By Chris Martinez, Founder Idiomatic
Most Customer Care 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 must 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 working with our clients such as Pinterest that the key to making Customer Care more influential in cross-departmental disagreements is to come to the table with quantified, contextualized, 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 product manager at Pinterest. What would be more helpful in your search to design the perfect online experience:
- A lot of people think photos should be bigger than they are today in the profile.
- 24% of our customers have written in complaining about not being able to see fine details in small print on their photos.
The first one is an offhand remark. The second is useful information. If almost a quarter of our customers have difficulty with the size of photos, that’s definitely something to address in the next sprint. Maybe we can improve zoom, or make the photos have more pixels. It’s a difficult statistic to ignore when we’re asking what customers want in a new feature.
Customer Care teams frequently struggle to quantify data from customer conversations in a meaningful way. Most smaller teams will rely heavily on disposition tagging. When a customer contacts your Care team about a specific issue, the agent can attach a pre-defined ticket tag or disposition code. Then, when compiling feedback for other departments, your Care experts can pull a report of the number of conversations filed under that tag or disposition code.
This is a good first approach. In the absence of an automated or AI solution, disposition code 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 code for everything, you need to be focused and pre-define the codes. Second, manual data review can be massively time consuming. Therefore, our advice is always to keep the number of codes relatively small and high-level to start, i.e., no more than 30-40 codes.
You can also evaluate AI solutions for understanding customer feedback automatically. For example, Pinterest’s Customer Experience team uses Idiomatic to code hundreds of thousands of cases a week to pull out actionable insights. Before Idiomatic, the manual effort was yielding an anecdotal and imprecise sketch of the customer experience. Switching to an artificial intelligence (AI) solution yielded better results with much more sensitivity. Instead of relying on the manual coding and analyzing of Customer Care conversations, Pinterest can see every trend reliably brought right to the surface.
Modern AI techniques such as Natural Language Processing (NLP) can read and analyze thousands of raw conversations rather than needing to search for keywords and phrases or tag manually. This helps identify trends you didn’t even think to look for (or have codes for), and turns customer feedback into data that helps resolve disagreements.
Regardless of how you do it, quantifying Customer Care conversation data makes it easier for other departments to act on Care driven insights. If nothing else, make sure your team is performing consistent high-level tagging of disposition codes.