In this webinar, we discussed how Hopin, an event technology platform, are using data analytics to nurture the cause and effect relationship between customer support and product teams, and how you can use VOC insights to influence the product roadmap in your own business.
Liz Pastor, VP of Product Support and Bhavik Patel, Director of Analytics from Hopin, talk about how they analyse customer support conversations to give product teams an unbiased view of customer pain points to influence strategies and keep the customer at the centre of business decisions.
Among their reporting, Hopin use SentiSum to analyse their customer support conversations in real time and gather insight on the topics that are driving positive and negative sentiments in customers, as well as churn risk.
Why is close collaboration between product and customer support important for the success of a company like Hopin?
All areas of a business impact the customer in one way or another, but none more directly than the team who are responsible for the very thing the customers are paying for - the Product team.
Customer support and product are incredibly intertwined. If a change happens within the product then it may increase or decrease the number of tickets coming into support, or even influence the sentiment of customer conversations.
"I think there's a huge opportunity to have more partnership, more common goals and more alignment amongst teams, so that not only can they rally together when ticket volumes go up but they can celebrate together when positive changes occur." - Liz Pastor
Product teams can be used to dealing with huge data sets from various different sources and can often not realise the rich data that exists within customer support that they might not be tapping into.
Diving into analyzed, quantitative data from within the customer support function can allow product teams to answer questions about customer pain points, do root cause analysis on issues and even track customer response to new releases.
"Now we have verbatim feedback directly from our customers, I feel like I've actually missed out in earlier parts of my career where we've probably lost an inordinate amount of insights just by not looking at this data." - Bhavik Patel
How can you objectively look at customer issues and identify opportunities for improvement?
"I think one of the biggest keys to using support data is to be able to understand and categorize your tickets. You can do that manually, which I know is never fun for teams, but you can also do that through ai. We use Sentisum."- Liz Pastor
As Liz describes in the webinar, it's important to have a level of subjectivity and understanding about your product and customers when looking at volumes and trends over time. If one particular area of your product is used five times more by your customers than any other area of your product, then it's no surprise that you would have higher ticket volumes relating to this area.
This is where being able to monitor trends and changes to your ticket categories over time will allow you to get a better view of any issues in different areas of the product, such as sudden in ticket volumes or the sentiment relating to an area of the product becoming more negative.
Product teams are able to self serve this data to shape their roadmap with an informed view of how much the customer is being impacted, and check how responses may have changed following different releases.
"When we were looking at some of this data in SentiSum, we realized that there were pockets of data where we were seeing low volumes of tickets but the total time to close was significantly higher, so it was taking the CS teams longer to close those types of tickets. I think when you start objectively looking at all of your challenges, you can start to understand where the barriers to success are and quantify them." - Bhavik Patel
What are the barriers to leveraging the right balance of quantitative and qualitative data?
Clever, intuitive technology exists and is only getting smarter and more accessible. So what can hinder organizations from accessing the data they need to make informed, confident decisions for their customers?
"I think this largely comes down to a cultural issue. A lot of product teams or design teams look at problems they're facing through their own lens and data sets they have available. I think if product managers took five, 10 minutes extra to engage other departments and data sets on issues, they might actually find themselves being able to solve them on a wider scale." - Bhavik Patel
Product teams deal with a lot of requests at once from all over the business, so it can be hard to know for sure which issues are most relevant to helping our customers in the best way.
Leaning on unbiased data allows you to see the impact of an issue on your customers in real-time and monitor success through ticket volumes and customer sentiment once a fix is released.
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If you enjoyed the insights we discussed in this webinar, check out our Support Insights podcast, where we speak to customer-centric department leaders about a different topic every 2 weeks.