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I began my involvement with contact centers – actually they were called call centers in those days -more than 20 years ago. I quickly learned that almost everyone involved in running a contact center is obsessed with metrics: queue times, average call handling times, agent utilization, average length of after-call work – the list seemed to be endless. Since joining Ventana Research I have carried out numerous benchmark studies into customer and contact center performance, and found things haven’t changed a great deal. The number of metrics has increased and old favorites are still high up on the list.

In my earlier research into contact center analytics,  I divided the metrics into two broad categories: financial and process. From a financial perspective, companies are most focused on seeing a comparison between actual operational costs and budgeted costs, and the average cost of handling different interaction types – calls, email and letters. Meanwhile, the research shows that as far as process metrics are concerned, things haven’t progressed a great deal. What I call efficiency metrics still dominate, and average call handling time is still number one. The one positive I take for the results is that compared to my earlier benchmarks in first contact resolution rates (FCR) have increased in importance, with 68 percent of respondents indicating it is an important metric. I view FCR as a mix of an efficiency and outcome metric because it reflects efficiency of operations, in that there will not need to be as many call-backs, and it is also an outcome metric, in that customers are likely to be more satisfied if they get their issues resolved at the first attempt. However, companies need to be careful how they measures FCR, because a customer might raise the same issue through another channel or come back at a later date with the same issue.

The most disturbing insight from the research is the percentage of companies that still rely on spreadsheets to produce their contact center and customer reports and analysis. Just under two-thirds (62%) of the respondents indicated they use spreadsheets as their primary tool, and 90 percent said they use spreadsheets on very regular basis. The challenges of using spreadsheets are manyfold: they are labor-intensive and prone to data entry errors, and there are often long gaps between the data being available and the final reports being distributed. However, as the research shows, by far the biggest issue is accessing all the data sources that include customer-related data. These now include transactional data in structured files, call recordings, text-based data such as CRM notes, email, survey forms, letters and social media posts, and machine-based data such as failed calls. To gain a full view of contact center performance and customers, organizations need to access all these forms of data, aggregate them, then produced consolidated reports and analysis to support all customer-facing activities. Given the sheer scale and complexity of these data sources, customer data falls squarely into the big data arena, requiring companies to investigate specialist tools that can not only access all forms of customer data but can do so at the speed of light so that the information is available to support real-time activities such as answering a customer call. The most advanced organizations also look for these tools to include predictive analytics capabilities so they can predict potential future customer activities, such as a propensity to terminate an agreement because of bad customer service.

In one of my recent blogs about the 2.0 world I noted that consumers have changed their communication habits, the channels through which they investigate and buy new products, and the ways they collaborate with other like-minded consumers. This means companies have to run to catch up. They need better views of their customers, interaction-handling performance and the outcomes of interactions. They therefore need to reevaluate the metrics they use to monitor and assess these critical activities and the technologies they use to produce them. In the past, companies struggled to build the business case to invest in analytics; my research shows that if companies use a better balanced set of metrics, including outcome metrics, then it is easier to build a case and derive full benefit from such investments.


Richard J. Snow

VP & Research Director

IBM recently announced its new Customer Experience Lab. During a briefingvr_inin_types_of_interactions_in_contact_center I learned that the lab is a response to what IBM discovered by interviewing more than a thousand CMOs, who are concerned about the explosion of data companies collect about their customers. This explosion is being driven by changing customer communication preferences and the way customers now interact with organizations, which I recently highlighted in my post about the 2.0 world. My research into the contact center in the cloud shows a similar trend; although traditional channels such as telephone calls and email are still the most popular, channels such as social media, instant messaging, text messaging and video are fast catching up.

IBM also saw that the world is changing and mobile and social not only impact the way customers interact with companies but also vr_bti_br_technology_innovation_prioritiesimpact how organizations need to work internally; they must create more joined-up processes and systems and allow employees to collaborate more easily on tasks. The trend to source systems in the cloud has also changed internal IT operations and the way business users access systems and information. All these changes mean that companies need to look at new technologies that allow them to analyze big data more rapidly so users can be provided with up-to-date customer information, just as I said in my recent blog post about a new a generation of customer analytics and big data. The Ventana Research benchmark into business technology innovation also mirrors this trend and shows that analytics is seen as a high priority for companies, with collaboration, mobile, cloud and big data of equal, growing importance, and perhaps somewhat surprisingly social still at its infancy.

Against this background, IBM decided to launch its Customer Experience Lab, which is an undertaking that probably no other organization could make. It is bringing together more than 100 consultants and researchers from 12 of its existing labs to focus on the customer experience. The Customer Experience Lab will focus on three areas initially: customer insight, customer engagement and employee engagement. Customer insight will focus on how to create a single view of the customer using all the available sources of customer data, including structured, unstructured and event data. It will also look at how to use these insights to predict customer behavior so that companies can plan future activities. Customer engagement will focus on what I call proactively managing the customer experience at every touch point – that is, using these insights to personalize responses and put them into the context of the customer journey and desired business outcomes. Employee engagement will focus on empowering employees using tools such as collaboration, social and analytics so they deliver excellent experiences but also deliver targeted key performance metrics. All three of these tasks will work within the context of analytics, social, mobile, presence, location, machine learning and the cloud.

As you might expect, this is not an entirely philanthropic exercise, although IBM was quick to point out that its services would be “heavily subsidized.” The Customer Experience Lab will work with clients on a four-stage approach: discover, scope and solution, prototype and deliver. Discover will use existing IBM processes to workshop with clients what they are trying to achieve and prioritize future activities. Scope and solution will assess how a task can be achieved and build a business case and roadmap to the desired goal. Prototype will build a prototype, test the solution with early adopters and map out an implementation plan with costs. Deliver will create the solution, integrate it into any existing environment and test whether it delivers the expected results. All of these stages use a combination of resources in the lab, including hardware, software and IBM business consultants. This is not just a theoretical exercise; it is designed to deliver real-world solutions to meet real-world goals.

I applaud IBM’s efforts and was pleased to hear that the solutions might include third-party products. My research shows that customer experience management is an immature market, and there is a lot of confusion about what exactly it is. For a long time people have claimed that customer service is the only differentiator; I believe that in fact the customer experience is the true differentiator. Several consumer research reports I have seen show that bad experiences often lead to customers defecting, or posting less than positive comments on social media, and we have all heard stories about what effect that can have.

As I pointed out in my blog post about the 2.0 world, customers have changed, so companies must change to keep up. The early messages I heard about the Customer Experience Lab make me think it will help companies recognize how they need to innovate customer service and change the way they engage with customers. Eventually I hope its work will result in you and me finding it easier to engage with companies and achieve better outcomes.


Richard J. Snow

VP & Research Director

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