Month: January 2024

Call groups, which are sometimes called ring groups, help make sure callers get connected to the right call center agent quickly and efficiently.

When someone calls, the call groups system shares the call with a specific team of agents or phone lines in a pattern that shortens wait times and helps resolve customer inquiries off the bat.

If a call group is well implemented, calls are spread out evenly amongst a team with minimal effort. The right agent gets to answer the call, and the customer’s request is usually resolved in a shorter time frame.

So is this system worth implementing?

The short answer: yes.

Call groups are a vital tool for any business looking to increase first-call resolution (FCR), which ultimately leads to improved customer satisfaction. If you’re looking to improve key metrics such as reduced dropped call rates or agent downtime between calls, call groups may be a good option for your organization.

Call Group Ring Patterns (and When to Use Each)

Call groups are more intricately organized by ring patterns, which are essentially the order in which the agents’ phones ring. A ring pattern is the core functionality of a call group, and it’s what makes the whole system customizable to a call center’s needs.

Ring patterns are managed by the VoIP telephone system or call center software. The technical workings of these patterns depend on the programming and algorithms used in the software.

By choosing the right ring pattern, you can make sure calls are handled efficiently, improving overall call distribution.

Here’s a breakdown of different ring patterns and when to use them:

Sequential ringing: For specialized small teams

Sequential ringing operates like a relay race, where the call is passed from one agent to another in a…

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Call deflection is a strategy used in customer service to manage high call volumes by directing callers to alternative support channels like a website’s knowledge base, email, FAQs page, or chatbot.

While it might reduce immediate call pressure, it doesn’t necessarily lessen the overall workload or solve customer problems. Often, issues are just delayed—or never resolved at all.

Simply rerouting customers to self-service options they’ve likely already explored doesn’t meet their need for a prompt and effective resolution. Customers may end up calling back, or agents might need to return calls, creating a loop of deferred solutions.

So why do so many call centers still rely on call deflection? It’s often seen as a quick fix to alleviate immediate pressure, without addressing the root causes of high call volumes. However, savvy call center workforce management teams are shifting their focus toward more effective solutions.

Call Deflection From the Customers’ Point of View

To best understand the impact of call deflection, let’s walk through the customer’s journey.

First, a customer encounters an issue and immediately turns to your website’s self-service options, browsing FAQs or help guides with no success.

Realizing that they’re not going to be able to solve their problem themselves, they decide to call your call center, expecting a direct conversation with an agent who can resolve their issue.

However, upon calling, they’re met with an automated system that deflects them back to the very online resources they’ve already found inadequate.

This loop of deflection not only heightens their frustration but also diminishes their trust in your brand.

At this stage, the customer is likely to feel ignored and undervalued, a sentiment that can significantly erode brand loyalty.

73% of consumers believe that when brands prioritize their time, it leads to…

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A good ol’ fashioned disaster can take your call center offline and do long-term damage at any point—so it’s good to have a solid recovery plan in place.

First, you should make a list of all your risks and their potential impacts, and then you can define your objectives and priorities.

Next, you’ll map out your team roles and create a communication plan.

Finally, make a point to document everything ahead of time so your plan will be there waiting for you whenever you need it.

What You Need Before You Start

Before you put together your disaster recovery plan, you’ll need a few things:

• The right team: Your team is the cornerstone of any disaster recovery efforts. Everyone should have some knowledge of the plan so when things go down, they’re ready. Have key members and roles for your disaster recovery team in mind.
• Examples of other call center plans: There’s no need to build your plan from scratch when you can learn from others who’ve been in your shoes when things went wrong. Find examples of other call center emergency plans and follow industry best practices when you build yours.
• VoIP provider failover coverage documentation: If your call center uses VoIP services or other call center software, refresh your memory on their failover coverage. Look over and note their mechanisms for failover and minimizing work interruptions.
• Critical systems and resources inventory: Write down your critical applications, systems, and other things your call center requires to run. Note which are top priority so you can focus on restoring those assets first if disaster strikes. Look for individual points of failure within each of these as well.
• Multiple communication channels: Draw up a disaster communication plan. Outline how and how often teammates and…

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AmplifAI is perhaps one of the most fruitful outworkings of the modern AI era. It’s a cutting-edge software that uses AI to improve the internal efficiency and performance of your business’s teams, saving time, reducing costs, and increasing profits down the line.

By integrating with your existing software setup, AmplifAI analyzes and sorts through every piece of data for every single employee in your company. It then condenses its findings, not into a broad overview filled with corporate buzzwords, but into a set of specific recommended best actions and performance metrics that speak to the ability of each and every employee.

Who AmplifAI Is For

Without a doubt, AmplifAI is intended for use by large enterprises with hundreds or thousands of employees, numerous divisions, and numerous teams within those divisions. Even AmplifAI itself does not deny this—its website specifically says, “Built for Enterprise Teams,” and its main clients are huge companies like Home Depot and GoDaddy.

The all-encompassing nature of AmplifAI’s software, as well as the solutions and recommendations it actually provides at the end, are simply too large-scale to be applicable to small businesses or startups.

There’s also a section on the website marketed to business process outsourcers, or BPOs. Though BPOs themselves can be of a smaller scale than enterprise-level businesses, the companies they provide services to are usually huge enterprises themselves—so the rule still stands.

AmplifAI Pricing

As if the verdict above needed any more justification, AmplifAI does not specify pricing on its website—this is very common for software solutions that offer tailored services to largely varied enterprises.

The upside to this sort of pricing scheme is that…

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ASR (Automatic Speech Recognition) allows us to interact with devices, such as smartphones and smart home assistants, using our voice. In call centers, ASR helps customers interact with automated systems and self-service menus without the need of a human operator.

This technology is useful for call centers because it can reduce wait times, improve your agents’ efficiency, and keep customers happy by giving them faster support.

It’s worth mentioning that ASR can also stand for answer-seizure ratio, a call center metric that call centers use to gauge the percentage of calls answered by agents in a specific amount of time.

For the purpose of this article, however, we’ll be talking about automatic speech recognition. We’ll discuss how it works, how to use it in your call center, and how to meet any challenges that come along with using it.

How Automatic Speech Recognition Works (In Detail)

To understand what a caller is saying, ASR breaks down human speech into small units and matches them to a database. This process, called acoustic modeling, uses algorithms, statistical models, and machine learning to match and decode sound patterns.

ASR software is trained to recognize different speech patterns, accents, and dialects. There are two main ways this is done:

1. A traditional, hybrid method that combines many language models. These include a lexicon of words and their pronunciations, patterns of speech, and language statistics. This method works, but it has some downfalls, such as lower accuracy and a need for humans to train the models manually.
2. An end-to-end deep learning method. This method doesn’t usually need an acoustic or language model, but adding them helps it be more accurate. This method requires less human intervention and tends to yield better results.

ASR is used…

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