Identifying and improving performance measures connected to the success or failure of today’s contact center are crucial for multichannel merchants.
Contact center metrics are typically grouped into five infrastructure categories:
- Operations management.
- Human performance management.
- Service level management.
- Customer satisfaction.
- Employee satisfaction.
To gauge the efficiency and effectiveness of contact center operations, tie key elements of the operation (e.g., activities, tie resources, and assets) to financial measures such as revenue, cost, and profit. This allows you to reduce the complexities of the contact center into simple, easy-to-understand goals.
For example, if the primary objective of the contact center is to sell the company’s products, tying the number of sales achieved to revenue and profit presents a clear picture of the value the center generates. Tying the number of sales achieved to the costs required to generate those sales creates a snapshot of performance for a single period, which can then be trended over time or compared to the performance of similar organizations.
Examples of metrics tied to operational performance include: revenue per contact; cost per seat; and profit per agent.
Human performance management:
You need to make sure your people are skilled and performing well. Contact center management develops performance management measurements because they want their people to be experts so they can interface with customers and deliver superior service. If the person doesn’t perform well, it means we need to coach them to increase their skills and knowledge. If they do perform well, then they receive reward and recognition.
When contact centers measure employees, they usually measure the following categories:
- Quality — Focusing on quality means processes are continually being improved. This is important because improved processes are usually accomplished in shorter periods of time, which translates to higher productivity. Contact centers typically use call monitoring and data accuracy to assess an agent’s quality performance.
- Productivity – These are measures of what agents accomplish in a given timeframe. For example, contact centers can look at the number of contacts handled per hour, the number of sales closed per day, or the number of contacts that were converted to sales during a week.
- Time usage – How agents use their time. One example is sign-on time. Of all the time the person is in the building, how much time are they available to help a customer? Schedule adherence can also be part of time usage.
Watch what standards you set for your agents. Each unique contact center application has its nuances. Consider the productivity metric of so many contacts per hour. One contact center department may have an average talk time of two minutes while the contact center’s internal help desk organization may have an average talk time of eight minutes. Therefore it is not reasonable to have the same contacts per hour standard for each.
Service level management:
Contact centers measure service level to evaluate whether their customers are gaining access to the contact center in a timely and satisfactory manner. Commonly used measurements for service level include:
- Service level — Typically contact centers say, “I have so many percentage of calls that I answer in so many seconds.” Many will say, “80% of my calls are answered in 20 seconds.”
- Percentage of calls abandoned — When a customer calls in and is placed on hold (in queue waiting for the next available agent), they may disconnect from the call – for whatever reason. Perhaps they were at work and calling on a break. Perhaps they were at home and their child just fell off the jungle gym. Or, maybe they just got frustrated and hung up.
- Percentage of calls blocked – In most instances this isn’t done at the contact center. The contact center can get a report from the phone company that tells them how many customers were not able to get through because of busy signals.
Many contact centers average data. They may look at 24 hours, a week or a month and say, “Over a month’s period of time, I only had a 5% abandon rate.” But if they were to look at times when their contact center is most busy, they could have had a 50% abandon rate. You need to be careful that you don’t do so much averaging that you are not looking at what your customers are really experiencing.
Rather than averaging key service level metrics, convert these metrics to a “percentage of half-hours meeting standards” format. Using this format, the company first determines what standard of performance it will strive to meet. For example, it may set an abandon rate goal at 3% or less of all calls. Next, it establishes the number of half-hour periods during the measurement timeframe that the goal must be met, for example 80% to 85%. So using the new metric, the abandon rate goal can now be stated as “3% or less of all calls will be abandoned during 80% to 85% of the half-hour periods measured.” This conversion provides the flexibility to accommodate call arrival variations, and eliminates the distortion sometimes caused by averaging.
Customer satisfaction assessments are used to determine customer perceptions about the quality of service delivery, agent system performance, and the overall process of service fulfillment.
To gather information dealing with customer satisfaction, look at customer surveys, first contact resolution metrics, and your customer retention and churn rates.
Recent research has demonstrated a strong relationship between employee satisfaction, customer satisfaction, and increased employee retention. It is also important to gather metrics dealing with employee satisfaction.
You do this by surveying employees, and looking at retention and turnover metrics for them as well.
With all of these metrics, ask yourself: “Why am I measuring this?” and “What am I doing with this measure?” If you can answer these questions, then you can determine how to adjust your metric formulas accordingly.
Kathryn Jackson is an associate with call center consultancy Response Learning Corp.