Emerging call center technology trends that promise to revolutionize call center strategy, operational processes, and the customer experience generate marketplace enthusiasm. They are hard to ignore as unproven capabilities capture the imagination and attention of vendors, users, and industry analysts. In this report, McIntosh & Associates cuts through the hype and identifies technologies with the potential to significantly impact call center operations and results. While the glamour of the latest “bright shiny object” is compelling, McIntosh offers a pragmatic framework for evaluating and investing in new technologies based on testing, proven results, and return on investment.
How Call Center Technology Trends Emerge
Call center technology trends emerge and gain prominence when a triggering event occurs; when a new technology or capability generates sufficient hype to break through the clutter and coalesce into something resembling a consensus. Markets focus on the new technology or capability creating a tipping point of enthusiasm.
Not surprisingly, call center technology trends flourish with sustained and extravagant promotion. Potential suppliers, fearful of missing out on the next big thing, join the race to over-promote the new capability. Industry sages unite to proclaim it a “game changer” and predict dominance. Users compete for positions as early adopters. It is at this point that the trend can no longer be ignored or marginalized; it demands attention. And thus, amid rhetorical hype, unrealistic expectations, and competitive flurry, trends are launched to follow a predictable pattern.
Gartner’s Emerging Technologies Hype Cycle
In 1995, the Gartner Group began tracking new technologies and published the Emerging Technologies Hype Cycle1. This model is a useful framework for understanding a technology’s forward momentum, frequent retreats, and eventual adoption or abandonment.
In today’s call center environment, technology trends are viewed as tools that when adopted, should quickly yield immediate and sustainable results. The graphic to the right illustrates the common expectation for an evolving trend. In reality, the lifecycle for emerging technologies more closely resembles a wild roller coaster ride with inconsistent accents and descents as soaring expectations are followed by disillusionment, eventual enlightenment, and finally pragmatic operational solutions.
There are five distinct phases detailed in the Hype Cycle, with the early phases more volatile than later lifecycle stages:
- Technology Trigger: A technology breakthrough or innovation that initiates the Cycle and drives early media interest and hype. At this point there are no pragmatic applications, and the anticipated benefit is conceptual.
- Peak of Inflated Expectations: The hype and excitement build as the capability is promoted, increasing visibility and raising expectations to unrealistic levels.
- Trough of Disillusionment: Early adopters face disappointment and disillusionment as first generation applications fail to meet expectations; interest in the technology is moderated by reality with interest waning and visibility declining.
- Slope of Enlightenment: Viable second and third generation products leveraging the technology begin to emerge as firms test and develop pragmatic business uses for the capability.
- Plateau of Productivity: Widespread adoption is common, with firms recognizing the technology’s relevance and value to the business.
While some new technologies can take 10 years or longer to evolve into effective business tools, others require decades to mature. The Hype Cycle may seem simplistic, but the evolutionary path for emerging technologies is seldom linear; in fact, multi-year comparisons of the Cycle reveal a pattern of forward progress, retreat, neglect, and then renewed interest and refinement before a technology is either discarded or finds its business application.
Inclusion in the Hype Cycle does not guarantee that a technology will mature as expected nor deliver value in the anticipated timeframe. For example, since 1995, speech recognition has appeared on the slope of enlightenment without significant movement, with time to maturity varying from less than two years to more than 10 years. As we have waited for speech recognition and natural language interaction processing to mature and be embraced by customers, the core underlying technology, speech recognition, has spawned multiple voice-enabled innovations. A few examples are highlighted in the box above with the year(s) they appeared on the Hype Cycle also noted.
Emerging Call Center Technologies Trends
More than 50 emerging call center technologies have been identified on the Hype Cycle; however, McIntosh believes that only a small subset of these technologies have the potential to significantly impact call center processes, operations, and customer experience initiatives. While some systems have been abandoned, other innovative technologies such as virtual call routing, home agent platforms, and automated self-service applications have demonstrated the value of patience and adopting based on proven results.
Listed below are the current emerging technologies that McIntosh finds most intriguing and believes should be evaluated, monitored, and tested to gauge their ability to contribute to call center efficiency and effectiveness.
GARTNER’S EMERGING TECHNOLOGIES HYPE CYCLE: Call Center Focus (2012)
Strategic Big Data
Big Data, the ability to collect and analyze enterprise level data, is a buzzword more than a specific technology. It is a compelling and elusive concept with soaring expectations in both the private and public sectors. Government agencies have invested hundreds of millions of dollars in big data projects, expecting the technology to tame the complexity of big government.
The ability to store, process, and analyze data is a proven capability. Big data’s value is in the scale of the data that can be collected and the granularity of the analysis. The challenges continue to be skill-based constraints rather than hardware and/or software limitations. Companies will always have an abundance of data; the limiting factor is an absence of the analytic skills and business acumen required to generate actionable insights through statistical analysis and modeling.
Call centers work with big data on a daily basis, tracking every second of every call and every agent activity through telephony, desktop, and workforce management applications. In the future, call centers will add to this operational cache with big data insights from customer interactions, systems external to the call center, and sources outside the enterprise. This convergence of data will be used to address common call center challenges such as recruiting reliability, employee retention, customer churn, revenue optimization, workforce productivity, and improved workload forecasting and workforce planning.
Tactical Big Data – Actionable Analytics
While big data is conceptually appealing at the enterprise level, the allure of actionable analytics is even greater for call center operators. Every day, call centers organize massive data streams into useable information, creating knowledge bases that inform decisions and enable action.
These knowledge bases are leveraged by multiple analytics approaches to deliver the information supporting call center decisions and actions leveraging:
- Social Analytics
- Speech Analytics
- Text Analytics
- Predictive Analytics
Social analytics, recognized as an emerging technology in 2007, is often perceived to be the newest “bright shiny object” in the call center arsenal. The hype surrounding anything related to social media borders on hysteria, with firms eager to mine and monetize these platforms.
More important than the scale of today’s social operations is the value social media may deliver in the future. Can it be monetized and become a differentiated sales and service channel? The verdict is out. Given the rapid changes within the social media landscape, continued monitoring is essential. Today’s leading social application or monitoring toolkit may be irrelevant or obsolete next year. What is becoming evident is that customers who engage on a social level exhibit higher levels of loyalty and generate higher average annual revenues than customers who do not interact via social platforms.
Whether firms are leveraging social media to optimize revenues among existing customers or to intensify the customer experience and thus impact loyalty, it is evident that social has the potential to impact profitability directly or indirectly. Firms seeking to exploit the social opportunity must demonstrate operational agility, constantly testing evolving social networks and tools while monitoring customers’ use of social networks. Social, more than other emerging technology, demands nimble operations.
Speech analytics is today’s most promising new technology. While deploying speech as a self-service channel has been challenging, the application of speech analytics to customer interactions is powerful. Speech analytics’ ability to correlate agent behavior to call outcomes and generate actionable voice of the customer insights is compelling.
However, like big data, speech analytics requires analytic skills and business savvy when mining for customer insights. This is not plug and play technology that can yield actionable insights through standard queries and reporting; it requires a baseline understanding of the customers’ mandatory requirements and service preferences to effectively leverage the tool. This understanding is acquired through multiple methodologies including Kano2 surveys, a proven approach used by McIntosh for cataloging customer requirements and preferences and identifying opportunities for differentiation. Once true customer requirements and preferences are understood, they become the foundation for developing the queries that mine customer interactions for actionable insights.
In the call center, text analytics enables the analysis of electronic communications including content from customer emails, online or email survey responses, CRM system transaction documentation, chat logs or transcripts, transcribed speech files, and website feedback. Like speech, text analytics has the ability, when leveraged skillfully, to deliver operational, customer experience, and competitive insights.
Predictive analytics, the ability to mine, analyze, and model the future based on historical data, is not a new concept. Credit scoring is a prime example of past efforts to predict future outcomes and minimize risk. Today’s call centers certainly are closer to anticipating and proactively engaging with customers, but few firms have chosen to leverage the insights that enable proactive service. The barrier is cultural. In order to leverage predictive analytics, call center leadership must break away from day-to-day reactive tasks and the comfort zone afforded by crisis management. This cultural shift cannot occur if management’s primary concern remains managing today’s call volume rather than planning for the future.
Call center predictive analytics have the potential to dramatically improve:
- Workload and workforce forecasts
- Customer segmentation models identifying new customers requiring specialized handling
- Customer churn prediction and prevention
- The ability to anticipate customer requests and deflect calls with proactive notifications
- Employee recruitment and retention tactics
Consistent for all analytics, the skills to effectively model the volume and variety of data, to mine historical and transactional data are in limited supply. Long-term talent must be developed, but in the short-term, acquiring the critical industry savvy, business acumen, and analytic skills to support predictive modeling will remain a challenge.
Cloud computing, the ability to store and process data and transactions virtually across a network, has enabled productivity and flexibility in call center IT operations. Call center migration from premise-based solutions to centralized, virtual operations began with early adoption of application service providers (ASPs) for specialized applications (e.g., speech self-service platforms and chat/email functionality) and gained acceptance with network management tools that managed call identification and delivery at the cloud level. Cloud computing within the call center continues to evolve, and today includes critical cloud-based telephony and VoIP/IP migrations.
Whether managed by the IT organization or hosted by an ASP, cloud-based applications enable nimble call center operations and support significant operational trends such as home agent deployments.
Speech Recognition and Natural Language
Speech recognition remains a compelling and productive innovation capable of delivering frictionless and convenient customer interactions. Its potential has not yet been fully realized, but there are pockets of outstanding execution that indicate the challenges are no longer technical but rather business maturity and the wisdom to apply the technology intelligently.
The most recent speech innovation, natural language questions and answers, is climbing the peak of inflated expectations. It will pass through the trough of disillusionment and eventually offer call centers the opportunity to simulate agent interactions for simple requests. Since complex touch tone automated applications remain customers’ least preferred communication channel3, a simpler voice interface enabled by the maturing of natural language functionality may finally deliver on the promise first seen in 1995.
Considerations Before Adopting an Emerging Technology
Whether you are considering moving to cloud based platforms, investing in big data, or seeking to test natural language capabilities, investing in emerging technologies and the processes enabled by these tools requires an understanding of customer requirements, preferences, and aspirations for service. While there is no absence of opinions on which interaction elements customers value, the only reliable source is the voice of the customer.
Two highly effective approaches for generating customer loyalty insights are the Kano survey methodology and the correlation of call observations and customer surveys. The two techniques leverage different approaches but both rely on actual customer feedback and analytics to generate statistically valid insights. On an ongoing basis, loyalty is best measured through surveys such as the Net Promoter Score (NPS®) 4, an approach that effectively gauges a firm’s ability to continually generate customer loyalty.
Based on extensive customer research, McIntosh recommends targeting the following customer impacts when considering investment in an emerging call center trend or technology.
- Increased Customer Accessibility: Does the new technology or process improve the customer’s access to your products or services? Does it improve channel accessibility and eliminate frustrating and irritating conditions such as lengthy wait times, call transfers, or forced channel switching?
- Expanded Customer Choice: Customers prefer to control their interactions and therefore resist processes or requirements that limit their options. Are you providing customers with alternative channels for resolution or information?
- Demonstrated Customer Respect: Does your new technology simplify customer communications? Does it demonstrate respect for the customer’s time by eliminating repetitive non-value requests or actions?
- Support for Issue Resolution: Does the technology enable frictionless, easy resolution? Industry data repeatedly shows that first contact resolution is the primary driver of satisfaction. Customers repeatedly return to the channel that offers them the greatest opportunity for resolution and are loyal to firms that excel at resolution.
Conclusion: Investment Guidance
The ability to recognize compelling capabilities frequently precedes the acquisition of skills required to operationalize these capabilities. While some emerging call center technology trends mature and find pragmatic business applications, many of the latest and greatest “bright shiny objects” become dull as hype exceeds reality. Before investing in an emerging technology, firms should consider the maturity of the technology and its current place on the Hype Cycle. By understanding a trend’s evolutionary path, firms can more realistically discern if a trend should be monitored, tested on a limited or expanded basis, or pursued as an investment.
For organizations seeking to invest in emerging call center technology trends, the Hype Cycle provides a cautionary framework. As evidenced by speech recognition’s lengthy and uneven evolutionary path, even the most promising technologies take time to deliver business value. It is challenging to accurately predict an emerging technology’s future. Call center leaders should avoid technologies that are positioned early in the Hype Cycle, monitoring each technology’s evolutionary path, testing and then deploying on a larger scale when warranted by demonstrated results. This approach will yield a more seamless implementation as well as the likelihood for a successful ROI and sustainable results.
- Gartner’s Emerging Technologies Hype Cycle, gartner.com.
- “Kano’s Methods for Understanding Customer Defined Quality,” Center for Quality of Management Journal, 1993, 1999.
- “How Consumers Research, Buy, and Get Service, 2011,” by Adele Sage, Forrester Research, Inc.
- Net Promoter® and NPS® are registered trademarks and Net Promoter Score and Net Promoter System are trademarks of Bain & Company, Satmetrix Systems and Fred Reichheld.