1. Traffic

The primary objective of your social media campaigns is to bring traffic back to your website or product pages. For some promoters, traffic is just enough. Say for example, for a site; they get paid for ads based on the volume of traffic. For the rest, the traffic needs to be converted into sales to meet their objective.

  1. Content analysis

As we have seen, creating content and sharing it through social media is an expensive and work-intensive affair. So, on a regular basis, you need to do content analysis as well to see if your efforts are getting recognized or not. You have to check whether:

  • Whether your images, videos, and text updates work the best?
  • Whether the content you share is in fine balance with the right mix or too much focused-on promotion?
  • Do you have enough engagement on the questions?
  • What changes are there on the social media platforms and what changes it demands from you?
  1. Sentiment analysis

The sentimental analysis covers the negative, positive, or neutral mentions on your brand. The latest social media tools are more focused on measuring the sentiments of the target audience over your brand through their social mentions about you. Even though these tools are not 100% accurate, it can surely be a good indicator of where you go wrong and what to correct.

These are some functional pointers included in social media analytics, which is a far wider specialty. However, it could be a good starting point if you master over these to streamline your social media campaigns to meet your online objectives.

Source: Social Media Analytics – What to Measure for Success? by thomassujain

Mar 26, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Ethics  Source

[ AnalyticsWeek BYTES]

>> Artificial Intelligence – Weekly Round-up by administrator

>> CNN Model Architectures and Applications by administrator

>> Two Things Everyone Needs to Know About Your CEM Program by bobehayes

Wanna write? 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 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]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q:How do you test whether a new credit risk scoring model works?
A: * Test on a holdout set
* Kolmogorov-Smirnov test

Kolmogorov-Smirnov test:
– Non-parametric test
– Compare a sample with a reference probability distribution or compare two samples
– Quantifies a distance between the empirical distribution function of the sample and the cumulative distribution function of the reference distribution
– Or between the empirical distribution functions of two samples
– Null hypothesis (two-samples test): samples are drawn from the same distribution
– Can be modified as a goodness of fit test
– In our case: cumulative percentages of good, cumulative percentages of bad

Source

[ VIDEO OF THE WEEK]

Understanding #BigData #BigOpportunity in Big HR by @MarcRind #FutureOfData #Podcast

 Understanding #BigData #BigOpportunity in Big HR by @MarcRind #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The data fabric is the next middleware. – Todd Papaioannou

[ PODCAST OF THE WEEK]

Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

 Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

Subscribe 

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[ FACT OF THE WEEK]

A quarter of decision-makers surveyed predict that data volumes in their companies will rise by more than 60 per cent by the end of 2014, with the average of all respondents anticipating a growth of no less than 42 per cent.

Sourced from: Analytics.CLUB #WEB Newsletter

Inside CXM: New Global Thought Leader Hub for Customer Experience Professionals

Inside CXMInside CXM, a new online global thought leadership hub for customer experience management (CXM/CEM) professionals, officially launched yesterday. Inside CXM is focused on bringing you the latest insights from the field of customer experience management. According to Inside CXM, their goal with this program is to provide valuable content via experts who have their finger on the pulse of the global customer experience marketplace.

I am happy to announce my partnership (disclosure – I am a paid contributor) with Inside CXM by providing unique content to their site. I join a host of other industry experts who cover topics like aligning your organization to deliver a unified experience, creating contextual customer journeys and using customer insights to build a smarter experience. These experts include Flavio Martins, Andy Reid and Molly Boyer. Check out the other contributors.

My first article for Inside CXM, “Why Customer Experience Management? To Leave the World a Better Place,” focuses on how businesses need to consider that the impact they have on customers goes well beyond their company walls and financial ledger. Business leaders need to remember that customers’ interactions with their company not only impact how the customers feel about the company, but the quality of those interactions can impact, positively or negatively, their personal lives. Customer experiences are, after all, a subset of all of life’s experiences.

 

Originally Posted at: Inside CXM: New Global Thought Leader Hub for Customer Experience Professionals by bobehayes

Mar 19, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Statistically Significant  Source

[ AnalyticsWeek BYTES]

>> 10 groups of machine learning algorithms by administrator

>> Towards Better Visualizations: Part 1 – The Visual Frontier by analyticsweek

>> How can you reap the advantages of Big Data in your enterprise? Services you can expect from a Remote DBA Expert by thomassujain

Wanna write? Click Here

[ FEATURED COURSE]

A Course in Machine Learning

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Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need… more

[ FEATURED READ]

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th Edition

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The eagerly anticipated Fourth Edition of the title that pioneered the comparison of qualitative, quantitative, and mixed methods research design is here! For all three approaches, Creswell includes a preliminary conside… more

[ TIPS & TRICKS OF THE WEEK]

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

[ DATA SCIENCE Q&A]

Q:What are the drawbacks of linear model? Are you familiar with alternatives (Lasso, ridge regression)?
A: * Assumption of linearity of the errors
* Can’t be used for count outcomes, binary outcomes
* Can’t vary model flexibility: overfitting problems
* Alternatives: see question 4 about regularization

Source

[ VIDEO OF THE WEEK]

#GlobalBusiness at the speed of The #BigAnalytics

 #GlobalBusiness at the speed of The #BigAnalytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

You can use all the quantitative data you can get, but you still have to distrust it and use your own intelligence and judgment. – Alvin Tof

[ PODCAST OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

 Understanding Data Analytics in Information Security with @JayJarome, @BitSight

Subscribe 

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[ FACT OF THE WEEK]

This year, over 1.4 billion smart phones will be shipped – all packed with sensors capable of collecting all kinds of data, not to mention the data the users create themselves.

Sourced from: Analytics.CLUB #WEB Newsletter

Measuring Customer Loyalty is Essential for a Successful CEM Program

Customers can exhibit many different types of loyalty behaviors toward a company (e.g., recommend, purchase same, purchase different products, stay/leave), each responsible for different types of business growth. Furthermore, when asked about their loyalty behaviors via relationship surveys, customers’ ratings of loyalty questions show that customer loyalty essentially boils down to three different types of customer loyalty:

  • Retention Loyalty: Degree to which customers will remain as customers or not leave to competitors. This type of loyalty impacts overall customer growth.
  • Advocacy Loyalty: Degree to which customers feel positively toward/will advocate your product/service/brand. This type of loyalty impacts new customer growth.
  • Purchasing Loyalty: Degree to which customers will increase their purchasing behavior. This type of loyalty impacts average revenue per customer.

These three distinct types of customer loyalty form the foundation of the RAPID loyalty approach. Using the RAPID loyalty’s multi-faceted approach helps companies understand how improving the customer experience can improve business growth in different ways. If interested, you can read my recent article on the development of the RAPID loyalty approach.

Product and Service Experience

Customer experience management (CEM) is the process of understanding and managing customers’ interactions with and perceptions about your company/brand. The ultimate goal of this process is to improve the customer experience and, consequently, increase customer loyalty. Two primary customer experience areas that are commonly assessed are the customers’ perception of their 1) product experience and 2) service experience. These two areas are shown to be among the top drivers of customer loyalty; customers who have a good experience in these two areas report higher levels of customer loyalty than customers who have a poor experience.

How does Product and Service Experience Impact Each Type of Customer Loyalty?

To understand the impact of the product and service experience on different facets of customer loyalty, I used existing survey data. Last year, Mob4Hire, a global crowd-sourced testing and market research community, and I conducted a worldwide survey, asking respondents’ about their experience with and loyalty towards their current wireless service provider. To measure the product and service experiences, respondents were asked to indicate their agreement about statements that describes their provider (1 to 5 – higher scores indicate agreement and better customer experience). As a measure of the product experience, we averaged respondent’s ratings across two questions: 1) good coverage in my area and 2) reliable service (few dropped calls). As a measure of the service experience, we averaged respondent’s ratings about their provider’s representatives across 5 areas: 1) responds to needs, 2) has knowledge to answer questions, 3) was courteous, 4) understands my needs and 5) always there when I need them. The survey also asked about the respondents’ loyalty toward their wireless service provider across the three types of loyalty: 1) retention, 2) advocacy and 3) purchasing.

To index the degree of impact that each customer experience dimension has on customer loyalty, I simply correlated the ratings of each customer experience dimension (Coverage/Reliability; Customer Service) with each of the three loyalty measures (Retention, Advocacy, Purchasing). I did this analysis for the entire dataset and then for each of the wireless service providers who had more than 100 respondents. Figure 1 contains the results for the impact of Coverage/Reliability on customer loyalty.

Figure 1. Impact of Product Experience on Retention, Advocacy and Purchasing Loyalty. Click image to enlarge.

As you can see in Figure 1, using the entire sample (far left bars), the product experience has the largest impact on advocacy loyalty (r = .49), followed by purchasing (r = .31) and retention loyalty (r = .34). Similarly, in Figure 2, using the entire sample (far left bars), the service experience has the largest impact on advocacy loyalty (r = .48), followed by purchasing (r = .34) and retention loyalty (r = .32). Generally speaking, while improving the product and service experience will have the greatest impact on advocacy loyalty, improvement in these areas will have an impact, albeit a smaller one, on purchasing and retention loyalty. I find this pattern of results in other industries as well.

Looking at individual wireless service providers in Figures 1 and 2, however, we see exceptions to this rule (Providers were ordered by their Advocacy Loyalty scores.). For example, we see that improving the product experience will have a comparable impact on different types of loyalty for specific companies (Figure 1 – T-Mobile, Safaricom). Additionally, we see that improving the service experience will have a comparable impact on different types of loyalty for specific companies (Figure 2 – Safaricom, MTN, Orange, Warid Telecom, Telenor, and Ufone). The value of improving the service experience is different across companies depending on the types of customer loyalty it impacts. For example, improving the service experience is much more valuable for Safaricom than it is for T-Mobile. Improving the service experience will greatly impact all three types of customer loyalty for Safaricom and only one for T-Mobile.  I suspect the reasons for variability across providers in what drives their customer loyalty could be due to company maturity, the experience delivery process, market pressures and customer type. Deeper analyses (e.g., stepwise regression, path analysis) of these data for specific providers could help shed light on the reasons.

Figure 2. Impact of Service Experience on Retention, Advocacy and Purchasing Loyalty. Click image to enlarge.

Benefits of Measuring Different Types of Customer Loyalty

Improving the customer experience impacts different types of customer loyalty and this pattern varies across specific companies. For some companies, improving the customer experience will primarily drive new customer growth (advocacy loyalty). For other companies, improving the customer experience will also significantly drive existing customer growth (retention and purchasing loyalty).

Companies who measure and understand different types of customer loyalty and how they are impacted by the customer experience have an advantage over companies who measure only one type of loyalty (typically advocacy):

  • Companies can target solutions to optimize different types of customer loyalty to improve business growth. 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.
  • Key performance indicators (KPIs) can be identified 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.
  • Companies are better equipped to quantify the value of their CEM program and obtain more accurate estimates of the Return on Investment (ROI) of the program. 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 new customer growth. When companies measure advocacy, purchasing and retention loyalty, the estimate of ROI is based on new and existing customer growth.

Final Thoughts

The primary goal of CEM is to improve customer loyalty. Companies that narrowly define customer loyalty 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.

Originally Posted at: Measuring Customer Loyalty is Essential for a Successful CEM Program

Mar 12, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data security  Source

[ AnalyticsWeek BYTES]

>> How to Solve 10 Healthcare Challenges with One Predictive Analytics Model by analyticsweek

>> 6 Big Data Analytics Use Cases for Healthcare IT by analyticsweekpick

>> The Usability of Dashboards (Part 1): Does Anyone Actually Use These Things? [Guest Post] by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

R Basics – R Programming Language Introduction

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Learn the essentials of R Programming – R Beginner Level!… more

[ FEATURED READ]

On Intelligence

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Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… more

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q:Is it better to have 100 small hash tables or one big hash table, in memory, in terms of access speed (assuming both fit within RAM)? What do you think about in-database analytics?
A: Hash tables:
– Average case O(1)O(1) lookup time
– Lookup time doesn’t depend on size

Even in terms of memory:
– O(n)O(n) memory
– Space scales linearly with number of elements
– Lots of dictionaries won’t take up significantly less space than a larger one

In-database analytics:
– Integration of data analytics in data warehousing functionality
– Much faster and corporate information is more secure, it doesn’t leave the enterprise data warehouse
Good for real-time analytics: fraud detection, credit scoring, transaction processing, pricing and margin analysis, behavioral ad targeting and recommendation engines

Source

[ VIDEO OF THE WEEK]

Solving #FutureOfWork with #Detonate mindset (by @steven_goldbach & @geofftuff) #JobsOfFuture #Podcast

 Solving #FutureOfWork with #Detonate mindset (by @steven_goldbach & @geofftuff) #JobsOfFuture #Podcast

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[ QUOTE OF THE WEEK]

I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding. – Hal Varian

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

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[ FACT OF THE WEEK]

The Hadoop (open source software for distributed computing) market is forecast to grow at a compound annual growth rate 58% surpassing $1 billion by 2020.

Sourced from: Analytics.CLUB #WEB Newsletter