As such, this interaction becomes the first impression that a new or potential customer makes, and your call center is the equivalent of your shop window on the high street — whether that call center is a colocated team in a single building, or more likely these days consists of a distributed team of agents working independently in their homes or other locations.
You only get one opportunity to make a first impression anywhere in life, so getting the quality of the customer experience right is essential. Measuring quality is not always straightforward, however, so the following tips may help you ensure you optimize the right metrics and aim for continual improvement.
In this article we’ll answer questions on: How to ensure a high quality customer experience? How to measure quality in your call center? What elements impact the quality of your call center?
Compare data meaningfully
Every organization in 2020 prides itself on being data driven, but data by itself is meaningless, and the age-old ‘garbage in, garbage out’ truism holds in the contact center as anywhere else. If you are looking for quality monitoring and areas to improve these can be subtle, and require a synthesis of different metrics — so it's essential to be sure you’re not comparing apples with oranges.
So, if you are seeking to optimize agent performance using call monitoring, you need to be clear about your goals, and then gather data which will help you achieve that, fairly and objectively.
For example, if you are listening in live to calls or a recording stored in the Ringover dashboard, you might conclude that Agent Angela’s professional, positive, and precise performance in a successful sale is greatly superior to Agent Beatrice’s call that you listen to next, where the caller was negative and belligerent, clearly not serious about buying — perhaps they were benchmarking or even gathering competitive intelligence. No sale was concluded, and a little bit of impatience was becoming evident in B’s responses towards the end of the call...
Qualitatively it might seem obvious whose performance was stronger — and there is undoubtedly much that could be learned from the second call, which could have been concluded more rapidly as soon as the enquirer was disqualified. So using the recording in this way for training and individual feedback is very valuable.
But for comparing one agent’s performance with another it would be very unfair, and if you were in the position comparing A and B’s performance then you would want to:
- Listen to multiple recordings from each agent — taken at different times of day/shift, and with different outcomes.
- Score the different types of calls — easy conversion of warm ready-to-buy enquirers, compared with those interactions which require skillful handling of objections, for example.
- Consider their overall sales performance — bearing in mind that one big result could both counterbalance and hide a string of unsuccessful conversations, and that there are always factors outside the control of the most experienced agent.
Look at their efficiency metrics such as agent utilization score, speed to answer, average handle time, and so on, to give you a comparative picture of each agent’s quantitative performance.
Some of this data will be easily accessible via the Ringover call center portal and easy to crunch away, generating all sorts of ratios and metrics. Other things will be more qualitative and time-consuming, like call monitoring in real time — so how much is ‘enough’ is a qualitative question too and depends on the stakes.
If A and B are competing for a promotion, or a raise, or to keep a single job in a downsized team, then your investment in monitoring will vary accordingly.
A smarter future
As such, one trend is to move towards 100% supervision, instead of sampling. Modern AI technology makes this possible, and indeed for some highly regulated industries it might be an essential compliance feature before too long. Already such industries — like finance and health — will need to keep recordings on file for lengthy periods, in case there are disputes or repercussions. Imagine the quality gains if you could analyze and score that raw data meaningfully, prior to archiving.
For example, you could extract the call logs from the Ringover dashboard, and use software to transcribe them into text, then use an algorithmically driven process to automate the scoring of each transcript, based on keywords, sentiment analysis, process adherence, and so on. Then you could slice and dice this data all manner of ways, to compare things like an agent’s performance on late or early shifts, or the emotional brand relationship of a long-term customer.
This approach is evolving, but has many advantages for the contact center, including feeding into your quality standards and risk assessment, as well as removing bias and sampling error from your call monitoring.
Understand the customer journey
In order to truly understand the quality issues at the heart of any issues with your call center, you need to put yourself in the position of the user, that is the caller. Whether they are calling for purchasing or support, if you design your processes and monitoring around them and their journey through the interaction with your business, you’ll be well-placed to pick up on all relevant quality issues.
This not only means listening to a call from start to finish when monitoring, but realizing that the call is not the entirety of the interaction. If they come to the phone stressed out and annoyed from an email or messaging exchange that has been unsatisfactory, or because of issues with a product that has frustrated them, they will bring that context and emotion with them.
The call itself might not only be an issue for your agent, maybe it will get escalated to a technical support specialist — so staying with the monitoring to understand how long the caller waits on hold, how much they have to explain over again, whether this is leading to first call resolution or have they had to get in touch repeatedly, all feeds into the experience.
Subsequently, follow-up matters: your IVR can collect a quick exit survey, or you might send them an email requesting feedback and NPS rating. All of this should be considered in evaluating the overall quality of the experience.
Your customers are not created equal!
In an ideal world you would have infinite call center resources and budget, and every interaction would be a blank slate on which to paint the perfect customer experience every time.
Back on Planet Earth, you have a CRM which clearly tells you who your VIPs are, the small percentage of clients whose lifetime value makes a vital difference to your bottom line. You also know who your high-risk accounts are, the people who have had for whatever reason become vulnerable to loss due to bad luck or mishandling. And of course the high-maintenance ‘barnacles’ who cling to the hull no matter what, despite generating very little income.
Your agents should not be expected to handle these calls in the same way, and similarly with prospects — just how big is the deal on the table, and how do you qualify the seriousness of any potential inquiry, and tag it accordingly?
This kind of classification of your users into different personas will drive quality in your call center, while supporting you in managing all the resources at your disposal. For example, it will help you decide which calls to monitor and compare, and how to allocate your agent resources most tactically: You might have one red-hot closer who must take the next call from the burning prospect, and another amazing listener with infinite patience who will make your wobbly client feel like the most special and important person they’ve ever had the pleasure to speak to. Still another may be expert at despatching the time wasters with such courteous efficiency, they rattle through the queue at a rate of knots when your call center is at its busiest, driving down cost per call and ancillary metrics like percentage of calls blocked.
Therefore, the definition of quality depends on the intended outcome, and you can use your IVR in conjunction with tags in your CRM to ensure the best routing of each call, and that you’re monitoring the right metric in respect of each interaction.
Calibrate for quality across the business
Finally, it’s important to remember that your call center does not operate in a vacuum, instead it is part of an organization with strategic objectives, such as to sell products or serve educational needs. As such, you cannot isolate its impact, and an effective quality management system will need to sync with metrics across your organization.
Similarly, you will want to make your contact center data visible across the organization, because of that pivotal role it fulfills — as shop window and frontline monitoring tool, for sentiment and satisfaction. Being the first point of contact for incoming enquirers of any kind, it’s essential the call center wins the support needed from internal stakeholders to perform this role well.
The omnichannel context
While the phone remains one of the most vital sources of insights and data from direct customer interaction, it should be remembered that increasingly today other channels are growing in popularity, as customers themselves become ever more tech-savvy and engaged with what their consumer smart devices can do.
As such your call center and its backend CRM will need to integrate with other channels, and share data and monitoring with them — because the customer journey may begin with an angry tweet which is followed by an email from support leading to a phone call in, switching to a video call for screen sharing, and so on.
Understanding the complex interplay of these interactions will take your quality monitoring to the next level, and help to break down the silos between these channels within your customer service operation — because while the different channels may have been added to your own offering at different times, to the customer it is essential to remember that doesn’t matter: It’s all one conversation, one interaction, to them — and if you cannot treat it as such, your quality ratings will suffer (for example, if you ask them to repeat the info they already gave your colleague in the social media team, because you cannot access it during the subsequent phone call).
Visualizing data for quality
Once you pull the data out of Ringover and your CRM you can analyse it and present it in different ways which will be of value to different stakeholders — because what quality means varies according to the eye of the beholder, for example:
- Agents can benchmark their own performance and improvement over time — sales professionals in particular love honing in on exactly what is getting them the results and rewards they crave
- Call center managers can track individual and team performance, to support staffing, training, onboarding, and endpoint management
- Supervisors can rapidly identify policy breaches (such as failing to read out a disclaimer, or use of inappropriate language), and design effective remediation strategies based on individual behavior patterns
- Quality auditors can evaluate the performance overall
- Product teams can identify support costs which are impacting on the profitability and reputation of a specific SKU, and take measures to fix the product at source, instead of generating endless escalations
Analysis and aggregation of call center metrics alongside other business intelligence data can identify patterns and trends over time, and show you how your calls unit is performing as a cost center. If this analysis is performed using machine learning algorithms for predictive insight, you can optimize the customer experience at a higher level — taking the best interactions, scrutinizing their common factors, and modelling for training and process development in the future.
In a competitive business environment the quality of your call center therefore has far-reaching implications, and it starts with getting familiar with the data in your Ringover dashboard, before using it intelligently to drive and optimise your entire organization’s performance.
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