Analyzing Big Data: A Customer-Centric Approach

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:

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