How do you leverage qualitative insights and data to build out a product messaging?

For me, the most helpful thing is to talk to customers. Here's an example of something I recently did to help inform messaging that hopefully sparks some ideas for you. 

When I first started at Figma, I wanted to better wrap my head around what "collaboration" meant. The word "collaboration" is so generic. Everything is "collaborative" and works "better together". So I wanted to get to the bottom of what parts of Figma's collaboration capabilities do customers really care about. 

Caveat: I'm not a trained researcher. But everyone and anyone should feel empowered to talk to customers. Sorry for any research faux pas here :)

I came up with a couple exercises I wanted to get customer feedback on. Mocked it up in a Figma file, shared a link to the file with the customer, and asked them to do the exercises live. 

Exercise #1) I wrote down all the common things related to collaboration I've heard customers say about Figma, like being able to have all their design files in one place on the Web or be able to co-edit a file together. Then I asked them to stack rank based on what's most important to their workflow. Here's a screenshot of that exercise.

Exercise #2) Then I wanted to test some sample messaging/copy. To do that, I created 3 landing page variations of a "collaboration" webpage and asked them to talk out loud as they read through the pages, express what things meant to them and talk about how the messaging made them feel. Here's a screenshot of that. 

What I learned was while we often think about "collaboration" in Figma as co-editing in a file, the async parts of collaboration are perhaps more important. It's like working in Google Docs. It's nice to be able to write copy together, but how often do you actually do that? What makes something like Google Docs also great is that it's one doc that's always up-to-date, that auto-saves, that has built-in versioning, that you can add comments, leave feedback and have conversations. 

I love this question because I’ve seen many product marketers create positioning and messaging solely based on gut instinct. To truly answer this question though, I think you need to back-up before positioning and messaging is even written. Before writing anything you should ask yourself what data you have to inform your unique POV — such as analyst reports, research your company has created, or third-party studies. This data should inform how you create positioning and messaging. Once you have written, then my recommendation is to test various statements on high-traffic pages like a product page or in-app resource. We try to do this as much as possible within HubSpot to inform the positioning and messaging we bring to the market, all backed by the data we begin the process with.

Data insights is a critical component of what I like to call “Evidence-based Marketing”. I like to use 3 types of data insights: User Research, Market Research, and Customer Proof-Points.
I use User Research data to help guide the user/customer journey map. This data is critical to help you map out all possible touch points that your message will hit.

I then use market data (industry stats, market reports or surveys) to quantify and magnify the problem that your users or customers are experiencing. This data can either show evidence of a problem that needs fixing or of a trend that your customers need to be made aware of. Data will add credibility to your statements. For example, we can all agree that a statement like “Business travelers spend a lot of time at airports” is less meaningful and memorable than “Business travelers spend an average of 360 hours, the equivalent of 15 days per year at airports”.

Finally, I like using customer success stories or proof points to support your statements around product benefits or solutions. For example, after partnering with Uber for Business, dealerships have seen an average increase of 10 points in customer satisfaction and 20% increase in revenue.

1/ always start with what you have. This can include product usage data, user data, customer support calls, sales calls, etc. literally anyone that is customer-facing can provide something of value.

2/ i use research to really undestand that primary motivators and constraints for my audience. the feature or product should fit in that specific context --> your product should solve for the motivation + constraint. qualitative feedback is particularly helpful here. 

Many people thinking about starting a business turn to Square. Their motivation is something like "I think my idea could be a real business; I want to make it happen, despite what people may say!". The constraint is "I don't know where to start and this is intimidating." So we position our product as easy to use and educational, for this particular audience. Does that make sense? 

I usually use qualitative insights in two ways: 1) uncover the “so-what” behind good messaging 2) test target response to messaging.

Qualitative studies are a great quick way to understand customer or prospect behavior, needs, and pain points, which help you identify meaningful insights that will make customers pay attention to your message. However, since qualitative studies are not statistically significant, they should be reinforced with quant data or insights from your CS and sales teams.

Qualitative studies are also a great quick way to gauge directional customer response to messaging. I described my approach in a different answer, but to sum up, I use UserTesting to evaluate if the target understands the message and how they feel about it e.g. confused, motivated, inspired, indifferent, etc. For example, in one of my previous roles we tested messaging full of marketing stats and got feedback that the message felt like a math equation. This was a clear call to simplify.

As product marketing is part of the product organization, we look at data analysis and north star metrics to help in our decision-making process. Customer research and market analysis help to identify the unique pain points to develop the differentiated value proposition. 

CRM data analysis, buyer persona research (interviews with customers and external) and competitive intelligence (with sales, customers, partners and win/loss) help articulate the right value at the right time for the right segment.