Uncovering Your Customer Experience Blind Spots with Quality Monitoring and Analytics………………………………...…. 3 1. Three Common Reasons Blind Spots Occur .......... 3 a. Figure 1: Where is quality measured?........... 2. Using Quality Monitoring for CX Analysis .............. 3. Completing QM Evaluations without Overwhelming Your Team ................................................................. 4. Creating CX Forms for Quality Monitoring ............. 5. Developing CX Questions…………………………….. a. Figure 2: CX questions/response……………. 6. Determining Root Causes with Quality Monitoring 7. Using Analytics to Automate CX Analysis…………. 8. Comparing Quality Monitoring and Analytics……… 9. Sentiment Analysis Methods…………………………. 10. Determining Root Causes with Analytics…………... 11. Keys to Achieving Ongoing Success………………..
Uncovering Your Customer Experience Blind Spots with Quality Monitoring and Analytics With 81% of companies recognizing customer experience (CX) as a competitive differentiator, 1 it’s important to understand if your contact center is doing all it can to positively impact customer retention and satisfaction. It doesn’t take much for customers to consider switching: 79% of customers say that a single bad experience is enough to cause them to consider taking their business elsewhere. 2
81% of companies recognize CX as a competitive differentiator. - DimensionData, 2017
You might think your call center is providing a satisfactory service, but consumers tell another story: less than half say they’re satisfied with their contact center experiences, regardless of channel. 3 Pinpointing your customer experience challenges is difficult without specific processes in place to uncover issues. Just like blind spots when driving your car, hidden problems can derail your well-intentioned efforts to deliver a positive customer experience. In this white paper, we’ll address specific ways you can use quality monitoring (QM) and analytics to identify hidden opportunities for improving your customers’ experiences.
Three Common Reasons Blind Spots Occur Blind spots are areas in your company’s performance where you have no knowledge of what is actually occurring. These issues can potentially have a big impact on customer experience, retention, satisfaction, and ultimately, profitably. There are three common reasons that blind spots occur: •
Reason #1: Customers Don’t Speak Up. One of the most common causes of blind spots is that the vast majority of customers won’t speak up if they’re dissatisfied. A typical business only hears from 4% of its unhappy customers, and 91% of those customers say they’ll never come back. 4
Reason #2: Insufficient Visibility into Channels. If you don’t have an omnichannel strategy, or a connected customer journey, it’s likely you have some blind spots. Customers want consistency of service across channels, especially if they interact with your organization in multiple ways using email, chat, or phone. Technology exists to help contact centers implement omnichannel and gain this much needed visibility. However, many contact centers are behind the curve in this area: only 21.3% have connected all their channels using a single platform and 19.6% have no connection at all between channels. 5
Reason #3: Inadequate Monitoring of Non-Voice Channels. One of the major issues is that many companies don’t have effective quality programs in place for their non-voice channels. Telephone interactions, as you might expect, are measured by 95.9% of companies, but the numbers drop dramatically when it comes to other types of customer contact. 6 (see Figure 1)
Figure 1: Most companies measure the quality of telephone interactions but measuring other types of communications is less common, DimensionData 2017 Global Customer Experience Benchmarking report
Using Quality Monitoring for CX Analysis Most contact centers are familiar with using quality monitoring to analyze interactions for agents’ performance. In this case, we’re suggesting that you also add CX analysis to your QM processes. To ensure you get visibility into all potential CX blind spots, you’ll need a QM process designed to monitor and evaluate three major areas: 1. Individual performance: What individual behaviors are positively and negatively influencing customer experiences? 2. Process performance: Are processes getting in the way of providing a satisfying experience? 3. Technical performance: Are systems, networks, or other technical issues impacting your ability to service customers? Since analyzing interactions using QM is a manual process, you’re probably concerned about managing your quality monitoring team’s bandwidth. They likely already have a full plate from analyzing interactions for agents’ performance – how can you possibly give them more to do?
Completing QM Evaluations without Overwhelming Your Team Ideally, you want to increase the number of net evaluations in order to add CX analysis to your QM process but without overwhelming your team. There are a few options to consider as you work out which solution works best for your organization: •
Dedicate a single resource on your QM team or rotate this responsibility throughout the team. Rotating the task is highly dependent on your team members’ skill sets – can everyone handle this type of responsibility or are one or two team members best suited for it? If your team’s skills are appropriate, then rotating tends to be a great option, because it gives the team some variety and drives a wider awareness of CX.
Add more resources, by potentially hiring additional resources or getting help from another department. Use an internal business analyst or special projects resource who may be better equipped at conducting this type of analysis than a QM evaluator.
Combine the addition of CX evaluations with current QM improvement projects to take advantage of expected productivity gains.
Make a “small” reduction in your current QM evaluations to account for adding in new CX evaluations
Creating CX Forms for Quality Monitoring In order to analyze interactions for customer experience issues, you’ll need to create CX-specific questions, which means deciding whether to add those questions to your current agent performance form or to create a separate CX form. The best practice recommendation is to create two different forms: one to analyze customer experience data and one for analyzing agent performance. Why? The evaluation process occurs from two different viewpoints: the customer’s and the business’s points of view. Using one form requires evaluators to switch mindsets in the middle of the analysis which can slow the process and introduce more errors. Using separate forms also simplifies the reporting process and makes it easier to get quantitative results, as it’s quicker to aggregate scores for each type of form and analyze them separately. Combining agent performance and CX analysis could also have a negative impact on agents’ acceptance, because agents only want to be evaluated on areas they have control over − not those that are outside their responsibilities such as technologies or processes. If you do decide to use a combined form, make sure your QM solution can exclude CX questions from your agents’ scores. Create sections in the form for each of your focus areas: • • •
Individual performance Process performance Technical performance
Next, develop questions for each of these three focus areas to analyze the CX data.
Developing CX Questions In agent performance evaluations, the goal is to eliminate subjectivity and use questions that make everything black or white. In this case, it’s ok if your CX questions are subjective, because the objective is to look for directional information in customer interactions and dig deeper later. Align your questions with typical analytics capabilities shown in Figure 2:
Pinpointing root causes with quality monitoring can be a rather lengthy process. It involves these steps: •
Running reports and tracking QM trends for new question scores
Implementing a method for flagging really good/poor interactions for quick review. (For poor interactions, create a proactive outreach process to turn the customer around.)
Focusing on lowest scores or sinking trends for deeper analysis
Quality Monitoring (QM) •
Reviewing actual interactions for insights into root cause
If you’d like to automate more steps in this process, consider using analytics. While quality monitoring does have value, you’ll likely get ten times more benefit with analytics.
Using Analytics to Automate CX Analysis Analytics is an alternative to using QM to manually analyze interactions. It’s faster and can analyze many more interactions across all contact channels than QM, which results in better trending and quantity data for more accurate and complete findings. It also drives deeper root-cause analysis, because results can be correlated to metadata that give a more complete picture of what drives a customer’s experience. Thanks to cloud technology, it’s also affordable and easy to maintain since there’s no need for on-premises server farms. And if your interaction volumes increase dramatically, analytics software can easily accommodate any change in data volumes.
Expands on an existing process so there’s no need to create a completely new one Encompasses voice interactions and easily applies to other channels as well Leverages existing technology investments
Analyzes a larger sample of interactions for more complete results Automates work so fewer dedicated resources are needed Drives deeper root-cause analysis due to correlating with meta-data and other information Supports all contact channels
While quality monitoring has its advantages, if you get good results with QM, you’ll likely do ten times better with analytics.
Sentiment Analysis Methods If you’ve looked at analytics in the past and found that it didn’t meet your needs, much has changed recently due to technology innovations. Analytics uses automated sentiment analysis to understand if an interaction was negative, positive, or neutral. Different sentiment technologies have come and gone over the years; here’s a brief overview of past and current sentiment analysis methods: •
Biometrics was the first analytics technology. It measured voice volume, pace of speech, and other physical factors. These were tracked for variances and used to predict sentiment. Speaking louder or faster was equated to negative interactions. The challenge in using biometrics was that it had difficulty actually distinguishing between positive and negative sentiment.
Word spotting was the next technology advancement. It classified certain words or terms as being positive or negative and assigned positive or negative values to them. It then counted the number of occurrences in a conversation and summed the values to determine sentiment. However, it had difficulty distinguishing between words that sounded the same but had different meanings such as “weight is too much” vs. “wait too much”.
Natural Language Analysis is used by most innovative analytics solutions today. It’s akin to automatically diagramming and parsing sentences to understand the context and meaning of words. This overcomes the limitations of biometrics and word spotting and produces far more accurate results.
Determining Root Causes with Analytics Analytics software can be used as part of a larger process to filter interactions and understand the root cause of issues. This is accomplished by going through a series of filtering steps: 1. First, the analytics software filters interactions by customer sentiment using Natural Language Analysis. 2. Next, the analytics software focuses on interactions that were found to have negative or mixed sentiment (meaning there are approximately equal amounts of negative and positive sentiment in the interaction). 3. Interactions are then analyzed for common themes such as discussion topics, agent sentiment, handling time, etc. 4. The root cause of the issue is determined such as did an agent issue or a process issue occur? 5. Once the analytics analysis is complete, you can take corrective action, monitor for ongoing changes, and document progress. Compared to quality monitoring, analytics is more effective than QM for analyzing CX, regardless of the number of CX questions you add or interactions you analyze. It can quickly compare results across all your communication channels, which is more difficult to do with quality monitoring. It also frees up your quality monitoring resources to be used elsewhere.
Keys to Achieving Ongoing Success Of course, regardless of whether you use quality monitoring or analytics to uncover your CX blind spots, none of it matters if your organization doesn’t implement an action plan to fix the issues that are uncovered. You’ll also need to clearly define roles and responsibilities and involve other departments, such as Billing, to resolve some issues. This is where high-level support is crucial to ensure that everyone adheres to the same goal: fixing issues so your customers have a better experience. Of course, uncovering your CX blind spots also means you’ll need to implement an ongoing monitoring program to be sure the plan is working and customers are experiencing improvements.
Conclusion Not having visibility into your organization’s blind spots can be deadly; uncovering them can pay big dividends. Because negative customer experiences are so damaging to your customer experience, they are low-hanging opportunities for improvement. Even if you don’t have analytics, don’t give up. Get started by implementing a quality process for CX. It’s worth the effort to gather the data and analyze it whether you use quality management or analytics.
3 Keys to a Successful CX Quality Monitoring Program A successful quality monitoring process for customer experience needs to include these three distinct areas: Individual performance Process performance Technical performance
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