Customer support is the natural result of progress. It accumulates as the product changes and new types of customers join, rising and falling as a form of customer experience debt.
Like “tech debt,” the normal result of engineering tradeoffs, support effort needs to be managed — but the changes often extend beyond the support team itself. Let your CX debt grow too big and the interest may not just be a huge incremental cost but a worse customer experience and ultimately a source of churn.
Every company wants to simultaneously grow faster, develop long-lasting customer relationships, and reduce support costs. Achieving this support miracle is a function of how effectively an organization can accurately assess where the effort is spent, diagnose what’s causing it, and invest in making changes in two categories:
- Examples **within the support function** : improved reply templates, deflection services, knowledge base posts delivered just in time
- Examples **elsewhere in the organization** : new forms of onboarding, customer success processes, enhancements to the product, or even changes to how the product is priced.
Put another way, customer support, like any customer-facing team, is a cost of revenue, an opportunity to expand revenue, and a source of data-driven prioritization for the company’s time. The trick is understanding the value of the data that comes through customer support and turning the firehose of (a) unstructured conversation text with (b) occasional survey data into useful insights.
I recently caught up with Darren Chait, co-founder of Hugo, to discuss scaling customer support and our shared experience in managing customer experience debt. We’ve learned from working with our own customers that managing that effort can be boiled down to three questions your team can ask itself on a regular basis.
Question 1: What’s driving our communication effort?
Customer support is a cost of goods sold. As your customer base and revenue grow, so does your customer support cost in order to maintain that same customer experience as a direct extension of the product. We’ve found, however, that many customer support teams don’t dive any deeper to understand how that cost is made up beyond a simple count of tagged tickets. And as I explored previously, it turns out every support team has trouble with their tags.
A good support team can typically understand the cost of customer support by product area, feature, or user segment. A great team can go further, diving deeper into the nuances of those support efforts to inform strategic decision making around growth, finance, product experience, and scaling customer support.
Look at your customer support data and ask yourself:
- Can we break costs down by product area, feature, segment, etc?
- Can we quantify it in terms of effort, escalations, existing solutions, etc.?
- Are there costs, or sources of cost, we know we’re _not_ measuring?
Question 2: What’s driving our customer outcomes?
One of the most interesting challenges in building fast-growing tech companies has been straddling qualitative vs. quantitative. At a time when so many companies define themselves as data driven, knowing how to translate qualitative insights or feelings to into quantitative metrics that others can understand can be difficult. Scaling customer support is no exception.
A common blind spot in organizations that value customer experience is making decisions solely based on the volume of interactions. For example, a high-volume problem related to login problems might naturally get prioritized for improvement, but it should also be balanced with improvements to a more complex problem that happens a fraction of the time but generates a much worse experience for your most valuable segment of customers.
It’s also possible for efficient solutions — knowledge base articles and automated deflection — to technically help your customer but in a way that leaves them quietly wishing the experience were better.
It’s well known that Amazon customers have a higher connection to the brand once they’ve had one well-handled support encounter, notwithstanding that the customer support encounter is typically triggered by a negative experience.
This is exactly what Hugo’s team has experienced, too. Customer experience through high-quality support is part of their differentiation, and they know that customers who have had positive support integration are 2.5x more likely to activate and become engaged with their meeting notes platform, than those they haven’t heard from. So, while the data says that our four most common support queries can be handled better with video content in their resources center, they made the decision to leave it to the human agents.
So, ask yourself: what is it that’s driving satisfied, or unsatisfied, customers? Long-running tickets? Long wait times?
Question 3: What’s changing?
Arguably the most important question is how things are changing over time. Operating under the assumption that the first two questions — what you’re spending your time on and what’s driving specific outcomes— don’t tell the full story is a good way to uncover other influences on the growth of customer support effort.
The specific questions to consider here are broader:
- How do we uncover emerging themes or underlying issues that aren’t currently tracked?
- When an issue bubbles qualitatively up as as “new and urgent,” how do we verify the scope and severity quantitatively?
- Who would have to know what, and when, in order to validate a new customer need and act on it?
Darren was recently talking to a customer about this exact issue. This Hugo customer is a large tech company that has seen tremendous growth over recent months, and as is often the case, customer support was first to feel the crunch as revenue grew. They have a great data-centric culture and were very effective at correlating support time spent with components of their product, new features, and emotional engagement from customers. As they scaled, these were the key drivers for resource allocation and strategy.
But, what they didn’t know was the role that customer support played in product definition. Their support team was not only a funnel for product insights and customer concerns, but in many cases was the source of suggested ideas and solutions for the product team. As they reorganized the support team purely based on support data and customer feeling, they lost this important role which in turn slowed down the business’ product machine. Lesson learned!
These three simple questions have proven to be an effective framework for evaluating your customer support effort as revenue scales. Together, they ensure that you consider every factor that influences your business’ ability to scale customer support. In doing so, you will maintain or improve customer experience while keeping a handle on CX debt.
For us, they serve as a frequent starting point for working with teams to better understand their data. Asking these questions and learning more about your organization’s support effort can also mean getting a firmer grasp on scaling efforts and identifying new opportunities for delight; leveraging your qualitative and quantitative data to find new ways to make your customer experience what differentiates you from the competition.