Aug 31, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Data security  Source

[ AnalyticsWeek BYTES]

>> Wrapping my head around Big-data problem by v1shal

>> Data Science Skills and the Improbable Unicorn by bobehayes

>> When shouldn’t you rely on data analytics – The danger of trusting polls by checcaaird

Wanna write? Click Here

[ NEWS BYTES]

>>
 Software to “Encode the Mindset” of Lawyers – Lawfuel (blog) Under  Prescriptive Analytics

>>
 Pulling Real ROI from a Sea of Big Data Analytics – insideBIGDATA Under  Big Data Analytics

>>
 Four things you should know about machine learning – GovInsider Under  Machine Learning

More NEWS ? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

image

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]

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

image

If you are looking for a book to help you understand how the machine learning algorithms “Random Forest” and “Decision Trees” work behind the scenes, then this is a good book for you. Those two algorithms are commonly u… more

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:Examples of NoSQL architecture?
A: * Key-value: in a key-value NoSQL database, all of the data within consists of an indexed key and a value. Cassandra, DynamoDB
* Column-based: designed for storing data tables as sections of columns of data rather than as rows of data. HBase, SAP HANA
* Document Database: map a key to some document that contains structured information. The key is used to retrieve the document. MongoDB, CouchDB
* Graph Database: designed for data whose relations are well-represented as a graph and has elements which are interconnected, with an undetermined number of relations between them. Polyglot Neo4J

Source

[ VIDEO OF THE WEEK]

Rethinking classical approaches to analysis and predictive modeling

 Rethinking classical approaches to analysis and predictive modeling

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

We chose it because we deal with huge amounts of data. Besides, it sounds really cool. – Larry Page

[ PODCAST OF THE WEEK]

Using Analytics to build A #BigData #Workforce

 Using Analytics to build A #BigData #Workforce

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

According to estimates, the volume of business data worldwide, across all companies, doubles every 1.2 years.

Sourced from: Analytics.CLUB #WEB Newsletter

Rise of Data Capital by Paul Sonderegger

Data capital will replace big data as the big topic of boardroom conversation. This change will have a big effect on competitive strategy, and CEOs need to make decisions and prepare to talk to their boards about their plans. To succeed, CEOs need to embrace the idea that data is now a kind of capital as vital as financial capital to the development of new products, services, and business processes. The implications are far greater than the spread of fact-based decision-making through better analytics. In some cases, data capital substitutes for traditional capital. In fact, the McKinsey Global Institute says that data capital explains most of the valuation premium enjoyed by digitized companies.

But, we’re getting ahead of ourselves. First, we need to acknowledge a few basics.

Because every activity in commercial, public, and private lives uses and produces information, no organization is insulated from the effects of digitization and datafication. Every company is thus subject to three laws of data capital.

  1. Data comes from activity.

Data is a record of what happened. But if you’re not party to the activity when it happens, your opportunity to capture that data is lost. Forever. So, digitize and “datafy” key activities your firm already conducts with customers, suppliers, and partners—before rivals edge you out. At the same time, look up and down your industry’s value chain for activities you’re not part of yet. Invent ways to insert yourself in a digital capacity, thereby increasing your share of data that the industry generates.

Contributed in The Big Analytics: Leader’s Collaborative Book Project Download your FREE copy at TheBigAnalytics
 

Source: Rise of Data Capital by Paul Sonderegger

Customer Churn or Retention? A Must Watch Customer Experience Tutorial

Care about Churn or Retention. Here is a brilliant watch for you.
Care about Churn or Retention. Here is a brilliant watch for you.

Customer retention and reduced churn is in high charts for most of businesses. So, how can companies work through their customer experience to achieve it. In the video below, TCELab touched some brilliant points that could help any company work through their strategy to build a Voice of Customer program.

The video is taken from one of our affiliate calls and it got a lot of positive response, so we decided to use it for educational purposes. If you don’t have 1 hour to spend, here is a trajectory for what is covered and when.

Happy scrolling. Don’t forget to share it with your network so that they could get things right as well.

0:00:07 What is Customer Experience Management (CEM)?
0:02:04 Why do CEO’s care?
0:04:15 Why CEM vendor should be excited?
0:07:15 What does CEM Program looks like?
0:07:45 Design of a CEM Program: CEM Program Components
0:11:20 Design of a CEM Program: Disparate Sources of Business Data
0:14:23 Design of a CEM Program: Data Linkage (connecting data to answer different question)
0:17:17 Design of a CEM Program: Integrating your business data (mapping organization silos with survey type)
0:20:58 Design of a CEM Program: Three ways to grow business… why just NPS is not enough?
0:25:40 TCELab product plug but some cross winds of CEM gold information
0:33:10 TCELab CLAAP Platform but some cross winds of CEM gold information
0:39:00 TCELab product execution process, time-lengths & other relevant information around it (information relevant to affiliate networks)
0:43:30 TCELab product lists (information relevant to affiliate networks)
0:52:40 TCELab case study: Kashoo + lot of good information for SAAS companies CEM program
Blog source

Source by v1shal

Aug 24, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
SQL Database  Source

[ NEWS BYTES]

>>
 Cloud Kings: ‘Geostorm’ Film Imagines Human-Controlled Weather – Space.com Under  Cloud

>>
 Greig: Farmer trust key to big data’s future – Manitoba Co-operator Under  Big Data

>>
 How to conduct an IoT pen test | Network World – Network World Under  IOT

More NEWS ? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

image

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]

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

image

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]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:Do we always need the intercept term in a regression model?
A: * It guarantees that the residuals have a zero mean
* It guarantees the least squares slopes estimates are unbiased
* the regression line floats up and down, by adjusting the constant, to a point where the mean of the residuals is zero

Source

[ VIDEO OF THE WEEK]

Making sense of unstructured data by turning strings into things

 Making sense of unstructured data by turning strings into things

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

It’s easy to lie with statistics. It’s hard to tell the truth without statistics. – Andrejs Dunkels

[ PODCAST OF THE WEEK]

#DataScience Approach to Reducing #Employee #Attrition

 #DataScience Approach to Reducing #Employee #Attrition

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

Remembering Steve Jobs

Remembering Steve Jobs. Click image to enlarge.

Everybody has an opinion about Steve Jobs. Please tell me how he has impacted your life in this brief survey.

I have read more about Steve Jobs after his passing than before. The outpouring of emotion and words of remembrance for him on the Web reflects the impact that he had on people who knew him and people who just used his products. I am part of the latter group.

Writing and Creating

I purchased my first computer, the Macintosh Plus, while I was in graduate school. I was amazed at the things I could do with this machine. I could write, play games (okay, mostly solitaire) and make art.  I wrote my first book, Measuring Customer Satisfaction and Loyalty, on that little magical box. My Mac allowed me to create everything for that book, from text and tables to fancy figures, helping me to describe complex ideas like sampling error. Sixteen years later, those exact figures still appear in the third edition of my book.

That book has greatly impacted my life and career. The process of writing the book helped me through a personal breakup. It helped me learn about the topic on which I was writing. It made me a better writer. The book itself even lead me into a career in helping companies improve the quality of the relationship they have with their customers. Without the computer that Steve Jobs created, I know my life would have been different than what it is today.

Defining Words

Writing and creating art are a big part of my life. To some degree, I have Steve Jobs to thank for that. I created the word cloud you see in this post, combining the words used to describe him after his passing with the image of him on the Apple.com site.  The words are based on many articles/quotes I found online today. Some words represented in this picture are from quotes from President Obama, Mark Zuckerberg, Guy Kawasaki, and Bill Gates, to name a few. The larger the font size, the more frequently that word was used to describe him. This picture represents how people define him, remember him.

I will leave you with words from Steve Jobs. I recently watched a recording of his 2005 commencement address to the graduating class of Stanford. While I enjoyed his entire address, one particular passage resonated with me.

“Remembering that I’ll be dead soon is the most important tool I’ve ever encountered to help me make the big choices in life. Because almost everything — all external expectations, all pride, all fear of embarrassment or failure – these things just fall away in the face of death, leaving only what is truly important. Remembering that you are going to die is the best way I know to avoid the trap of thinking you have something to lose. You are already naked. There is no reason not to follow your heart.”

Steve Jobs

Thanks for following your heart, Steve.

Source by bobehayes

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

[  COVER OF THE WEEK ]

image
Weak data  Source

[ AnalyticsWeek BYTES]

>> Betting the Enterprise on Data with Cloud-Based Disaster Recovery and Backups by jelaniharper

>> Movie Recommendations? How Does Netflix Do It? A 9 Step Coding & Intuitive Guide Into Collaborative Filtering by nbhaskar

>> Big Data: Career Opportunities Abound in Tech’s Hottest Field by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 The secret to cloud security: elasticity | Healthcare IT News – Healthcare IT News Under  Cloud Security

>>
 Salesforce Introduces Einstein Account-Based Marketing, Built on the World’s #1 CRM Platform – MarTech Series (press release) (blog) Under  Sales Analytics

>>
 Cloud Control: Data Security Hazards and How to Avoid Them – The Legal Intelligencer Under  Data Security

More NEWS ? Click Here

[ FEATURED COURSE]

CS109 Data Science

image

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

[ FEATURED READ]

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

image

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for e… 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:Is it better to design robust or accurate algorithms?
A: A. The ultimate goal is to design systems with good generalization capacity, that is, systems that correctly identify patterns in data instances not seen before
B. The generalization performance of a learning system strongly depends on the complexity of the model assumed
C. If the model is too simple, the system can only capture the actual data regularities in a rough manner. In this case, the system poor generalization properties and is said to suffer from underfitting
D. By contrast, when the model is too complex, the system can identify accidental patterns in the training data that need not be present in the test set. These spurious patterns can be the result of random fluctuations or of measurement errors during the data collection process. In this case, the generalization capacity of the learning system is also poor. The learning system is said to be affected by overfitting
E. Spurious patterns, which are only present by accident in the data, tend to have complex forms. This is the idea behind the principle of Occam’s razor for avoiding overfitting: simpler models are preferred if more complex models do not significantly improve the quality of the description for the observations
Quick response: Occam’s Razor. It depends on the learning task. Choose the right balance
F. Ensemble learning can help balancing bias/variance (several weak learners together = strong learner)
Source

[ VIDEO OF THE WEEK]

Understanding How Fitness Tracker Works via @STEAMTribe #STEM #STEAM

 Understanding How Fitness Tracker Works via @STEAMTribe #STEM #STEAM

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Getting information off the Internet is like taking a drink from a firehose. – Mitchell Kapor

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ 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

Development of the Customer Sentiment Index: Lexical Differences

This is Part 2 of a series on the Development of the Customer Sentiment Index (see introduction, and Part 1). The CSI assesses the extent to which customers describe your company/brand with words that reflect positive or negative sentiment. This post covers the development of a judgment-based sentiment lexicon and compares it to empirically-based sentiment lexicons.

Last week, I created four sentiment lexicons for use in a new customer experience (CX) metric, the Customer Sentiment Index (CSI). The four sentiment lexicons were empirically derived using data from a variety of online review sites from IMDB, Goodreads, OpenTable and Amazon/Tripadvisor. This week, I develop a sentiment lexicon using a non-empirical approach.

Human Judgment Approach to Sentiment Classification

The judgment-based approach does not rely on data to derive the sentiment values; rather this method requires the use of subject matter experts to classify words into sentiment categories. This approach is time-consuming, requiring the subject matter experts to manually classify each of the thousands of words in our empirically-derived lexicons. To minimize the work required by the subject matter experts, an initial set of opinion words were generated using two studies.

In the first study, as part of an annual customer survey, a B2B technology company included an open-ended survey question, “Using one word, please describe COMPANY’S products/services.” From 1619 completed surveys, 894 customers provided an answer for the question. Many respondents used multiple words or the company’s name as their response, reducing the number of useful responses to be 689. Of these respondents, a total of 251 usable unique words were used by respondents.

Also, the customer survey included questions that required customers to provide ratings on measures of customer loyalty (e.g., overall satisfaction, likelihood to recommend, likelihood to buy different products, likelihood to renew) and satisfaction with the customer experience (e.g., product quality, sales process, ease of doing business, technical support).

In the second study, as part of a customer relationship survey, I solicited responses from customers of wireless service providers (B2C sample). The sample was obtained using Mechanical Turk by recruiting English-speaking participants to complete a short customer survey about their experience with their wireless service provider. In addition to the standard rated questions in the customer survey (e.g., customer loyalty, CX ratings), the following question was used to generate the one word opinion: “What one word best describes COMPANY? Please answer this question using one word.

From 469 completed surveys, 429 customers provided an answer for the question, Many respondents used multiple words or the company’s name as their response, reducing the number of useful responses to be 319. Of these respondents, a total of 85 usable unique words were used by respondents.

Sentiment Rating of Opinion Words

The list of customer-generated words for each sample was independently rated by the two experts. I was one of those experts. My good friend and colleague was the other expert. We both hold a PhD in industrial-organizational psychology and specialize in test development (him) and survey development (me). We have extensive graduate-level training on the topics of statistics and psychological measurement principles. Also, we have applied experience, helping companies gain value from psychological measurements. We each have over 20 years of experience in developing/validating tests and surveys.

For each list of words (N = 251 and N = 85), each expert was given the list of words and was instructed to “rate each word on a scale from 0 to 10; where 0 is most negative sentiment/opinion and 10 is most positive sentiment/opinion; and 5 is the midpoint.” After providing their first rating of each word, each of the two raters were then given the opportunity to adjust their initial ratings for each word. For this process, each rater was given the list of 251 words with their initial rating and were asked to make any adjustments to their initial ratings.

Results of Human Judgment Approach to Sentiment Classification

Table 1.  Descriptive Statistics and Correlations of Sentiment Values across Two Expert Raters
Table 1. Descriptive Statistics and Correlations of Sentiment Values across Two Expert Raters

Descriptive statistics of and correlations among the expert-derived sentiment values of customer-generated words appears in Table 1. As you can see, the two raters assign very similar sentiment ratings to words for both sets. Average ratings were similar. Also, the inter-rater agreement between the two raters for the 251 words was r = .87 and for the 85 words was .88.

After slight adjustments, the inter-rater agreement between the two raters improved to r = .90 for the list of 251 words and .92 for the list of 85 words. This high inter-rater agreement indicated that the raters were consistent in their interpretation of the two lists of words with respect to sentiment.

Figure 1. Distribution of
Figure 1. Distribution of Sentiment Values of Customer-Generated Words using Subject Matter Experts’ Sentiment Lexicon

Because of the high agreement between the raters and comparable means between raters, an overall sentiment score for each word was calculated as the average of the raters’ second/adjusted rating (See Table 1 or Figure 2 for descriptive statistics for this metric).

Comparing Empirically-Derived and Expert-Derived Sentiment

In all, I have created five lexicons; four lexicons are derived empirically from four data sources (i.e., OpenTable, Amazon/Tripadvisor, Goodreads and IMDB) and one lexicon is derived using subject matter experts’ sentiment classification.

Table 2. Descriptive Statistics and Correlations among Sentiment Values of Customer-Generated Words across Five Sentiment Lexicons (N = 251)
Table 2. Descriptive Statistics and Correlations among Sentiment Values of Customer-Generated Words across Five Sentiment Lexicons (N = 251)

I compared these five lexicons to better understand the similarity and differences of each lexicon. I applied the four empirically-derived lexicons to each list of customer-generated words. So, in all, for each list of words, I have 5 sentiment scores.

The descriptive statistics of and correlations among the five sentiment scores for the 251 customer-generated words appears in Table 2. Table 3 houses the information for the 85 customer-generated words.

Table 3. Descriptive Statistics and Correlations among Sentiment Values of Customer-Generated Words across Five Sentiment Lexicons (N = 85)
Table 3. Descriptive Statistics and Correlations of among Sentiment Values of Customer-Generated Words across 5 Sentiment Lexicons (N=85)

As you can see, there is high agreement among the empirically-derived lexicons (average correlation = .65 for the list of 251 words and .79 for the list of 85 words.

There are statistically significant mean differences across the empirically-derived lexicons; Amazon/Tripadvisor has the highest average sentiment value and Goodreads has the lowest. Lexicons from IMDB and OpenTable provide similar means. The expert judgment lexicon provides the lowest average sentiment ratings for each list of customer-generated words. The absolute sentiment value of a word is dependent on the sentiment lexicon you use. So, pick a lexicon and use it consistently; changing your lexicon could change your metric.

Looking at the the correlations of the expert-derived sentiments with each of the empirically-derived sentiment, we see that OpenTable lexicon had higher correlation with the experts compared to the Goodreads lexicon. The pattern of results make sense. The OpenTable sample is much more similar to the sample on which the experts provided their sentiment ratings. OpenTable represents a customer/supplier relationship regarding a service while the Goodreads’ sample represents a different type of relationship (customer/book quality).

Summary and Conclusions

These two studies demonstrated that subject matter experts are able to scale words along a sentiment scale. There was high agreement among the experts in their classification.

Additionally, these judgment-derived lexicons were very similar to four empirically derived lexicons. Lexicons based on subject matter experts’ sentiment classification/scaling of words are highly correlated to empirically-derived lexicons. It appears that each of the five sentiment lexicons tells you roughly the same thing as the other lexicons.

The empirically-derived lexicons are less comprehensive than the subject matter experts’ lexicons regarding customer-generated words. By design, the subject matter experts classified all words that were generated by customers; some of the words that were used by the customers do not appear in the empirically-derived lexicons. For example, the OpenTable lexicon only represents 65% (164/251) of the customer-generated words for Study 1 and 71% (60/85) of the customer-generated words for Study 2. Using empirically-derived lexicons for the purposes of calculating the Customer Sentiment Index could be augmented using lexicons that are based on subject matter experts’ classification/scaling of words.

In the next post, I will continue presenting information about the validating the Customer Sentiment Index (CSI). So far, the analysis shows that the sentiment scores of the CSI are reliable (we get similar results using different lexicons). We now need to understand what the CSI is measuring. I will show this by examining the correlation of the CSI with other commonly used customer metrics, including likelihood to recommend (e.g., NPS), overall satisfaction and CX ratings of important customer touch points (e.g., product quality, customer service). Examining correlations of this nature will also shed light on the usefulness of the CSI in a business setting.

Source: Development of the Customer Sentiment Index: Lexical Differences

Aligning Sales Talent to Drive YOUR Business Goals

5steps_analytics
A confluence of new capabilities is creating an innovative, more precise approach to performance improvement. New approaches include advanced analytics, refined sales competency and behavioral models, adaptive learning, and multiple forms of technology enablement. In a prior post (The Myth of the Ideal Sales Profile) we explored an emerging new paradigm that is disrupting traditional thinking with respect to best practices: the world according to YOU.

However, with only 17% of sales organizations leveraging sales talent analytics (TDWI Research), it seems that most CSO’s and their HR business partners are gambling — using intuition as the basis for making substantial investments in sales development initiatives. If the gamble doesn’t pay off, then the investment is wasted.

Is your sales talent aligned to your company’s strategy of increasing revenue? According to the Conference Board, 73% of CEO’s say no. This lack of alignment is the main reason why 86% of CSO’s expect to miss their 2015 revenue targets (CSO Insights). The ability to properly align your sales talent to your company’s business goals is the difference between being in the 86% or the 14%.

What Happens When You Assume?

Historically, sales and Human Resource leaders based sales talent alignment decisions — both development of the existing team and acquisition of future talent — on assumptions and somewhat subjective data.

Common practices include:

  • Polling the field to determine the focus for sales training
  • Hiring sales talent based largely on the subjective opinion of interviewers
  • Defining your “ideal seller profile” based on the guidance of industry pundits
  • Making a hiring decision based on the fact that the candidate made Achiever’s Club 3 of the last 5 years at their previous company
  • Deploying a sales training program based on what a colleague did at their last company

Aligning sales talent based on any of the above is likely to land your company in the 86% because these approaches fail far more times than they succeed. They fail to consider the many cause-and-effect elements that impact success in your company, in your markets, for your products, and for your customers. As proof of their low success rate, a groundbreaking study by ES Research found that 90% of sales training [development initiatives] had no lasting impact after 120 days. And the news isn’t any better when it comes to sales talent acquisition; Accenture reports that the average ramp-up time for new reps is 7-12 months.

Defining YOUR Ideal Seller Profile(s)

So how does your organization begin to apply the “new way” (see illustration below) as an approach to optimize sales performance? It begins with zeroing in on the capabilities of your salespeople that align most closely to the specific goals of your business. In essence, it means understanding what the YOUR ideal seller profiles are.

Applying the new way begins with specific business goals of your company. What if market share growth was the preeminent strategic goal for your organization? Would it not be extremely valuable to understand which sales competencies were most likely to impact that aspect of your corporate strategy? The obvious answer is yes; and the obvious question is how align and optimize sales to drive increased market share?

How does a CSO identify where to target development in order to have the biggest impact on business results?

By using facts as the basis for these substantial investments. Obtaining facts requires several essential ingredients. The first is a rigorous, comprehensive model for sales competencies; that is, a well-defined model of “what good looks like” for a broad range of sales competencies. This model can be adapted for a specific selling organization, and provides the baseline sales-specific assessments (personality, knowledge, cognitive ability, behavior, etc.).

Then, by applying advanced analytics, including Structural Equations Modeling (SEM) – we can begin to identify cause-effect relationships between specific competencies and the metrics and goals of YOUR organization. With SEM, CSO’s can statistically identify the knowledge and behavior that set top-performers apart from the rest of their team. With this valuable insight, the organization can now align both talent development and acquisition to the company’s most important business goals.

Sales Talent Analytics Provide Proof

Times have changed. The days of aligning sales talent based on gut feel, assumptions or generally accepted best-practices are over. By leveraging sales talent analytics, today’s sales leader can apply a proven 3-step approach to stop gambling and get the facts to statistically pinpoint where to focus development of the sales team, quantifiably measure the business impact / ROI of that development, and improve the quality of new hires. But buyer beware; not all analytical approaches are equal. The vast majority leverage correlation-based analytics which can lead to erroneous conclusions.

By the way we’re not eschewing well designed research that provides insights into broader application of best practices. Aberdeen Group found that best-in-class sales teams that leverage data and analytics increased team quota attainment 12.3% YOY (vs. 1% for an average company) and increased average deal size 8% YOY (vs. 0.8%)

It’s time to define the ideal seller profile for YOUR company. In our next post in this series, we answer the question – how do we capitalize on that understanding to drive the highest impact on our business goals?

Source: Aligning Sales Talent to Drive YOUR Business Goals by analyticsweekpick

Aug 10, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Pacman  Source

[ AnalyticsWeek BYTES]

>> The New Analytics Professional: Landing A Job In The Big Data Era by analyticsweekpick

>> The Reliability and Validity of the Consumer Financial Protection Bureau (CFPB) Complaint Database by bobehayes

>> Linking Constituency and VoC Metrics by bobehayes

Wanna write? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

image

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]

Data Science from Scratch: First Principles with Python

image

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn … 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 does NLP stand for?
A: * Interaction with human (natural) and computers languages
* Involves natural language understanding

Major tasks:
– Machine translation
– Question answering: “what’s the capital of Canada?”
– Sentiment analysis: extract subjective information from a set of documents, identify trends or public opinions in the social media

– Information retrieval

Source

[ VIDEO OF THE WEEK]

Advanced #Analytics in #Hadoop

 Advanced #Analytics in #Hadoop

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Without big data, you are blind and deaf and in the middle of a freeway. – Geoffrey Moore

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Poor data across businesses and the government costs the U.S. economy $3.1 trillion dollars a year.

Sourced from: Analytics.CLUB #WEB Newsletter