{"id":43120,"date":"2012-07-30T12:02:00","date_gmt":"2012-07-30T19:02:00","guid":{"rendered":"http:\/\/customerthink.com\/big_data_advances_in_customer_experience_management\/"},"modified":"2012-07-30T12:02:00","modified_gmt":"2012-07-30T19:02:00","slug":"big_data_advances_in_customer_experience_management","status":"publish","type":"post","link":"https:\/\/customerthink.com\/big_data_advances_in_customer_experience_management\/","title":{"rendered":"Big Data Advances in Customer Experience Management"},"content":{"rendered":"

\"\"I gave a talk last week on Big Data and Customer Experience Management and how Big Data will change how companies think about their Customer Experience Management programs. This talk was part of a larger webinar on competitive analytics that was co-sponsored by TCELab<\/a> and Omega Management<\/a>. Some of the content below appears on prior blogs but not in the format below. When the webinar becomes available online, I will update this blog post with the link.<\/p>\n

——————–<\/p>\n

I think of Big Data as an amalgamation of different areas that help us try to get a handle on, insight from and use out of large, quickly-expanding, diverse data. To me, Big Data refers to the idea that companies can extract value from collecting, processing and analyzing vast quantities of data. In fact, McKinsey and Company, in a report from October), concluded that businesses who can get a better handle on these data will be more likely to outperform<\/a> their competitors who do not.<\/p>\n

Three Vs of Big Data<\/strong><\/h3>\n

When people talk about Big Data, they are typically referring to three characteristics of the data<\/a>: 1) Volume<\/strong>, 2) Velocity<\/strong> and 3) Variety. <\/strong>First, the amount of data being collected is massive. Hardware companies use sensors to collect data about the performance of their solutions housed in their clients’ company. Second, the speed at which data are being generated\/collected is very fast (consider the streams of tweets). Finally, companies need to deal with different types of data, both structured and unstructured data.<\/p>\n

Big Data Landscape<\/strong><\/h3>\n
\"Forbes<\/a><\/p>\n

Figure 1. Big Data Landscape image was created by Dave Feinleib (dave@vcdave.com; blogs.forbes.com\/davefeinleib<\/a>); We have modified the image to show the reader where TCELab fits into the Big Data space. The original image can be found here<\/a>.<\/p>\n<\/div>\n

Because extremely large data sets cannot be processed quickly using conventional database systems, companies have created new ways of processing (e.g., storing, accessing and analyzing) this big data. Data can be housed on multiple servers for quick access and employing parallel processing of the data (rather than following sequential steps).<\/p>\n

The graphic in Figure 1, illustrates the major players in the Big Data field. At the bottom, we have the technology companies who have developed ways of processing massive amounts of data quickly. At the next level, we have infrastructure companies who provide a platform on which to integrate Big Data technology into companies’ current infrastructure. At the top of the graphic, we have the Big Data companies who help provide insight into the business data where they use science, predictive modeling and other techniques to solve a specific problem. So, it is important to be clear when talking about Big Data companies.<\/p>\n

Three Big Data Approaches<\/strong><\/h3>\n

Brian Gentile, CEO of Jaspersoft, argues for a solution-oriented approach to understanding the value of Big Data<\/a>. Before selecting a vendor, you first need to understand the problem you are trying solve. Keep in mind that Big Data is not just about analyzing data quickly; it is about analyzing data intelligently, perhaps with some theory-driven analyses. Gentile’s three Big Data approaches include:<\/p>\n

    \n
  1. Interactive Exploration<\/strong> – good for discovering real-time patterns from your data as they emerge<\/li>\n
  2. Direct Batch Reporting<\/strong> – good for summarizing data into pre-built, scheduled (e.g., daily, weekly) reports<\/li>\n
  3. Batch ETL (extract-transform-load)<\/strong> – good for analyzing historical trends or linking disparate data sources based upon pre-defined questions. Sometimes called data federation, this approach involves pulling metrics from different data sources for purposes of understanding how all the metrics are related (in a correlation sense) to each other.<\/li>\n<\/ol>\n

    So, be sure to understand that Big Data is not just about quick analysis<\/em> of your data. It is also about integration<\/em> of different sources of data. I will come back to this notion of data integration, or Batch, ETL, later in the post.<\/p>\n

    Value from Analytics<\/strong><\/h3>\n
    \"IBM<\/a><\/p>\n

    Figure 2. Businesses get value from their data using analytics. The graph was recreated for presentation. The original image can be found here<\/a>.<\/p>\n<\/div>\n

    In a late 2010 study<\/a>, researchers from MIT Sloan Management Review and IBM asked 3000 executives, managers and analysts about how they obtain value<\/strong> from their massive amounts of data. They found (see Figure 2) 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.<\/p>\n

    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<\/strong>. There are simply not enough people with Big Data analysis skills. In fact, McKinsey and Company<\/a> estimated that the US faces of huge shortage of people who have the skills to understand and make decisions based on the analysis of big data.<\/p>\n

    The MIT\/IBM researchers also found that six out of 10 respondents cited innovating to achieve competitive differentiation<\/em> as a top business challenge. Additionally, the same percentage of respondents also agreed that their organization has more data than it can use effectively<\/em>. Clearly, companies want to differentiate themselves from the competition yet are unable to effectively use their data to make that happen. I think the primary Big Data problem is one of applying appropriate analytic techniques to business data.<\/p>\n

    Customer Experience Management<\/strong><\/h3>\n

    Customer Experience Management<\/strong><\/a> (CEM) is the process of understanding and managing customers’ interactions with and perceptions about the company\/brand.<\/strong> The goal of CEM is to improve the customer experience in order to optimize customer loyalty. Top businesses who have implemented CEM programs realize that these programs can be data intensive, generating millions of data points about their customers’ attitudes, online behaviors, and even their interactions with a given employee, just to name a few. To optimize the value from these data, companies need to apply appropriate analytics to provide insights about how to increase customer loyalty.<\/p>\n

    Disparate Sources of Business Data<\/strong><\/h3>\n
    \"\"<\/a><\/p>\n

    Figure 3. Different Business Data Sources Used for Linkage Analysis<\/p>\n<\/div>\n


    <\/p>\n

    CEM practitioners rely heavily on customer feedback as the source of their insights, yet businesses have many different sources of business data that could provide additional customer insight (see Figure 3). These data sources fall into these general buckets:<\/p>\n

      \n
    1. Operational<\/li>\n
    2. Financial<\/li>\n
    3. Constituency (includes employees, partners)<\/li>\n
    4. Customer Feedback<\/li>\n<\/ol>\n

      Big Data federation principles can be applied to these disparate data sources to improve your ability to extract insights about the reasons behind customer loyalty and sustained business growth.<\/p>\n

      Data Integration is Key to Extracting Value<\/strong><\/h3>\n
      \"\"<\/a><\/p>\n

      Figure 4. Companies who integrate operational data with their customer feedback get the insight that drives customer loyalty<\/p>\n<\/div>\n

      In my research on best practices in customer feedback programs, I found that the integration of other sources of business data with customer data is necessary for an effective CEM program<\/a>. As you see in Figure 4, companies who integrate operational measures with customer feedback (those are the green bars) were more satisfied with their customer feedback program and had higher customer loyalty rankings in their industry compared to companies who did not integrate those data.<\/p>\n

      Linkage Analysis<\/strong><\/h3>\n

      Integrating different business metrics to understand how they relate to each other is sometimes referred to as the process of Business Linkage Analysis<\/strong><\/a>. How you integrate\/link your different metrics depends on the problem you are trying to solve or the question you are trying to answer.<\/p>\n

      For example, here are three popular questions that can be addressed using linkage analysis of disparate data sources.<\/p>\n

        \n
      1. What is the $ value of improving customer satisfaction\/loyalty?<\/li>\n
      2. Which operational metrics have the biggest impact on customer satisfaction\/loyalty?<\/li>\n
      3. Which employee\/partner factors have the biggest impact on customer satisfaction\/loyalty?<\/li>\n<\/ol>\n

        Each question requires different datasets, merged at the right level for the appropriate analysis.<\/p>\n

        \"\"<\/a><\/p>\n

        Figure 5. Integrating business data to gain customer insight<\/p>\n<\/div>\n


        <\/p>\n

        Integrating your Business Data<\/strong><\/h3>\n

        Figure 5 illustrates some common ways companies integrate disparate data sources. The columns represent the different types of customer feedback sources and customer metrics. The rows represent the other data sources and metrics: financial, operational and constituency. Even though many different data sources can be integrated, I refer to this approach as a “customer-centric” approach<\/a> because the data are organized to gain insight about the causes and consequences of customer satisfaction\/sentiment\/loyalty.<\/p>\n

        Depending on the question are you trying to answer, you will use a combination of different sources of data. For example, when dealing with questions around financial metrics, you can integrate those with customer feedback at the relationship level via relationship surveys and social media sources. For operational or constituency-related question, you will need to consider other data sources and integration level (e.g., link data at transaction level instead of customer level).<\/p>\n

        How Big Data can Advance Customer Experience Management<\/h3>\n

        Next, I am going to conclude by talking about how Big Data can transform the field of customer experience management. I think Big Data principles can advance the field of customer experience management space in three important ways.<\/p>\n

        1. Ask\/Answer Bigger Questions<\/strong><\/h3>\n

        First, I think that the application of Big Data principles can help you ask and answer bigger questions about your customer. A successful CEM program is designed to deliver a better customer experience which translates into a more loyal customer base. The source of data in most CEM programs is gathered through customer feedback tools like surveys and social media sites. 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.<\/p>\n

        Businesses can now ask, and, more importantly, answer these types of questions.<\/p>\n