Improving call center metrics with knowledge management
Knowledge management is growing in importance
Based on the findings of the TSIA annual report on the state of knowledge management, this is what John Ragsdale, Distinguished Vice President at TSIA, says about the importance of knowledge management (KM).
“Though KM has historically been primarily an area of interest for support, I’m seeing more organizations, including professional services, managed services, and customer success, formalizing the capture and sharing of knowledge.”
Benefits of knowledge
Using knowledge management in contact centers is known to improve productivity of customer service agents, consistency in responses, use of new information, and compliance with industry rules. This is by no means an exhaustive list of its benefits.
When used well, in line with best practices, knowledge management enables breakthrough enhancements to operational metrics, not only transforming the contact center but transcending it in many ways. And in this, KM is aided by the new technologies of the time, like artificial intelligence, intelligent search, and reasoning.
It literally pays to run an efficient contact center
A cost analysis of channel usage reiterates why contact centers need to be efficient. According to Gartner, Inc., agent-assisted channels such as phone, live chat, and email cost an average of $8.01 per contact. Combine that with the fact that, according to another Gartner research, only 9% of customers report resolving their issues completely through self-service, and it is obvious that a massive proportion of customer service traffic is coming over these expensive channels.
These are some call and contact center metrics that knowledge management impacts in the contact center:
- First-Contact Resolution (FCR)
- Average Handle Time (AHT)
- Average Speed to Answer (ASA)
- Annual Training Hours (ATH)
- Call deflection / Email deflection / Chat deflection
- Product returns and exchanges
- Dispatch avoidance rate
Contact center and call center metrics explained, with supporting examples from our Global 2000 clientele
FIRST-CONTACT RESOLUTION (FCR)
One of the key customer-focused contact center metrics, First-Contact Resolution can significantly reduce consumer effort. While FAQs, search, and topic-tree browsing can help with simple queries, more sophisticated technologies like Artificial Intelligence (AI) are essential to resolving issues of medium to high complexity at first contact.
A premier telco client was able to improve FCR by 37% across thousands of contact center advisors when they made it mandatory for all agents to use guided help, a capability enabled by Case-Based Reasoning (CBR), an AI technology. In fact, any agent there is now able to handle any call, the “holy grail” in contact center customer service!
AVERAGE HANDLE TIME (AHT)
While it is a good metric for the customer as well, AHT is more of an internal metric for customer service operations. It is important to bear in mind that AHT without FCR can only increase customer effort and defection. Happily, KM, when done right, can transform both of these seemingly conflicting metrics.
Impact on metric
A premier banking client reduced AHT by 67% while improving FCR by 36%, leveraging AI to guide customers to answers! In fact, advisors in its contact center used the same technology to guide customers through processes such as account opening and other banking transactions while complying with industry regulations!
AVERAGE SPEED TO ANSWER (ASA)
ASA might better be called ASORA, the Average Speed to One Right Answer!
Without the “right,” ASA might mean speed to the wrong answer, increased customer effort, and you know how that goes. With the proliferation of customer touchpoints, it is important to have a centralized omnichannel knowledge management system that is in consistent use to make sure that the customer gets the single right answer regardless of channels or even people within a single channel.
Impact on metric
In fact, the telco client mentioned earlier leverages the same AI technology and omnichannel knowledge base across its contact centers and hundreds of retail stores to deliver single right answers fast, regardless of touchpoint!
ANNUAL TRAINING HOURS (ATH)
Contact centers have been spending huge amounts on agents—onboarding, training of new agents, and providing ongoing training to existing agents. At 35-40%, agent attrition is exceptionally high in contact centers, adding to overall training costs.
In fact, US companies spent $87.6 billion on employee training in 2018 alone. On average, organizations spent 11 percent of their budget or $235,077 on learning tools and technologies. Yet agent knowledgeability, in the context of customer service, remains a big issue.
How do you reduce training needs without compromising service quality? Again, KM delivers the answer.
Impact on metric
With CBR/AI technology, a leading global bank was able to reach the #1 spot in customer service NPS and reduce ATH by 50%, even as it expanded to 11 countries with mostly novice agents in its workforce! With the same technology, a telco reduced induction training time by 43% and time-to-competency by half.
(Note that reducing the need for training also reduces shrinkage, which is the amount of time lost due to agents’ breaks at work, sick time, training, holidays, etc., another commonly used contact center metric.)
CALL / EMAIL / CHAT DEFLECTION
Customers increasingly prefer self-service, and contact centers benefit from it as they look to cost-effectively meet increasing demand for service. However, robust Knowledge Management is critical to delivering digital self-service. One of the popular metrics for measuring digital self-service effectiveness is the number of calls or emails or chat requests that were successfully deflected.
Impact on metric
Contextual and intelligent self-service enabled a retailer to deflect up to 60% of email requests, while a media and legal services giant deflected 70% of requests for agent-assisted email and chat customer service!
PRODUCT RETURNS AND EXCHANGES
In today’s hypercompetitive marketplace, many branded manufacturing firms, retailers, telcos, and others accept product returns or exchanges, and eat the costs in processing them. Called by various names depending on the industry—No Fault Found (NFF), No Trouble Found (NTF)—many of these returns and exchanges are unwarranted where the products were not faulty but the customer thought so (and the contact center could not resolve the problem.) NFF costs many organizations tens millions of dollars each year, but here’s the good news: KM can address this issue head on.
Impact on metric
A large telco has reduced unwarranted handset exchanges by 38% using AI-powered problem resolution for the consumer at its contact center, while improving FCR by 19% and call quality by 23%!
DISPATCH AVOIDANCE RATE
Unresolved problems that could have been avoided with smart problem resolution through the contact center result in unnecessary truck rolls or engineer callouts. Depending on the industry, each such visit can cost from a couple of hundred to a few thousand dollars, biting into the business’ operating margin.
Impact on metric
With omnichannel AI deployed in the contact center and on the website, a leading white goods manufacturer was able to save tens of millions dollars every year by reducing such wasted truck rolls. A water utilities firm was able to save ~$5M per year by reducing unnecessary engineer callouts, and even improved FCR by 30%!
These metrics are only the tip of the iceberg. There are other areas transformed by KM, including agent churn reduction, regulatory compliance, reduction of website abandonment, customer journey progression, and softer metrics like agent morale and customer satisfaction.
Of course, there’s one caveat: Remember “different strokes for different businesses.” The metric that makes sense for one brand might not make sense for another. Force-fitting Walmart-style metrics to a Nordstrom brand intent can kill your brand! You should align your customer service contact center and knowledge management goals with your business goals.