96% of companies think it’s important to make their customers happy. That’s probably not a surprise to you —happy customers mean less churn, more referrals and higher growth rates. There’s no debate about the importance of making customers happy. But the question then becomes… how do you actually make customers happy?
…only 31% of customers actually feel that companies are likely to act on feedback…
Common wisdom tells us that customers are happiest when their concerns are heard and addressed. When companies take the time to understand what their customers need, they deliver valuable features, fix the right bugs and keep customers for longer.
However, only 31% of customers actually feel that companies are likely to act on feedback. As customers ourselves, we can relate because filling out a complaint form often feels like yelling into a black hole. Why do so many organizations agree that it’s important to make their customers happy, but don’t give the impression that they are really listening?
Historically, it’s really difficult to truly listen to customers. In our attempts to get closer to customers, we’ve discovered that companies make many (incorrect) assumptions.
- Companies believe All Hands Support is an easy fix to bridge the gap between customers and engineers… but it’s not.
- Customer Support believes they can make informed decisions and product recommendations from thousands of customer conversations … but they can’t.
- Product teams believe they will understand what drives customers by looking at usage data… but they won’t.
Buying into these myths leaves companies thinking that they are listening to customers, when in reality they are only listening to the loudest voices. And sometimes those loudest voices aren’t even customers —they are coming from inside the building.
In this post, we break down the myths customer-focused companies currently face and share how we believe they can do better.
Where All Hands Support Breaks Down
One of the first things startups do once they’ve decided to become customer focused is launch an All Hands Support program. Zapier, Statuspage and Front have all used and advocated for All Hands Support in the past. This program is designed to bring non-customer facing employees into the support loop by talking directly to customers. Engineers, marketers and product people are all given the chance to interact with real customers.
There are several benefits to All Hands Support.
- Engineers become more empathetic to the plight of frustrated customers.
- Product and marketing teams understand what customers need on a whole new level.
- Support has flexible resources to pull from if the queue gets busy.
But there are also limitations to All Hands Support.
One of the biggest changes support teams point to is how quickly bugs get fixed when engineers are the ones interacting with customers. It’s talked about like this is a good thing – but it’s often not! If an engineer jumps on a couple support tickets a week, they have a very narrow view of what customers are asking. When they divert their limited time to fixing a bug mentioned by one customer, what happens to the other thousands of customers experiencing a bigger, more serious bug?
All Hands Support can make companies customer-distracted rather than customer-focused.
This means companies are spending time and resources on fixing problems that aren’t as important.
It’s tempting to use All Hands Support as a one-and-done customer listening program. While it’s a good starting point, it’s not really helping organizations hear what their entire customer base is saying. All Hands Support can make companies customer-distracted rather than customer-focused.
Why Customer Support Can’t Listen Objectively
Instead of bringing the rest of the company to support, we need a better way to get support insights to the rest of the company.
The truth is, customer support teams have struggled with getting a seat at the table for years. They have deep wells of knowledge from talking with customers all day long, but the problem they face is the same problem All Hands Support faces. Each support agent’s view of the customer is based on the small subset of customers they’ve interacted with.
If you asked one agent for the biggest issue customers face, they are making this decision based on a small percentage of total conversations. The bigger your support team, the larger this divide gets. For support teams of 10, each agent deals with one tenth of the volume. They probably still have a good idea of the biggest dissatisfaction drivers, but grow that team to 50 and each agent only see 2% of the total volume.
…you can only tag what you’re already looking for…
Many teams try to understand overall trends by tagging tickets into themes. But there’s a major drawback to this system – you can only tag what you’re already looking for. The insights gained from tagging are constrained by the fact that teams need to identify possible trends before tagging. The hierarchy of tags determines the types of questions you can answer – and you might miss questions that you haven’t thought of yet.
So when support tries to influence product decisions, they come with flawed data. Processing enormous amounts of customer conversations often requires making some assumptions. Instead of using all the data to make a hypothesis, support teams are forced to make a decision and then analyze the data to back it up.
As Scottish writer Andrew Lang suggests, we “use statistics as a drunken man uses lamp posts – for support rather than for illumination.” Our conclusions are based on what we go looking for. If we’re looking for customer conversations that support our point, we’ll find them. But we might be missing the most pressing concerns, simply because we don’t know to look for it.
Why Behavioral Data Isn’t Enough
Product teams often believe that actions speak louder than words. To data-driven product teams, what customers say they will do doesn’t mean as much as what they actually do. These product teams rely heavily on usage data to make decisions about new features and prioritization.
Behavioral data provides great insights into the paths your customer takes through your product. It can show you what features are the most used and segment users. It can show you a lot about how your customers are using your product currently.
The why is just as important as the what when it comes to being customer-focused.
The problem, however, is that behavioral data tracking isn’t predictive. It can’t tell you what your customer is about to do or wants to do, only what they’ve already done. Tracking clicks won’t show you the intention behind customer actions. Did they mean to start a new report? Or did they just want to refresh the data in the existing one? The why is just as important as the what when it comes to being customer-focused. Only qualitative data can uncover these insights.
Secondly, it’s not possible to track what doesn’t exist. If customers are looking for new functionality, watching what they do with the current product won’t show that. You need to be listening to what they’re asking for with their words.
Customers might use your product the same way for weeks before suddenly jumping ship to a competitor. Except, if you dig into their support conversations you may find it wasn’t so sudden. They’ve been telling you for weeks that the interface is confusing, that they really need a new feature, and that they aren’t happy. But if you’ve only been watching their usage data, you wouldn’t know they already had a foot out the door.
Using AI to Illuminate Customer Concerns
To really focus on what customers are saying, you need a much more powerful engine. You need to be able to dig through several thousand conversations and find the trends. Humans just aren’t built for that kind of analytical calculation while also answering tickets.
That’s why using AI for customer support insights helps companies become smarter. Instead of relying on gut instinct and fragmented perspectives, companies can actually crunch the data to draw conclusions from real customer conversations. With AI in customer support you can:
- Utilize all the data you generate in customer operations. AI is one tool that allows teams to broaden perspective beyond All Hands Support: instead of relying on anecdotal, fractured ideas companies can utilize automation to scale up the listening efforts to all customer interactions, transforming all of this freeform qualitative feedback into quantitative data.
- Understand the questions you didn’t think to ask: AI surfaces trends you didn’t even think to look for. Instead of pulling data to support pre-formed assumptions, you can look comprehensively at the entire data set built from the ground up. You can do this by assessing the root cause of each individual interaction and uncovering trends instead of relying on your notions of what you should be tracking. Getting this objective view (unmarred by your customer support agents’ preconceived notions), can be an invaluable resource in uncovering insights.
- Uncover the why behind behavioral data: AI helps you listen to the reasons customers act the way they do, supplementing the product usage data you might already be collecting. Instead of knowing that customers don’t use something and then doing customer interviews or focus groups to find out why you can listen to the reasons your customers are already giving you.
If you categorize data correctly, you can turn qualitative data into quantitative data, putting numbers to data that has traditionally been seen as anecdotal and biased. For example, tweets from customers are excellent examples of sentiment. But how do you compare the sentiment around one product launch to another?
Instead of greasing the squeaky wheel, pay attention to the trends of your entire user base. What’s the biggest concern this month? How often does this feature get mentioned? Are customers looking for option A or option B? What words are they using to describe their problems? Intelligent tools can surface these answers for you – without even being told what to look for.
The next era of customer-focused companies is coming. We finally have the right technology to make informed decisions about where to dedicate our resources. Stop believing in the myths and put the machines to work to start listening to your customers.