Category Archives: Analytics

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.

contact-center-applications

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/ 

 

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.

The 80/20 Rule Is Dead! You Can Do More with Social Media Data

In Shifting the Paradigm of Contact Center Interaction Tracking, we spoke of a paradigm shift that needs to be considered in standard contact center metrics. For the sake of all great social experiences, let’s revisit one of the most common and basic measures of contact center success – the service level.

Relying on service level to measure customer satisfaction

Oddly enough, service level has always been somewhat of a misnomer and often wrongly applied.

Service level was created to satisfy a base driver since contact centers needed a way to quantify how well they looked after their callers. And thus was born the 80/20 rule of quantification –  where 80% of calls are answered within 20 seconds.

Makes sense, doesn’t it? Speak directly with your customers within a certain amount of time, within their threshold of patience, and everyone will be happy. Maybe not!

Service level today is so ubiquitous that hosted service providers and support organizations set their contracts to meeting service level agreements (SLAs) with financial repercussions tied directly to that service level metric.

While contact centers may have shifted those parameters over the years with a 90/10 or 70/30 ratio as the measured bar of success, it’s become tougher to generally apply that rule to customer communications.

At its fundamental level, meeting that quantified threshold does not directly equate to providing “good service” to your customer base.

What is good service? It is entirely held in the eye of the customer and how they feel about their unique individual experiences with your enterprise including how they perceive interactions with your brand. Service level was an incredibly indirect metric to equate waiting with dissatisfaction but that is not the only factor.

Measuring customer satisfaction – back then and now

Years ago, in the voice-only world, where the 80/20 rule was created, this metric along with mailed surveys from the marketing department would be the standard to gage customer satisfaction. However, both were separate entities that had virtually no correlation other than to say: “You can’t blame us that survey scores are low. We’re answering the phones quickly”.

Survey tools today have come a long way to capturing that customer feeling. However, the tendency of customers is to only complete a survey if their experience was negative and they feel the need to report a behavior or unsatisfied outcome.

This is a reactive process with lengthy delays before action can be taken. The damage often has already been done and their likelihood to recommend (Net Promoter Score- NPS) has been reduced. Historically a negative customer experience would simply make interesting conversation over coffee.

Today is a brave new world where technology affords us a better chance to gain visibility into the fundamental definitions of good customer service.

But with the advent of social media, it’s important to start looking at social media data, since social has become the means to vent and report poor experiences with businesses or otherwise. Folks do like to complain! This pseudo-friendship world increases the audience that will hear about any shortcomings your business may have. The ability to “share” and “retweet” grows that audience exponentially if the reader is moved by the initial statement.

Fixing customer experience before it is too late

That just means that today you simply must get it right because everyone will hear about it if you don’t.  Unfortunately, that ideal is unachievable; perfection is unrealistic; and mistakes will undoubtedly be made. So, the question becomes, how can I mitigate the risk associated to bad customer experiences being broadcast throughout social media?

Leveraging social media data

Today we have the technology to view and access social media data through social media interfaces. A targeted push to collect social media profiles (via Twitter “follow”-ships” or Facebook likes, to name a couple examples) allows you then to collect and use social influence as a decision-making parameter when prioritizing their incoming interactions.

Those with a larger social network may have a higher priority to satisfy to hopefully leverage a “post” about positive experience as grass roots marketing or to lessen the broadcast impact of a possible negative experience. It is then up to the business to balance customer value and social influence in that prioritization.

Deploying intelligent routing that listens for social media data

Waiting in a queue and missing service level targets still has impact on the overall experience. But technologies today, such as intelligent routing, leverages customer value and social influence; virtual queuing and overflow routing all exist to eliminate that wait, reducing the overall impact of a service; level metric is the be all satisfaction statistic for contact centers. Intelligent prioritization lets you dictate which customers get faster service, thus reducing negative blow back towards your brand.

Relying on Net Promoter Score to make better decisions

So, what will fill that void in the future? The clue may have been dropped in an earlier statement. Net Promoter Score (NPS) should be the metric that drives your business decisions. “How many of our clients would recommend us to a friend?”  We should make sure they all do, and keep that in mind for all our business decisions.

The technologies discussed herein all provide the ability to collect directly or indirectly data elements to produce a score at a client level or directly improve their experience.

Turning Customer Interaction Data into a Competitive Advantage

Most contact centers use interaction data to justify or support contact center metrics, such as average call handle time, speed of answer, abandonment and even first call resolution.

But when you think about it, doesn’t it make more sense to capture data at the event and interaction level? This would allow for much more powerful analysis, as well as the ability to think outside the standard box of metrics every legacy system provides.

It’s actually very difficult to turn even excellent performance in most standard contact center metric categories into a sustainable competitive advantage.

In this blog, we will look at how one well-known company’s use of customer interaction data has become a key part of their success.

A Modern-Day Romance: Big Data and Customer Journey

It’s hard to escape noticing the trend categorically described as Big Data. Gartner predicts that by 2020, there will be 26 billion common household devices with the capability to being connected and sharing data. If you really think about it, it kind of starts to get a little unnerving. So, our advice is … don’t think about it!

Not only is data being tracked in aggregate, but it’s being tracked by businesses in the context of trying to understand your journey or your customer experience.

In the past, companies primarily focused on delivering their customers a tangible good or service. But why wasn’t this sustainable? If your market was attractive, then someone somewhere was working on delivering it better and cheaper.

Companies then began focusing on our “relationship”. Today, most organizations are focused on customer experience or the customer journey. But it’s different than the relationship and loyalty concept. It’s based on the concept of the “experience economy”, which started in the early 2000’s, but really has emerged as the guiding principle for customer service organizations. If you want some background, read this Harvard Business Review article “Welcome to the Experience Economy” by B. Joseph Pine and James H Gilmore.

The reality is – most organizations have more information than they know what to do with.

Now Playing: Netflix

Let’s look at Netflix and how they leverage their customer information to enhance the customer experience. The upstart was one of the main factors that drove Blockbuster to bankruptcy; even though, Blockbuster seemed to have all the advantages. Blockbuster at its peak was a $5B company; Netflix was started with $2.5M in startup cash. And today, according to some, it is worth in the neighborhood of $68B!

What made Blockbuster disposable was the fact they were focused on the rental transaction and were not focused on customer experience. Blockbuster had the opportunity to collect our interaction data – but they didn’t.

On the other hand, Netflix did something with each customer interaction. They tracked what customers liked and made suggestions. And they got better and better. Blockbuster only had a loyalty program, something like a free rental every 10 times.

It wasn’t just about the streaming technology. Yes, it was a threat, but Blockbuster had the financial resources to build it or buy it. They failed by allowing Netflix to create a competitive advantage of customer interaction intelligence. Netflix leveraged this intelligence to delight customers, by personalizing each and every customer experience.

So, Where Do We Go from Here?

The key is creating a company culture that does not focus solely on tasks. And the first step is to see if you have any system or data silos to be able to know your customer’s interactions.

The key to overcoming all barriers is to focus on the fact that the contact center is the front line of the customer experience. Typically, it is a key nexus to capturing the interaction data that can be leveraged strategically.

Contact centers should push the organization to view the contact center as strategic. Setting the stage that customer experience has to be viewed with an eye toward making the customer’s life better is every bit as important as making the contact center’s life better.

The key lies in making the connection between the customer interaction data, transforming that into valuable information and leveraging that to improve every customer interaction.

In today’s world, simply satisfying customers will not retain them. By turning data into insights, contact centers can personalize each interaction to deliver a little extra to delight them. And this what creates a real competitive advantage.

Shifting the Paradigm of Contact Center Interaction Tracking

A common scenario of interaction event tracking

Let’s paint the picture of a common series of events in the contact center.

1.  A technical or performance issue is suspected – either through anomalies in high-level aggregate reports or an agent/customer complaint. From a management stand-point, the immediate question is: “Is this a recurring problem or a singularity?”.

2.  Depending on severity, technical resources then get assigned for a deeper analysis to validate and isolate the occurrence, which often comes through a time-consuming data analysis by those technical resources involving log files or interaction level data.

3.  If a pattern can be identified, an organization may create another historical report or real-time dashboard to deliver a count or list of repeat occurrences.

4.  Companies can then attempt to take corrective measures and insert a process to react to an issue recurrence through that same reporting mechanism.

3 major issues with this process

A typical issue resolution process is a slow process in a fast-paced technological environment where every second counts to the bottom line and Net Promoter Scores drive business success.

1.  Historical reports are important, but it is a reactive solution that sometimes delivers limited information only. Validation and isolation of issues must be done through other means of analysis, which is often manual.

2.  The log in report creation, delivery, and presentation of information makes the resulting returns unactionable for the initial issue occurrence. If new parameters or data elements need to be tracked for the new issues, companies are forced to let data accumulate for issue recurrence validation. You can only track from the creation date forward without backwards analysis into how long this has been going on.

3.  Real-time dashboarding presents aggregate level data, which is not granular down to the interaction level. When looking at aggregated data, it often will take several occurrences to move an average or count metric to exceed a threshold resulted in repeated poor customer experiences that are not auctioned.

We’ve come to accept the above, along with the few dissatisfied customers that experience issue recurrence, while resolution is driven by this slow process.

However, what if we can action poor experience in real-time?  The concept is not new and some technology has been around for very specific scenarios. Overflow routing is one example. The longer customers waited, the worse they viewed their experience – so why not have them speak to someone with another focus in the contact center who’s free. Variations on a theme saw skills-based routing with a timeout extension of the target pool to include more and more potential agents as time elapsed in queue.  But, these are very specific issues.

Why do we still do it this way?

Standard metrics and contact center best practice are set based on our technical ability to monitor system, agent and interaction behavior. When innovation stalls or is stagnant over an extended period –  that standard becomes ingrained in the industry. This happens because companies stop bothering to look for alternative or better tracking methods and accept the limitations as industry norms.

But, every so often there’s a technical leap that pushes limitations to new levels, allowing us to do more and do it better.

Shifting to a new process with innovative technology

A more flexible and agile approach would mean the ability to automatically monitor all contact center activity from an agent, interaction and technology behavior approach. This requires the need for data to be captured at the interaction level.  This concept is also one that has existed for a long while.

This is a slow moving process that adds significant time in an ability to take action on a captured event.

Only recently have we seen integrated products with the ability to action system, agent and interaction behavioral data in a real-time manner. We’ve seen companies out there doing real-time voice sentiment analysis and identification, so that immediate action can be applied in the case of a negative or positive tone.

Artificial Intelligence platforms such as Watson, Alexa and Siri are starting to be integrated to filter event data streams and apply machine learning to potential outcomes. AIl can still be somewhat costly and complex to set up the types of learning one hopes to achieve but it is closer to an everyday reality.

Interaction level data capture will permit regressive analysis once an issue is discovered. If you’re capturing all the events – it is a matter of applying your identification algorithm to old data to see how long this has been occurring. Interaction level monitoring through an agile platform can identify single occurrences and action can immediately be taken as mandated.

Now, a new world best practice scenario

1.  Report a problem through customer/agent complaint, reporting anomaly or pattern analysis of agile event level monitoring system

2.  Analyze with agile event level data capture application to assess impact or existence of hypothesized issue

3.  Identify the fix and either attempt to eliminate or reduce occurrences to an acceptable level

4.  Create historical report to communicate success or failure of fix over time

5.  Use agile event level data capture application to monitor for issue recurrence and automatically alert to take immediate action on resolution. Problem calls can now be flagged for added service as needed until problem is eliminated.

You can imagine scenarios now where your interaction level data monitoring can detect multiple call-backs of a high value customer and immediately enter a callback record into an escalation queue to make sure their issues are dealt with correctly.

Negatively flagged voice tones can initiate immediate barge in of managers to de-escalate. Agents who place calls immediately on hold can be flagged and action taken immediately instead of letting the behavior repeat for multiple customers.

…..And so, a new way of managing customer experience is born. We can see with this possibility more can be achieved in a truly agile manner in every sense of the word. For each now to imagine new scenarios that they would want to monitor and react immediately in their day to day operations.

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.

5 Keys to Effective Customer Journey Maps

As companies compete for market leadership position, it is quite easy for them to focus internally – either on processes to improve efficiency in delivery of their product, or on research and development to remain competitive. This can distract from understanding the market for which they are competing. Understanding how the market views, interacts and deals with their company is equally important.

Customer Journey Maps are an effective tool for companies to document customer perspective and identify key interaction points to monitor measure and improve upon the entire customer journey. This practice results in enhanced customer satisfaction, reduced churn, increased revenue, and greater employee satisfaction. The more touch points you have, the more complicated it becomes.

Creating Customer Journey Maps can be arduous, but the end result, if done correctly, can help all facets of the company understand how customers interact with, and perceive the company in the market. Consider these points when creating Customer Journey Maps:

1. Don’t rely on generic client demographic data, instead determine the segmentation of your customer base

Find an appropriate balance between high level demographic based research and result data from an existing customer base.  For example, the general expectation is that older customers are less likely to use alternative communication channels, such as chat, social media or SMS.

However, in the print media industry, a segment of their interactions come from a more senior population who own multiple properties and migrate between them throughout the year. These senior clients are largely migratory and do not own a land line. Instead, they perform their interactions from a mobile device and are proficient in the use of alternative media channels.

Often, decisions are made on general assumptions about customer behavioral traits that aren’t always true. Most companies don’t regularly gather customer perspectives or share the insights when they do. But without an outside view on what is important, and what does or doesn’t work, your journey map will lack an accurate view of the customer, leading to decisions based on incomplete or flawed information.

2. Avoid analysis paralysis

Given the breadth of data available, it’s easy to include lots of it. This can result in dizzying complexity.

Remember, you are creating a tool to help you easily understand the customer and identify what is most important to them.

Create customer journeys that represent the largest customer interaction segments to achieve consensus to move forward with design, measurement and optimization. As with any collaborative process, define a decision structure with the right levels of empowerment. The goal is not to make everyone happy, instead, find the most efficient solutions to satisfy the customer experience.

Keep your strategic goals in the forefront to guide you in your employment of journey maps.

3. View as a living iterative process

What may be true today may not be true tomorrow. Invest in efforts to maintain a customer journey map that evolves according to the changing needs of the customer. Customer habits can change quickly in the new social world and must be reviewed regularly to address new habits.

4. Establish key interaction points

Identify points of bottleneck, inefficiency, and positive service levels. Journey events of significant impact have a greater bearing on the customer’s perspective of the company. Great journey maps separate critical moments from the rest.

A customer journey map helps to identify gaps, and disjointed or painful customer experiences, such as:

  • Gaps between information channels when users receive mixed messaging across various channels
  • Gaps between departments where users get frustrated with internal communication issues

5. Measure value at key interaction points

Contact Centers are a collection of complex software processes that generate a tremendous amount of interaction data. Most contact centers rely on traditional analysis, such as manual data gathering, text editors and generic log analysis tools in an attempt to understand the data and the customer experience.

Identifying those key customer touch points is not enough. You must set up your environment to correctly measure and track outcomes around key interactions.

A centralized system that breaks down the silos of measurement, minimizes the need for multiple tools, establishes a common set of measurements, and offers a holistic view of all interactions is a key consideration. The solution must have the ability to:

  • Capture all events around all customer interactions and easily enable analysis of that data
  • Provide a near real-time visibility to trends and issues
  • Provide the ability to anticipate trouble in key interaction areas
  • Allow quick drill down and provide cradle to grave visibility of the entire interaction experience

Investigating a customer experience from cradle to grave with traditional tools requires intensive manual efforts and consolidation of data form various systems. Streamlining tools, such as CIMplicity Visualizer, captures as much of the experience as possible, by reducing analysis and maintenance overhead.

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)