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Big Data has Big Implications for Customer Experience Management

Customer Experience

Big Data has Big Implications for Customer Experience Management image bigdatalinkageredonwhiteTCELabsmall 300x300Unless 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 a 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

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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.

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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

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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, 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.

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