Last year we discussed metrics as a key component of getting live chat performance management right, geared towards an audience who may be unfamiliar with data collection and analysis. We also took a quick look at collecting baseline data. In this post, we’ll cover the actions you should be taking in response to the data you’ve collected.
If you are encountering challenges to your performance management efforts due to low-quality data collection (especially as a result of poor data entry practices by your staff, and/or an inadequate CRM integration), please contact us.
As a reminder, responding to data exists within the context of an active five-step process:
- what to ask (metrics)
- how to answer (metrics)
- getting “Before” right (metrics + data collection)
- how to react to “After” (data collection + action)
- your next “After” (data collection + action, perhaps metrics also)
Once you’ve established the parameters of your problem and how you plan to go about solving it (which tells you what to measure and how to measure it), and have gathered enough data to compare against your baseline, it’s time to adjust what your team is doing in response to that data, and to consider how you might continue to improve, going forward.
In an attempt to keep the conversation clear, I’ll continue to ground it in our example from last time: reducing wait time and transfers with smarter routing, rather than with additional headcount and/or with training the entire tier one team so that transfers aren’t necessary.
We determined that we should track average wait time and number of transfers per day (or per week, depending on website traffic/volume of transfers), and that we might want to track the reason for the transfer. Tracking the reason for transfer is worth considering because there is a significant difference between the need for transfer being due to the agent not having the answer the visitor needed (which probably took some time to determine, reducing our efficiency and frustrating the customer), vs. due to the visitor making an incorrect selection and us needing to transfer them as a result of their user error (potentially frustrating for the customer, but a relatively immediate fix).
Step Four: How to React to “After”
There are really only three types of changes you should be making in response to the data you’ve collected: change practice/process, change data collection, or change priorities.
Changing practice is the most straightforward result of data collection. You had a theory: you could reduce transfers and wait times through better routing. The data supported it: after tracking daily transfers for 2+ weeks, you believe that customers with billing questions should automatically be directed to a specialized customer service rep rather than to general support. Now you just need to move forward and implement the changes necessary (in this case, update your chat/phone/etc software to accommodate this improvement to routing). You’re ready for Step Five, but don’t stop collecting data yet! You’ll need that data to demonstrate the significant impact your proposed change has had, not only to your superiors, but to your staff who’ve had to accommodate your data collection needs, and potentially to your peers who’ll want to learn from the best practice your process has helped to establish!
When you realize that you don’t feel confident enough about the data you’ve collected to warrant changing practice, but still feel strongly that your hypothesis is correct, it’s time to change the way you collect that data. Perhaps you didn’t track the reason for the transfer when you first started collecting data. As a result, the data seems to indicate that, on average, transfers aren’t impacting service- but that doesn’t align with your real-world experience. You try filtering out quick, user error-based transfers. This change in how you collect the data significantly impacts the average, and now you have the quantitative basis for making a confident improvement to your team’s practices/processes. See “changing practice” above.
Changing priorities happens when, after collecting data, you discover that your hunch was incorrect. While transfers are a painful experience on a daily basis, it is a trivial issue for a small percentage of your customer/visitor population—and as such your time and resources are better-allocated towards solving other, bigger problems. This doesn’t mean you did something wrong; this result might mean your improvement efforts in a particular focus area have plateaued. For instance, maybe your routing is fully optimized; there’s no other changes you could be making that will result in greater efficiency (without tradeoffs that simply aren’t worth the trouble)- great job getting to that point! You’re ready for Step Five.
Step Five: Your Next “After”
A defining (and also, admittedly, exhausting) component of performance management is that it is continuous; it represents a cultural shift for an organization. That shift being: from a generic, repetitive “incremental raising of the bar” for your team’s or organization’s overall goals, to identifying specific, measureable ways you and your staff can improve your work on a daily basis to meaningfully impact those overarching, strategic goals.
If you’ve made it to this point, you’ve already tested a theory and had it proven right (or demonstrated that your hypothesis doesn’t have the ROI it needs to in order to be worth the trouble of implementing). In either case, you’re ready to test your next theory about how your team can improve. Keeping with our example, you’ve set up smarter routing that now directs customers with billing questions directly to the person or team best-equipped to handle them, rather than clogging tier one and frustrating customers whom tier one is not equipped to help—now what? If the transfer rate is still a problem, try to identify why that might be. Zero in on that “reason for transfer” in your data collection. If the transfer rate is looking good, find another high-impact opportunity. Maybe have your post-chat survey only ask one question (e.g. “how would you rate our customer service?”), but based on the response to that question, trigger a longer feedback collection process (this could work for getting more information about how to calm an angry customer, or could provide great testimonial content for your marketing team!).
You might also want to have a look at this article on ICMI, specifically the section on secondary impact. These kinds of conversations can help inform what direction to move in next with your continuous improvement efforts!
Again, if you are encountering challenges to your performance management efforts due to low-quality data collection (especially as a result of poor data entry practices by your staff, and/or an inadequate CRM integration), please contact us.