CUSTOMER ANALTYICS BEST PRACTICES

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1 CUSTOMER ANALTYICS BEST PRACTICES Gain a competitive advantage by improving customer experience / success programs through the collection, analysis and reporting of customer data. WHAT S INSIDE We studied 80+ companies to determine the effectiveness of 25 practices in formal customer (e.g., customer experience, customer success) programs. Analytical Leaders (companies that use analytics to gain a competitive advantage) infuse analytics practices throughout their entire customer program, from setting strategy to delivering bob@businessoverbroadway.com 2017 Appuri, Inc. Page 1 of 22 insights. Competing on analytics requires focusing analytics efforts on improving customer insights (not reducing costs), adopting machine learning, integrating data silos and having access to data experts/scientists. BOB E. HAYES, PHD Business Over Broadway

2 OVERVIEW We surveyed 80+ customer-centric professionals in companies with formal customercentric programs (e.g., customer experience, customer success) to identify analytics best practices across all components of customer programs. We divided the companies into three groups based on their ability to use analytics to create a competitive advantage: Analytical Competitors (32% - respondents satisfied with company s use of analytics), Analytical Rookies (41% - respondents indifferent to company s use of analytics) and Analytical Amateurs (27% - respondents dissatisfied with their company s use of analytics). When we compared these three groups, we found that different segments incorporated customer analytics practices to varying degrees. Analytical Leaders had customer programs that: 1) had access to a data scientist, 2) presented results in executive dashboards, 3) employed machine learning to gain customer insights, 4) conducted indepth research using customer data and 5) integrated data silos Business Over Broadway Page 2 of 22

3 Table of Contents Sample Description... 4 Creating a Competitive Advantage with Analytics... 5 Activities in Customer Programs... 5 State of Analytics in Customer Programs... 7 Analytics Primarily Used to Improve Loyalty and Reduce Costs... 7 Adoption of Analytics Practices in Customer-Centric Programs... 8 Data Science Skills of Customer Professionals... 9 Roadblocks in Customer Programs Customer Analytics Best Practices Analytical Leaders Focus Efforts on Customers Analytical Leaders Infuse Analytics throughout their Customer Programs Strategy/Governance Best Practices Business Integration Best Practices Method Best Practices Reporting Best Practices Research Best Practices Summary Adopt these Key Practices in your Customer Program to Improve the Value of Your Analytics Business Over Broadway Page 3 of 22

4 Sample Description We studied 88 companies that have a formal customer program (e.g., customer experience (46%), customer success (30%), other (24%)) to determine the extent to which they incorporate analytics practices into their program. Most respondents were from the US (86%) and worked in a B2B (50%) or B2C/B2B (42%) company. About half (48%) of respondents were from companies with 5000 or more employees; 40% of respondents were from companies with 50,000 or more employees. Most respondents worked in the IT (29%), Healthcare and Medical (12%), Financial Services (8%) and Aerospace and Defense (8%) industries. Data were collected from January to March Potential respondents were invited to complete a survey on customer analytics best practices via the researcher s blog and social media profiles Business Over Broadway Page 4 of 22

5 Creating a Competitive Advantage with Analytics Companies continually look for ways to outperform their competitors. One way they are trying to get ahead is through the application of analytics on their data. Researchers 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. Researchers from MIT and SAS showed that analytical-leading companies (those that use analytics to create a competitive advantage) adopted analytics practices to a greater degree than analytical-lagging companies. In the current study, we asked respondents to indicate how satisfied they were with their company s use of analytics to create a competitive advantage. We found that 32% of respondents were satisfied with their company s use of analytics to create a competitive advantage. 41% were neither satisfied nor dissatisfied about their company s use of analytics while 27% were dissatisfied. Activities in Customer Programs Businesses, trying to stay ahead of the competition, adopt formal customer-centric programs. While these programs may have different names (e.g., customer experience, customer success, customer loyalty), the ultimate goals of these programs are the same: to ensure customers are happy, receiving value from the company s solution and are loyal. All customer programs are not the same. Companies have the option of adopting certain customer-focused activities. For example, some companies adopt the practice of sharing customer results company-wide, while other companies limit sharing to the executive team. Some companies use customer metrics to set company strategy while other companies rely strictly on financial metrics to guide their strategy. The sum of the 2017 Business Over Broadway Page 5 of 22

6 adopted activities essentially defines the company's customer program. These various activities can be grouped into six areas or components: Strategy addresses how companies incorporate customer metrics into their longterm plans/vision/mission to help achieve its objectives and goals Governance describes the formal policy around the formal customer program: Rules, Roles, Requests Business Integration involves embedding customer program (e.g., processes/data) into other business operations and processes Method addresses the means by which customer data are collected and what gets measured Reporting addresses analysis, synthesis and dissemination of customer metrics Research is concerned with how companies provide additional customer insights by conducting deep dive research using sophisticated analytic methods (predictive modeling, machine learning) 2017 Business Over Broadway Page 6 of 22

7 State of Analytics in Customer Programs We wanted understand how companies use analytics in their customer programs. We looked at four areas: 1) primary use analytics, 2) how companies generally structure their customer programs and 3) accessibility to data science skills and 4) common roadblocks in customer programs. Analytics Primarily Used to Improve Loyalty and Reduce Costs We asked respondents to indicate the primary areas in which their company uses analytics. While companies use analytics for many different purposes, the most popular areas include improving customer loyalty and reducing enterprise costs. The least popular areas in which analytics are used include making real-time decisions and identifying new markets. The least popular areas in which companies use analytics include accelerating development of new products, making real-time decisions and identifying new markets Business Over Broadway Page 7 of 22

8 Adoption of Analytics Practices in Customer-Centric Programs Customer programs generate much data. The value of your data, however, is only as good as the analytics practices you adopt. We wanted to understand the extent to which companies adopted 25 specific analytics practices across six program components (Strategy/Governance, Business Integration, Method, Reporting, Advanced Research). The adoption rate varied widely across the 25 practices. The most widely adopted practices included using multiple survey methods (80% of respondents indicated their program adopted this practice), having an executive champion of the program (74%) and integrating the program into business technology and processes (74%) Business Over Broadway Page 8 of 22

9 The least adopted practices included using machine learning for learning insights (38%), using social media to determine customer sentiment (35%) and incentivizing employees using customer metrics (33%). It appears that fewer than half of customer programs adopt analytics practices related to advanced research activities (e.g., conduct in-depth studies and employ customer data platforms. Data Science Skills of Customer Professionals We asked customer professionals to indicate their proficiency in five data science skills as well as their access to teammates who are experts in those same five skills. While customer professionals were competent in two skills (i.e., Business knowledge and Technology), they lacked adequate proficiency in Programming, Mathematics and Statistics. Additionally, these customer professionals do not have teammates who can fill those quantitative skills. Like the customer professionals themselves, their teammates lack expertise in Programming, Mathematics and Statistics. One positive finding is that 66% of these customer professionals said that they have access to a data scientists/analyst within 2017 Business Over Broadway Page 9 of 22

10 the company to help them make sense of their data. So, while they may not have the quantitative prowess needed for successful data science projects, they have access to data professionals to help them find the patterns and associations in their data. Roadblocks in Customer Programs We asked respondents to indicate their biggest roadblocks that hinder their customer program to improve customer loyalty (e.g., recommendations, up/cross-sell). Respondents indicated that, on average, they are experiencing two big roadblocks in their customer programs. The most popular roadblock mentioned by these customer professionals was the lack of integration of customer insights into business operations (59%). Only 14% of respondents said their program does not provide customer insights. So, while companies are getting insights from their data, they are experiencing difficulty putting those insights into operations Business Over Broadway Page 10 of 22

11 Customer Analytics Best Practices In this section, we will look at identifying best practices in customer programs by exploring how analytical leading companies differ from analytical lagging companies in their customer programs. For example, we wanted to determine if companies that are able to use analytics to gain a competitive advantage structured their program differently (in how they use data and the practices they adopted) compared to companies who were not able to use analytics to gain a competitive advantage. In the current study, companies were segmented into three groups based on how well they use analytics to create a competitive advantage. Segmentation was based on the response to the following question (see page 4): How satisfied are you with your company s use of analytics to give them a competitive advantage? (0 Extremely Dissatisfied to 10 Extremely Satisfied); 0-3 = Analytical Laggards, 4-6 = Analytical Challengers; 7-10 = Analytical Leaders. Analytical Leaders Focus Efforts on Customers We compared the three groups on how they primarily use analytics in their company. Analytical leaders use analytics differently than analytical laggards. Generally speaking, the top use of analytics for Analytical Leaders was to generate customer insights. For example, a majority of Analytical Leaders, more so than Analytical Laggards, use analytics to improve customer loyalty (79% vs. 25%) and to increase customer understanding (71% vs. 33%). Analytical Laggards, however, tended to use analytics primarily to reduce enterprise costs and improve resource allocation Business Over Broadway Page 11 of 22

12 Analytical Leaders Infuse Analytics throughout their Customer Programs Our results revealed that Analytical Leaders, more so than their counterparts, structure their customer programs differently by embedding analytics practices throughout all aspects of their customer program. Analytical leading companies tended to adopt more customer program practices compared to less analytically savvy companies. While this trend occurred for each component of the customer program, adoption rates differed greatly in these areas: Research (delta between Leaders and Laggards = 65%) and Method (delta = 50%) Next, we will take a deeper look at each program component Business Over Broadway Page 12 of 22

13 Strategy/Governance Best Practices Strategy reflects the overarching, long-term plan of a company that is designed to help the company attain a specific goal. For customercentric companies, the strategy is directed at improving the customer experience. While strategy is necessary to build a customer-centric culture, companies need to create formal policy around the customer feedback program that supports the strategy. The governance surrounding the customer feedback program helps foster and maintain a customer-centric culture by operationalizing the strategy. Analytical leaders, compared to analytical laggards, show greater adoption in the following areas: Use customer metrics to set company vision, goals Customer program championed by top executives Use customer metrics in executives incentive compensation plan Customer metrics as important as financial metrics 2017 Business Over Broadway Page 13 of 22

14 Business Integration Best Practices The area of Business Integration addresses the extent to which the organization embeds elements of the customer program (including processes and data) into other business operations and processes. For the customer-centric company, customer programs play an important role in the management of the business. The integration of customer metrics into the daily operations of running the business keeps the customers needs in the fore of the management and front-line employees mind. Analytical leaders, compared to analytical laggards, show greater adoption in the following areas: Include program results in company / executive dashboards Communicate goals of customer program to the entire company Integrate customer feedback with CRM system to help resolve issues Use social media / online brand communities to resolve specific customer complaints 2017 Business Over Broadway Page 14 of 22

15 Method Best Practices The area of Method of the customer program is concerned about determining which customer metrics will be measured and the ways in which the customer data are collected Analytics leading companies, compared to analytics lagging companies, show greater adoption in the following areas: Use multiple survey methods to capture customer feedback Provide validation evidence of the quality of customer data Measure different types of customer loyalty (e.g., churn, recommendations, up/cross-selling) Monitor social media and online brand communities for comments 2017 Business Over Broadway Page 15 of 22

16 Reporting Best Practices The Reporting component of your customer program addresses how customer data are analyzed, summarized, synthesized and reported. Analyzing the customer data and disseminating the resulting insights are essential elements to using data to gain a competitive advantage. Loyalty leaders know how to best summarize and present the customer feedback so the company is able to make useful business decisions. Analytics leading companies, compared to analytics lagging companies, show greater adoption in the following areas: Present customer research to external audiences Benchmark customer metrics with competitors Report customer metrics for specific customer segments Calculate customer sentiment using social media sources 2017 Business Over Broadway Page 16 of 22

17 Research Best Practices Customer-focused research using customer data can provide additional insight into the needs of the customer base and increases the overall value of the customer program. This research extends well beyond the information that is gained from the typical reporting tools that summarize customer metrics with basic descriptive statistics. Now, business leaders are able to leverage predictive analytics to help them predict future events so that they can capitalize on opportunities and mitigate risk. Analytics leading companies, compared to analytics lagging companies, show greater adoption in the following areas: Integrate/merge disparate data silos with customer metrics Use machine learning to uncover customer insights Have access to a data analyst / expert / scientists to help make sense of data Conduct in-depth applied research using customer data to find insights 2017 Business Over Broadway Page 17 of 22

18 Summary We asked customer professionals about their analytics practices within their company. We found that adoption rates of customer analytics practices in customer programs varied widely. While some practices were adopted by over 70% of companies, a few were adopted by less than 40% of companies. The use of machine learning in customer programs was only adopted by 38% of the companies. This study showed that customer programs appear to lack the analytical rigor needed to extract insights from their customer data. Less than half of the companies surveyed used a customer data platform to deliver automated insights and even fewer companies report using machine learning to gain insights. Customer professionals lacked proficiency in quantitative skills, limiting their capability of extracting insights from their data. It s important to note that, in our prior study of data professionals, proficiency in statistics skills were a top driver of analytics project success, even for job roles that were not primarily quantitative in nature. Specifically, we found that Business Managers who were highly proficient in statistics and statistical thinking were more satisfied with their work than Business Managers who were less proficient. I suspect that customer professionals would benefit from having deeper knowledge of statistics. Customer programs now process a lot of data, and I believe that customer pros need to keep pace and develop basic quantitative skills if they want to put that data to use. Customer professionals who are better equipped at mining and visualizing their data will not only generate better insights, but will ask better questions. We also found that the biggest roadblock in customer programs revolves around translating insights into operational practice. Fortunately, many service providers are addressing this problem by helping companies set up a seamless link between the service provider's insight engine (typically machine learning capabilities) and their customers' operational systems. For example, these machine learning service providers are able to integrate data silos and extract customer insights via machine learning. These insights (in quantitative form) can then be fed back into the company's marketing automation or customer success systems to build workflows that can automatically trigger a targeted marketing campaigns or notify a customer success manager of an at-risk account. Adopt these Key Practices in your Customer Program to Improve the Value of Your Analytics While the adoption of many customer program practices differentiates analytical leading companies from analytical lagging companies, a handful of these practices appear to be more 2017 Business Over Broadway Page 18 of 22

19 important than others. The figure below summarizes the key differences among companies who differ on their ability to use analytics to gain a competitive advantage. Below are five key customer program practices companies can do to ensure they stay ahead of their competition: 1. Ensure team members of your customer program have access to a data analyst/expert/scientist to help them make sense of their data. Customer programs necessarily deal with a lot of different types (structured and unstructured) coming from many different sources (e.g., surveys, CRM, sales, support). Because customer professionals do not have the necessary skills in statistics and math, providing them access to data experts/scientists will improve their chances of making sense of and getting value from their data. These data professionals can apply the right type of analytics to surface insights that can help answer important business questions. Without these data skills, companies may be missing important insights. 2. Present customer program results in company/executive dashboards. Customer programs can generate many types of useful customer insights (e.g., descriptive, 2017 Business Over Broadway Page 19 of 22

20 predictive, prescriptive) to help stakeholders monitor trends in satisfaction, identify drivers of loyalty and prescribe remedies that optimize your improvement efforts. The customer insights reported in dashboards need to be relevant and simple. Different stakeholders will require different types of information. While executives require customer insights to help set long-term company goals and strategy, customer-facing employees require customer insights to help immediately resolve a customer s complaint. Keep reporting simple; don t overwhelm the dashboard with too many customer metrics. Use one or two high-level metrics (e.g., customer satisfaction, churn rates, customer health score). Presenting customer program results to the entire company and executives helps build a culture that is aligned around the needs of the customers. 3. Use machine learning techniques to uncover customer insights. When customer professionals have integrated all the disparate data silos, they can be overwhelmed with the number of variables they must manage and analyze. Even if they have access to seasoned data professionals, these data pros are only human. They are simply unable to manually and quickly sift through the sheer volume of data to find optimal predictive models. Instead, to create the best predictive models, customer professionals can rely on the power of machine learning to quickly and accurately uncover the underlying insights. Machine learning algorithms continually learn, and the more data they ingest, the better they get. Coupled with the processing capability of today, these algorithms, compared to humans, can provide customer insights quickly. 4. Conduct applied research using customer data to find customer insights. Analytics don t occur in a vacuum. Whenever you analyze your data, you need to focus your analytics on answering specific questions. Building a comprehensive research program is a good start at ensuring that the questions you answer are relevant and useful to your company s mission and objectives. Additionally, the research agenda will help guide the work of the data scientists to optimize the value of the analytics techniques they use to answer those questions. 5. Integrate the data from your customer program with other data sources. Business leaders need a unified, integrated picture of their customers. Having access to all data that are relevant to your customers benefits all stakeholders. Front-line heroes are better equipped to handle individual customer needs. Additionally, data professionals are able to build better (more accurate) predictive models when they have all the 2017 Business Over Broadway Page 20 of 22

21 relevant data. One approach to unifying your data is to leverage a customer data platform (CDP). Customer data platforms employ predictive analytics and machine learning to help surface customer insights. Also, CDPs utilizes APIs to other platforms that help support retention, lifecycle marketing and re-engagement marketing programs; that is, the insights you gain from a CDP can be integrated into your existing sales, marketing and support workflows. Customer programs generate a lot of data. Consider the data from customer surveys, CRM systems, Web analytics and support systems, to name a few. Consequently, customer programs need to include efforts to improve how companies govern, collect and analyze that data. Keeping pace in the Big Data world requires companies to rethink their customer programs, including the skills needed for customer professionals, the tools they use and the executives who support it all. This report provides a look into what you can do to ensure your customer program supports the overall analytics efforts of your company Business Over Broadway Page 21 of 22

22 ABOUT BOB E. HAYES, PHD I am Business Over Broadway (B.O.B.). I like to solve problems through the application of the scientific method. I use data and analytics to help make decisions that are based on fact, not hyperbole. My interests are at the intersection of customer experience, data science and machine learning. Business Over Broadway Business Over Broadway Page 22 of 22