How customer service leaders can influence product decisions

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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:

  1. How many other people have this problem?
  2. What’s really going on?
  3. 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:

  1. A few people think screens should be bigger than they are today.
  2. 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.
You can also evaluate AI solutions for understanding customer feedback automatically. For example, Upwork’s customer experience team used to have three people reviewing hundreds of cases a week to pull out actionable insights. The manual effort was yielding an anecdotal and imprecise sketch of the customer experience. They switched to an AI solution that yielded 10 times as much data analysed in about a third of the time. Instead of relying on the manual taggingand analysing of support conversations, Upwork can see every trend brought right to the surface.
 
Modern AI techniques such as Natural Language Processing (NLP) can read and analyse 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 tags for), and turns customer feedback into data that helps resolve disagreements.
 
Regardless of how you do it, quantifying support conversation data makes it easier for other departments to act on support driven insights. If nothing else, make sure your team is performing consistent high-level tagging.

“But what is really going on?”

Contextualise your data
 
When presented with data, most logical people will want to dig in deeper. They’ll want to know the “why” behind the numbers. Who are these people? What led them to think this way? Can we characterise these users further? That “why” can often be uncovered by analysing second level trends, or contextual patterns that emerge from within the subset of data. Secondly, context becomes even more compelling when quantitative data is backed up with meaningful qualitative
stories.
 
Combining the quantitative with the qualitative is the true secret to driving product decisions with support data.
 
If you’re using AI to surface trends, it’s easy to leave the cross referencing to the computer. There’s no need to pull up every ticket with the relevant tag for further analysis. The AI will have already identified the second level trends within the subset of tickets. Product owners can dive deeper with a single click. This transparency brings even more weight to the arguments customer support teams have been making all along.
Combining the quantitative with the qualitative is the true secret to driving product decisions with support data.
Even if you aren’t using AI it’s key to keep examples all in one place. While product wwners aren’t easily convinced by two or three anecdotal stories, they will want to read 20 tickets to have a better idea of what customers are talking about. If you’re using tagging, linking statistics to the tickets responsible for those statistics can be difficult.
 
It’s crucial that you do the hard work of putting all these tickets into one place instead of just pulling two or three examples because product owners are unlikely to start searching through help desk conversations to find the necessary tickets that it will take to convince them.
 
To settle disagreements of what customers really want, teams need easy access to second level trend analysis and qualitative stories.

“Why should I care?”

Curate engaging data
 
Finally, voice of the customer data needs to be engaging. You can have all the quantified data you want, but if it no one reads it, you’re not going to make a difference. Curation of data can help make reports more relevant to each department.
 
To us, curation means:
  • Trimming out irrelevant or misleading data.
  • Aligning feedback with current company goals.
  • Reducing noise or less important trends.
One way to automatically curate engaging data is through the use of sentiment analysis. NLP classifies customer conversations by emotion and tone, so teams can pull out the most extreme feedback to distribute.
 
Intercom uses sentiment analysis to send VoC data across the company in a weekly report. Often this kind of weekly report is difficult to get engagement from. But when they started being able to zero in on the most extreme sentiment they found colleagues were excited to receive the reports each week.
 
Enthusiasm? About reports? When you’re providing the information colleagues need to do their job better, that’s a very possible reaction to a report.
 
Don’t bring a knife to a gunfight
 
Maybe equating a product discussion to a gunfight is a bit extreme. But the advice is sound. When you’re advocating for the needs of your customers, it’s your responsibility to prepare properly. If you try to influence product decisions with opinions, don’t be surprised when support loses its seat at the table.
 
Preparation takes time, but if you put that time in you can get great results. Instead of bringing gut feelings, unsubstantiated claims and anecdotal feedback, support teams need to develop quantified, transparent data to backup their requests. AI can help with that, but step one is recognising that it’s going to take effort and making a commitment to invest resources in preparing yourself for success. Good luck!
 
Christopher Martinez is COO of Idiomatic.

About Chris Martinez

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.

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