Executive Summary

Today’s intense competition requires that companies know as much as they can about their customers in order to anticipate their needs and serve them better. Implementing initiatives such as customer value, customer experience, price and profitability optimization, contact center optimization and risk mitigation requires in-depth understanding of the options available and the information technology prerequisites needed to support each option.

In pursuit of these goals, businesses collect, track and analyze data from broader and deeper sets of data sources than ever before. While structured data remains the most common form, organizations are learning also to exploit semistructured data (for example, text) as well as more complex data such as voice and image files. Seeking an advantage, they use analytics across the business to assess customer value, the nature and quality of the customer experience, the effectiveness of interaction-handing processes, contact center performance, compliance with regulations and more. Indeed, customer data is one of the most promising areas for the application of analytics; it can deliver significant value when used to deepen insight, sharpen decision-making and provide alerts when situations require attention from managers or executives.

Seeing this opportunity, software vendors now offer a variety of packaged analytic applications and specialized tools. Creating applications tailored for vertical industries is considerably easier than it has been in the past because tools are designed so business managers as well as analysts and statisticians can use them. Techniques for processing large data sets (“big data”) have evolved quickly, making it possible to extract insights from masses of data for practical business purposes. Among the analytics options, predictive analytics makes it possible to anticipate changing business conditions and helps users respond sooner and more intelligently to them. Web-based platforms that handle big data enable companies to use advanced analytics easily and economically. At the same time, advances in mobile technology provide access to analytics from smartphones and tablets. Cloud computing and mobile devices facilitate collaboration between users in different business units.

But managing information and the analytics derived from them in pursuit of a competitive advantage is neither a simple nor a trivial undertaking. In our view, prospective buyers’ poor understanding of best practices and functional requirements for analytics creates significant issues in most companies, as do deficiencies in their software and data environments. These hinder their ability to improve control over business processes and make it more difficult for them to choose new technology that can deliver the value they seek.

Enter this latest Ventana Research benchmark research, on the State of Customer Analytics. It was designed to examine in depth the needs for and use of analytics and metrics by all lines of business involved in customer-related activities. In designing this research, one major goal was to provide prospective buyers with a better understanding of their organization’s requirements and thus improve their ability to formulate a successful internal business case. Another emphasis is to assess interest in using mobile and collaborative technologies to determine their potential for facilitating faster, easier and broader use of customer analytics.

Organizations require a robust and accurate understanding of their customers; this research finds that in most instances they are not getting it. Only one in five organizations is operating at the highest Innovative level of performance in their use of customer analytics, while more than one-quarter (27%) perform at the lowest Tactical level. The remaining half (53%) have room for improvement, placing at the middle two levels, Strategic and Advanced, in our Performance Index analysis.

The research finds that organizations are more likely to be satisfied with their technology if they prioritize better analysis of customers and their activities. Much discussion recently has been devoted to the 360-degree view of the customer and how to analyze interactions across every engagement channel to adequately depict and understand the customer journey. A complete view of all interactions is clearly valuable, but most organizations are assembling only a small percentage of the data required to achieve that complete view of the customer journey. The research thus suggests that the 360-degree view is today a conceptual goal rather than a clearly defined set of components.

More than half of organizations use feedback (75%), demographics (64%) and interactions (59%) as part of their 360-degree view; fewer include sales (48%), marketing (42%) and social media analytics (39%). The research thus finds that fewer than half of organizations (47%) have a complete view of their customers. One striking finding is that more than half of organizations (51%) do not share that view of the customer internally across lines of business. Organizations that assemble a 360-degree view of their customers are more likely to be satisfied with customer analytics (39%) than those that don’t (14%).

Organizations are satisfied at higher rates with the processes they use to generate customer analytics, with two-thirds of organizations (66%) satisfied. Most organizations said they find it easy (31%) or very easy (19%) to collect the data they need to create useful customer metrics and performance indicators; fewer than one-fifth (18%) said they found it difficult.

However, most organizations spend less time on the analysis of their data than they do on the mundane tasks of preparing that data for analysis. Two-fifths of their time is spent reviewing data (20%) and preparing data for analysis (19%), neither of which yields actionable insights, while less than 10 percent is spent performing advanced analytics (2%), assembling scenarios for analysis (3%) and preparing annotations and charts for presentation (3%). Evaluating how time is allocated in the customer analytics process is critical to being able to make improvements and maximize the value of this investment.

The research makes clear that organizations have the best success when customer analytics is a team effort. In almost half (46%) of participating organizations the responsibility for customer analytics lies with a team dedicated to that function; more than half (25%) of those teams consist of analysts. Organizations using dedicated teams reported satisfaction with their customer analytics process more often than those relying on their IT organization or using a shared team or individual users. A wide variety of individual skill sets across roles, from analysts to managers, is essential to creating and taking advantage of customer analytics.

With tools and teams in place, the research finds that organizations are reaching for deeper insights through advanced analytics. More than half (55%) of organizations indicated they view advanced analytics as very important and an additional 29 percent said they are important. And they reported that engaging in such advanced analysis yields information that’s viewed as high quality. Among advanced analysis capabilities, the most important are the ability to process large volumes of data (63%), predictive capabilities (54%) and the advanced visualization of information (51%). With an array of meaningful analytics, organizations are able to better understand their customers and more effectively guide future actions.

Nonetheless, organizations’ confidence in the quality of the information being generated by customer analytics is not uniformly high. Most participants in the research indicated they are either very confident (27%) or confident (47%) in the quality of the information their systems are generating, but more than one-quarter (26%) said they are only somewhat confident or not at all confident. Similarly, most organizations told us their customer analytics data is very accurate (15%) or accurate (48%), but one-third reported it is only somewhat accurate (26%) or not accurate (7%). We attribute this partly to the persistence of spreadsheets as an analytic tool in half of participating organizations; spreadsheets are difficult to work with in an enterprise setting, inflexible and prone to errors. Improving confidence in customer analytics requires tools that enable reliable processing of a wide variety of customer-related data types that span every type of interaction.

For more than two-thirds (68%) of organizations, the most important analytics are those that are focused on customers (for example, CSAT, NPS, CES and CLV), followed by financial (60%), agent performance (53%) and contact center operations (45%) analytics. Organizations report that they use a wide array of important metrics. Topping the list are those associated with customer satisfaction and profitability, cited as important by 59 percent and 52 percent, respectively. Other important metrics include financial ones such as revenue attainment and adherence to budget, and process metrics such as call outcomes, sales conversion rate and agent quality scores. Other customer-related metrics include customer lifetime value, cost to serve and, lower on the list of priorities the research yields, the net promoter score.

The largest percentage of organizations (42%) have important metrics available for use in two to five business days after the period close; 17 percent deliver the metrics immediately on close and an additional 12 percent deliver them within one day. Organizations most often review their important metrics and indicators on a monthly (25%) basis, but almost as many do it daily (20%), weekly (21%) or quarterly (20%). This research reveals widespread dissatisfaction with the dedicated technology that vendors are offering for customer analytics. Half of participating organizations (49%) reported that they view the cost of these offerings as too high, while more than one-third told us that new tools do not offer sufficiently significant advances to induce them to replace their existing approach (36%). One in five (20%) said there is not enough value in dedicated tools.

These attitudes are typical of organizations that have not prepared a thorough business case with which to effectively evaluate technology — a situation that often leads to unsatisfactory results. In our view organizations seeking to improve their competitiveness must address their technology challenges and embrace collaborative approaches to gain the most value from customer analytics.

Our research finds that the use of both cloud computing and mobile technology in customer analytics is increasingly popular, with cloud computing cited as important by 44 percent and mobile technology by 42 percent of organizations. While customer analytics systems traditionally have been deployed on-premises, fewer than two in five (37%) organizations report that this is still the case: Cloud computing options are now preferred by the majority of organizations (55%). Most organizations (53%) also are using mobile technology to access customer analytics; the remainder (46%) plan to deploy it soon or at some point in the future. Among those that already have, a substantial majority (88%) reported a level of satisfaction with their mobile technology for customer analytics.

Key Insights

This benchmark research yielded the following important general findings and key insights regarding customer analytics. (We discuss performance levels in the Performance Index portion of the full research report; the actual questions asked in our survey are in an appendix to the research report. Specifics of organization sizes are in the appendix “About This Benchmark Research.”)

Customer analytics need significant improvement.
Organizations need a complete and accurate understanding of their customers. In most instances they are not getting it. Our Performance Index analysis finds that only one-fifth (20%) of organizations are operating at the highest Innovative level of performance in their use of customer analytics, while more than one-quarter (27%) perform at the lowest Tactical level. The remaining half (53%) place at the middle two levels, Strategic and Advanced, indicating room for improvement. More detailed analysis shows that close to one-half of organizations (45%) do poorly in how they manage and use the information associated with customer analytics. In the Process category organizations do somewhat better, with only a bit more than one-quarter (28%) placing at the lowest-performing Tactical level. Organizations perform better in the people-related aspects of customer analytics, with our analysis placing more than one-quarter (27%) at the Innovative level and the fewest (13%) at the Tactical level. Our research finds that the larger the organization, the better it likely performs.

The research findings make clear that organizations struggle with customer analytics but also indicate that most already recognize shortcomings as well as the importance of prioritizing improvement. Two-thirds (66%) of participants said they view it as very important to have a better analysis of their customers and activities. Most said they want better customer analytics in order to improve their customer service strategy (61%), improve the customer experience (58%) and improve the outcomes of interactions (48%). Among industry sectors, improving the customer experience was most important to finance, insurance and real estate industries (76%). The research finds that organizations indicating that it’s very important to have better analysis of customers and their activities are more likely to be satisfied with their technology.

Organizations face an imperative to improve customer analytics.
Overall, satisfaction with customer analytics efforts is not high. Only one-quarter of organizations (27%) reported that they are satisfied, with the largest portion of participants (44%) saying they are somewhat satisfied with their customer analytics efforts. At 38 percent, executives are more satisfied than those at other employee levels. Satisfaction is higher (41%) among manufacturing companies than in other industry sectors and higher in IT (43%) than in business units (25%). Almost half (42%) of organizations that are satisfied with the processes used to generate customer analytics are satisfied with the analytics themselves.There are substantial reasons for organizations to put efforts into improving their customer analytics. Users reported a list of benefits of customer analytics, topped by better communication and knowledge sharing across the organization (59%), improved customer experience and satisfaction (50%), increased sales (50%), better business analytics (48%) and increased productivity (45%). Two in five organizations overall (39%) said they will change how they generate and apply customer analytics over the next 12 to 18 months; among the very largest of organizations, a majority are planning to make a change. Driving change are the desires to create new revenue opportunities and to improve business processes (each 66%), improve the customer experience (59%) and increase workforce productivity (45%).

To improve, organizations must build an appropriate business case as well as identify the budget required for and potential value of the investment. A solid business case requires quantification of time saved on activities and clear priorities for software with which to design and deploy customer analytics. The research finds that evaluation criteria are important in the following order: usability (65%), functionality (61%), reliability (59%) and manageability (57%), followed by adaptability (52%), TCO/ROI (45%) and vendor validation (41%). The research makes clear that effective evaluation of customer analytics requires a comprehensive assessment.

Organizations spend too much time wrangling data and not enough time performing analyses.
Two-thirds of organizations (66%) are satisfied with the processes they use to generate customer analytics. Satisfaction increases with organization size — the largest organizations, those with more than 10,000 employees or $10 billion in revenue, are also the most satisfied (each approximately 80%). Where there is dissatisfaction, it mainly concerns data not being readily available (75%) and, in distant second place, a lack of skilled resources (46%). Most organizations said they find it easy (31%) or very easy (19%) to collect the data they need to create useful customer metrics and performance indicators; fewer than one-fifth (18%) said it was hard.

However, analysis of time spent on each of the component elements of processing customer analytics shows that the distribution of time allocated to the component processes is often less than optimal: Most organizations spend the largest portion of their time reviewing data (20%) and preparing data for analysis (19%), neither of which yields actionable insights, and the least time performing advanced analytics (2%), assembling scenarios for analysis (3%) and preparing annotations and charts for presentation (3%). Notably, manufacturing companies spend even more time preparing data (29%). Understanding how time is allocated in the customer analytics process is critical to understanding where to prioritize improvements and maximize the value of an investment.

Having the right team and skills for customer analytics is essential.
Handling customer analytics appears to work best as a team effort. Close to half (46%) of participating organizations reported that the responsibility for their customer analytics lies with a team dedicated to that function; more than half (25%) of those teams consist of analysts. In an additional 15 percent of organizations, a team of analysts is shared across business units or IT. The very largest of organizations (more than 10,000 employees or $10 billion in revenue) are more likely to have a dedicated team of analysts (each approximately 35%). Organizations using dedicated teams reported being satisfied with their customer analytics process more often than those relying on their IT organization or using a shared team or individual users.

More than one-third (38%) of the organizations that rate their skills in creating customer analytics as excellent are able to generate them immediately, and almost three-quarters (74%) of organizations that rate their skills as excellent indicate they are very confident in using them. Many of the organizations’ customer analytics teams are well skilled, and skilled teams produce excellent customer analytics. Three in five organizations described their team’s skills as above average (39%) or excellent (20%). Once again, bigger is better: The very largest of organizations most often indicated they have excellent skills. A wide variety of individual skill sets across roles, from analysts to managers, is essential to creating and taking advantage of customer analytics.

Organizations want customer analytics to yield predictive insights.
With tools and teams in place, organizations are reaching for deeper insights through advanced analytics. More than half (55%) of organizations indicated advanced analytics are very important and an additional 29 percent said they are important. Moreover, the research finds that engaging in such advanced analysis yields information that’s viewed as high quality. The most important advanced capabilities are the ability to process large volumes of data (63%), predictive capabilities (54%) and the advanced visualization of information (51%).

Attitudes toward two aspects of advanced customer analytics are worthy of note. Predictive analytics is almost universally important: It is very important for two in five organizations (42%), important to about as many (40%) and somewhat important to 16 percent; only 2 percent of participants cited it as not important. In addition, financial services organizations in particular identified as important the ability to process large volumes of data (76%) as well as to use predictive analytics (72%) more often than other organizations. The very largest of organizations also view these two capabilities as particularly important.

Customer analytics should be presented in the forms that suit an organization’s particular needs. This research finds a number of presentation components are critical to use with customer analytics: charts (70%), reports (62%), visualizations (59%), tables (55%), documents (55%), text (36%), natural language summary narratives (34%) and maps (32%). To be effective, analytics should be presentable in a variety of forms that can tell a story about an organization’s customers and help guide future actions.

For accuracy and depth in customer analytics, the devil is in the details.
Organizations’ confidence in the quality of the information being generated by customer analytics is not uniformly high. Most participants in the research indicated they are either very confident (27%) or confident (47%) in the quality of the information their systems are generating, but more than one-quarter (26%) said they are only somewhat confident or not at all confident. Similarly, most organizations told us their customer analytics data is very accurate (15%) or accurate (48%), but one-third reported it is only somewhat accurate (26%) or not accurate (7%). We attribute this partly to the persistence of spreadsheets as an analytic tool in half of participating organizations; spreadsheets are difficult to work with in an enterprise setting, are inflexible and are prone to errors. Email (45%) and business intelligence systems (44%) are close seconds as sources of data. Almost three-quarters (71%) of organizations that use spreadsheets heavily for customer analytics report that spreadsheet use makes it difficult to produce accurate and timely customer analytics.

The most common source of customer analytics data is, not surprisingly, customer feedback, cited by 52 percent of organizations. Email (43%), social media (43%) and BI systems (43%) also rank highly. The types of data that are included in customer analytics comport with this sourcing: text (47%), event (44%), social media (42%), voice (35%), images (32%) and video (24%). Analysis of organizations’ plans shows that usage of text and social media will decline while video will increase. Voice is a critical data source as it represents the conversation between a customer and the organization. Improving the accuracy and depth of customer analytics requires the ability to access and process a wide variety of data types that span every type of interaction.

It’s essential to use the proper metrics from customer analytics.
For more than two-thirds (68%) of organizations, the most important analytics are customer-focused (for example, CSAT, NPS, CES and CLV), followed by financial (60%), agent performance (53%) and contact center operations (45%) analytics. Executives, however, place a lower importance on customer-focused analytics (48%) than they do analytics concerning managerial roles (75%).

Applying analytics is the first step to generating useful metrics. The most important metric organizations derive from customer analytics is the customer satisfaction score (59%). Other important metrics are, among financial metrics, customer profitability (52%), revenue attainment and adherence to budget (each 48%). The most important process metrics are call outcomes (47%), sales conversion rate (41%) and agent quality scores (33%).

The other customer-related metrics important to organizations are customer lifetime value (48%) and cost to serve (39%). The net promoter score was only seventh in importance (26%). In the very largest of organizations (more than 10,000 employees or $10 billion in revenue), the customer satisfaction score was even more important (cited by approximately two-thirds of those organizations); it also was identified as important significantly more often (80%) in financial services industries.

The largest percentage of organizations (42%) are delivering important metrics in two to five business days after the period close; 17 percent deliver the metrics immediately on close and an additional 12 percent deliver them within one day. Organizations most often review their important metrics and indicators on a monthly (25%) basis, but almost as many do it daily (20%), weekly (21%) or quarterly (20%). Metrics aren’t effective if they can’t be applied to processes and interactions in a timely manner.

Technology challenges are widespread in customer analytics.
Organizations are using a variety of approaches in customer analytics. In addition to their unwise reliance on spreadsheets (78%), organizations’ current top choices are analytics tools as part of business applications (66%), analytics tools as a component of a suite of business applications (55%), stand-alone analytics tools (52%) and dedicated customer analytics tools (49%). Among planned deployments in the next 24 months, predictive analytics and modeling tools are the top choice (35%), followed by stand-alone, general-purpose analytic tools (30%), analytics tools as part of a suite of applications (28%) and dedicated customer analytics tools (27%). Organizations that use predictive analytics and modeling tools are most likely to be satisfied with the processes they use to generate customer analytics (82%).

This research reveals widespread dissatisfaction with the dedicated technology that vendors are offering for customer analytics. Half (49%) reported that the cost of these offerings is too high, while more than one-third told us that new tools do not offer sufficiently significant advances, leaving them satisfied with their existing approach (36%). One in five (20%) said there is not enough value in dedicated tools. Our research has consistently found that organizations are more likely to be satisfied with technology when they have used a properly designed business case that comprehensively identifies investments.

The effective use of customer analytics technology requires collaboration among people and across processes to be effective. More than four in five participants (86%) said it is important to collaborate directly with others in the organization in using customer analytics tools. More than half (54%) are already using business and social collaboration technology with customer analytics, another 17 percent said they will begin to use it within 12 months and 22 percent said they will begin to use it at some point in the future. Overall satisfaction with available business and social collaborative capabilities is high, with more than four in five organizations either satisfied (40%) or somewhat satisfied (46%). Organizations must overcome technology challenges and embrace collaborative approaches to gain the most value from customer analytics

Cloud computing, machine learning and mobile are critical for customer analytics.
Organizations looking to improve their customer analytics are focusing on the next generation of digital technologies now becoming mainstream in business, such as cloud computing (44%) and mobile technology, cited as important by 44 percent and 42 percent, respectively. While customer analytics systems traditionally have been deployed on-premises, only 37 percent of organizations reported that this is still the case: Cloud computing options are now collectively preferred by the majority of organizations (55%). Among cloud deployments, private (27%) and hybrid cloud (13%) are the two most popular approaches. Most (51%) organizations reported they are satisfied with their cloud-based customer analytics and an additional 32 percent are somewhat satisfied.

A significant majority of organizations (89%) indicated that it is important to use machine learning as part of their customer analytics in the organization. Most are already using it, having deployed it more than a year ago (31%) or within the last 12 months (22%). Another one-fifth (22%) are planning to do so in the next year. Those already using it overwhelmingly are satisfied, either completely (48%) or somewhat (42%). Machine learning is the foundation for applying advanced analytics and developing models for artificial intelligence.

Most organizations (53%) are using mobile technology to access and use customer analytics; the remainder (46%) plan to deploy it soon or at some point in the future. Among those that already have, a substantial majority (88%) reported some level of satisfaction with their mobile technology for customer analytics. More than half (57%) of organizations that have been using mobile technology to access and use customer analytics said they are satisfied. Mobile clearly is becoming a critical component of analytics

Data and analytics enable a 360-degree view of the customer experience.
The sophistication of organizations’ customer analytics varies significantly. This research makes clear that the most important use of customer analytics in organizations is to improve the customer experience (72%), followed by using it to create a customer service strategy and to identify customer service improvements (both 57%) as well as to be able to personalize customer experiences (50%). Managers in particular told us they focus most on strategy creation (67%), followed by efforts to improve the customer experience (50%). Reflecting other findings, acquiring data about the customer experience is more important to large organizations.

Developing quality insights from analytics requires a comprehensive view of the customer. Much discussion recently has been devoted to the importance of the 360-degree view of the customer and how to analyze interactions across every engagement channel to adequately understand the customer journey. While a 360-degree view is undoubtedly valuable, most organizations are assembling only a small percentage of the data required to achieve a complete view across the customer journey. The research finds that more than half of organizations include feedback (75%), demographics (64%) and interactions data (59%) as part of their 360-degree view, but fewer include sales (48%), marketing (42%) and social media (39%) data. Furthermore, more than half of organizations (51%) do not share that view across the entire organization. While most organizations said they are planning to provide a 360-degree view, almost a third (30%) said they plan to do so in the next 24 months and most (48%) do not have a schedule to do so. Organizations that have a 360-degree view of its customers are more likely to be satisfied with customer analytics (39%) than those that don’t (14%). The research finds that the path to a 360-degree view is still an aspirational work in progress; attaining it requires self-assessment and a plan to improve

10 Best Practice Recommendations

This benchmark research reveals significant new insights into the evolving nature and use of customer analytics. For organizations considering how to optimize the use of customer analytics in their organization, we offer the following recommendations.

Recognize the shortcomings in modern customer analytics and aim to overcome them.

Organizations need a complete and accurate understanding of their customers. In most instances they are not getting it. However, our research finds that most recognize shortcomings and the need for growth. Two-thirds of organizations said they view it as very important to have a better analysis of their customers and activities to improve their customer service strategy, the customer experience and the outcomes of interactions. Organizations indicating that it’s very important to have better analysis of customers and their activities are more likely to be satisfied with their technology. We urge organizations to work to understand the importance of customer analytics and that improvement begins with a careful examination of the effectiveness of the people, processes, information and technology that make customer analytics possible.

Respond to the imperative to improve customer analytics. 

Overall, satisfaction with customer analytics efforts is not high. Only one-quarter of organizations reported that they are satisfied, with the largest portion of participants saying they are somewhat satisfied with their customer analytics efforts. There are substantial reasons for organizations to put efforts into improving their customer analytics. Users reported a list of benefits of customer analytics, topped by better communication and knowledge sharing across the organization, improved customer experience and satisfaction, increased sales, better business analytics and increased productivity. To improve, build a business case for modernizing customer analytics from design to usage that includes technology as well as processes to identify the budget required for and potential value of the investment.

Spend less time wrangling data and more time performing analyses.

Two-thirds of organizations are satisfied with the processes they use to generate customer analytics. Our research suggests they should not be. Our analysis of time spent on each of the component elements of processing customer analytics shows that time allocation is often less than optimal: Most organizations spend the largest portion of their time reviewing data and preparing data for analysis, neither of which yields actionable insights, and the least time in performing advanced analytics, assembling scenarios for analysis and preparing annotations and charts for presentation. Aim to better understand how time is allocated in the customer analytics process to effectively prioritize improvements so that time is invested in the aspects of the analytics process that will yield value.

Build a team with the right customer analytics skills.

Handling customer analytics works best as a team effort. Close to half of participating organizations reported that the responsibility for their customer analytics lies with a team dedicated to that function; more than half of those teams consist of analysts. Organizations using dedicated teams reported satisfaction with their customer analytics process more often than those relying on their IT organization or using a shared team or individual users. And almost three-quarters of organizations that rate their skills as excellent indicate they are very confident in using them. Take care when assembling your team: Skilled teams with members from across functional and technology roles will produce the best possible customer analytics.

Aim to develop customer analytics that yield predictive insights.

With tools and teams in place, organizations are reaching for deeper insights through advanced analytics. More than half of organizations indicated advanced analytics are very important and an additional 29 percent said they are important. Moreover, the research finds that undertaking such advanced analysis yields information that’s viewed as high quality. The most important advanced capabilities are the ability to process large volumes of data like that produced by big data technologies and the advanced visualization of customer information. Predictive analytics is almost universally important: It is very important for two in five organizations and important to about as many. Investigate technology that increases the science in your customer analytics and uses visualization and predictive methods to provide better, more actionable insights.

Eliminate spreadsheets.

Despite advances in technology, organizations’ confidence in the quality of the information being generated by customer analytics is less than universal. True, most participants in the research indicated they are either very confident or confident in the quality of the information their systems are generating, but more than one-quarter said they are somewhat confident or not at all confident. Similarly, while most organizations told us their customer analytics data is very accurate or accurate, one-third reported it is either somewhat accurate or not accurate. We attribute this situation partly to the prevalence of spreadsheets as a critical source of data; spreadsheets are difficult to work with in an enterprise setting and are prone to errors. If you feel your customer analytics are not delivering the competitive edge you need, curtail your spreadsheet use and examine the array of available modern tools and techniques to gain all the insights from your data.

Use the right metrics.

Organizations are using an array of metrics that they derive from customer analytics. The research places these metrics into one of three categories: those focused on the customer, financial metrics and process metrics. Participants in this research said the most important of the metrics they use is the customer satisfaction score. Among financial metrics, organizations cited customer profitability, revenue attainment and adherence to budget as important. The most important process metrics are call outcomes, sales conversion rate and agent quality scores. While many of these metrics can provide valuable insight, all organizations are different. It’s critical to consider your organization’s specific needs when developing metrics and prioritizing their use and impact.

Address the technology challenges in customer analytics.

This research reveals widespread dissatisfaction with the dedicated technology that vendors are offering for customer analytics, which the research suggests has led to an unwise reliance on spreadsheets. Half reported that the cost of these offerings is too high, while more than one-third told us that new tools do not offer sufficiently significant advances, leaving them satisfied with their existing approach. One in five said there is not enough value in dedicated tools. Organizations are more likely to be satisfied with technology chosen on the basis of a business case that effectively evaluated potential investments. Overcome technology challenges and get the most out of customer analytics by considering your organization’s needs and building a thorough business case.

Embrace cloud computing, machine learning and mobile in customer analytics.

Organizations looking to improve their customer analytics are focusing on the next generation of digital technologies, now becoming mainstream in business: cloud computing, mobile technology and machine learning. While customer analytics systems traditionally have been deployed on-premises, only 37 percent of organizations reported that this is still the case: Cloud computing options are now collectively preferred by the majority of organizations. More than half (57%) of organizations that have been using mobile technology to access and use customer analytics said they are satisfied. Mobile is a critical component of analytics — not just to consume customer analytics but to make decisions and act. And a significant majority of organizations indicated that it is important to use machine learning as part of their customer analytics in the organization. Machine learning is the foundation for applying advanced analytics and developing models for artificial intelligence. Those already using it are overwhelmingly satisfied, either completely or somewhat. We strongly advise exploring how these three advancing digital technologies can improve your customer analytics.

Utilize data and analytics to enable a 360-degree view of the customer experience.

Much discussion recently has been devoted to the 360-degree view of the customer and how to analyze interactions across every engagement channel to adequately represent the customer journey. A 360-degree view is undoubtedly critical, but most organizations are assembling only a small percentage of the data required to achieve a complete view across the customer journey. The research finds that three-fourths of organizations include feedback and more than half include demographics and interactions as part of their 360-degree view, but fewer include sales, marketing and social media. Furthermore, more than half of organizations do not share that view internally. It’s important to be thorough and include all available information in assembling a 360-degree view of customers; organizations that do are more likely to be satisfied with customer analytics than those that don’t. Conduct a self-assessment on your 360-degree view of customers and determine where improvement to your data and analytics efforts can have an impact.

About Ventana Research

Ventana Research is the most authoritative and respected benchmark business technology research and advisory services firm. We provide insight and expert guidance on mainstream and disruptive technologies through a unique set of research-based offerings including benchmark research and technology evaluation assessments, education workshops and our research and advisory services, Ventana On-Demand. Our unparalleled understanding of the role of technology in optimizing business processes and performance and our best practices guidance are rooted in our rigorous research-based benchmarking of people, processes, information and technology across business and IT functions in every industry. This benchmark research plus our market coverage and in-depth knowledge of hundreds of technology providers means we can deliver education and expertise to our clients to increase the value they derive from technology investments while reducing time, cost and risk.

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Appendix: About This Benchmark Research

Methodology

Ventana Research conducted this benchmark research on the web from June 2017 through November 2018. We solicited survey participation via email, our website and social media invitations. Email invitations were also sent by our media partners and by vendor sponsors.

We presented this explanation of the topic to participants prior to their entry into the survey:

Today’s intense competition requires companies to know as much as they can about their customers in order to anticipate their needs and serve them better. Implementing initiatives such as customer experience, price and profitability optimization, contact center optimization and risk mitigation requires analytics and technology. This benchmark research is designed to examine the state of the art in customer analytics to determine how organizations can improve through a range of best practices and technology-driven improvements.

The following promotion incentivized participants to complete the survey:

Upon completion of the research, all qualified participants will receive a report on the findings of this benchmark research to support their organization’s efforts, along with a $25 Amazon.com gift certificate. In addition, all qualified participants will be entered into a drawing to win one of 25 benchmark research reports and a 30-minute consultation, a package valued at US$1,495 or €1,232. Thank you for your participation!

Qualification
We designed the research to assess the use of and plans for customer analytics across organizations and industries. Qualification to participate was presented to participants as follows:

The survey for this benchmark research is designed for customer-, call center- and contact center-related business and IT managers connected with managing or operating any customer-related activities, offering outsourced contact center services or involved with the purchasing of technology for this area. Solution providers, software vendors, consultants, media and systems integrators may participate in the survey, but they are not eligible for incentives and their input will be used only if they meet the qualifications. Incentives are provided to qualified participants in the research and also are conditional on provision of accurate and verifiable contact information including company name and company email address that can be used for fulfillment of incentives.

Further qualification evaluation of respondents was conducted as part of the research methodology and quality assurance processes. It entailed screening out responses from companies that are too small, questionnaires that were not materially complete, or those where the submission is from an inappropriate submitter or appears to be spurious.

Demographics

We designed the survey used for this research to be answered by executives and managers across a broad range of roles and titles working in organizations. We deemed 212 of those who clicked through to this survey to be qualified to have their answers analyzed in this research. In this report, the term “participants” refers to that group, and the charts in this section characterize various aspects of their demographics and qualifications.