Today we continue our series exploring the ways call centers can see direct, measurable benefits by adding a call-back solution. We’re walking through the paths to “hard” ROI, such as reducing abandon rate, telco costs, or (today’s topic) handle time, one post at time. If you missed part 1, you can read it here.
With this series, we’re setting aside the other major upside to call-backs – increased customer satisfaction. Often the pursuit of happier customers is sufficient enough for all call centers to give this technology a close look, but we recognize that some organizations are more motivated by quantitative benefits, or need additional “ammunition” to obtain budget approval.
This post is fairly detailed and analytical. If you want a more high-level introduction, try 5 Reasons You Need Call-Backs in Your Call Center.
Intro to Handle Time
Average Handle Time (AHT) is a straightforward metric. The handle time of a call is the sum of 3 numbers, which are illustrated in Figure 1:
1. Talk time: The time an agent and caller spend talking to each other.
2. Hold time: The time a caller spends on hold, not including the initial hold time prior to reaching an agent. In other words, “hold time” in this case only counts the seconds where an agent has put the caller on hold (usually to research a solution or confer with a colleague). You can call this “interstitial hold time” to distinguish it from the initial hold time. Another way to avoid confusion is to call the initial hold time the “queue time”, since that term is never used for the interstitial hold time.
3. Wrap-up time: The time an agent spends on post-call work after the conversation ends. It’s important to limit this to work that is directly related to the call, and exclude general non-call work.
Average Handle Time is the sum of these times averaged across all calls in a given time frame (usually a day), as shown in figure 2.
AHT is critical to controlling costs in the call center because agent time comprises the bulk of the operating costs of a call center. A research paper by Strategic Contact modeled 5 different kinds of call center situations and provides this helpful visualization. To see which model is closest to your call center, refer to the original report.
If we take the average across those models, we see that the variable labor costs (“CSRs and Supervisors”) account for roughly 80%, so let’s use that for illustrative purposes.
Since a lower AHT means less labor required per call, it’s a logical step to see that a reduction in AHT leads to reduced cost per contact. One can even use that 80% figure to infer that a 10% reduction in handle time will yield an 8% reduction in cost per contact, over the long run.
Pitfalls of AHT as a Metric
Although AHT is a powerful metric, there are pitfalls in relying on it too much. AHT measures agent efficiency, but not effectiveness.
Agent behavior is impacted dramatically by the metrics that management chooses to monitor. If agents are being graded on AHT, there is a risk they will rush through calls and not solve the problems. Many companies have found that de-emphasizing AHT will lead to increased customer satisfaction and first call resolution (FCR). For more on the relationship between AHT and FCR read Improving First Call Resolution in Your Call Center.
A great example of this trade-off can be seen in a webinar we did with Jeannie Sugaoka, Senior VP of Support Services, at Tech CU (Technology Credit Union). Watch it on-demand here.
Jeannie said, “We want our customers to feel cared for, and for every call to be ‘one-and-done’… so we don’t manage handle time.” This is one of those policy decisions that has powerful ramifications. The decision not to watch handle time, sends a strong signal to the agents about how they should approach calls and the kind of style they should use. It’s no surprise that TechCU enjoys a very high customers satisfaction score (C-SAT).
These two aspects of AHT are easily reconciled. One can set aside AHT as an agent performance metric, but continue to search for ways to lower it. Adding call-backs to your call center is one way you can do this.
The Happiness Dividend
Anyone who has worked as an agent, or managed them, knows that it can be a stressful job. Frustrated callers makes everyone’s job harder. How much do callers dislike hold time? A lot. According to Consumer Reports, “long wait on hold” and “bad hold music” rank as the 3rd and 6th most common complaint. (Only “Can’t get a human” ranks higher and, arguably, that complaint is about hold time as well.)
Removing the frustration of hold-time leads to happier callers and less “venting”. That venting itself can eat up valuable time in a call. Furthermore, happier callers can, in general, be serviced more quickly. Thus the “happiness dividend” is the reduction in AHT due to callers being less annoyed.
Admittedly, the impact of this effect can be difficult to forecast for your particular company. It depends on your average speed to answer (ASA), your baseline customer satisfaction scores, the demographics of your callers and the nature of the calls.
At Fonolo, we saw this effect in action during a deployment with Allstream. (The link to a short video case study is below.) Kent McInall, Director of Quality Assurance for Allstream said: “There [increased] employee satisfaction of not speaking with a frustrated end user… nobody is saying ‘Oh my god I was on hold for 12 minutes’ anymore… [instead] we’ve got a bunch of pleasant people wanting to speak to each other… ”
[fonolo_overlay_video src=”105149051″ title=”Allstream Video Case Study” time=”2:36″]
A second way that call-backs can help is through pre-call questions. You can use the interaction time prior to connecting with an agent to answer some of the questions that agents will need (for example, “what’s your name and account number?” or “what’s the reason for your call?”). This information can be passed to the agent so that the conversation is more streamlined.
If the call-back interaction takes place through the web or mobile device, then you can take advantage of the visual interfaces those channels have to make the pre-call questions extremely efficient. (Especially in the case of long alphanumeric codes, where spelling out each letter phonetically can fray the nerves of both caller and agent.)
Some sample math to put this in context: If an average question takes 20 seconds to ask and answer, and 3 questions can be off-loaded from talk time, then it’s a full minute removed from the call.
For the sake of walking through an ROI calculation, let’s revisit our fictional company “ExampleCo” from part 1 of this series, and assign some reasonable values for AHT reduction: 5% for the “happiness dividend” and 10% for the pre-call questions.
We can’t apply this 15% reduction in handle time directly to cost-per-call because of the various overhead costs attached to each call (as per figure 3). For the sake of this exercise, we’ll use the 80% adjustment factor as discussed earlier.
Next, we need to know how many calls will be affected. In other words, the fraction of callers that request a call-back. If you read part 1, you will recall that this fraction is itself comprised of the “Offer Rate”, how often call-backs are offered, and the “Take-up Rate”, how likely callers are to accept the offer. Rather than go through that exercise again, let’s just use a 20% overall call-back rate here. This allows us to calculate a total savings as shown in Figure 4.
So now we see the savings. Next, our ROI calculation requires understanding the cost as well. To keep things simple, let’s make some assumptions about the call-back solution you purchase:
- No initial fee
- Price based purely on the number of call-backs used
- No concurrency limit
With this pricing model, the ROI is quite straightforward. The company saves $120,000 and uses 100,000 call-backs. To be in the black, the cost per call-back needs to be $1.20 or less. (You’ll be happy to know that Fonolo meets the criteria above and would comfortably fit this ROI case. But we encourage you to explore other options on the market, as well.)