Every organization has a group of customers that are held close and dear to the organization, whether due to loyalty program members, the revenue or profit margins they generate, the new market segment they represent or countless other reasons. One of our business partners recently explored ways to better serve their highly valued small business customer segment. Since better service can only result by understanding customer expectations first and foremost, that’s exactly where our analysis begins. If you remember in Part 1 of this series, I talked about some figures related to customer acceptance of a virtual queue as well as some positive figures about one company’s successful adoption.
The figure above leverages two independent data sources – one, the switch data of wait time for each customer and two, the customer experience survey results. Analysis revealed a number of key findings:
1. For every additional minute of wait time, satisfaction with wait time degraded by 3.20 satisfaction points.
2. More importantly, for every additional minute of wait time, brand satisfaction degraded by 1.51 points.
3. The point of diminishing return was identified as 2 minutes; agent availability sooner than 2 minutes of wait time provided no lift in satisfaction levels.
As a result of these findings, our business partner decided to “trigger” its virtual queue messaging when (business) customer wait time was expected to exceed 2 minutes. The satisfaction curves and point of diminishing return will likely be different for every industry and every company but the need to identify the point is universal.
If you have determined that you need or will be implementing Virtual Queuing, chances are you have already analyzed the impact of your customers’ wait time on your brand satisfaction. This same analysis can be (and should be) used to help determine when to initiate your virtual queue messaging for each key segment of your customer base. Without this analysis, you will simply be guessing as to when to trigger the message invitation. Guessing minimizes the potential ROI and perhaps unduly burdening your call centers.
One note of caution: Much like Work Force Management software, virtual queues work best in large call center environments where the activities of a single agent or customer have little impact on the outcome of the prediction algorithm. Please consider your environment before applying the approach described above. Since many virtual queue technologies do not interface with WFM software, you should avoid setting unrealistic customer expectations that create dissatisfaction with an effort to improve the experience.
