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Recently my colleague Tony Cosentino wrote an analyst perspective asserting that big data analytics will displace net promoter score (NPS) for more effectively measuring the entire customer experience. This prompted a response from Maxie Schmidt-Subramanian, asserting that big data and NPS aren’t the only ways to measure customer experience success. The main point of Tony’s piece, as I interpret it, is that NPS is just a number, but big data analytics can reveal much more about customer behavior and intentions, and it can link these to business outcomes. On the other hand Maxie argues that whether or not companies use NPS, when it comes to measuring the customer experience, they rely too much on surveys and no one metric does the entire job. While to a large extent I agree with both arguments, from a business perspective I don’t think either addresses three very important questions. The first is what actually is the customer experience? Second, how should it be measured? And third, what is the best use of big data in relation to customer experience?

I recently wrote about how to deliver EPIC customer experiences. This acronym includes four elements that go a long way toward defining a superior customer experience: It must be Easy (in availability of channels at times of the customer’s choice, and in use of technology), Personalized, In context (reflecting previous interactions) and above all Consistent (presenting the same timely information regardless of channel, whether assisted or self-service). That said, I believe that what is most important, for both customer and company, is thevr_Customer_Analytics_02_drivers_for_new_customer_analytics outcome of the interaction. Was the problem resolved to the customer’s satisfaction? Did the caller find and purchase the products or services being sought? Of course there are other considerations such as the cost of the interaction and the customer’s subsequent value to the organization.

Regarding the second question, various metrics are useful to assess different outcomes and the true customer experience. Our benchmark research into next-generation customer analytics illustrates this point, showing that companies use on average 11 metrics to assess customer-related activities: Among the most widely used, three are financial (adherence to budget, customer service costs and customer profitability), five are process-oriented (including call outcomes, performance vs. service level agreement and agent quality scores), and three are customer-specific (customer satisfaction, cost to serve and lifetime value). Perhaps in contrast to popular opinion, NPS ranked only fifth among customer-specific metrics. Our research also finds that improving customer experience is a top priority and driver for improving in 63 percent of organizations.  Overall the results strongly suggest that most companies are undecided on how to measure the customer experience, but they  seem to agree one metric isn’t enough.

That brings us to big data, and to analytics applied to it. Companies, especially large ones serving consumers, have always had a lot of customer data, including from CRM, ERP, billing and other business applications to interaction-related data in call recordings, email letters and other forms. Recently the volume and variety both have increased significantly because companies often have web, email, IVR recordings, text records, social media surveys, Web scripts, chat scripts, instant vr_Customer_Analytics_09_technology_used_for_customer_analyticsmessages, social media posts, video recordings and output from mobile apps. But most companies can’t do much with all this data. Our benchmark research into next-generation customer analytics finds that the most common tools used to produce metrics, reports and analysis are spreadsheets and general-purpose business intelligence tools. While each of these has its uses, both require considerable manual effort, and neither can process unstructured data (such as voice, text and events) or expose insights from the content; they can’t, for example, determine customer satisfaction because of what was said. Nor do they make it easy to gather data from multiple sources; for example, before purchasing a new product, a customer might have had multiple visits to the company website, chat sessions with contact center agents, phone calls with people in several business groups, filled out feedback surveys and posted a comment on social media complaining how difficult the process had been. To achieve all three, systems must be able to link data from multiple sources and apply data, speech and text analytics; these are common capabilities of several big data analytics products. The foundation of big data is important as it was found to be the second most important technology category for customer analytics in 60 percent of companies after collaboration and 44 percent of companies are using it today according to our next generation customer analytics research. My main point is that big data is ultimately not just about volumes and speed of change but about understanding the data companies have and putting the information to use to deliver the desired outcomes.

My most recent research studies show that the majority of companies run their communication channels independently of each other and business groups chase their own goals so that there is little collaboration between them; these disconnections are among the reasons most customer experiences are far from EPIC. To improve we recommend that companies take the following steps. First and foremost in a multichannel world is understanding actual customer journeys, which I have written about. These journeys cross channels and business groups, extend throughout the customer life cycle and differ for individual products and services. Big data is needed to ingest and process the great volumes and many types of data involved, including all the data associated with a named customer, and analytics is necessary to produce analysis and metrics. These tools can help companies understand the outcomes of all those journeys and identify ways to improve them. In addition companies can benefit from using predictive analytics to examine past journeys and use them and scenarios to predict likely outcomes of current or future journeys; for example, if customers that go down a certain path often stop being customers, the company should find ways to influence them to take more productive paths.

Secondly, companies should rethink the metrics they use. Our customer analytics research finds that companies often claim to be trying to improve one aspect of service – for example, customer satisfaction – but measure another – say, average handling time. Once again metrics should align with desired outcomes: If cost control is important, measuring handling times makes sense as these have a direct correlation of costs, but if customer satisfaction is most important find metrics such as customer satisfaction measured over time and customer vr_Customer_Analytics_03_key_benefits_of_customer_analyticsvalue that relate to it. Our next generation customer analytics research finds that the largest benefit from analytics is to improve the customer experience according to over half (55%) of organizations.

Thus it is clear that companies need a balanced set of metrics that are directly related to what they are trying to achieve and are shared across the organization. The last point is very important and ties to Maxie’s point that “humans need a concept to rally around.” For example, I know of a company in which everyone’s compensation depended to some extent on customer satisfaction scores. Leaving aside whether they were measuring this objectively, it stopped employees from doing things that might result in bad customer experiences and thus lower customer satisfaction scores; one obvious example is selling customers the wrong product. One metric I endorse is customer lifetime value. This is an outcome metric that addresses both sides of the cost and revenue equation, is a strong indicator of customer loyalty and reflects both customer experience and employee performance.

To build on Tony’s and Maxie’s analyses let me finish with four observations:

  • The right analytics, whether called big data or not, can reveal more about the customer experience than any metric. It can also precisely calculate metrics such as lifetime value that require multiple data sources.
  • It is likely that companies will go on using surveys, albeit using more channels, as a means of gaining feedback from customers. However, companies can gain more value by using speech and text analytics to gain broader insights that reflect customers’ feelings and predict their likely actions.
  • Companies should adopt real-time or near-real-time customer journey maps, showing outcomes and including predictive capabilities, to help manage and improve the customer experience.
  • There is no golden customer experience metric; customer lifetime value is probably the closest. So it is necessary to use multiple metrics, which companies should share more and use to drive action as there is no point in metrics for metrics’ sake.

Customer experience has become a key differentiator for many companies. However getting it right is not easy. So I recommend that organizations take into account my observations as they strive to create more loyal and thus more valuable customers.


Richard J. Snow

VP & Research Director

Our benchmark research into next-generation customer engagement finds that the top priorities in customer service for companies are to improve the customer experience (said 74%) vr_NGCE_Research_06_changes_to_improve_engagementand their customer service performance (70%). To do this, the technological steps most companies expect to improve customer engagement are to deploy collaboration systems, redesign the customer portal, deploy internal mobile applications, deploy mobile customer service apps and use social media for customer service. All of these we regard as potentially innovative and required digital technologies. Deeper analysis of the results finds key primary drivers for these priorities. Employees across the organization are handling customer interactions, but customers expect consistent responses no matter who they engage with. Customers are using more electronic channels of engagement, but here, too, they expect consistent responses. People on both sides are engaging more while they are on the move, so mobile support for employees and customers has become essential. Let’s consider how each of these five technologies can help companies meet these challenges and improve customer engagement.

Collaboration. Various established forms of collaboration are found in business systems, including file sharing, instant messaging, Web-page sharing, application sharing, discussion forums and unified communications that allow users to see who is available to take over interactions or confer with. Each of these has a part to play, but more innovative tools use Facebook-like social media techniques that enable employees to message each other or defined teams, share files, exchange ideas and generally collaborate on resolving individual or general customer service issues. They also facilitate sharing of performance recognition information, which can spur employees to perform better. Because these tools mirror personal social media use, they are likely to attract and be adopted by employees.

Customer Portal. Despite the popularity of newer methods, our research shows that many consumers still use corporate websites to look up information, find answers to questions, report issues and make purchases. However many company sites have not evolved from providing static information such as Q&A lists, and our research also shows that less than half of such visits are successful and force customers to use alternative channels. To match customer expectations, companies need portal systems that can personalize content and support dialogue with customers to help resolve their issues, for example, chat or interactive video.

Internal Mobile Apps. More employees engaging with customers today do so away from their desks; they include contact center supervisors, sales personnel and mobile customer service engineers. Each of them requires access to systems that provide alerts, advise of next best actions and supply information to help resolve customer issues. Companies therefore need systems that allow such employees to access these capabilities on their smartphones or tablets.

Mobile Customer Service Apps. These apps must attract consumers, often in forms much like games. They have to go beyond simply providing browser access to the enterprise website and at the minimum provide basic transactional capabilities; they must, for example, help customers look up the balance of an account, pay a bill, track a delivery or raise a service issue. As mobile app development capabilities improve, companies will need to do even more and enable customers to purchase goods or services, use self-service to resolve issues on their own or collaborate with customer service employees on voice calls, chat sessions or video calls. The key in moving forward is to connect such apps with internal transactional systems so they can display personalized information, remove the necessity for customers to re-enter data if they continue the engagement through another channel, and bypass system such as IVR so they can engage  directly with the employee most likely to resolve their issue.

Social Customer Service. Our research shows that at present, most companies use social media for marketing, for example through a corporate page on LinkedIn or YouTube videos about its products. More advanced companies have created a team to track and respond to tweets about the company, but most of these are reactive and don’t support significant dialogue because of the confidential nature of much of the data that would need to be shared to resolve a customer issue. Others have set up social media forums where customers can engage with other customers to resolve their issues or gather information. Yet we find that companies over manage these interactions to interject with their preferred solutions.  I believe that technical limitations such as accepting only 140 characters per post and insufficient security of information will prevent social media from having a major impact on customer engagement and expect companies to gain more success using less controlled forums.

Nevertheless, I do not doubt that each of these technologies can, if used properly, help companies innovate in customer engagement. But customer preferences will evolve along with technology; in particular the Internet of Things will rapidly expand human interaction with smart devices. So I believe rather than just making innovations in customer engagement, companies will have to find tools that enable them to disrupt the whole process. I think this will happen primarily through two technologies: self-service and analytics. I recently wrote that companies need to keep up with the digital age and match or even keep ahead of consumer expectations to engage through more digital channels and particularly to use these to resolve their own issues. As I outlined above this will mean that companies will have to improve IVR, make their customer portals more interactive and make their mobile apps smarter.

One instance could be visual IVR. As far as I can see most of these systems are actually smart mobile apps that are designed with menus and options that match customer expectations, rather than simply collecting information prior to a customer talking to an agent. Another is virtual agents. In simple terms this is voice-activated software designed to offer customers options that meet their expectations and provide responses personalized according to the caller and what he or she is trying to achieve. A third, the very latest advance in this area, is interactive video. It provides capabilities similar to those of virtual agents but presents the customer personalized information – for example, the latest bill – that can be used to guide the dialogue. In all these areas the application must be accessible through mobile devices, programmed with the customer in mind and connected with systems that can provide personalized information.

I have maintained for a long time that companies can’t improve customer engagement or customer service unless they know their customers, including what they buy, how many questions theyvr_Customer_Analytics_05_dissatisfaction_with_customer_analytics ask, how many complaints they make, what channels of engagement they use, what actions they take, their intentions going forward, how they feel during and after every engagement, and many more. As the list shows, gaining this view can be complicated. The issues begin with data; the more systems a company has, the more data it generates. Likewise, the more channels of communication a company supports, the more data it generates. Our research into next-generation customer analytics finds that accessing all this data is a problem for the majority of companies. To take full advantage of their data, companies have to deploy advanced systems that ingest all sources of customer data (including transactions, interactions and events), link all data related to one customer and produce analyses in forms suitable for each type of user.

One new type of tool to provide such comprehensive views is real-time journey maps. They show the interactions a customer has with the company, the channels used and how the person crosses channels to complete each interaction. A key capability is to show the outcome of journeys, that is, not just the channels used but the outcome – perhaps a new sale, an issue resolved or an increase in customer satisfaction score. By looking at actual journeys companies can understand why they went the way they did and what must be changed to produce better outcomes, for the customer and the company. The most advanced systems include predictive capabilities, which can, for example, analyze past journeys and predict what might happen in the future.

Organizations that deploy these disruptive technologies will also be in position to change their business models so they engage with customer in new, more effective ways. Those that do can get ahead of the competition and improve business performance by exceeding customer expectations.


Richard J. Snow

VP & Research Director

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Ventana Research


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