How Hotjar are using SentiSum to eliminate manual processes in discovering product feedback

We caught up with Nick Moreton, Director of Support at Hotjar and self proclaimed “SentiSum evangelist” about how he’s harnessing customer support insights to integrate customer feedback into Hotjar’s product feedback loop with SentiSum.

At the time of this case study, Hotjar have only been using SentiSum for a few short weeks, but the benefits are already beginning to show for Nick and his team.

He was kind enough to answer a few of our questions on what the journey has been like so far, and what he envisions Hotjar’s next steps will be using these new insights.

How does SentiSum work?

Step 1

SentiSum integrates with Zendesk to uncover detailed insights from support tickets.

Step 2

Qualitative topics are surfaced enabling closer CS and Product collaboration.

Step 3

Product strategies are influenced and aligned with customer feedback data.

How are Hotjar using SentiSum to share customer support insights with the product team?

Our use of SentiSum so far has been very much centred on topic identification. I'm interested in finding out what's driving an experience for the customer, what's driving them to have to raise a support ticket, and I think that's where we are already seeing the real power of SentiSum's analytics.

We are already beginning to use those insights to inform our conversations with product teams so that we’re influencing our changes and updates based on what the customers are saying. As time goes on we want to enable the product team to engage more and more with the SentiSum platform so that they can self-serve to find those customer support insights.

The product team will have their own login to the SentiSum dashboard to go and look for the topics customers are talking about and apply them to their product strategy, which I’m really excited for because that would enable us to fully integrate product and support with one another.

The way I see SentiSum is, it's not about viewing product feedback, it's about discovering product feedback.

We can say “Hey, let's go and discover what our customers are saying.”, because the feedback is all tagged and logged in detail for us automatically in the SentiSum dashboard.
We aren’t limited to the data that we’ve picked up on in our manual processes.
That really excites me.

So far, we're putting a lot of work into these initial stages with SentiSum to focus on improving the granular accuracy so that when someone goes to discover that product feedback, we are confident that they’re getting the most value out of it.

We've also started to bring a second source of feedback into SentiSum that is more of a product feedback mechanism. It's not quite ready yet to explore, but we are already seeing the opportunity there. If we can get the same level of insight from different feedback sources it's gonna be a super powerful thing to help us drive decisions because we’ll have a wider view of different voices of a customer, different perspectives and different avenues of feedback all in one platform.

Watch our support insights podcast episode featuring Nick where we talk about his thoughts and advice on representing the voice of the customer in the product feedback loop.

How has SentiSum saved you time in uncovering customer sentiment/voice of the customer?

Before we started using SentiSum we had a basic system for logging product feedback.

If we had a ticket that said, for example, a heatmap isn't working properly, we'd manually log it and tag it, then we'd have to wait for a period of time from that moment to gather data on that issue.

From there we’d need to go into the Zendesk explore product and build some queries to pull the data, and from there we’d have to create some visualisations and create a dashboard.
Those manual processes are time consuming and bring a whole load of subjectivity, (and just going into Zendesk Explore is an absolute nightmare anyway).

Using SentiSum all of those steps basically disappear because it's automatically categorised all of the tickets for us and displayed them in the dashboard.

When we first brought in our data, SentiSum categorised all of our support data from the previous year, so we've got historical data, as well as ongoing data that we can filter and monitor from the dashboard.

The biggest thing for me which, aside from time saving, is the powerful thing in our example is - when we were doing our manual analysis in Zendesk, we’d have to decide what stories we want to investigate, what stories we want to tell to then go and get the data for it.

With SentiSum you go in, you open it up and you see the stories being told to you. You see an increase in a particular topic and immediately understand why.

We haven't had to think about what story we want to tell. We go in and read the book and find out what the story is.

And you pick up on those stories a lot earlier than you might have done using the kind of subjective methods you had before.

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