Five Emerging Call Center Technology Trends to Watch in 2017

Blog Post created by robertstanley Employee on Aug 4, 2017

The newest call center technology advancements are focusing on deeper insights and customer satisfaction.


Over the past several years, call center technology has played a big part in shifting the industry’s focus towards a better customer experience.  Overall wait times are down.  Calls are transferred less often before a solution is found.  More communication channels like text, chat, social and email are being taken seriously.  The patterns within these actions are clear- call centers are getting serious about improving customer satisfaction on the customer’s terms.


How is technology empowering these advancements?  Let’s take a look at some of the latest trends-


Delving Deeper with Analytics

Collecting data has never been difficult for call centers- they have mountains of it on hand from recordings of customer interactions.  The real challenge has been finding ways to transform that data into actionable insights, preferably ones that can empower call center managers to fine-tune their teams.


Best-of-breed speech and customer interaction analytics solutions automate the Quality Assurance (QA) process and reveal customer insights across multiple channels including – calls, chat, email, texts, social media and surveys. These systems transform call center performance management by converting 100% of customer conversations into transcripts that can be categorized, tagged, and scored based on business KPIs.  They also capture metadata from any source – recorder, chat system, CRM, and evaluate every interaction for sentiment and acoustical values.


Transcripts and recordings are easily searchable for specific good and bad language and behaviors. Insights can be used for training all agents on areas they all need to improve and for coaching specific agents on the areas just they need to improve. Supervisor and agent dashboards incentivize self-evaluation and online training. In addition, real-time analytics provides alerts to agents and supervisors while a call is happening. These alerts can remind agents about proper greetings, compliance language, and upsell and cross-sell scripts. In addition, speech analytics is being used to uncover root cause. It’s great to know that there are large blocks of silence on a call, but it’s even better to know why. Is there an issue with IVR routing, is it difficult for agents to find the information the customer is asking for, is it a training issue, or are agents just wasting time? All of this information can be revealed through speech analytics.


Advancements in both the analytics technology and speech recognition engines are improving the accessibility of these solutions to small and mid-sized companies and those who are not quite ready to deploy a full-featured solution.  In addition, the functionality of analytics technology can now be expanded with the use of APIs, which are enabling analytics solutions to grab data, functionality and services from other systems and vice-versa.


Businesses who are serious about better understanding customers and improving their experience throughout the entire journey are turning to speech and customer engagement analytics. Other return on investment values include: automating QA, fine-tuning agent performance, improving compliance adherence, and increasing sales/collection.


Omnichannel Support & Behavior Analysis

While 2013 and 2014 surveys found up to 79% of customers still prefer phone support over any other method; that statistic itself is misleading.  Just four years ago, nearly 8 out of 10 chose phone support because they didn’t have any other choice; at least not one that seemed viable at the time. In many of these cases customers picked up the phone after having unanswered emails and striking out on every other channel they could find.  The data indicated that a lack of quality support through email, social media and text was driving customers away.


Today, the Aberdeen group finds that 58% of businesses now use eight channels to communicate with their customers.  While that’s certainly a step in the right direction, the latest call center technology can now analyze and sync all those cross-channel conversations to get a much better idea of customer preferences.


More Comprehensive Self-Service Tools

Likewise, a major industry complaint in recent years has been the lack of self-service available to customers for smaller tasks that really don’t require much human interaction. For instance, think about something as simple as a consumer requesting a credit card limit increase in an emergency situation.  While we all know that an algorithm ultimately makes that decision, the consumer would have to call customer service, navigate through an automated menu, explain the situation and eventually have a service rep go through the same process the customer could have completed themselves ten minutes earlier.  These types of obstacles make little sense from a service standpoint.


The solution in this instance also goes back to better data analysis through state of the art call center technology.  Not only can companies create self-service alternatives for their customers, but they can also see how those digital elements are interacted with to know how to fine tune them.  This is also where machine learning and artificial intelligence are quickly entering into the equation to automate most of the basic level tasks that service reps handle today.


Cloud-Based Solutions & the Rise of Virtual Workers

Cloud-based communication have drastically reshaped the landscape of call centers in recent years since it allows companies to quickly expand or condense their operations at will depending on the business climate.  Without the hindrance of data being tied to geography, it has opened doorways to countless call center business models that weren’t even possible five years ago.  Both hybrid and full-scale cloud solutions will continue to gain popularity in the near future.


Bringing data to the cloud has also allowed call centers to move away from large physical locations and take advantage of remote workers across the globe.  Modern collaboration tools keep workers as connected as traditional employees while slashing the cost of doing business.  The result is flexible, more efficient personnel that are better equipped to handle a variety of client needs.


A Unified Customer View with APIs

For many years, companies have been using several disparate business systems that were not able to easily integrate with one another. A major disadvantage of these siloed applications is that they made it very difficult, and IT resource intensive, to extract information from one system so that it could be shared with another. This is no longer the case.


For example, some speech analytics solutions have APIs with CRM systems such as Salesforce, or data visualization solutions such as Tableau, as well as big data warehouses. Through the API, specific bits of data can be grabbed from these systems and used in the speech analytics dashboard. Or they can be pulled from the speech analytics platform and used by another business application. In addition to data, APIs enable the use of services and functionality, such as adding a workflow step from one system into another.


The recent widespread development and use of Application Programming Interfaces (APIs) is allowing businesses to invest in best-of-breed speech analytics solution and easily integrate with other existing systems to pull data from one into another for a single unified view of the customer.


Big Data & Predictive Analytics

Predictive analytics is a term you’ll become quite familiar with in the near future as call centers learn how to better use their current and historical data to predict the best ways to serve customer needs.  This relatively new computer science combines machine learning with statistical analysis and queries to predict future trends, challenges and opportunities before they actually happen. 


Data centers are capitalizing on this technology by using it to better understand their customers across all communication types.  For instance, how long is too long to leave a customer on hold?  How many clicks will a user make before leaving a FAQ database in frustration?  What is the optimal amount of automated interaction?  Predictive analytics can provide those answers and so much more.


What emerging technologies are you using or considering?