What is the secret to customer loyalty? The answer, straight from 50,000 consumers who took part in a massive survey by Corporate Executive Board, was: Make it easy to get service. In other words, reduce their effort.
To find the recipe for the ever-elusive ease, Forrester asked 5,000 consumers (on our behalf) about their biggest pain points in getting customer service. Again from the horses' mouths, the answers (by far) were lack of contact center agent knowledge and inconsistency of answers across touchpoints, followed by the inability of Web sites to deliver answers. With a common knowledge theme running across the pain points, the panacea is clearly an intelligent and unified omnichannel knowledge management (KM) system.
Done with the right technology, process, people, and best practices, KM reduces customer effort, which, in consumers' own words, creates loyalty. Beyond this strategic differentiator, KM also enables breakthrough enhancements to operational metrics, which not only transforms the contact center but also transcends it in many ways. Here are sample metrics and corresponding real-world examples from some of our clients at eGain. One caveat: 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 is not a good idea.
First contact resolution (FCR), one of the key customer-focused contact center metrics, can significantly reduce consumer effort. While FAQs, search, topic-tree browsing, etc., 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. As an example, a premier telco client was able to improve FCR by 37 percent across thousands of contact center advisors when it mandated that all agents use guided help, a capability enabled by case-based reasoning (CBR), an AI technology. In fact, any agent is now able to handle any call, the holy grail in contact center customer service!
Average handle time (AHT) is a good metric for the customer as well, but 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. As an example, a premier banking client reduced AHT by 67 percent while improving FCR by 36 percent, 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) might better be called ASORA, the average speed to one right answer. ASA might mean speed to the wrong answer and 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. 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.
Another common metric is annual training hours. According to the 2015 Dimension Data Global Benchmarking Report, contact center training budgets are ironically being cut by as much as 60 percent, even as agent roles continue to become more complex. It's no wonder that consumers point to knowledge-related issues as the main sticking point in getting good customer service. How do you reduce training needs without compromising service quality? Again, KM delivers the answer, pun intended! With CBR/AI technology, a leading global bank was able to reach the #1 spot in customer service NPS and reduce annual training hours by 50 percent, 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 percent 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.
Companies can also look at 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 KM is critical to delivering digital self-service. One of the popular metrics for measuring digital self-service effectiveness is the number of calls/emails/chat requests successfully deflected. Contextual and intelligent self-service enabled a retailer to deflect up to 60 percent of email requests, while a media and legal services giant deflected 70 percent of requests for agent-assisted email and chat customer service.
Another consideration is 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, including No Fault Found (NFF) or 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. A large telco has reduced unwarranted handset exchanges by 38 percent using AI-powered problem resolution for the consumer at its contact center, while improving FCR by 19 percent and call quality by 23 percent.
Then there's the 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. With omnichannel AI deployed in the contact center and on the Web site, 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 more than $5 million per year by reducing unnecessary engineer callouts, and even improved FCR by 30 percent.
These metrics are only the tip of the iceberg. There are other areas transformed by KM, including agent churn reduction, regulatory compliance, reduction of Web site abandonment, customer journey progression, and softer metrics like agent morale and customer satisfaction. I don't mean to channel Francis Bacon here, but it's true that some technologies improve customer service on the margins, some enable incremental improvement, and only a handful actually transform it. Knowledge management (KM), AI included, is a technology that clearly falls into the last category.
Anand Subramaniam is senior vice president of marketing at eGain.