Tag Archives: data analytics

4 Common Contact Center Challenges and How to Solve Them

In previous years, the contact center was seen as an operational necessity, an important but non-strategic wing of an organization. That has changed.

Customer experience (CX) is now understood to be among the most powerful differentiators for businesses today. Contact centers, and the technologies enabling them, now feature prominently in strategic decision making.

Many organizations know their current system for engaging with customers could be improved, but with new technologies, new vendors and new jargon seeming to appear every day, it’s difficult to determine the best place to start.

This blog will introduce four common contact center challenges you may be facing and show how four organizations overcame them.

Siloed Technologies

Silos are one of the great challenges facing contact centers today. A “silo” refers to a technology that is disconnected from the others.

Different departments acquire technology (billing system, dispatch system, WFM, etc.) based vendor strength, but they are not tied together, which hinders the ability to create smooth transitions between departments to support an end-to-end process. Data becomes siloed as the customer view is carved up between these systems. There is no master customer “system of record.”

The impact on the enterprise? Agents are forced to play the role of integrator, using multiple screens and numerous applications to resolve customer requests. Customer metrics sink due to the complexity of support. Agent turnover increases. Management lacks actionable insights into contact center events and trends. Maintenance costs rise, and rise, over time.

If technology silos are hurting your CX, the need for change is clear. The best course forward may not be.

Technology silos and process silos — you can have one, but you usually have both. Technology silos often lead to process silos.

See how SMART Technologies successfully overcame siloed systems and achieved a flawless customer experience without impacting their day-to-day operations.

Inflexible Infrastructure

Another common challenge facing contact centers is the use of inflexible, on-premise systems. Often requiring continual hardware upgrades and “quick fixes” to remain functional, these systems are the underlying cause of many of the silos mentioned above.

They also cause a heavy drain on the IT department’s resources, as more time is spent managing complexity than thinking strategically about where technology can create an advantage.

Many organizations dealing with inflexible infrastructure understand the benefits of migrating to a modern cloud contact center: minimum required investment to deploy, maintain and support; ease of managing multichannel communications; improved employee performance and CX; and the ability to adapt to changing business needs.

But there’s a natural resistance to change that stems from the fear of disrupting day-to-day operations.

See how a medical technology company with strict parameters seamlessly transitioned from an an end-of-life system to Salesforce to reduce maintenance and support costs, achieve unprecedented agent visibility and experience a 5X increase in order processing.

Introducing & Managing Multiple Channels

Customers demand more connected experiences than ever before. They expect to be able to communicate with you digitally, and expect you to provide a consistent journey across all touchpoints, whether they’re engaging in a web chat, texting or speaking to a representative by phone.

Many contact centers avoid adding digital channels because their legacy infrastructure won’t support it, and they don’t want to add new siloes to an already complex system. Others add digital channels, but struggle to integrate their data into the agent desktop so agents lack context and information on prior interactions.

Another difficulty related to managing multiple channels is integrating it properly with WFM systems, which makes it difficult to effectively forecast, plan and schedule agents across channels.

The end result of all these challenges — whether it’s a lack of channels or channels being poorly stitched together — is that you run the risk of customers churning to competitors that offer more consistent and personalized experiences.

See how a publicly traded financial services company with limited customer care channels and disconnected legacy technology turned their customer experience into an asset by introducing digital channels, modernizing their systems and optimizing the agent desktop.

Lack of Contact Center Visibility

Another harmful side effect of complex, siloed contact centers is that they hinder an organization’s ability to spot and resolve problems. Delivering consistent CX is a challenge; it’s near impossible if you can’t identify areas that need improvement.

Lacking a modern infrastructure, many organizations necessarily turn to the IT department to identify and fix problems with agents or operations, as they are the only ones with the technical know-how to navigate the system and manually analyze the log files. But dedicating IT resources to solve non-IT issues isn’t a sustainable solution.

See how a Fortune 500 transportation logistics company with the challenges above leveraged an operational analytics application to sift through massive amounts of data in minutes to identify and resolve issues, improve agent performance and achieve unprecedented visibility into their contact center operations.

About Aria Solutions

Aria Solutions is a customer engagement center solutions company that helps some of the world’s biggest organizations achieve unified customer engagement centers free of silos.

Over the past 22 years we have empowered 550,000 agents and completed over 1,200 successful projects, collaborating with our customers to help them achieve their business goals and find better ways to serve their customers.

Visit our about page to learn more about Aria Solutions or contact us today if you’d like guidance on how your organization can overcome your contact center challenges.

Thanks for reading!

How To Organize Support For Your Contact Center Applications

As contact centers become more complex and expand to support more channels like web chat and social, it’s important to organize support for contact center applications in a way that allows teams to design consistent customer experiences.

Most contact centers organize support around siloed applications, having one team that supports the voice channel and a separate team that supports social channels, like Facebook. But when support is organized this way and spread across multiple application-specific teams, gaps in support are created and must be fixed as they can affect both the efficiency and effectiveness of the contact center.

Organizing support by functional skills not by application silos

Instead of organizing support by contact center application silos and having multiple application specific teams, contact centers should try the approach of organizing support around functional skill sets.

Seen in Figure 2 below from the Forrester report “Mind the Gap When Organizing to Support Contact Center Applications”, by looking at functional skill sets like agent desktop that can span a group of applications, contact centers can gain leverage.


One team to support queuing-and-routing

Companies that don’t have one complete solution for their contact center, are supporting queuing and routing for digital channels like web chat and social through their website teams and ACD, IVR, and CTI support for the voice channel usually has its own team. Having this legacy telecom silo, creates a gap in support and makes it difficult for both teams to design consistent customer experiences.

Merging these teams to support queuing and routing contact center applications, means they will be working on the same customer experience design goals and requirements, which results in more consistent experiences across all channels.

Another reason why it’s important to do this now is there are emerging trends like AI matched routing that will likely force this organizational shift eventually (Future-Proof Your Customer Service: Build An AI-Infused Cognitive Contact Center, Forrester Research, February 2018). This new way of routing is different from skills-based routing in that through AI, agent matching is done in real-time and based more on attributes such as:

  • Agent performance histories
  • Skill development priorities
  • Customer histories

AI matched routing needs to look at the experience customers have on all channels so to implement this and other emerging trends, it’s essential for queuing and routing to be one team.

One team to support the agent desktop

[easy-tweet tweet=”Agents can have 10 to 30 applications open simultaneously.” template=”light”]But to reduce employee and customer frustration, improve handle time, and lower training expenses, agents need the right tools and an agent desktop that shows them a 360˚ view of the customer on one screen, should that be the goal.

In order to not add to the list of agent applications, contact centers should be selective about what ends up on the agent desktop, and they should monitor agent use to further refine the user interface (Design Your Contact Center To Be Customer-Centric, Forrester Research, August 2017). The best way to do this is to have one team that supports the agent desktop, instead of a bunch of separate teams supporting different contact center applications agents use.

Organizing support for the agent desktop in this way will focus efforts on usability and developing a more in-depth knowledge of all applications appearing on the agent desktop. Making it easier for agents to engage with customers has many benefits for the contact center, including:

  • Reducing agent turnover and increase efficiency
  • Increase customer satisfaction scores
  • Increase revenue
[easy-tweet tweet=”Companies with agent desktop optimization programs enjoy 44% greater customer retention rates. ” template=”light”]The first step to driving these results is to have one team that supports the agent desktop to optimize the user interface and help improve customer engagement (Design Your Contact Center To Be Customer-Centric, Forrester Research, August 2017).

Contact center operations to support CC data analysis

Business analysts in contact centers are faced with the challenge of navigating through many reporting systems. Having data stored in disparate systems is a major pain point for contact center managers too – as they need manually consolidate reports to be able to forecast and schedule.

Since expertise of data structures and databases frequently resides within business technology rather than in the contact center itself, this causes a gap in the effectiveness that analytics can bring to the contact center. To remedy this, organizations should move contact center data analysis from the business technology to the contact center business unit itself.

Contact centers are awash with data, but there is still a struggle to integrate it and drive process management (Design Your Contact Center To Be Customer-Centric, Forrester Research, August 2017). Having contact center operations support data analysis for the contact center can help the organization understand the data analysis needs of the contact center, resolve any data visibility pain points that contact center operations managers experience, and help run the contact center more effectively and efficiently.

A few things organizations need to do is:

  • Understand how subtle changes in historical data can affect WFM

WFM teams depend on historical data but they also need to know of any anomalies or shifts in that data over time. Business Intelligence (BI) teams should assist WFM teams in analyzing these shifts and helping them determine if system upgrades or database changes in a certain timeframe caused any anomalies.

  • Understand that queuing and routing changes affect reports

Changing real-time business rules can affect how data flows into analytics systems so the team that supports queuing and routing must work with the BI team so both teams understand what reports will be affected from routing changes.

  • Map and manage the many sources of truth

BI professionals should work with contact center business analysts to map existing systems and determine which reports should come from which system. For example, in scenarios were customer feedback is captured in an IVR then integrated with quality monitoring data, which system is best suited to show customer satisfaction trends? This needs to be determined for the contact center to get the right insights and make better decisions.

Configuring contact center applications drives not only the customer experience but the agent and management experience as well. How contact centers organize support results in the overall efficiency and effectiveness of the contact center.

To learn more about how to optimize operational performance of the contact center by picking the right contact center organizational model and developing a “living” RACI model, download this complimentary Forrester report https://aria2019.wpengine.com/forrester-report-support-contact-center/ 


Looking at Contact Center Metrics in a Customer-Centric Way

Contact Centers, as an industry, have been around since the advent of telephony technology. That technological era has also enabled us to measure and track contact center activity quite accurately. For too many years, contact centers were focused on operating contact centers as efficiently as possible, not providing outstanding customer experience. Our early ability to track and measure activity resulted in a series of metrics being adopted to monitor the overall contact center performance.

These traditional metrics include average handle time, average wait time, occupancy, idle time, and service level among others. These metrics largely have a philosophical basis in Fredrick Taylor’s “The Principles of Scientific Management”, which still have a certain degree of operational relevance.

Even though managing to those metrics resulted in running contact centers efficiently (and thus became the new norm to benchmark against), it also resulted in contact centers blindly managing to those metrics alone, without any analysis of how they are servicing and satisfying their customers’ expectations today. In true Fredrick Taylor style, we still will find those same metrics present in most dashboards and reports used today.

But what about measuring the customer experience?

The issue with traditional metrics is that they are largely inward facing to a company. Customer service has evolved since those early days and the relevance of traditional metrics have waned in the current age of customer engagement. It is a fair question to now ask if these traditional metrics still make sense as the benchmark?

Today, contact centers increasingly understand the importance of providing excellent service to their customers, and as a result, they are adopting a customer-centric engagement approach. All aspects of customer contact need to be weighed for effectiveness and striking the perfect balance to maintain a continuous relationship.

For organizations to compete and differentiate themselves, departments such as marketing, sales and customer service must coordinate their varying points of contact across all channels – not to inundate the recipient, but to strategically keep the relationship alive.

Shifting from traditional to customer-centric metrics approach

With a customer-centric focus, new metrics should be introduced. Net Promoter Score, the likelihood of recommending your business to another, has traditionally been a metric tracked by marketing as a measure of success of the company brand.

Contact centers today have a deep impact to that brand. With the technical ability to offer surveys to customers to review their contact experience across all media channels – can provide insight for quality of relationship management. Surveys can be skewed, because someone receiving poor service may be more motivated to report on a bad experience, as opposed to someone who feels they got the experience they expected.

Businesses can implement technology that evaluates the tone of voice during a conversation via a multitude of media channels and rate the relative satisfaction of a customer through language and tone. Everything can be combined to represent how your customer experience is impacting that relationship.

Leveraging both approaches effectively

Traditional metrics can continue to help with internal efficiencies, but the actual metric result should be modified with a focus on the customer. For example, service level has widely been accepted in the industry to be the percentage of calls answered within a threshold and that it should align with the 80/20 rule (80% of calls answered within 20 seconds). The “Calls in Queue” or “Time in Queue” metrics that meet a certain threshold are alternative ways of measuring the same – customer wait.

In a customer-centric focus of contact center one may argue that great service and relationship management would mean never having to wait in queue. “Calls in Queue” and “Time in Queue” would then strive to be zero and service level would strive to be a 100/0 rule (100% of calls answered within 20 seconds).

Technology such as virtual queuing can be introduced to facilitate the drive to those metric values in concept. With virtual queuing, customers no longer need to sit on the phone until an agent becomes available. They can schedule a callback at a convenient time or just have the agent call back when their turn comes up.

Traditional evaluation would see this solely as inefficient. But today, one can and should calculate the financial impact on sales and recurring revenue to a reduction in “Net Promoter Score” and how managing to new metric values versus old metric values could impact that “Net Promoter Score metric.

Unlike the Fredrick Taylor days, measuring the relationship satisfaction today can effectively be translated to a financial impact, as technology now permits us to effectively evaluate satisfaction. Metrics change and accepted values shift as the expectation of great customer service and the company’s relationship with their customers.

Why will your customers come back and recommend you to others? Because you’re measuring how well you’re delivering what the customer wants.

Measuring Agent Productivity in an Omnichannel World

As customer call centers have evolved into customer care centers, managers have had to adapt with new strategies for going beyond solving customers’ problems to finding ways to delight them at every touch point. The rewards can be great — customer loyalty and brand advocates — but only if customer care is done right.

Making sure that happens involves a complex melding of best practices in technology, process, and agent performance. One element that brings all three of these factors together is an omnichannel approach.

Ensuring a smooth and consistent experience between channels — phone, email, text, and chat — is the key to success in omnichannel customer care delivery. But to ensure that success, you need to know what’s working, especially in terms of agent efficiency.

In the old voice world you measured the length of phone calls, how many calls per hour, and the percentage of issues resolved during the first call, pretty straightforward, but with agents using multiple channels in a given shift, those measurements no longer tell the whole story. Let’s look at the challenges of measuring agent activity and productivity in an omnichannel world and how you can overcome them:

Challenge #1 – Blending

Within the omnichannel world you might have each agent assigned to just one channel, which keeps measuring their performance simple. However, another approach is having any given agent assigned to multiple channels simultaneously. For example, an agent might take calls, but respond to emails or chats when call volume is slow.

While this approach serves to increase overall efficiency for the care center, a single agent may not be as productive as they would be using just one channel at a time, especially when asked to handle too many channels.

The first step to optimal performance is appropriate performance measurement. For example, let’s say you have two pools of agents handling inbound phone calls:

  • Pool #1 handling only calls[su_spacer size=”10″]
  • Pool #2 responding to emails in between calls[su_spacer size=”10″]
  • Expectations for Pool #2 handle times should be adjusted accordingly, given that these agents need time to shift between channels and tools.[su_spacer]

This scenario becomes increasingly complex as you add more channel types and combinations, and you must determine the level of granularity needed to be effective at managing your workforce according to contact load. The best approach is to be as granular as possible on initial setup, and then aggregate metrics as you choose to ignore certain levels of differentiation to simplify ongoing management.

Challenge #2 – Simultaneous Interactions

Simultaneous interaction handling is not recommended. Agents juggling more than one customer at a time are more likely to exhibit lapsed concentration, mistakes in execution, and accidental sharing of private information.

However, there are care centers out there pushing those boundaries, so some agents will be handling multiple channels simultaneously. These agents may be engaged, for example, on a phone call and a chat at the same time.

So, in a five-minute window, if a given agent is talking on the phone to one customer and chatting with another, the appropriate measurement of time spent might be five minutes for each interaction, for a total of ten minutes.

This means that an agent could potentially have more interaction time (say six hours) than their actual shift time (four hours). Managers may need to adjust the processes and tools they use to accurately measure results.

Challenge #3 – Virtual Queuing

When no agents are available, virtual queuing allows customers to schedule a call back when call volume is low or hold their place in the queue and call back when it’s their turn. This method is more convenient for customers, but — to ensure it works most efficiently — care centers must properly attribute the queue time of the interaction even when there is no call physically waiting in the telephony environment.

With falsely short queue times, measurement systems may offer lower numbers than expected as there appear to be more calls handled within acceptable queuing thresholds. It’s also important to note that virtual queuing strategies (and the metrics used to measure their effectiveness) only work if there are down times during which agents can make return calls.

Invest in the Right Technology

The key to accurate measurement in the omnichannel environment is the right technology. Granularity is important because you must be able to group by agent skill set or interaction type combinations. Those base metrics can later be combined to achieve the desired level of reporting.

Therefore, it’s prudent to seek out tools, such as Aria’s Visualizer, that capture and display data at the distinct interaction level. The key is to get reports of all events (including interactions, routing, and meta data) within an environment, so you can later analyze that data in ways that are meaningful to your care center and your company.

You also need to be able to accurately capture presence data, and precisely attribute handle times to each channel method. The right tool for these tasks is one that seamlessly connects your customer relationship management (CRM) system, such as Salesforce, with your workforce management (WFM) system. This type of application can provide accurate metrics like the average queue wait time, or average interaction handle time (including all channels), which help with forecasting and staffing.

Finally, having all channels operating within one system can assist in measuring critical metrics. Commonly, an agent will be working within two or more channels, using a separate tool for each one. Because the systems are separate, so is the data gathering, meaning there could be inconsistencies in how performance is measured. Unified agent desktop technology helps in connecting all channels to one system in order to gather consistent, complete, and useful data.

Bottom line: the challenges of working within an omnichannel environment don’t have to outweigh the benefits. The investment you make in omnichannel operations may come back to you many times over, but only if you can optimize agent performance. The best way to do that is to recognize the challenges, and make sure you have the right tools to address them.

Agent Desktop Optimization: How Many More Screens Can Your Agent Handle?

Successful contact centers have always relied on speed. It’s about getting more done, in less time. To better manage customer conversations and respond to their inquiries in a timely manner, contact center agents need access to the right information immediately. The trouble is – most contact centers face data silos and believe that expensive, custom integration for agent desktop optimization is their only alternative.

It all comes down to inefficient infrastructure. Agents are forced to work with disparate IT systems that don’t talk to each other, making access slow and labor-intensive.

Over the years, mergers and acquisitions have shoehorned incompatible technology or multiple data sources together, making the infrastructure even more complex.

Wasted time and unnecessary costs

The average agent spends 15% of their time searching for the information across disparate systems to respond to customer inquiries (the Aberdeen Group research). This results in a lost cost of productivity of $1.57 million each year for a 300-seat contact center.

It’s a daily battle, and it gets them down. They’re not as productive as they want to be. They can’t serve customers as well as they need to, and there’s nothing they can do about it.

The extra ongoing costs of managing and maintaining disparate and legacy systems mean your business is bleeding money where it doesn’t have to.

Improving agent efficiency with unified, omnichannel technology

Modern contact centers need to add channels like voice, email, chat, mobile, social media to serve their customers. And the number of channels and tools used by contact center agents is expected to increase. This, coupled with data silos, means organizations need to rethink priorities and find alternative solutions.

According to 2016 Aberdeen Research “Optimize your customer experience strategy, get data right”, delivering consistent omnichannel conversations depends on companies providing all relevant employees with a unified view of their customer journey. So, it’s important that contact centers integrate this omnichannel approach into their agent desktop technology.

By investing in an optimized agent desktop, agents no longer have to switch between several screens to find the information they need. It’s simply there when they need it. To achieve this, agent technology needs to be integrated with multiple data sources, such as CRM solutions like Salesforce, while also providing access to legacy databases, such as billing systems or custom in-house systems.

There are platforms and solutions available that make integration faster and more powerful than ever before. So, improving agent’s access to information through agent desktop optimization may not be nearly as costly as you think.

The faster an agent can access what they need, the happier they’ll be. The same goes for their customers. Optimizing agent technology to speed up customer service should be a priority. For example, Amazon patented its ‘OneClick’ purchase process to retain what it believes is an enormous business advantage in terms of customer service. Amazon achieves extremely high conversion from its existing customers. Since the customer’s payment and shipping information is already stored on Amazon’s servers, it creates a checkout process that is virtually friction-less.

It’s this “friction-less” transaction concept that is needed in your contact center. Whether answering a customer’s question, selling them a product, or providing a service, enabling your agents to perform friction-less transactions should be the goal. Creating an integrated solution is a big part of this process.

In fact, integrated solutions are already delivering. The majority of companies improving agent productivity and performance can access relevant customer data through a single screen, therefore, improving first contact resolution and customer retention rates. For any business concerned about employee retention, this is the way to ensure it.

Contact us today to learn how Aria can help your contact center maximize agent productivity and ensure customer satisfaction across multiple channels.

Visualizer Success Stories by West Corporation and PacifiCorp

At G-Force 2015, Aria Solutions’ product team together with Daniel Vetro, Director of Information Services from West Corporation, and Todd McCall, Voice Systems Engr. 3 from PacifiCorp, discussed how CIMplicity™ Visualizer is helping to streamline operations and improving the customer journey.


Chris: CIMplicity Visualizer is an analytics tool developed by Aria, and is available now on the Genesys AppFoundry. It actively listens to your interaction events that are transmitted in the Genesys environment, collects the events, and stores them in a flat file storage format and then makes the results visible in our Visualizer application.

Ron: Dan, could you talk about the biggest challenge you face on a day-to-day basis?

Daniel: When you’re a service provider, you have a lot of clients that are trying to get that information and then make sense of it in different formats. A big part of leveraging this tool is the ability to validate when an incident or an issue comes in. It allowed us to get in much faster and validate that it is in fact an issue, along with a visual representation. It takes a lot of the time out of the debate that is spent on identifying if there is really a problem.

Ron: Todd, how about you? What are some challenges that you are faced with at PacifiCorp when running your daily operations?

Todd: We are a single tenant, but we do have two contact centers. Each contact center is on its own routing engine. That creates two sets of log files. And you know, 20 megabytes of log files can be very difficult to work with. What Visualizer brings to the table is the ability to merge log files, so I can merge multiple files together, and now I have a one hour image of the call center, or alternatively, the full day visual representation. For us, it is the segmentation and the size of the log files.

Ron: Daniel, what were some motivations around selecting Visualizer?

Daniel: For us, another big motivation was staff. We had some decent turnover at different periods. You have to teach new hires about logs and documentation. It’s much easier when you have a centralized tool that allows you to do that. We can get teams up to speed much faster, by focusing on the platform and not on the logs.

Daniel: For us, another big motivation was staff. We had some decent turnover at different periods. You have to teach new hires about logs and documentation. It’s much easier when you have a centralized tool that allows you to do that. We can get teams up to speed much faster, by focusing on the platform and not on the logs.

Ron: Chris, what about some other clients we have talked to? I know we’ve had some feedback from some users about identification of patterns and trends. What have you seen in that area?

Chris: The rendering of visual images leads to pattern recognition. We are able to see what successful calls, activities and patterns normally look like. One of our clients noticed a series of very dense calls that have arrived with very short established times through Visualizer. It stood out in Visualizer and did not fit the standard pattern of normal calls. When we looked deeper, we found that the agent was taking the calls and immediately transferring them back into the original queue. This would improve his Total Calls Handled metric and AHT for that day, but the customer was having the experience of being double-queued. This would not have been detected in standard agent reports but stood out quite clearly in Visualizer.

Ron: The visual representation really does help expose what would be virtually impossible to see through log analysis or other traditional forms of research. Todd, could you give an example around how you were leveraging Visualizer, in an effort to improve operations as you have transitioned from Avaya?

Todd: Actually, Visualizer is the first tool in our tool kit. Whenever we get a report from the business about an issue, this is the first place we go to. We had an event where the call center called us and said that they were not getting any more calls. This is incredibly frightening, particularly on Saturday. So, I opened Visualizer and I could immediately see five-six hours of activities. As you scrolled down, you could see this one VDN that was just black with the activity. This is where all calls were landing, and I knew exactly where to start troubleshooting.

Ron: Great! Chris, what are some other things that you have seen when working with other clients around proactively identifying things before an issue is reported?

Chris: If you are looking at IVR ports in Visualizer, the pattern would be standardized – a ringing an event, followed by and established where the port would play treatment or a message. In Visualizer, it would appear as a short yellow, followed by blue. But, simply scrolling over IVR ports and seeing ports that have only a yellow color indicates quickly and clearly that the customer, sent to those ports on, is only experiencing continuous ringing. Visually, this fact jumps out quickly and can be dealt with proactively.

Ron: Would you agree that focus on the facts and data tends to improve communications with customers? And how has Visualizer helped with making that a more collaborative process?

Daniel: From the perspective of a service provider, often the people we deal with, when analyzing issues, are non-technical in background. They are often looking at the problem from a call flow or business perspective. Providing a visual representation makes the conversation a much easier one to have, instead of pointing to lines of text from various log files. Visualizer has helped tremendously with the ability to export metrics and provide screenshots of the actual call activity.

Ron: After the initial kickoff, how long did it take the project to complete and put you in a position of collecting production data?

Daniel: It was very fast. We completed installation in 3 separate environments. The work was completed in 5 business days, which also included some brief on-site Knowledge Transfer. The touch point sessions have been a huge benefit for us. It has allowed the team to familiarize themselves with the application, and be able to bring back real-life scenarios to the sessions. We compare how traditional methods would have solved this issue to what Visualizer can now do in a matter of a few seconds, as opposed to hours.

Todd: It was pretty simple actually. It was very straightforward and easy to understand. It built my confidence in having to administer the system going forward. The actual installation took about an hour and a half, and the overall project took about 3 weeks to actively be collecting production data. A great addition is the post deployment touch-point calls to allow us to use the product and return with questions on functionality and assistance in constructing useful queries.

Ron: Was there any special change or accommodation to work flow or process, as a result of this new Visualizer implementation?

Daniel: We still have people who are used to traditional log analysis methods, but we’re pushing to get customers into the mindset of going to Visualizer as the first step, when dealing with issues. With Visualizer, we find that instead of escalating through the tiers to an engineering level, we’re continuing to push the conversations down to the lowest possible level of support. This has proven to be an unexpected benefit from Visualizer. Previously, items that had to go to more senior and more expensive technicians are now being handled at the lower levels. That’s allowed for more resources to be freed up to work on revenue generating projects.

Todd: Sending someone an email with log text is hard to understand. Our process is now changed to provide Visualizer screenshots. And through those images, our businesses can see and understand exactly the issue at hand. Visual representations from Visualizer speak volumes in seconds, faster than we can even explain the issues verbally.

Ron: What are some of the other best practices seen with other clients?

Chris: Best practice for all our Visualizer clients has been to make Visualizer the first step in the analysis process. Typically, in Genesys environments, the bulk of the time analyzing issues is spent triaging of logs and the gathering of logs from various servers in the environment. As many Genesys users know, logs tend to roll over very quickly in large environments, resulting in calls spanning multiple logs from a single application. Calls that may be transferred across sites now involve multiple TServers applications. Visualizer’s consolidation of all this information gets you to that point quicker. Visualizer cuts out that whole lead time of gathering the information, so users start with the issue… For example, a 5000-seat contact center has reduced resolution times from 4 to 6 hours down to 30 mins to 1 hour. The director of this group has stated that if Visualizer were to go away, they would all quit “en masse”.

Audience: Is there a situation that you can’t track with Visualizer?

Todd: We were trying to track an event with Visualizer, and couldn’t find it there. We then checked our SIP logs, SIP Proxy logs, Tserver logs and couldn’t find it there. It turned out that the call never hit our Genesys platform. If you don’t see it in Visualizer, it didn’t go to the Genesys environment.

Chris: If an interaction has touched the Genesys environment in any way, it will generate an event and that will be captured by Visualizer. Because Visualizer is listening to the data stream directly, it is not dependent on the logs themselves. We’ve found that when the event string becomes too long, logs can potentially truncate the event, thus missing a piece of the overall picture.