As a Support leader, your queue may feel like an emergency room. The frenetic pace of agents tackling inbounds is like a team of intake staff, doctors, and nurses attending to a flurry of patients in various states of infirmity.
Imagine the ER’s usual suspects — a heart attack, a sprained ankle, an unexplained high fever, and a hypochondriac with a hangnail. Then there’s the steady stream of obscure cases, from rare allergic reactions to the toddler who swallowed a quarter, to the DIY carpenter covered in superglue — the ER sees no shortage of variety and relies on careful processes and procedures to prioritize medical attention for patients facing the greatest immediate risk.
Support teams, too, see a mixed bag of cases every day scattered across the urgency and complexity spectrum, and prioritization is a constant challenge. What if the ER staff treated the heart attack the same way that they treated the sprained ankle?
Nobody wants to spend time in the ER waiting room, and everyone wants their case addressed quickly. In order to understand, measure, and improve your customer experience, you need to identify high-priority cases quickly. In this article, we discuss a prioritization framework that will help you understand what has the greatest impact on your CX, and where your attention is needed most, so you can prioritize resources effectively.
Support Teams Need a Better Prioritization Framework
For many overloaded Support teams, the default prioritization logic is first-in, first-out (FIFO). This isn’t surprising. First, as consumers, we’re accustomed to the popular refrain, “Your call will be answered in the order it was received.” Secondly, many industry-standard customer support systems are optimized for this logic, helping companies close a high volume of transactional conversations as quickly as possible.
But the strategic importance of Support has come a long way, especially as forward-thinking organizations rethink their approach to customer feedback — FIFO logic no longer stands up to the high expectations that today’s leading Support teams must meet, internally and externally. The Support teams of today’s leading SaaS companies are not some rote assembly line and are expected to navigate the nuances of customer relationships and to be great partners to Customer Success, Product, and the rest of the organization.
Support conversations are not created equal. It sounds straightforward enough that a simple how-to question from a freemium user can probably wait longer than an irate enterprise customer who is missing data from one of their reports — but the plot thickens with several thousand cases a month fielded by different agents and spread across different channels.
Support leaders have long cited the 80/20 rule as a descriptor of resource allocation across cases, whereby 20% of cases typically consume 80% of team effort. However, in a recent study across millions of support interactions, we found that effort and operational costs are much less predictably distributed — 80% of agent effort is distributed across 40% of conversations. There are a few important takeaways here — (1) Support is dealing with more complexity than expected, (2) Support resources are spread more thinly than you may think, and (3) Unknown unknowns are likely contributing to rising costs and reduced productivity.
These findings magnify both the importance and difficulty of prioritization. Two important prioritization considerations are relative urgency and relative complexity. These considerations will help you most accurately represent the impact of a case on the quality of your customer experience.
Do you focus on cases (a) affecting customers that represent the most Annual Recurring Revenue ("ARR")?, (b) from customers with repeat issues, (c) related to this morning’s outage, or (d) all of the above? Support teams from leading CX organizations have no other choice than answer (d), but it’s only possible to tackle (a) through (d) effectively with a rock-solid prioritization framework that is consistent enough to leverage automation, and dynamic enough to accommodate unknown unknowns.
According to Forrester, 77% of customers say respecting their time is the most important thing companies can do to provide good online customer service. Indications of urgency can include the language and sentiment that customers use to describe the issue and its impact on usability and their own effort, and the status of the customer.
Support teams are unfortunately no stranger to colorful language — colorful or not, the language that customers use to describe how an issue is affecting them can be a highly effective prioritization lever. That said, Support teams are well-accustomed to scenarios where issues that turn out to be relatively trivial make a big entrance as customer-declared emergencies. Customers don’t necessarily mean to misrepresent their situation, they simply want a response ASAP, and terming the issue as “serious” usually helps speed things up. This is why it’s important to use a combination of contextual factors to determine relative urgency.
Negative sentiment in combination with mentions of your core platform functionality may well be a very serious usability issue. For example, if you make HR software and a customer isn’t able to search through candidate records, that issue affects the core functionality of your product. On the other hand, if they’re not able to tag a candidate profile with a specific attribute, though annoying, it’s probably an issue that can wait a little bit.
Aside from overall contract value, important status-driven factors that can influence relative urgency include customer tenure and renewal date. Aside from simply rewarding loyalty, another reason to pay attention to customer tenure is that loyal customers are more likely to recommend you to others and to purchase new products and services from you in the future. Conversely, you may also want to prioritize cases from new customers who have just started using your product, and may still be within the trial period. An outstanding Support team can be a great defense against churn — a study by Kolsy found that 67% of churn could be stopped if a customer’s issue is solved in their first support interaction.
Another consideration could be time to the customer’s renewal date. Combining customer attributes into tiers that prescribe an urgency threshold can save you time in the moments where every second counts. For example, someone on a mid-tier plan for five years could have the same status as someone who’s been on your top-tier plan for two years.
Complexity is another important dimension that should influence case prioritization. Some drivers of complexity can include whether or not documentation already exists for the issue, whether or not the customer has already tried a set of recommended workarounds, and whether cross-functional teams like Engineering or Product need to be brought in. We know that a user permissioning issue is typically much easier to solve than a report that is inexplicably displaying incorrect data. However, the presence of unknown unknowns is perhaps the most problematic cause of complexity. With the fast pace of feature releases at most leading SaaS companies, the emergence of new issues is inevitable, and it’s simply not possible to anticipate every potential issue that customers may encounter.
Leveraging AI to find similarities between cases can help you manage complexity more efficiently so that you can prioritize more effectively — this way, your team is better equipped to focus on solving the complex issue at hand, rather than trying to pattern-match and determine next steps. The ability to see trends across cases in real-time also means that unknown unknowns stay that way for much shorter periods of time, ensuring a less costly resolution process that is better positioned to make a positive impact on the customer experience.
The more complex an issue, the longer it will typically take to solve. The ability to gauge complexity early means you can set appropriate expectations, streamline cross-functional communication, make things right, and improve your customer experience, much faster than you would in the absence of a solid prioritization framework.
Triaging cases can be very time-consuming and expensive when left to manual effort and traditional process, so having the ability to rapidly understand where cases fall in your prioritization framework will significantly increase peace of mind, and productivity, both in the moment and across planning horizons.
AI-Assisted Logic: Next-Generation Prioritization
Using AI-assisted prioritization can help you develop a prioritization framework that reflects what moves the needle for your customers based on customer sentiment and customer effort, and your business, based on cost of service. Incorporating AI can help you route cases by overall business impact, instead of just timestamp or channel. Considering additional factors like customer language and attributes, and whether the nature of the issue is recurrent, can help you understand cases' relative urgency and complexity so that you can prioritize them effectively. Furthermore, scoring cases by sentiment and effort can help you predict escalations so you can solve issues before they become risks. Thoughtful escalation logic reduces noise and maximizes productivity, while automation keeps all stakeholders on the same page, ensuring nothing slips through the cracks.
Though all support conversations are important, they are certainly not all created equal, in urgency, complexity, or their impact on the quality of your overall CX. Your ability to prioritize effectively is paramount, and incorporating AI and automation into your process will increase your Support team’s performance with less effort, reduce cost per case, and equip Support to be a net contributor to your CX and strategic partner to your organization.