How can customer service leaders get a seat at the product table - and what data should they use to support their case?
The first step of being heard by product is actually putting yourself in their shoes to better understand their challenges. Imagine you’re busy compiling data from multiple stakeholders, it can be difficult to know who to listen to first. If Mary from sales says customers need one thing, Bob from support says customers need another, and a customer interview reveals a third possibility - who’s right?
When it comes to influencing product decisions, we need to come armed with trustworthy, meaningful data.
Most support leaders inherently know what customers want, we have a deep understanding of the needs of our users, and we care deeply about getting that information into the hands of decision-makers. But retrieving meaningful data from thousands of user conversations is difficult. And when arguing with, say, sales, we have to compete with cold, hard, revenue figures.
The good news is that our daily interactions with users give us the data we need to be influential. If we track data in the right ways, we can then go to product with more confidence and have a positive effect on development strategy. We’ve seen at Idiomatic that the key to making support more influential in cross-departmental disagreements is to come to the table with quantified, contextualised, curated data. Doing so will help you answer Product’s three most common objections:
- How many other people have this problem?
- What’s really going on?
- Why should we care?
“How many other people have this same problem?”
Quantify your data
Imagine you’ve landed a new role as a smartphone designer. What would be more helpful in your search to design the perfect phone:
- A few people think screens should be bigger than they are today.
- 24% of our customers have written in complaining about not being able to read small print on their screens.
The first one is an offhand remark. The second is useful information. If almost a quarter of ourcustomers have difficulty with visibility, that’s definitely something to address in the new smart phone. Maybe we can improve zoom, or make the screen bigger. It’s a really difficult statistic to ignore when we’re asking what customers want in a new model.
Support teams frequently struggle to quantify data from customer conversations in a meaningful way. Most smaller teams will rely heavily on ticket tagging. When a customer contacts support about a specific issue, the agent can attach a pre-defined ticket tag. Then, when compiling feedback for other departments, support can pull a report of the number of conversations filed under that tag.
This is a good first approach. In the absence of an automated or AI solution, tagging can be a useful resource. There are two main limitations of which to be wary. First, you need to know what you’re looking for; you can’t tag for everything, you need to be focused and pre-define the tags. Second of all, manual data review can be massively time consuming. Therefore, our advice is always to keep the number of tags relatively small and high-level to start, i.e., no more than 30-40 tags.
Support teams frequently struggle to quantify data from customer conversations in a meaningful way.
“But what is really going on?”
Combining the quantitative with the qualitative is the true secret to driving product decisions with support data.
“Why should I care?”
- Trimming out irrelevant or misleading data.
- Aligning feedback with current company goals.
- Reducing noise or less important trends.
About Chris Martinez
Chris is the Founder of Idiomatic, a company that uses artificial intelligence to help customer care teams work smarter. Their AI layer gives companies customer driven ways to improve their customer experiences by mining textual data to automatically understand and categorize conversations with customers at scale. This lets companies systematically know what their customers are saying and how they can improve. Prior to Idiomatic, Chris founded and ran data for Glow (www.glowing.com), a leading publisher of mobile health apps for women. Glow was spun out of HVF, Max Levchin’s (a co-founder of PayPal) startup studio, where Chris was an Entrepreneur-in-Residence. Previously, Chris spent many years camped at Stanford, racking up a BA in math & computer science from Stanford University, a JD from Stanford Law School, and an MBA from the Stanford Graduate School of Business.