Big Data has Big Implications for Customer Experience Management

Unless you have been living under a rock, you know that Big Data is the latest buzz word in the world of business. The concept of Big Data is broad one and I consider it an amalgamation of different areas that help us try to get a handle on, insight from and use out of data. Pat Gelsinger, President and COO of EMC, in an article by the The Wall Street Journal said that Big Data refers to the idea that companies can extract value from collecting, processing and analyzing vast quantities of data. Businesses who can get a better handle on these data will be more likely to outperform their competitors who do not.

When describing Big Data, people typically refer to three characteristics of the data: 1) Volume: the amount of data being collected is massive; 2) Velocity: the speed at which data are being generated/collected is very fast (consider the streams of tweets); and Variety: the different types of data like structured and unstructured data. Another characteristic of the data that, I think, warrants attention is the source of the data. Businesses data can come from different sources. These are:

  1. Operational: Operational data contain objective metrics that measure the quality of the business processes and can come from a variety of sources. Hardware providers use sensors to monitor the quality of their implementations. Customer Relationship Management (CRM) systems track the quality of call center interactions (e.g., call length, response time).
  2. Financial: Financial data contain objective metrics that measure the quality of financial health of the company and are typically housed in the company’s financial reporting system.
  3. Constituency (includes employees, partners): Constituency data contain attitudinal metrics as well as more objective metrics about specific constituents. Human Resources department has access to a variety of different types of data, ranging from employees’ performance histories and completed training courses to survey results and salaries. Partner programs track partner information, including attitudes, financial investments, and sales growth.
  4. Customer: Customer data contain attitudinal metrics. Large enterprises rely on their Enterprise Feedback Management systems to capture and analyze data from such sources as surveys, social media and online communities.

One way businesses are making sense of their data is by linking them together.

Business Data Integration

In a study on customer feedback programs, I found that business data integration played a crucial role in the success of the programs. Specifically, loyalty leading companies, compared to their loyalty lagging counterparts, integrated different sources of business data into their customer feedback data.  They linked their customer feedback data to operational data, financial data and constituency data. By linking disparate data sources to their customer feedback data, companies gain insight about what is important to the customers.

But data integration is a difficult problem. Within a given company, data are housed in different systems. HR has their own system for tracking employee resources. The call center tracks data on their CRM system. Finance tracks their data on yet a different system.  What approach can companies take to integrate all their data? In a recent interview, Anjul Bhambhri, VP for Big Data for IBM, talked about how business can solve their Big Data integration problem with respect to data silos:

“My response and suggestion – and we’ve actually done it with clients – has been that, you leave the data where it is. You’re not going to start moving that around. You’re not going to break those applications. You’re not going to just rewrite those applications… just to solve this problem. Really, data federation and information integration is the way to go. Data is going to reside where it is.”

Anjul Bhambhri, VP for Big Data, IBM

The problem of Big Data for businesses is one of applying appropriate data federation and analytic techniques to these disparate data sources to extract usable insight to help them make better business decisions. Companies who can extract the right insights from their business data will have a competitive advantage over others who can not.

Next, let us turn to the field of Customer Experience Management to see how the application of Big Data principles can help companies gain insight from their business data to help them grow their business.

Customer Loyalty is our Ultimate Criterion

Customers play a critical role in the success of any business; customer loyalty is key to business growth. Businesses that have customers who engage in more loyalty behaviors (e.g., stay longer, recommend, continue buying, increase share-of-wallet, more clicks/views) toward their company experience faster growth compared to businesses that have customers who engage in fewer loyalty behaviors. The key to growing one’s business, then, is to understand how to improve customer loyalty.

Customer Experience Management

Customer Experience Management Program Components

One way companies are trying to improve customer loyalty is through customer experience management (CEM). CEM is the process of understanding and managing your customers’ interactions with and perceptions of your company or brand. A CEM program consists of a set of organized actions that support the goal of CEM. While a CEM program has many moving parts, an easy way to organize those pieces into six components of a CEM program (see figure to the right).

The source of data in most CEM programs, not surprisingly, is customer feedback data. Businesses gain customer insight primarily by collecting and analyzing customer feedback data from different sources, including customer feedback surveys, social media sites, branded online communities and emails. Using customer feedback data, companies identify the customer experiences that are closely linked to customer loyalty and use that information to allocate resources to improve those customer experiences, and, consequently, increase customer loyalty.

Three Implications for CEM

Customer feedback is just one type of data that need to be analyzed and managed. By integrating different business data silos, businesses can more fully understand how other business metrics could impact or be impacted by customer satisfaction and loyalty. The impact that Big Data integration will have in CEM falls in three related areas: 1) Answering bigger questions about customers; 2) Building companies around the customers; 3) Using objective measures of customer loyalty.

Implication 1: Answer Bigger Questions about Customers

A successful CEM program is designed to deliver a better customer experience which translates into a more loyal customer base. As mentioned earlier, the source of data in most CEM programs is through customer feedback tools (e.g., survey, social media). Businesses gain customer insight primarily by analyzing customer feedback data with little or no regard for other data sources. By linking disparate data sources to their customer feedback data, companies gain insight about their customers that they could not achieve by looking at their customer feedback data alone.

Here are a few important business questions that can be addressed by linking disparate data sources to customer feedback.

  • Where do we set operational goals in our call centers (e.g., number of handoffs, length of wait time) to ensure we maximize customer satisfaction?
  • How many hours of training do employee need to ensure they can satisfy their customers?
  • Which call center metrics are the key determinants of customer satisfaction with the call center experience?
  • Where do we need to invest in our employee relationship (e.g., across the employee experience touch points) to ensure they deliver a great customer experience?
  • Do customers who report higher loyalty spend more than customers who report lower levels of loyalty?

Companies who integrate their business data to understand the correlates of customer satisfaction and loyalty can better answer these questions and, consequently, have a much better advantage of effectively allocating their resources in areas that they know will help improve the customer experience and maximize customer loyalty and business growth.

The process merging disparate data silos depends on the question you are trying to answer. You will need to apply appropriate data federation and aggregation processes to build specific data models for statistical analyses and interpretation for each question. For example, studying the impact of employee satisfaction on customer satisfaction requires a different data model than when studying the impact of call center metrics on customer satisfaction.

This entire process of data integration is sometimes referred to as Business Linkage Analysis. The interested reader can explore the outcome of this data federation and aggregation process below. I developed three customer-centric data federation processes and data models to help companies use their existing data to address some of those Big Questions presented above.

  1. Linking operational and customer metrics: We are interested in calculating the statistical relationships between customer metrics and operational metrics. Data are federated and aggregated at the transaction level.  Understanding these relationships allows businesses to build/identify customer-centric business metrics, manage customer relationships using objective operational metrics and reward employee behavior that will drive customer satisfaction.
  2. Linking financial and customer metrics: We are interested in calculating the statistical relationships between customer metrics and financial business outcomes. Data are federated and aggregated at the customer level. Understanding these relationships allows you to strengthen the business case for your CEM program, identify drivers of real customer behaviors and determine ROI for customer experience improvement solutions.
  3. Linking constituency and customer metrics: We are interested in calculating the statistical relationship between customer metrics and employee/partner metrics (e.g., satisfaction, loyalty, training metrics). Data are aggregated at the constituency level. Understanding these relationships allows businesses to understand the impact of employee and partner experience on the customer experience, improve the health of the customer relationship by improving the health of the employee and partner relationship and build a customer centric culture.

Implication 2: Build your company around your customer

The success of a CEM program depends on the adoption of certain business practices in each CEM component. While there are several best practice standards, the major success drivers are related to strategy/governance, business process integration, and applied research. Companies who adopt best practices in these areas have higher levels of customer loyalty compared to companies who do not adopt these practices.

Integrating different sources of business data helps build a customer-centric company by building interest across the company in understanding what impacts the customer experience. Because the integration of different business data would necessarily involve key stakeholders from each organization, the mere act of integration would be a catalyst for further cross-organizational discussions about the customer. Applying a customer-centric data federation and aggregation approach to business data integration would help senior leaders understand how their organization (and its metrics) impacts the customer.

The results of customer research become more applicable to other organizations when you are using their data in your research. Different data owners (e.g., senior leaders) can now start asking (and answering) questions about their metrics and how they are related to the customer experience. Expanding the use of customer data to other departments (e.g., HR, Call Center, Marketing)  helps the entire company improve processes that are important to the customer. Here are some examples of how companies are using this type of research to build a customer-centric culture.

  • Identifying and building customer-centric operational metrics for executive dashboards
  • Removing the noise from executive reports by including only customer-centric business metrics (known to be predictive of customer satisfaction)
  • Integrating customer feedback into operational systems (CRM) so front-line employees understand the interactions and attitudes of their customers
  • Conducting in-depth customer research using all business data to continually gain customer insight and gain a competitive advantage

Big Data technologies and processes can go a long way in helping you support your CEM program.  By taking a customer-centric approach to your Big Data, you will be able to literally build the company (its data) around the customer.

Implication 3: Use Objective Loyalty Metrics

Despite the existence of objective measures of customer loyalty (e.g., customer renews contract, recommends you, buys more), CEM programs rely on customer surveys as a way to assess customer loyalty. Measures of customer loyalty typically take the form of questions that ask the customer to indicate his or her likelihood of engaging in specific types of behaviors, those deemed important to the company/brand.

CEM professionals (me, too) typically use these self-report measures as our only measure of customer loyalty when analyzing survey data.  While these loyalty metrics do provide reliable, valid and useful information, you are always interested in what customers really do. By linking up financial data and customer feedback data, you would be able to understand how the customer experience impacts real customer loyalty behavior using objective metrics, like purchase amount, products purchased, products liked, products shared and renewed contract.

End-of-quarter financial reports include customer loyalty metrics (e.g., churn rates, ARPU, repurchase rates) with no information about the factors that might impact those numbers. Traditionally analyzed at the end of the quarter as standalone metrics, these objective loyalty metrics provide no insight about how to improve them. Linking satisfaction with the customer experience to these objective loyalty measures, however, lets you build predictive models to help you understand the reasons behind your financial metrics. This is powerful stuff.

Could we stop using self-reported customer loyalty metrics? It would make the loyalty measurement debate a moot point. I think, though, the use of self-reported customer loyalty metrics will always be used. Survey-based loyalty metrics allow companies to quickly and easily gauge levels of customer loyalty and provide a forward look into the future about customer loyalty.

Summary

The era of Big Data is upon us and the Big Data problem for business is one of linking up their disparate data silos with customer feedback data in order to identify the correlates of customer satisfaction and loyalty. A major hurdle in solving this problem involves applying appropriate data federation and aggregation methods across the different data silos. This data federation process results in usable datasets with the right metrics culled from different data sources to answer specific questions or hypotheses.  Once the metrics are pulled from their respective data sources, businesses can apply statistical modeling to answer important questions about the causes of customer satisfaction and loyalty.

Big Data principles have a role in CEM programs. Integrating other sources of business data with your customer feedback data can help you extract much more value from each of your data sources. By linking up these data sources, companies will be able to ask and answer bigger customer experience questions, embed the importance of the customer across different organizations/departments and provide the use of both subjective and objective metrics of customer loyalty.

Originally Posted at: Big Data has Big Implications for Customer Experience Management by bobehayes

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

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

[ DATA SCIENCE Q&A]

Q:How to clean data?
A: 1. First: detect anomalies and contradictions
Common issues:
* Tidy data: (Hadley Wickam paper)
column names are values, not names, e.g. 26-45…
multiple variables are stored in one column, e.g. m1534 (male of 15-34 years’ old age)
variables are stored in both rows and columns, e.g. tmax, tmin in the same column
multiple types of observational units are stored in the same table. e.g, song dataset and rank dataset in the same table
*a single observational unit is stored in multiple tables (can be combined)
* Data-Type constraints: values in a particular column must be of a particular type: integer, numeric, factor, boolean
* Range constraints: number or dates fall within a certain range. They have minimum/maximum permissible values
* Mandatory constraints: certain columns can’t be empty
* Unique constraints: a field must be unique across a dataset: a same person must have a unique SS number
* Set-membership constraints: the values for a columns must come from a set of discrete values or codes: a gender must be female, male
* Regular expression patterns: for example, phone number may be required to have the pattern: (999)999-9999
* Misspellings
* Missing values
* Outliers
* Cross-field validation: certain conditions that utilize multiple fields must hold. For instance, in laboratory medicine: the sum of the different white blood cell must equal to zero (they are all percentages). In hospital database, a patient’s date or discharge can’t be earlier than the admission date
2. Clean the data using:
* Regular expressions: misspellings, regular expression patterns
* KNN-impute and other missing values imputing methods
* Coercing: data-type constraints
* Melting: tidy data issues
* Date/time parsing
* Removing observations

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Harsh Tiwari talks about fabric of data driven leader in Financial Sector #FutureOfData #Podcast

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Tomorrow’s Data Visualizations: From Real-Time Data Discovery to Resource Allocation

Yesterday, data visualization tools were a salient component of the data discovery process. Organizations across verticals became comfortable publishing the results of business intelligence and analytics tools to the commonly found visualizations that gave them meaning.

Tomorrow, data visualizations will play an even greater role in data management. They will not only inform analytics on business applications, but also those on the underlying systems that allow organizations to maximize both them and their overall business worth.

“The value of data gets better if you have more data,” MapR Vice President of Product Management Anoop Dawar explained. “Which means, it’s in the best interest of the organization to bring every business unit onto a shared services infrastructure. But in order to do that the shared services infrastructure has to provide data feeds to each business unit and real time visibility into how your workloads are doing.”

Cross Enterprise Visualization
Visualizations become imbued with the potential to provide information across the entire enterprise, in real-time, when organizations utilize a shared platform for both operations and analytics. That way, the same visualization mechanisms that are apposite for particular business units are also deployed by the IT departments operating them—which enables organization-wide sharing of data pertinent across domains. In fact, this cross-enterprise utility of data visualizations forms the essential difference between the tools that are currently found in popular visualization platforms, and those that were specifically designed for an analytics/operations shared platform. “What is being visualized is actually completely different,” Dawar maintained. “Tableau or QlikView are usually visualizing a business dashboard. We’re talking about visualizing your cluster operations. Its giving you the health and heartbeat of the pulse of the cluster and how it’s running.”

The Business and IT
The fundamental degree of insight that these visualizations offer helps both the business and IT. In many instances, the latter is aided in its ability to assist the business by helping to determine critical business questions about system optimization. In the example of fraud detection, a fairly commonplace application of a singular platform for both analytics and operations, “If I can do a better job but take a longer time, is that better for me, or should I be more risk-taking to deliver a better experience?” Dawar asked. “That’s a business decision, but to drive that decision I need to go to the cluster operations and find out how much time I’m taking on each of these systems.” Moreover, the visualizations that are part of MapR’s Spyglass Initiative are easily shared between parties, so users can “say hey, I built this dashboard that’s really important for my application, and I want to share it with the actual person who cares about this application so instead of them asking me how the system is doing, they can look at it as well,” Dawar mentioned.

The Issue of Scale
If the customization and the intuitive ease of use of the dashboards of yesterday and today are the same, their deployments are markedly different. “Getting a better sense of what’s going into your cluster allows you to allocate appropriate resources, especially when you have some jobs that are extremely resource intensive, some that might be lighter, and some that might have different user groups,” MapR Senior Director of Product Marketing Dale Kim denoted. More notable, perhaps, is the issue of scale, which is frequently a direct consequence of the quantities of big data expediently pumping through today’s organizations. Such scale not only applies to the underlying systems’ ability to process that data, but also to the visualization mechanisms that are attempting to represent them in real-time, if need be. “When you have a big data converged application, your monitoring problem also becomes a big data problem,” Dawar mentioned. “It needs to have this scale out architecture where you can choose how long and how much of the data you have, what kind of granularity you want to have it show, and have the system take care of all of this.”

Expanding Visualization Utility
The overarching importance of visualizations for systems on a sole platform for analytics and operations is in extending the usefulness of these mechanisms by supplementing their traditional utility. Leveraging these tools to illustrate business analytics and analytics for their underlying systems helps to identify how each of these facets is impacting the other. Furthermore, it enables organizations to make the requisite adjustments to get the sort of performance they need for competitive advantage. “Visualization is a very powerful tool to fingerprint the steady state of the system and what’s considered the heavy state,” Dawar reflected. “Therefore, now you can do preventive maintenance, you can figure out anomalies, and you can project into the future on how your state is changing as more and more workloads come onto your cluster.”

For the time being, future projections into real-time operations and analytics platforms are likely to include the Internet of Things, in which the ability to actually visualize workloads and changing data states is invaluable. Specifically, these visualizations can help organizations gauge “where should the processing for a certain system lie,” Dawar remarked. “In IoT systems you really have to make a very good determination of what processing happens at the edge, and what processing happens at the core for economic reasons, machine learning reasons, and also for security reasons.”

The real-time graphic representations of time-sensitive data facilitated by visualization tools could prove highly influential in making those determinations—and others.

Originally Posted at: Tomorrow’s Data Visualizations: From Real-Time Data Discovery to Resource Allocation

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

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Data Mining

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

[ DATA SCIENCE Q&A]

Q:How 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

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@AnalyticsWeek Keynote: The CMO isn't satisfied: Judah Phillips

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Big Data is not the new oil. – Jer Thorp

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140,000 to 190,000. Too few people with deep analytical skills to fill the demand of Big Data jobs in the U.S. by 2018.

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Moving on Up: The Challenges of Cloud Security

Many organizations are not in the technology business, and the work of hosting their own infrastructure can be a distraction from their core business activities. By leveraging the services of cloud service providers, these organizations can leave infrastructure management to the experts and reap the benefits of sharing infrastructure costs with other cloud customers.

However, the differences between maintaining infrastructure in the cloud and on-premises deployments aren’t all to an organization’s benefit. The cloud is a very different environment than what IT staff and employees are used to when working with company-owned systems. These differences can create significant challenges when trying to adapt to the cloud.

Many serious cloud-related challenges come in the field of cloud security. The move of company infrastructure from within the organization’s perimeter (on company owned and controlled systems) to systems that are under the control of a third-party vendor requires a very different approach to security. And, with the rapid adoption of the cloud, failing to meet these challenges can result in a devastating data breach.

Challenges of the Cloud

Adoption of cloud computing by enterprises is growing rapidly. In 2018, it was estimated that over three quarters of enterprises are using the cloud, and that number is only expected to grow in the coming years. Unfortunately, one thing that isn’t keeping up with the rapid pace of cloud adoption is cloud security. Securing the cloud is a very different proposition than securing on-premises systems. As a result, a large number of data breaches occur due to improperly secured cloud repositories every year.

Understanding the potential challenges of cloud security is an important component of overcoming them. Three of the biggest problems faced by IT professionals when trying to secure an organization’s cloud deployment are the rapid adoption of cloud computing, the lack of visibility into infrastructure leased on the cloud, and security misconfigurations in cloud deployments.

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Analyzing Big Data: A Customer-Centric Approach

Big Data

The latest buzz word in business is Big Data. According to Pat Gelsinger, President and COO of EMC, in an article by the The Wall Street Journal, Big Data refers to the idea that companies can extract value from collecting, processing and analyzing vast quantities of data. Businesses who can get a better handle on these data will be more likely to outperform their competitors who do not.

When people talk about Big Data, they are typically referring to three characteristics of the data:

  1. Volume: the amount of data being collected is massive
  2. Velocity: the speed at which data are being generated/collected is very fast (consider the streams of tweets)
  3. Variety: the different types of data like structured and unstructured data

Because extremely large data sets cannot be processed using conventional database systems, companies have created new ways of processing (e.g., storing, accessing and analyzing) this big data. Big Data is about housing data on multiple servers for quick access and employing parallel processing of the data (rather than following sequential steps).

Business Value of Big Data Will Come From Analytics

In a late 2010 study, researchers from MIT Sloan Management Review and IBM asked 3000 executives, managers and analysts about how they obtain value from their massive amounts of data.  They found that organizations that used business information and analytics outperformed organizations who did not. Specifically, these researchers found that top-performing businesses were twice as likely to use analytics to guide future strategies and guide day-to-day operations compared to their low-performing counterparts.

The MIT/IBM researchers, however, also found that the number one obstacle to the adoption of analytics in their organizations was a lack of understanding of how to use analytics to improve the business (the second and third top obstacles were: Lack of management bandwidth due to competing priorities and a lack of skills internally). In addition, there are simply not enough people with Big Data analysis skills.  McKinsey and Company estimates that the “United States faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.”

Customer Experience Management and Big Data

The problem of Big Data is one of applying appropriate analytic techniques to business data to extract value. Companies who can apply appropriate statistical models to their data will make better sense of the data and, consequently, get more value from those data. Generally speaking, business data can be divided into four types:

  1. Operational
  2. Financial
  3. Constituency (includes employees, partners)
  4. Customer

Customer Experience Management (CEM) is the process of understanding and managing customers’ interactions with and perceptions about the company/brand. Businesses are already realizing the value of integrating different types of customer data to improve customer loyalty. In my research on best practices in customer feedback programs, I found that the integration of different types of customer data (purchase history, service history, values and satisfaction) are necessary for an effective customer feedback program. Specifically, I found that loyalty leading companies, compared to their loyalty lagging counterparts, link customer feedback metrics to a variety of business metrics (operational, financial, constituency) to uncover deeper customer insights. Additionally, to facilitate this integration between attitudinal data and objective business data, loyalty leaders also integrate customer feedback into their daily business processes and customer relationship management system.

While I have not yet used new technology that supports Big Data (e.g., Hadoop, MapReduce) to process data, I have worked with businesses to merge disparate data sets to conduct what is commonly called Business Linkage Analysis. Business linkage analysis is a problem of data organization. The ultimate goal of linkage analysis is to understand the causes and consequences of customer loyalty (e.g., advocacy, purchasing, retention). I think that identifying the correlates of customer metrics is central to extracting value from Big Data.

Customer-Centric Approach to Analyzing Big Data

I have written three posts on different types of linkage analysis, each presenting a data model (a way to organize the data) to conduct each type of linkage analysis. The key to conducting linkage analysis is to ensure the different data sets are organized (e.g., aggregated) properly to support the conclusions you want to make from your combined data.

  • Linking operational and customer metrics: We are interested in calculating the statistical relationships between customer metrics and operational metrics. Data are aggregated at the transaction level.  Understanding these relationships allows businesses to build/identify customer-centric business metrics, manage customer relationships using objective operational metrics and reward employee behavior that will drive customer satisfaction.
  • Linking financial and customer metrics: We are interested in calculating the statistical relationships between customer metrics and financial business outcomes. Data are aggregated at the customer level. Understanding these relationships allows you to strengthen the business case for your CEM program, identify drivers of real customer behaviors and determine ROI for customer experience improvement solutions.
  • Linking constituency and customer metrics: We are interested in calculating the statistical relationship between customer metrics and employee/partner metrics (e.g., satisfaction, loyalty, training metrics). Data are aggregated at the constituency level. Understanding these relationships allows businesses to understand the impact of employee and partner experience on the customer experience, improve the health of the customer relationship by improving the health of the employee and partner relationship and build a customer centric culture.

Summary

The era of Big Data is upon us. From small and midsize companies to large enterprise companies, their ability to extract value from big data through smart analytics will be the key to their business success. In this post, I presented a few analytic approaches in which different types of data sources are merged with customer feedback data. This customer-centric approach allows for businesses to analyze their data in a way that helps them understand the reasons for customer dis/loyalty and the impact dis/loyalty has to the growth of the company.

Download Free Paper on Linkage Analysis

Originally Posted at: Analyzing Big Data: A Customer-Centric Approach

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

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[  COVER OF THE WEEK ]

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>> Explaining Data Teams to HR by analyticsweek

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

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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]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ 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

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[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @ScottZoldi, @FICO

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @ScottZoldi, @FICO

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[ QUOTE OF THE WEEK]

Data are becoming the new raw material of business. – Craig Mundie

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

14.9 percent of marketers polled in Crain’s BtoB Magazine are still wondering ‘What is Big Data?’

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The Changing Landscape of Customer Acquisition, Engagement and Retention in 2020

Marketing has evolved over the years from a largely intuitive exercise based on an executive’s judgment and experience, into becoming a data-obsessed occupation. A hundred years ago, department store magnate John Wanamaker was famously quoted as saying that he knew half of his advertising spend was wasted. The problem, he said, was that he didn’t […]

The post The Changing Landscape of Customer Acquisition, Engagement and Retention in 2020 appeared first on TechSpective.

Source: The Changing Landscape of Customer Acquisition, Engagement and Retention in 2020

How to Become a Customer Experience Analytics Innovator

MIT Sloan Management Review

Companies continually look for ways to outperform their competitors. One way is to apply analytics to their treasure trove of data. Researchers, for example, have found that top-performing businesses were twice as likely to use analytics to guide future strategies and guide day-to-day operations compared to their low-performing counterparts. Earlier this year, MIT and SAS released the results of a study which identified some of the reasons behind these differences. They surveyed 2500 respondents (55% being executives) across a variety of industries about how their organizations use data to advance their business objectives.

Three Ways to Improve the Value of Analytics for your Business

The researchers compared Analytical Innovators (businesses where analytics created a competitive advantage and has helped innovation) with Analytically Challenged (businesses where analytics did not create a competitive advantage and did not help innovation). They found that Analytical Innovators adopted different analytic practices compared to Analytically Challenged. According to their findings, if you want to be an Analytical Innovator, you need to:

1. Use analytics to solve customer-centric problems rather than company-centric problems

MITSMR-SAS-Data-Analytics-Report
Figure 1. Analytical Innovators use analytics differently. Figure from MIT/SAS report: From Value to Vision: Reimagining the Possible with Data Analytics.

Analytical Leaders mainly use analytics to address issues that directly impact the customers: improve customer understanding, identify new markets, accelerate development of new products/services and make real-time decisions (see Figure 1). Analytically Challenged, on the other hand, primarily use analytics to address problems about internal matters: reduce costs and improve resource allocation. Unless those actions impact the customer experience (e.g., savings redirected toward product development), addressing those problems do not help improve long-term customer relationships.

One way to improve customer understanding is to collect customer feedback. Consider including customer experience (CX) metrics as part of your analytics strategy. Using various CX metrics as part of your analytics will help you identify business areas that matter to your customer and ways to improve customer loyalty.

2. Aggregate/Integrate your business data

MITSMR-SAS-Data-Analytics-Report-Aggregation
Figure 2. Analytical Innovators are very effective at capturing, aggregating, analyzing and gaining insight from data compared to their counterparts. Figure from MIT/SAS report: From Value to Vision: Reimagining the Possible with Data Analytics.

Analytical Innovators, more so than their counterparts, are more effective at capturing, analyzing and gaining insights from data (see Figure 2). More importantly, they are more effective at aggregating/integrating data. In the world of Big Data where businesses have access to a wide variety of disparate data, data aggregation and integration becomes paramount. I found that businesses that integrate different data (i.e., operational, financial, constituency) with the customer feedback data have higher customer loyalty rankings within their industry compared to businesses who do not integrate their data with customer feedback. Taking a customer-centric data integration approach to analyzing your business data can help you uncover deeper customer insights to help you understand the causes and consequences of customer satisfaction and loyalty.

3. Support the sharing of analytic best practices and insights throughout the company

MITSMR-SAS-Data-Analytics-Report-Support
Figure 3. Analytical Innovators experience much support in the dissemination of data insights. Figure from MIT/SAS report: From Value to Vision: Reimagining the Possible with Data Analytics.

As seen in Figure 3, Analytical Innovators report that their organization supports the sharing of analytics across the company.  By supporting the integration of data insights throughout the company, Analytical Innovators are less likely to make decisions based on hunches about what they think is happening; instead, they are better able to draw fact-based conclusions and allow employees and stakeholders to use those insights to drive both tactical and strategic decision-making, respectively.

Summary

Data analytics play a critical role in helping businesses create a competitive advantage and drive innovation. The MIT/SAS study showed that, to maximize the value of analytics, you need to:

  1. Use analytics primarily to increase value to the customer rather than to decrease costs/allocate resources.
  2. Aggregate/Integrate different business data silos and look for relationships among once-disparate metrics.
  3. Gain executive support around the use of analytics to encourage sharing of best practices and data-driven insights throughout your company.

Analytical Innovators use data differently than their counterparts. While the specifics may vary from Innovator to Innovator, common themes emerge across these analytical leaders. For the interested reader, I recently provided a case study (see: The Total Customer Experience: How Oracle Builds their Business Around the Customer) that illustrates how one company uses these analytical best practices to help improve the customer experience and increase customer loyalty.

————————–

tcethebookfinalsmall

 

Buy TCE: Total Customer Experience at Amazon >>

In TCE: Total Customer Experience, learn more about how you can integrate your business data around the customer and apply a customer-centric analytics approach to gain deeper customer insights.

 

 

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Mar 05, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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>> Sisense Hackathon 2020: Make Something Awesome With AWS by analyticsweek

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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

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Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:Give examples of bad and good visualizations?
A: Bad visualization:
– Pie charts: difficult to make comparisons between items when area is used, especially when there are lots of items
– Color choice for classes: abundant use of red, orange and blue. Readers can think that the colors could mean good (blue) versus bad (orange and red) whereas these are just associated with a specific segment
– 3D charts: can distort perception and therefore skew data
– Using a solid line in a line chart: dashed and dotted lines can be distracting

Good visualization:
– Heat map with a single color: some colors stand out more than others, giving more weight to that data. A single color with varying shades show the intensity better
– Adding a trend line (regression line) to a scatter plot help the reader highlighting trends

Source

[ VIDEO OF THE WEEK]

George (@RedPointCTO / @RedPointGlobal) on becoming an unbiased #Technologist in #DataDriven World #FutureOfData #Podcast

 George (@RedPointCTO / @RedPointGlobal) on becoming an unbiased #Technologist in #DataDriven World #FutureOfData #Podcast

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[ 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]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Juan Gorricho, @disney

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Juan Gorricho, @disney

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[ FACT OF THE WEEK]

Every second we create new data. For example, we perform 40,000 search queries every second (on Google alone), which makes it 3.5 searches per day and 1.2 trillion searches per year.In Aug 2015, over 1 billion people used Facebook FB +0.54% in a single day.

Sourced from: Analytics.CLUB #WEB Newsletter