The Importance of Workforce Analytics

Although organizations must make decisions based on a variety of factors and perspectives, few are as important as human resources when it comes to taking action. A company’s workforce is vitally important but concurrently one of the more complex sides of operating a business. Instead of clean, hard data, employees can present a variety of qualitative factors that are hard to put into numbers that work for analytics.

Even so, an organization’s human capital is perhaps its most important asset. Building an in-depth understanding of your staff can, therefore, deliver better answers and give you a competitive edge. More than acting as a way of punishing employees, however, workforce analytics—sometimes called people analytics—can empower your team by providing better insights as to what works and doesn’t. Furthermore, it can help uncover all the tools employees need to succeed. Let’s begin by breaking down the meaning of workforce analytics.

What are Workforce Analytics?

Workforce analytics, which is a part of HR analytics, are used to track and measure employee-related data and optimize organizations’ human resource management and decision-making. The field focuses on much more than hiring and firing by also concentrating on the return on value for every hire. Moreover, it highlights more specific data that assists with identifying workplace trends such as potential risk factors, satisfaction with decisions, and more.

Additionally, workforce analytics can evaluate more than just existing staff by also analyzing the trends that surround employment. For instance, companies can see which periods of the year have a higher number of applicants and adjust their recruitment efforts, or measure diversity efforts as well as employee engagement without having to resort to more invasive or subjective methods that may provide false positives.

What Are some Key Benefits of Workforce Analytics?

More so than tracking the number of employees and what they’re making, workforce analytics provides a comprehensive view of your organization’s workers designed to interpret historic trends and create predictive models that lead to insights and better decisions in the future. Some of the key benefits of workforce analytics include:

  • Find areas where efficiency can be improved with automation – While workers are an asset to a company, sometimes the tasks they do can reduce their productivity or provide minimal returns. Workforce analytics can discover areas where tasks can be relegated to machines via automation, allowing workers to instead dedicate their efforts to more important and valuable activities.
  • Improve workers’ engagement by understanding their needs and satisfaction – More than simply looking for firing and hiring information, workforce and people analytics can help a company understand why their employees are not performing their best, and the factors that are impacting productivity. This is more to maintain the current workforce instead of replacing it. The goal is to uncover those factors affecting performance and engagement and to overcome them by fostering better conditions.
  • Create better criteria for hiring new staff and provide a better hiring process – Finding new talent is always complex regardless of a company’s size or scope. Workforce analytics can shed light exactly on what is needed from a new hire by a department based on previous applicants, their success, and the company’s needs. More importantly, they can understand new candidates based on this historical data to determine whether they would be a good fit or not. For instance, a company seeking to hire a new developer may think twice about hiring a server-side programmer after several previous hires with similar experience didn’t work out.

What Key Metrics Should I Track for Workforce Analytics?

  • Employee productivity – We still talk about the 9 to 5 work day, but the current reality for many employees dictates that work hours tend to be more flexible and variable. As such, measuring productivity by the number of hours worked is no longer fully accurate. Instead, creating a productivity index which includes a few different data points will give a much better idea of how employees are performing.
  • Early turnover – Another important area that is often neglected when measuring satisfaction is how quickly employees are leaving on their own. A high early turnover rate is an indicator that things are not working both in terms of meeting expectations and employee satisfaction.
  • Engagement – This may seem superfluous, but employees who are engaged with their work are more likely to be productive. Measuring engagement includes tracking employee satisfaction, stress levels, and employees’ belief in the company’s ideals. High engagement is a great sign that HR is doing its job.

Conclusion

Focusing your data gathering internally can help you improve your company’s productivity. By honing in on your human resources and finding ways to empower your team, people analytics can boost your company’s efficiency, leading to happier and more productive colleagues.

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Jul 11, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Insights  Source

[ LOCAL EVENTS & SESSIONS]

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[ AnalyticsWeek BYTES]

>> The Challenges Canadian Companies Face When Implementing Big Data by analyticsweekpick

>> Voices in AI – Episode 89: A Conversation with Doug Lenat by analyticsweekpick

>> BARC Survey Shows New Benefits from Embedded Analytics by analyticsweek

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Statistical Thinking and Data Analysis

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This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and n… more

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The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

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People love statistics. Statistics, however, do not always love them back. The Signal and the Noise, Nate Silver’s brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Da… more

[ TIPS & TRICKS OF THE WEEK]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ 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

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[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

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[ QUOTE OF THE WEEK]

The data fabric is the next middleware. – Todd Papaioannou

[ PODCAST OF THE WEEK]

Understanding #FutureOfData in #Health & #Medicine - @thedataguru / @InovaHealth #FutureOfData #Podcast

 Understanding #FutureOfData in #Health & #Medicine – @thedataguru / @InovaHealth #FutureOfData #Podcast

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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

Webinar: Improving the Customer Experience Using Big Data, Customer-Centric Measurement and Analytics

pivotal-logo-taglineI recently gave a talk on how to improve the customer experience using Big Data, customer-centric measurement and analytics. My talk was hosted by the good people at Pivotal (recently Cetas).

You can view the webinar by registering here or you can view the slides below. In this webinar, Improving the Customer Experience Using Big Data, Customer-Centric Measurement and Analytics, I include content from my new book “TCE – Total Customer Experience: Building Business Through Customer-Centric Measurement and Analytics.” I discuss three areas: measuring the right customer metrics, integrating disparate data silos and using Big Data to answer strategic business questions. Using the right customer metrics in conjunction with other business data, businesses will be able to extract meaningful results that help executives make the right decisions to move their company forward.

In the book, I present best practices in measurement and analytics for customer experience management (CEM) programs.  Drawing on decades of research and practice, I illustrate analytical best practices in the field of customer experience management that will help you increase the value of all your business data to help improve the customer experience and increase customer loyalty.

 

Originally Posted at: Webinar: Improving the Customer Experience Using Big Data, Customer-Centric Measurement and Analytics by bobehayes

Jul 04, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data security  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ FEATURED COURSE]

Master Statistics with R

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In this Specialization, you will learn to analyze and visualize data in R and created reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform fre… more

[ FEATURED READ]

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

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Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored f… 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 do you think about the idea of injecting noise in your data set to test the sensitivity of your models?
A: * Effect would be similar to regularization: avoid overfitting
* Used to increase robustness

Source

[ VIDEO OF THE WEEK]

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

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

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[ 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  John Young, @Epsilonmktg

 #BigData @AnalyticsWeek #FutureOfData #Podcast with John Young, @Epsilonmktg

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[ 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

How Google Is Using People Analytics To Completely Reinvent HR

goo22_2-copy
First of two parts

If you haven’t seen it in the news, after its stock price broke the $800 barrier last month, Google moved into the No. 3 position among the most valuable firms in the world.

Google is clearly the youngest firm among the leaders; it has surprisingly been less than a decade since Google’s IPO.

Most companies on the top 20 market cap list could be accurately described as “old school,” because most can attribute their success to being nearly half a century old, having a long established product brand, or through great acquisitions. Google’s market success can instead be attributed to what can only be labeled as extraordinary people management practices that result from its use of “people analytics.”

A new kind of people management

The extraordinary marketplace success of Google (and Apple, which is No. 1 on the list) is beginning to force many business leaders to take notice and to come to the realization that there is now a new path to corporate greatness.

“New path” firms dominate by producing continuous innovation. And executives are beginning to learn that continuous innovation cannot occur until a firm makes a strategic shift toward a focus on great people management.

A strategic focus on people management is necessary because innovations come from people, and you simply can’t maximize innovations unless you are capable of recruiting and retaining innovators. And even then, you must provide them with great managers and an environment that supports innovation.

Unfortunately, making that transition to an innovative firm is problematic because almost every current HR function operates under 20th century principles of past practices, efficiency, risk avoidance, legal compliance, and hunch-based people management decisions. If you want serial innovation, you will need to reinvent traditional HR and the processes that drive innovation.

Shifting to data-based people management

The basic premise of the “people analytics” approach is that accurate people management decisions are the most important and impactful decisions that a firm can make. You simply can’t produce superior business results unless your managers are making accurate people management decisions.

Many do argue that product R&D, marketing, or resource allocation decisions are instead the most impactful decisions. However, each one of those business decisions is made by an employee. If you hire and retain mostly mediocre people and you provide them with little data, you can only assume that they will make mediocre decisions in each of these important business areas, as well as in people management decisions.

No one in finance, supply chain, marketing, etc. would ever propose a solution in their area without a plethora of charts, graphs, and data to support it, but HR is known to all too frequently rely instead on trust and relationships. People costs often approach 60 percent of corporate variable costs, so it makes sense to manage such a large cost item analytically.

Another major problem in HR is its traditional reliance on relationships. Relationships are the antithesis of analytical decision-making. The decision-making “currency” for most business decisions has long been data, but up until now, HR has relied on a different currency: that of building relationships.

In direct contrast, Google’s success has to be attributed in large part to the fact that it is the world’s only data-driven HR function. Google’s business success should convince executives at any firm that wants to grow dramatically that they must at least consider adopting the data and analytically based model used by Google. Its approach has resulted in Google producing amazing workforce productivity results that few can match (on average, each employee generates nearly $1 million in revenue and $200,000 in profit each year).

How does the Google approach reinvent HR?

HR at Google is dramatically different from the hundreds of other HR functions that I have researched and worked with. To start with, at Google it’s not called human resources; instead, the function is called “people operations.” The VP and HR leader Laszlo Bock has justifiably learned to demand data-based decisions everywhere.

People management decisions at Google are guided by the powerful “people analytics team.” Two key quotes from the team highlight their goals:

  • “All people decisions at Google are based on data and analytics.”
  • The goal is to … “bring the same level of rigor to people-decisions that we do to engineering decisions.”

Google is replacing the 20th century subjective decision-making approach in HR. Although it calls its approach “people analytics,” it can alternatively be called “data-based decision-making,” “algorithm based decision-making,” or “fact or evidence-based decision-making.”

Top 10 reasons for Google’s people analytics approach

The people analytics team reports directly to the VP and it has a representative in each major HR function. It produces many products, including employee surveys that are not anonymous, and dashboards. It also attempts to identify insightful correlations and to provide recommended actions. The goal is to substitute data and metrics for the use of opinions.

Almost everyone has by now heard about Google’s free food, 20% time, and wide range of fun activities but realize that each of these was implemented and are maintained based on data. Many of Google’s people analytics approaches are so unusual and powerful, I can only describe them as “breathtaking.”

Below I have listed my “Top 10” of Google’s past and current people management practices to highlight its data-driven approach:

  1. Leadership characteristics and the role of managers –ts “project oxygen” research analyzed reams of internal data and determined that great managers are essential for top performance and retention. It further identified the eight characteristics of great leaders. The data proved that rather than superior technical knowledge, periodic one-on-one coaching which included expressing interest in the employee and frequent personalized feedback ranked as the No. 1 key to being a successful leader. Managers are rated twice a year by their employees on their performance on the eight factors.
  2. The PiLab — Google’s PiLab is a unique subgroup that no other firm has. It conducts applied experiments within Google to determine the most effective approaches for managing people and maintaining a productive environment (including the type of reward that makes employees the happiest). The lab even improved employee health by reducing the calorie intake of its employees at their eating facilities by relying on scientific data and experiments (by simply reducing the size of the plates).
  3. A retention algorithm — Google developed a mathematical algorithm to proactively and successfully predict which employees are most likely to become a retention problem. This approach allows management to act before it’s too late and it further allows retention solutions to be personalized.
  4. Predictive modeling – People management is forward looking at Google. As a result, it develops predictive models and use “what if” analysis to continually improve their forecasts of upcoming people management problems and opportunities. It also uses analytics to produce more effective workforce planning, which is essential in a rapidly growing and changing firm.
  5. Improving diversity – Unlike most firms, analytics are used at Google to solve diversity problems. As a result, the people analytics team conducted analysis to identify the root causes of weak diversity recruiting, retention, and promotions (especially among women engineers). The results that it produced in hiring, retention, and promotion were dramatic and measurable.
  6. An effective hiring algorithm – One of the few firms to approach recruiting scientifically, Google developed an algorithm for predicting which candidates had the highest probability of succeeding after they are hired. Its research also determined that little value was added beyond four interviews, dramatically shortening time to hire. Google is also unique in its strategic approach to hiring because its hiring decisions are made by a group in order to prevent individual hiring managers from hiring people for their own short-term needs. Under “Project Janus,” it developed an algorithm for each large job family that analyzed rejected resumes to identify any top candidates who they might have missed. They found that they had only a 1.5% miss rate, and as a result they hired some of the revisited candidates.
  7. Calculating the value of top performers – Google executives have calculated the performance differential between an exceptional technologist and an average one (as much as 300 times higher). Proving the value of top performers convinces executives to provide the resources necessary to hire, retain, and develop extraordinary talent. Google’s best-kept secret is that people operations professionals make the best “business case” of any firm in any industry, which is the primary reason why they receive such extraordinary executive support.
  8. Workplace design drives collaboration – Google has an extraordinary focus on increasing collaboration between employees from different functions. It has found that increased innovation comes from a combination of three factors: discovery (i.e. learning), collaboration, and fun. It consciously designs its workplaces to maximize learning, fun, and collaboration (it even tracks the time spent by employees in the café lines to maximize collaboration). Managing “fun” may seem superfluous to some, but the data indicates that it is a major factor in attraction, retention, and collaboration.
  9. Increasing discovery and learning – Rather than focusing on traditional classroom learning, the emphasis is on hands-on learning (the vast majority of people learn through on the job learning). Google has increased discovery and learning through project rotations, learning from failures, and even through inviting people like Al Gore and Lady Gaga to speak to their employees. Clearly self-directed continuous learning and the ability to adapt are key employee competencies at Google.
  10. It doesn’t dictate; it convinces with data — The final key to Google’s people analytics team’s success occurs not during the analysis phase, but instead when it present its final proposals to executives and managers. Rather than demanding or forcing managers to accept its approach, it instead acts as internal consultants and influences people to change based on the powerful data and the action recommendations that they present. Because its  audiences are highly analytical (as most executives are), it uses data to change preset opinions and to influence.

Article originally published Here.

Originally Posted at: How Google Is Using People Analytics To Completely Reinvent HR

Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData

[youtube https://www.youtube.com/watch?v=wpyobHjuybM]

In this podcast Steve Goldbach & Geoff Tuff from Deloitte sat with Vishal to discuss their recently release book “Detonate”. They shared their insights on a cleaner way to create strategies for a future proof and transformation friendly organization. Their tactical suggestions goes a long way in helping install a robust strategy to increase responsiveness.

Steve / Geoff’s Recommended Read:
Geoff’s suggestion:
Cloud Atlas: A Novel by David Mitchell The Opposable mind https://amzn.to/2rA5BAV
The Last Days of Night: A Novel by Graham Moore https://amzn.to/2rAvErB

Steve’s suggestion:
Thinking, Fast and Slow by Daniel Kahneman https://amzn.to/2ryIx5C
The Opposable Mind: How Successful Leaders Win Through Integrative Thinking by Roger L. Martin https://amzn.to/2Kds7Y2
The Big Short: Inside the Doomsday Machine by Michael Lewis https://amzn.to/2KaFgRI

Podcast Link:
iTunes: http://math.im/itunes
GooglePlay: http://math.im/gplay

Geoff’s Bio:
GEOFF TUFF is a principal at Deloitte and a senior leader of the firm’s Innovation and Applied Design practices. In the past, he led the design firm Doblin and was a senior partner at Monitor Group, serving as a member of its global Board of Directors before the company was acquired by Deloitte. He has been with some form of Monitor for more than 25 years. He holds degrees from Dartmouth College and Harvard Business School.

Steve’s Bio:
STEVEN GOLDBACH is a principal at Deloitte and serves as the organization’s chief strategy officer. He is also a member of the Deloitte U.S. executive leadership team. Before joining Deloitte, Goldbach was a partner at Monitor Group and head of its New York office. Goldbach helps executives and their teams transform their organizations by making challenging and pragmatic strategy choices in the face of uncertainty. He is an architect, expert practitioner, and teacher of the variety of strategy methodologies developed and used by Monitor Deloitte over the years. Serving clients in many industries, including consumer products, telecommunications, media and health care, Goldbach helps companies combine rigor and creativity to create their own future. He holds degrees from Queen’s University at Kingston and Columbia Business School

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

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Register your interest @ http://play.analyticsweek.com/guest/

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Keywords:
#FutureOfData #Leadership #Podcast #Future of #Work #Worker & #Workplace

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Jun 27, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Tour of Accounting  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> The Big Data Challenge: Generating Actionable Insight by analyticsweekpick

>> @EdwardBoudrot / @Optum on #DesignThinking in Data Driven Products by v1shal

>> Apache Spark for Big Analytics by thomaswdinsmore

Wanna write? Click Here

[ FEATURED COURSE]

Probability & Statistics

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This course introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and… more

[ FEATURED READ]

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

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Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored f… 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: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]

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

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

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[ QUOTE OF THE WEEK]

Big Data is not the new oil. – Jer Thorp

[ PODCAST OF THE WEEK]

Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

 Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

Subscribe 

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[ FACT OF THE WEEK]

We are seeing a massive growth in video and photo data, where every minute up to 300 hours of video are uploaded to YouTube alone.

Sourced from: Analytics.CLUB #WEB Newsletter

Santa 2.0, What Santa could do with technology

Santa 2.0, What Santa could do with technology
Santa 2.0, What Santa could do with technology

Small 24-hour window to deliver all the present, how complicated the task is for Santa. Beyond the imagination of any logistics company today. So, doing this huge task in short amount of time, what should Santa do? Certainly, leveraging technology as catalyst goes a long way in making sure Santa’s day is not as crazy as it sounds. 1.8 million Kids under 14 years of age to serve, 24hours to spare. Delivering Gifts, Picture Stops with kids are some of many things Santa is tasked to do.

In the infographics below, financesonline put together some state of the art technologies to help Santa be a better, faster and optimal Santa. Santanet, Naughtiness Detector, Global Gift Network, Power boosters to name a few. Hyperloops, Amazon drones, Bounce Image explorer and Optical camouflage technologies are also used as well. So how much it all costs? A whooping $226b. This is price tags for helping Santa do his job better.

Let me stop talking and take you to this amazing infographics:

Cyber Santa Claus: Amazon Drones, Supercomputers and Other Cutting-edge Technologies Santa Can Use To Deliver Christmas Gifts
Designed by www.financesonline.com | Author: Julia Trello | Visit our Google+

Source: Santa 2.0, What Santa could do with technology by v1shal

House holding Analytics for Insurance

In a recent survey done by the West Monroe Partners, 2/3rds of insurance carriers indicated that they weren’t sure about the quality or accuracy of their data. Insurance as a business is driven by sales agents. Which means that nearly 70% or more of insurance business comes from sales agents. Most of the times, the end consumers relationship and day to day dealings are with the sales agent, who then interfaces with the carrier. Renewals, cross sell and retention efforts are driven by the sales agent.

 

While this works well for the carrier in terms of reach and scale, it does put them at one big disadvantage – lack of complete visibility into their customers profiles. The carrier is largely dependent on the agent to provide accurate and up-to-date information. This would include demographic data, policy relevant data, addresses etc… This opacity into customer data has many challenges:

 

Incorrect or Stagnant Data

Often when customers change addresses, or if there is a notable change in their life stage such as getting married or starting a family or getting a promotion etc…, these updates do not find their way back to the carrier.

Operational Inefficiencies / Contactability

While the sales agents are critical to the business of the carrier, the quality of customer information is paramount. Incorrect contact details such as incorrect email ids or WRONG contact numbers hamper a carrier’s efforts in cross sell, retention and even risk assessment.

Individual and Households

Most insurers look at policy holders in isolation. It may be quite possible that more than one individuals have different or multiple policies from the same insurer. As such the premium impact of the household is larger than that of the individuals. Insurers cannot optimize for cross sell or upsell keeping in mind the overall portfolio of the household.

Customer Feedback

When the policy holders relationship is with the sales agent, the carrier may not be able to get timely feedback from their consumers. In such cases, the carrier is never really sure of the end consumer experience (CX) it delivers.

 

Household Analytics

House-holding is the process of identifying connected individuals or households in an insurance portfolio. This is important from the point of view of an insurance company because it gives them a chance to run campaigns involving households rather than individual customers. It also gives them a chance to arrest cascading lapses in a household when they have a view of the households of risky customers.

 

Given a portfolio of policies, the process of house-holding involves 2 steps:

  1. Identifying individual customers (or persons) in the portfolio
  2. Identifying relations between the individuals identified in step 1 in order to be able to identify households.

 

There is a variety of information available about relationships between customers in the policy portfolio. This includes:

 

  1. The ‘proposer – insurer’ pair in every policy. (The fact that person A has bought a policy insuring person B is, barring group insurance, indicative of a relation between person A and person B.
  2. Common addresses for different customers – indicating that those individuals are staying at the same place.
  3. The nominee information and corresponding relationship information stored by insurance companies.

 

Potentially all of this information can be used to establish households, but before doing this we need to bear one important fact in mind. There are 2 kinds of errors that can occur in house-holding: False Negatives and False Positives.

We have detailed these in our white paper on Householding and it can be downloaded from here.

 

Advantages of Household Analytics

Householding is an important activity that can help unearth critical linkages between policy holders. A few immediate benefits that householding can provide are:

Contactability

By understanding which household a person belongs to, it may be possible to reach out to their alternate contact / email addresses. This is critical when there are important policy related updates that need to be communicated.

Cross Sell / Up Sell

The skill to identify and position a product that may be relevant to a household or an individual based on their portfolio can improve cross sell or upsell efficiencies. The customer will also appreciate a timely and relevant value proposition.

Risk Assessment

For an insurer, risk assessment at a household level in addition to the individual level can help write better premiums.

 

For more details on how householding analytics can help you, reach out to us below.

 

Contact

Source: House holding Analytics for Insurance by analyticsweek

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

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[  COVER OF THE WEEK ]

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[ AnalyticsWeek BYTES]

>> Customer Loyalty Resource for Customer Experience Professionals by bobehayes

>> How Similar Are UX Metrics in Moderated vs. Unmoderated Studies? by analyticsweek

>> Hacking journalism: Data science in the newsroom by analyticsweekpick

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CPSC 540 Machine Learning

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Machine learning (ML) is one of the fastest growing areas of science. It is largely responsible for the rise of giant data companies such as Google, and it has been central to the development of lucrative products, such … more

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Storytelling with Data: A Data Visualization Guide for Business Professionals

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Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. Th… 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:How do you take millions of users with 100’s transactions each, amongst 10k’s of products and group the users together in meaningful segments?
A: 1. Some exploratory data analysis (get a first insight)

* Transactions by date
* Count of customers Vs number of items bought
* Total items Vs total basket per customer
* Total items Vs total basket per area

2.Create new features (per customer):

Counts:

* Total baskets (unique days)
* Total items
* Total spent
* Unique product id

Distributions:

* Items per basket
* Spent per basket
* Product id per basket
* Duration between visits
* Product preferences: proportion of items per product cat per basket

3. Too many features, dimension-reduction? PCA?

4. Clustering:

* PCA

5. Interpreting model fit
* View the clustering by principal component axis pairs PC1 Vs PC2, PC2 Vs PC1.
* Interpret each principal component regarding the linear combination it’s obtained from; example: PC1=spendy axis (proportion of baskets containing spendy items, raw counts of items and visits)

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Unraveling the Mystery of #BigData

 Unraveling the Mystery of #BigData

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I’m sure, the highest capacity of storage device, will not enough to record all our stories; because, everytime with you is very valuable da

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[ FACT OF THE WEEK]

29 percent report that their marketing departments have ‘too little or no customer/consumer data.’ When data is collected by marketers, it is often not appropriate to real-time decision making.

Sourced from: Analytics.CLUB #WEB Newsletter