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Actuate, which develops commercial versions of the open source Business Intelligence Reporting Tool (BIRT) technology, recently held a one-day event in London. My colleague Mark Smith covers Actuate’s products, but I was impressed by the simplicity of the company’s message, the core of which is that the ActuateOne suite of products allows companies to extract data from multiple data sources, use one product to analyze it and present the results in multiple formats in response to individual user requirements. A key component of this visualization is how easily it can display the results on smartphones and tablets. Actuate presenters demonstrated these capabilities in a lengthy session designed to show that this is “BI for the layman”; that is, after some from help from IT in setting up access to data sources, users can do everything else through the software’s drag-and-drop capabilities. My recent benchmark research into contact center analytics suggests that such simplicity is critical for more business users to adopt BI; if companies are to move away from using spreadsheets to produce their customer and contact center analyses, in addition to being able to do more the new products will have to be as easy to use as spreadsheets.

The company’s ActuateOne X2BIRT product uses another drag-and-drop capability to highlight words in a document and effectively create metadata item that defines the field. This allows users to create a structured definition for documents that can then be used during analysis, for example, where a customer’s name appears on a bill. In the simplest case this allows users to extract the names of customers that appear in PDF files in archived bills. In more complex uses it turns unstructured text-based documents into “structured” data files that can be integrated with and analyzed by other products in the Actuate suite. In this way companies can analyze text-based documents received from and sent to customers in an effort to uncover customer issues and trends to supplement their customer analysis. Actuate is not known for customer analytics, but with the acquisition of Xenos earlier this year it is moving more into this space, especially in the area of customer communications.

During the day there was a lively panel discussion in which the panelists responded to questions from the audience. On one of the key topics companies increasingly have to confront, much was said about “big data,” on which my colleague David Menninger focuses. From my simple perspective this is nothing new. Contact centers have always been awash with data: call records, phone recordings, email, faxes, CRM records and many more; indeed a contact center with a few thousand seats can generate and process millions of records every week. There are obviously issues about where and how to store and process all of this data, but the most important issue is how to make use of it, and a significant portion of data today is unstructured. So it was good to hear Actuate is working to make the analysis of big data simpler, faster and more affordable.

One of the panelists, industry analyst James Governor, put this into the context of what information can be derived from all this data and what that information can be used for. He and I agree that companies have yet to get to grips with two key challenges. First is data consolidation. Customer data sits in multiple places within an organization, and it is key that business units are persuaded to share it so it can be brought together to produce a single source of customer information for use throughout the organization and for different purposes; the same source should contribute to, for example, setting customer-facing strategies, focusing marketing and sales campaigns, helping agents make better decisions as they try to resolve customer contacts and other uses. Second is identifying the kind of metrics and information that are important to companies trying to stay competitive in this tough economy. James put forward the view that companies need to focus more on customer behavior and the likely impact on customer behavior of marketing messages, sales calls, social media content, product features, an agent’s attitude, IVR menus and other sources, or as I recently wrote, how customers are likely to react to moments of truth in their contacts. Understanding this requires analysis of masses of historic and current data, both structured and unstructured. It will be interesting to see what Actuate does in this area as it develops more customer-related solutions.

Do you still use spreadsheets to analyze your customer data? Do you have plans to adopt any of the new analytics products now on the market?


Richard Snow – VP & Research Director

It never ceases to amaze me, when you ask people what their business objectives are and how they are measured, how often the two have little in common. This has been the case consistently in the research I have carried out over the last eight years into customer service and contact center performance. The main objective for contact centers is to improve customer satisfaction, but the key performance metric is average call-handling time. Despite hours of contemplation and discussions with colleagues, I still can’t see how one relates to the other.

Furthermore, most companies don’t pay enough attention to the impact metrics have on behavior; for example, sales people and their managers will bend all the rules they can to hit sales targets so everyone earns their commissions, even though this often produces downstream issues such as customers ringing up to complain about what they have been sold, or not being able to repay the loan they took out to make the purchase.

My research and experience talking to contact center managers show that contact centers focus primarily on efficiency metrics related to people and process. For instance, my recent benchmark research into contact center analytics shows that 65% of companies use four or more metrics, and one-fourth use six or more; these include queue lengths, average handling times, hold times, transfer rates, call volumes, silent time, first-call-resolution rates, agent quality scores, and others. Yet these are not what matter most to executives, whom the results show are more interested in customer satisfaction by customer and communication channel, product or service profitably and customer retention rates. Like the disconnect between average call-handling time and customer satisfaction, these two conflicting objectives indicate that companies need to review their key performance metrics to balance efficiency and effectiveness, and to ensure the metrics they use generate the right behavior.

Where should companies start? How about with the cliche that “the customer is king,” which is back in favor today? If you follow social media it seems not all companies have got the message. Debates continue as to whether customers or profits are more important, and how many complaints customers have to make before companies change policies and processes. You can read more about bad customer experiences online than you can about good ones.

Advocates of “the customer is king” argue that you should adopt customer-focused metrics such as net promoter scores, customer effort scores and customer value. I have nothing against any of these, but in isolation they are not particularly useful; it might be good to know customers say they might recommend your company, but do they actually do it? And if they do, can you measure the impact? Metrics become useful only if they produce change.

This is why I favor first-contact-resolution rate as a balance between efficiency and effectiveness. It saves money if issues are resolved the first time, and customers are more satisfied if issues are resolved promptly to their benefit. But first-contact-resolution rate can be even more useful when linked with other metrics and actions. Applied to agents, for example, it lets companies identify best practices and adjust process and training so more agents can resolve more issue the first time. Linked to customers, it can tell who are the difficult customers and how they can be handled in the future. It can help identify why issues occur and what can be done to generate fewer calls. It can influence behavior, because agents will strive harder to resolve more calls at the first attempt. It can influence call-routing rules, so that more calls are routed to agents who resolve more issues the first time. Companies that think outside the box and across processes and lines of business can uncover even more benefits.

The point is that companies should review their key performance metrics to determine whether they help or hinder progress toward achieving business goals and whether they drive the right behavior. As they do this, companies need to understand the types of metrics needed from people and process that contribute to performance metrics. You need to look at how you produce their metrics. Most business-related metrics cannot be derived from a single source of data, and the “simple” task of cutting and pasting data into spreadsheets is time-consuming and prone to error.

Data analysis is too complex a process to attempt with a spreadsheet when you’re trying to integrate data from multiple sources. But there are now several products that can automate the analysis of data drawn from multiple business applications, speech recording, text (including social media) and desktop usage and subject it to operational, customer, agent, process, social media, cross-channel, predictive and root-cause analytics and produce actionable information.

Whether the customer is king or not, I recommend companies take at look at these products, because they support the production of more effectiveness-related metrics and can help improve operational and business performance.

Have you reviewed your contact center metrics lately? Are you thinking of adopting any of the new analytics products now available? If so, tell us more and collaborate with me.


Richard Snow – VP & Research Director

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