How a Knowledge Base Reduces Average Handle Time

It’s a fact: Following the implementation of a call center knowledge base, average handle time (AHT) is measurably reduced. And as agents and customers become familiar with accelerated search and more efficient help options, AHT metrics will reach new lows. (Customer satisfaction will reach new highs.)

Key takeaways and core values:

• Average handle time defined
• Why AHT is a critical metric for contact centers
• Why call center support resolution takes too long
• 5 ways a knowledge base directly reduces AHT
• A knowledge base can help…or hinder
• Measure how deployment impacts AHT
• How KMS Lighthouse can reduce AHT for your organization
• FAQs

What Average Handle Time Is and Why It Matters for Contact Centers

AHT is not the “talk time.” Average handle time is a critical call center metric that includes 3 elements:

1. Total talk time – The duration of time an agent is interactively speaking with the customer
2. Total hold time – The customer’s interval waiting (on hold) as the agent seeks information and resources
3. After-call work (ACW) – Post-interaction time the agent spends documenting the call in the appropriate software, as well as follow-up emails and customer file updates.

A common formula used is:

Total Talk Time + Total Hold Time + ACW Time ÷ Total Number of Interactions = AHT

Customer Service Average Handle Time Impacts CSAT

Today’s customers expect you to value them and their time. Customer satisfaction scores (CSATs) can make the difference between sailing your ship or sinking it. Positive customer experiences can give you the competitive edge in a volatile marketplace. In fact, Gartner says customer experience is more than a metric; it’s “the primary battleground for business survival.”

An AI-powered knowledge management system (KMS) speeds the call center process by providing fast, accurate information to agents and customers. Customers appreciate faster resolutions. They are also empowered to seek the information they need for buying, tracking, or returning merchandise. Chatbots and AI-powered search tools help customers to easily access and manage their own account information.

Call center average handle time reduction results in as much as 150% higher customer satisfaction scores.

An AI-driven platform helps companies reduce average handle times, increase first-call resolutions, and enable efficient self-service. Tracking AHT is one of the most-measured standards for effective customer relationship management practices.

Important Contact Center Metrics

Best practices for call centers include several most-watched operational measurements:

• Critical/essential metrics

• Abandon rate percentage (ARP) – Percentage of customers who hang up before the call is answered
• Adherence to Schedule (SA) – How closely agents follow designated work times
• Agent Occupancy (OCC) – Time percentage agents spend on active calls & follow-ups vs. idle time
• Average speed of answer (ASA) – Average time to answer a call
• Cost per call (CPC) – Average cost per call center interaction
• Customer satisfaction score (CSAT) – Measures customers’ contentment with the service
• First-call resolution (FCR) – Percentage of calls resolved in the first interaction
• Grade of Service (GoS) – Percentage of calls answered within a specified timeframe
• Net promoter score (NPS) – Long-term customer loyalty and likelihood to recommend

• Important metrics

• Agent attrition rate (ATR) – Agents leaving the company
• Containment rate – Percentage of interactions handled by chatbots, etc., without human help
• Customer effort score (CES) – How easily the customer got help
• Forecasting accuracy – Predicted contact volume vs. actual volume
• Transfer rate – Calls passed on to another agent or department

• Operational Metrics

• Agent performance – Quality assurance score, SA, and utilization rate
• Efficiency – After-call work time, AHT, and cost per call
• Quality – CSAT, FCR, and NPS
• Service – ARP, ASA, and percentage of calls blocked

Why AHT Is a Most-Watched Operational Metric

Average handle time percentages also serve as a measurement used in decision-making and management of operational expenses. Consistently high AHTs can pinpoint employee training and onboarding gaps, confusing processes, and outdated information. Reduced AHT allows agents more time to tackle more calls, which lowers the cost per contact. Call volumes can be more accurately forecast, and staffing levels can be better balanced with AHT contact center knowledge base data.

AHT is a critical metric because it pinpoints how efficiently call center resolutions are reached. But it’s equally important to balance customer care. Too much emphasis on lowering AHT can lead to lower customer experience ratings.

Why Agents Take Too Long To Resolve Calls

On average, customers expect no more than an 8-minute hold time. Once connected, they seek resolution within 6 minutes or less. How long customers expect their contact center interaction to last also varies by industry:

• Financial services – 3-8 minutes
• Healthcare – 3-10 minutes
• Retail, e-commerce – 2-4 minutes
• Technical support – 8-10 minutes
• Travel, hospitality – 3-4 minutes

Why do agents take too long to provide customers with a helpful resolution?

• Complex, multistep processes – Complicated, lengthy security verifications, vague escalation pathways, and intricate wrap-up documentation can prolong AHT.
• High call volume – An unexpected spike in demand can overwhelm the call center staff.
• Inadequate training – Under-trained agents are intimidated when faced with multifaceted, difficult problems.
• Inefficient interactive voice response (IVR) – Getting caught in an IVR loop is a leading cause of dropped calls. It can lead to misdirected calls and too many transfers; customers must repeat information to two or more agents. IVRs cause customers to feel undervalued.
• Inefficient technology – Slow load times for CRM software can be a deal-breaker for call centers. But technology disconnects are often the real issue for too-high AHT:

• Disconnected CRM databases – Agents must manually hunt for and capture customer information using multiple screens.
• Information knowledge gaps – Agents must spend time toggling between databases to connect product information and troubleshooting fixes.
• Outdated data – Poorly maintained content can force agents to backtrack, seeking more up-to-date information sources.

Trust Is the Bottom Line

Research reveals 83% of consumers said a good customer experience increases their confidence in a company or brand. No matter how quickly – or how nicely – a customer call is handled, if the resolution doesn’t meet customer expectations, you’ve risked your reputation.

5 Ways a Knowledge Base Directly Reduces Average Handle Times

With a KMS implementation, call center agents – and especially your customers – can gain increased confidence in high-quality, accurate information. Your brand’s dependability and reputation for truth soars with an AI-driven,centralized knowledge base.

Centralization Ensures Information Consistency

Without a knowledge base, agents must rely on their own or others’ documentation, which may not have up-to-date information. Sifting through multiple information sources takes longer and can lead to varied and unreliable responses among agents. When every agent can access consistent, current content, agents’ and customers’ confidence increases substantially.

As product information, pricing, and policies change, a centralized knowledge base replaces outdated information with single-point updates for everyone in the organization. AHT is lowered with less confusion, fewer mistakes, and a greater number of first-time call resolutions.

1. Agents Can Focus on More Complex Customer Interactions

While customer self-service percentages are not measured by AHT, an AI-driven knowledge base encourages customers to self-seek answers using chatbots, FAQs, etc. Call center agents receive fewer repetitive questions and are free for more complicated issues resolution.

2. Agents Get the Training They Need, Faster

The implementation of a knowledge management system leads to 70% faster agent onboarding. As customer service training time is reduced, the quality of learning comprehension also excels. Agents’improved accuracy and efficiency benefit customers, positively impacting AHT.

3. Analytics Improve Access Time

Continuous data optimization occurs as a centralized knowledge base evaluates information that is seldom used, what’s needed, and more. Outdated information is eliminated, redundant data is consolidated, and finding content that is accessed most often becomes faster.

4. Interactive Guidance Speeds the Process

An AI-driven knowledge base is much more than an information repository. It also provides logical, streamlined processes and prompts. Agents and self-help customers can navigate decision trees faster and more effectively. Even complex queries can be more easily satisfied.

5. Knowledge Gaps & Silos Eliminated

Knowledge gaps can lead to inaccuracies by inexperienced agents. And in many contact centers, veteran agents have stockpiled valuable information.

A knowledge base implementation will include capturing department-specific and employee-owned information. Data and processes are then centralized for enterprise-wide, 24/7 availability. This consolidation reduces new agents’ dependency on others. Faster decision-making reduces AHT.

The Difference Between a Knowledge Base That Hurts and One That Helps

We are drowning in information and starving for knowledge. -Rutherford D. Rogers

A knowledge base implementation sets the stage for a new, enterprise-wide culture. The knowledge center itself is an asset, but the new-and-improved solutions will only work if your entire organization is invested in its success. Employees must be on board before, during, and after the KMS implementation.

Why would departments and staff resist participating in capturing and documenting knowledge?

• “Knowledge is power.” They see job security in hoarding information and serving as go-to experts.
• Processes evolve too fast & they “can’t keep up with” making edits.
• They are not good writers.
• They’re too busy and see it as extra work.

An Inefficient Knowledge Base Hurts

A knowledge base that hurts is…

• Dysfunctional – If the knowledge base is difficult to navigate, it creates more problems than it solves. Poor and inconsistent formatting makes articles harder to read and understand.
• Irrelevant – In addition to incorrect or outdated information, a knowledge base filled with topics that no one searches for can slow overall navigation. But if it lacks answers to common questions, users will abandon access efforts.
• Messy – A knowledge dump is a waste of time for contact center agents. Too much unstructured data and content that is incorrect, outdated, and unverified causes frustration and eventually, a lack of trust.

As much as 30%-50% in productivity can be lost by agents searching for correct answers.

An Effective Knowledge Base Helps

A knowledge base that helps is…

• Accessible 24/7 – The user-focused design provides solutions when and where they are needed. Information is no longer department-specific, but available to all.
• Integrated – The programs in which users work are seamlessly connected to vital company software.
• The best solution – Articles solve problems with easy-to-follow instructions and step-by-step processes.
• Trusted – Assigned experts ensure content is accurate and current.

A knowledge management system that helps your company can save an average of 2.5 hours/day per worker.

AI Makes Crucial Difference in KMS Effectiveness

Having a presence of information is not the point of a knowledge base investment. The difference between an inefficient and an efficient knowledge base is maintenance, trust, and usability.

A knowledge base platform that hurts your company can cause a 20% drop in productivity. To change a hurtful knowledge base to a helpful one, you must “clean up the clutter” and make information easier to search and faster to find. It’s equally critical to encourage a cultural change:

• Encourage and monitor user feedback.
• Ensure contextual integration within CRMs and other organization-specific software.
• Establish maintenance protocols.
• Reward useful contributions.
• Standardize and unify frequently used data.

The good news is that AI automatically fixes most knowledge-capture problems. (And employees don’t have to become technical writers.)

AI retains contextual knowledge from real-time support tickets, meeting notes, troubleshooting logs, and more. It then automatically generates the necessary documentation. And rather than forcing employees to search the knowledge base, AI brings the information into service portals, software (like Salesforce), and communication platforms (like Slack). AI-generated content supports employees in completing tasks.

How to Measure KMS Deployment Impact on AHT

Before any knowledge management system implementation, key performance indicators (KPIs) should already be available. Organizations should also have these average handle time formulaic numbers:

Total Talk Time + Total Hold Time + ACW Time ÷ Total Number of Interactions = AHT

In addition to KPIs, the most important post-deployment measurement is to note the changes in average handle time:

• After-call work time
• Total hold time
• Total talk time

A system for agents to rate the KMS solution process should be deployed the same time as the KMS go-live.

10 KMS Deployment Benchmarks and KPIs

1. Adoption/engagement – Within the first 90 days, determine how many and how often employees use the KMS (average benchmark is ≥ 70%).
2. Error reporting – This determines what needs improvement.
3. Knowledge contribution – How many new articles, edits, or approved solutions have been submitted?
4. Knowledge recurrence – Note how many times a particular article was accessed and if it reveals common issues or needs that require more permanent solutions.
5. Search/findability – Measure how often users find first-search solutions (target benchmark is > 80%).
6. Self-service – What is the percentage of customers who took advantage of self-help options since KMS implementation?
7. Time-to-solve – Determine the average duration to resolution using KMS.
8. Training time – Measure the onboarding/training time reduction for new employees.
9. User satisfaction – One of the most important metrics is agents’/customers’ satisfaction levels.
10. Verification – Determine the percentage of articles that have been verified/updated.

How KMS Lighthouse Reduces AHT in Contact Center Environments

Just as there’s a difference between helpful and harmful knowledge bases, there’s a difference between enterprise KMS providers. While other knowledge management companies sell centralized document systems, we wrote the book.

• Call center AHT lowered more than 30%
• Hold times reduced by up to 40%
• Training time cut as much as 70%

KMS Lighthouse can customize contact center solutions to work with your unique business environment because we built our own proprietary platform. For example, we can enable your employees to easily navigate searches in their own natural language. Our system isn’t modified to use AI because it was created with AI-powered search capabilities and solutions.

Ways KMS Lighthouse reduces AHT include:

• Direct integrations into systems like Amazon Web Services (the world’s largest cloud-computing platform), Azure, Freshworks, Genesys, Microsoft Dynamics 365, Salesforce, and Zendesk so your agents won’t lose time toggling between screens.
• Dynamic decision trees quickly and easily guide agents through even complicated troubleshooting processes.
• No technical skills are needed by users, which reduces IT support and AHT time.
• Onboarding is shortened; new agents can reach their AHT goals within 90 days.
• Standardized templates are provided that include pricing and policy details for faster talk time and wrap-up.

To learn more about centralizing enterprise information and measurably reducing AHT, request a free demonstration.

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