In this column, I did a little ranting and raving about some bad customer service experiences I’ve recently had. Bad customer service is bad business. It’s easier and cheaper to maintain a current customer than acquire a new one.
Yet when companies hit rough economic times, service budgets are often among the first to be cut. There are no easy decisions in times like these, but the damage resulting from customer dissatisfaction and frustration can long outlast economic downturns. The amount of effort and money required to correct a negative perception of a company is likely to be much higher than what it would cost to initially satisfy customers with quality support services.
I’d like to continue this discussion and make it more relevant to advertising technology by focusing on technology solutions for improving customer service.
The idea behind many of these solutions is to use automation and relational data warehousing to make customer service more efficient. When possible, organizations should seek to give customers the tools they need to find answers by themselves.
At the same time, it’s absolutely critical the system be designed to facilitate resolution of the issue should a customer not be able to find what she needs unassisted. This might be accomplished through live chat, computer desktop remote control, or pushing the consumer to a call queue to speak with a live representative. The key to maximizing the efficiency of such a system is all the components need to talk to each other.
When the customer arrives in the call center, the agent should have immediate access to that customer’s records, any previous inquiries (through any channel) related to the issue, and what’s already been done to address the problem.
Technology can help in several functional categories.
The Knowledge Base
The online, self-service knowledge base might be the core of any customer service application. I’ve seen it done well, and I’ve seen it done very poorly. Subtle differences can make or break a system. The best systems are those permitting flexibility in search options. Ideally, users should be able to select from a Boolean search engine, an “expertly” guided search, a natural language search, and so on. If the user starts with a broad, general search, a good system will use a decision tree to ask key questions to narrow it. Many systems also employ automated learning, allowing them to constantly refine the rankings of potential solutions based on customer feedback.
Enterprise customer service applications often include facilities for handling responses to customer inquiries. Let’s say a customer searched the knowledge base and didn’t find what he was looking for. So he emails the support center. Most systems can automatically reply with a generic message that includes a rough timeline for response by an agent. Some systems also use complex models to identify keywords and key phrases in the message that are likely to require specific responses. The system might then fire off an email with a potential solution and/or route the request to a queue where a live agent can address the concern.
Of course, there are potential pitfalls in automated responses that go as far as to suggest solutions. The user may already have found that solution in the knowledge base and deemed it unhelpful. The engine may produce an irrelevant result. These situations can be very frustrating for the user but can often be avoided if the system constantly keeps a record of all customer interactions. If the system knows which knowledge base articles a user reviewed before emailing the support center, it can avoid sending duplicates. It has a better idea of what the user wants.
This is one of the easiest ways to improve the efficiency of customer service. A single agent may be able to handle four simultaneous customer discussions, giving the user immediate access to potential solutions from a live agent while allowing the agent time to do on-the-spot research to resolve the situation. Live chat can be extremely useful in online shopping and order processes. A potential customer might have last-minute questions about a product that aren’t answered as part of the on-site product description.
Most systems include analytical applications for service center managers. These interfaces allow managers to check response time and overall efficiency, accuracy of responses (through any channel), and other critical metrics. Managers use these reports to quickly get a sense of how the operation is performing. They can take appropriate action to address potentially negative situations.
These are the functional categories of the customer service management solutions driven by Web technology. Some systems offer other modules that further enhance customer service. As with many enterprise-class software packages, costs can be high. The expense is likely justifiable when considering the potential increase in overall customer satisfaction and retention.
Jeremy is taking the week off. This column ran previously.
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