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How to Increase ROI for Your Custom Development Projects

It’s an exciting time to be in IT these days! Digital transformation is gaining in popularity, one element being the move of IT infrastructure to the cloud. Many different platforms that are available today provide a lot of opportunities to build exciting functionality through custom development and platform integrations.

On top of that, companies are looking for more options to provide their customers with self-service opportunities, either through customer portals or through bots using traditional and modern service channels such as telephony, SMS, or instant messaging apps like Facebook Messenger or WhatsApp.

All of this requires a significant number of process automations and system integrations, which allows companies to build custom solutions for their internal and external services, so they can provide better customer service and more efficient processes.

Why is all of this important?

Because companies that ignore the digital transformation will miss out on the next generation of customers, and continuously lose ground on their competition, which will be faster and better at delivering business value.

Why digital transformation is affecting project development

Software development is at the heart of the digital transformation!

Here are some examples of contact center and business cloud platforms, innovating for the digital transformation era:

Overall, more and more companies are transforming their applications into platforms or at least investing heavily into APIs that allow for deeper integration.

Unfortunately, the increasing demand for custom development comes with a maintenance cost attached. And that cost can vary significantly, based on the quality of the architecture and implementation of any given solution. If a company relies on an integration to submit orders from their CRM to the order management system, the integration becomes an additional point of failure (in addition to the two systems involved). An error in the code could prevent the orders from being submitted, and in a worst case scenario, this error might not even be noticed right away.

In theory, those automations provide a defined business value, but the reality is that this value is reduced by technical debt accrued during development. This includes not only errors in the implementation that cause the integration to fail, but also the additional effort needed to implement new business requirements in the future. The latter is very difficult to track, since there is no reference point to what a “perfect” system looks like. Nevertheless, it exists, and I have seen simple feature requests take a substantial amount of time refactoring the initial solution because it did not follow best practices.

This brings us to the last question: What can be done to reduce the negative effect that software quality issues can bring to a project or a business solution?

3 steps for improving the quality of your projects

I recommend the following 3 key steps as a starting point to improving the quality and outcome of your custom development projects. There are obviously more things that you can do, but from my experience, the following actions are essential for maintaining the ROI of any project.

1. Use software design patterns and development best practices

Design patterns have been proven to be successful when it comes to increasing code quality in a product. Not only do they introduce consistency between developers, they also ensure that the developed code is easy to understand and extend as your business solution evolves. Applying those patterns even to the smallest components will help to reduce bugs and, consequently, maintenance costs further down the road.

2. Introduce quality processes and automated testing

These elements are core to any software application development these days. First of all, any code developed, whether it is big or small, should be tracked in a version management system such as Git. It is critical that developers can review the history of a file to understand the context of how it evolved. With that, it is also possible to introduce code reviews as a standard process of everyone’s work. Any changes should be peer reviewed to ensure that standards are followed, and to reduce the risk of introducing errors. Adding automated tests, such as unit or acceptance tests, will further improve the quality of the solutions. While code reviews ensure the quality and correctness of the implementation at the current time, automated tests are there to detect regression introduced by future modifications of the code. All automated tests should be executed every time a new change is introduced to the environment.

3. Add Reliability Engineering

This step is the most difficult. Reliability Engineering extends the solution by building a framework of utilities and services that business solutions use for the implementation of the use cases. Those utilities and services are designed to handle malfunction of the business logic by recording incidents, raising alerts, applying strategies of self-healing, or possibly even initiating a restart of an entire system or component. For example, in the situation described above where the integration between the CRM and order management system would fail, notifications could immediately be sent out to IT that the integration is broken. If the reason for the failure was a connection timeout, which could mean that the order management system does currently not have enough resources available to process the order, the integration could queue the record and process it at some later time, assuming that more resources will then be available and the order submission will succeed.

In the best case scenario, human intervention is not needed, but if it is, it is important that every failure is handled consistently. While it is understood that production issues will occur, the goal of the framework is to detect those early and to collect critical diagnostic information that will significantly speed up the root cause analysis. Reporting this information will enable IT to make the right decisions to solve the issue quickly.

It should be noted that many of today’s cloud platforms have these features baked in. The key is to utilize their framework to make sure application and platform issues are handled in the same fashion. There obviously must be a balance between value and implementation time when deciding what reliability features should be included in a solution.

Nonetheless, any steps taken in this area will further secure the ROI, by ensuring that the final solution will work as expected. If it does not, it will have the smallest impact possible on the organization.

The value in following these steps & breaking down silos

It needs to be understood that any of these steps will add development time for the initial implementation of any use case. However, it should be noted that this time increase is mostly just theoretical nature. In practice, if those measurements are not applied, a project typically requires more time for bug fixes and re-work of those use cases, often exceeding the time spent adding those quality processes.

Breaking down the silos between enterprise systems adds a lot of value to the organization. This is an important part of the 4th industrial revolution. However, the applications that link those systems together are becoming a critical element for a company’s operations and must therefore be set up using strategies that ensure quality and flexibility for the future. This let’s your organization focus on continuous expansion rather than chasing the mistakes made in the past.

Stop Poor CX! A New Way of Analyzing Operational Issues on PureEngage

It’s common for issues to pop up during day-to-day contact center operations and with a lot of customer interaction and agent activity within your PureEngage platform, many of those issues may be hard to find.

Contact centers continuously monitor metrics to:

  • Analyze data that affects customer experience (CX)
  • Ensure that performance goals are met
  • Catch issues

The tools they use often only show summarized data and don’t help much when the “devil is in the details”. When issues fall within what those tools look for and capture, they are easy to spot and solving them is usually straightforward. For example, if the call volume is unexpectedly high, the contact center will make more agent time available (e.g. bringing on more agents or canceling off-queue time).

However, for issues lurking beneath the surface, evidence may show up in the CX metrics, but standard tools don’t offer an easy way to pinpoint the detailed contact center activities behind the issue. Worse still, is that there are likely activities causing issues that companies don’t even know about.

For example, there could be agent behavioral issues occurring, such as this issue (happened to one of our clients), where some agents were placing customers on hold immediately after greeting them and increasing customer frustration. While this issue was caught by accident (by the VP of Customer Service’s office assistant!), the contact center didn’t have the tools to know who was doing this, how long it had been happening, nor how widespread this hidden issue was.

These more complex issues take time to identify and figure out, causing a lot of time to pass before a resolution is found and put in place. For contact center directors and customer experience executives, this means that their key customer experience metrics (like NPS and CSAT) are affected.

Wouldn’t it be great if contact centers could find those hidden and complex issues much faster, and reduce the impact on customer experience? This is possible but requires contact centers to shift from traditional methods.

Making do with traditional methods and tools

Contact centers use many tools to measure customer experience and identify customer experience problems when they arise. They look at contact center operational metrics (often aggregated data), analyze conversations, or pick up issues directly reported by customers and agents.

Reports, analytics (such as customer journey analytics), WFM, and QA, are tools that contact center professionals use to see how customer experience is impacted (such as higher call volumes, or agents adhering to their schedules), but these tools barely offer a glimpse into those harder to find day-to-day operational problems.

Basically, contact centers are “making do” with the tools they have.

New methods and technology to detect poor customer experience in real-time

To understand those hard to find or unknown issues affecting CX, without spending tons of time sifting through low-level system details, contact centers need to adopt a new way and new technology.

There are new and emerging ways to more quickly, accurately and proactively examine the contact center activity for issues and problematic agent behavior that can affect the customer experience. These methods and technology also help monitor these issues to keep them at bay.

For example, Aria’s Visualizer for Genesys that works on PureEngage helps contact centers:

  • Identify agent behavior and compliance automatically by listening to ALL calls instead of just spot checking a small sample of calls.
  • Analyze calls by learning and improving what is defined as a problem call.
  • See detailed analysis in real-time that captures all the low-level activity in a contact center. This will provide a visual picture of the customer experience, making it easier to spot issues you didn’t even know you had and that are beyond the reach of listening to calls.

Contact centers now have options for resolving issues that happen in day-to-day operations and can reduce the effect on customer experience. Visualizer provides the detailed insights that find the issues quickly so they can be actioned immediately, and affect a smaller number of customers.

 

Top 3 Reasons Why You Need Customer Interaction Analytics

Businesses are losing $62 billion per year through poor customer service (Serial switchers strikes again, NewVoiceMedia, Jan. 2016). This means the new goal must be getting every interaction right.

Forrester Research predicts that by 2020, insights-driven businesses, like Baidu and Netflix will quadruple their revenue from $333 billion to $1.2 trillion, growing eight times faster than global GDP (Top Five Imperatives To Win In The Age Of The Customer, May 23, 2017).

Customer-centric companies like these are a real threat to a lot of businesses who choose to prioritize areas like manufacturing and distribution, instead of better understanding their customer needs and wants and delivering personalized experiences.

To deliver such experiences, you need insights; but to get insights, you need to start using all your data the right way. Once you get to a phase when you no longer have data silos across business systems, it is important to start looking for a good operational, employee, and customer interaction analytics application. And here are the top three reasons why:

1. Customer retention

No one would argue that customer retention is critical for survival. Your brand and company Net Promoter Score (NPS) is constantly being reevaluated with every interaction handled and socially-shared commentary by your customer base.

Back in the day, a negative customer experience would simply make interesting conversation over coffee. But with the advent of social media, customers that are not able to resolve issues, can blast their complaints to a global audience that will hear about any shortcomings your business may have.

A lot of companies out there focus more on customer acquisition than on customer retention. But did you know that it costs five times as much to attract a new customer than to keep the existing one, and if you only increased your customer retention rates by 5% your profits could increase by 25% to 90% (Customer Acquisition Vs. Retention Costs, Invesp, Dec. 2015).

The best way to increase your customer retention rate is to improve customer experience.

2. Agent behaviors

Your front-line agents are also a crucial element to your customer experience. By gaining visibility into your agent interactions you can make better decisions.

But do you really know what is going on? Are you able to easily see the whole picture when it comes to your agent performance?

Traditionally, agent behavior analysis has been based on a set of standardized aggregated metrics that were supposed to summarize good agent performance. But to provide good customer service, aggregated metrics just don’t cut it and so you need to look at customer interaction analytics.

Granularity and automation are needed to look for negative behavioral patterns (placing customers immediately on hold, flashing Not Ready while Ready, etc.) in your agent performance and flag them immediately.

The ROI in this case can be simple. As an example, let’s assume a fully loaded agent cost of $65K. That translates to $280/day/agent. If we manage to identify 15 mins of lost time for every agent day and assume a 300-agent contact center. It translates to $2700/day which over the course of 220 work days in a year is just under $600K!

And that is not even considering the impact a negative behavior may have on the customer experience or revenue opportunities.

3. The “hidden” costs of support

Most firms are swimming in data, but they’re only using about a third of it. Worse, only 29% say they are good at translating the result of data and analytics into measurable business outcomes (Top Five Imperatives To Win In The Age Of The Customer, May 23, 2017).

As a matter of fact, the top two challenges preventing organizations from making use of analytics are “ensuring data quality from a variety of sources” and “accessing data from a variety of sources” (Pick A Powerful Pilot To Propagate Customer Analytics, July 19, 2017).

Speed of issue resolution carries a significant impact. The quicker issues can be resolved, the less chance there would be of repeating poor customer experiences, thus minimizing the impacts to your brand. Outages that critically impact service delivery are a ticking money bomb. Financial impacts increase exponentially the longer you are unable to service customers. One of the largest banks in Canada implemented Aria’s Visualizer (business and support analytics) and improved support response times by 75%. Such customer interaction analytics applications cannot be underestimated.

Systems that deliver customer interaction analytics in an efficient and accessible manner empower lower tiered or less senior support staff to extract necessary system information required for issue resolution. This can help focus the energy of senior technical staff on critical and future facing initiatives.

Business analytics resources can also leverage data on agent and system behaviors to make better operating decisions.

Lack of proper visibility into these three areas: customer experience and retention, employee behavior, and support issues – translates into a significant financial impact. Analytics applications like Aria’s Visualizer allow for an improved unified approach to visualizing interactions level data, strategic support approach and behavioral performance analysis which can help you provide top rated customer service.

To sign up for a demo of Aria’s Visualizer visit the Genesys AppFoundry or the Aria’s Visualizer product page.

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.

THE PANEL DISCUSSION HIGHLIGHTS:

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.

FULL BREAKOUT SESSION (37:02 minutes)