“Product-Led Growth” is the new buzzword on the block. I will confess, I spent a full year coming across it in podcasts and twitter threads before I bothered to Google it. In case you haven’t bothered yet, I’ll save you a search — according to the Product-Led Growth Collective, it’s when:
“user acquisition, expansion, conversion, and retention are all driven primarily by the product itself. It creates company-wide alignment across teams — from engineering to sales and marketing — around the product as the largest source of sustainable, scalable business growth.”
PLG requires concentrated focus on the end users of a product (that’s you and me!). It means that software companies are now differentiating on the basis of their products, rather than based on sales relationships or marketing speak alone. Think Zoom in the Cisco Webex / Skype era.
Software, particularly B2B software, doesn’t always sell because of how well it solves the end users’ problems.
Let’s take expense management software as an example. If you’ve worked at a company with more than 50 employees, you’ve probably worked with some really bad expense management software. I once worked with software that was so frustrating that I almost paid for my expenses out of pocket, just to avoid it (almost).
So why did my company buy such terrible software? Because the person signing the cheque (a finance executive with an assistant to do his or her expenses) was not the same person as the end user (you, me, and said assistant).
Today we live in a world brimming with technology in our personal lives. I can call a cab with 3 taps on my phone and have a pool table delivered to my house in 2. The expectations of the end users have gone up, and executives have started listening to them. Add to this falling prices of software which enable us to make purchase decisions without c-suite approvals, and you have a recipe for change.
Suddenly, end user needs and experience matter a lot more for B2B software — it’s more important than ever to understand what customers and potential customers are doing, and why.
Understanding what your customers are doing is a solved problem — the Amplitudes and MixPanels of the world frequently charge companies 6-figures to know what their customers are doing on every product page, at every panel.
What still remains a comparative mystery is why. Clicks and events cannot reveal a customer’s motivation or the frustration that made them switch away from your product.
Today this question is answered by a patchwork of solutions. PMs speak to a few customers, sales teams ask for new features, customer success asks for major bugs to be fixed, and the rapidly growing function of User Research steps in to answer important questions.
You can’t — without understanding your customers’ motivations and needs, you’re making crucial product decisions by taking a shot in the dark, which can lead to expensive mistakes.
It’s no wonder that one of the biggest concerns Product Managers spoke to us about was deciding what and how to prioritize. With millions of dollars of development at stake and the probability of success of decisions unknown, it’s easy to understand why PMs struggle with these questions everyday. Some have even had their fingers burnt.
There are ways to improve the probability of success of product decisions and the earlier you invest in them in the product development cycle, the more cost effective it is.
One approach that Product teams are relying on more and more is to bring User Research in before design and development for large product changes. One e-commerce company that overhauled their checkout flow based on User Research insights saw a $300 million increase in annual revenue.
Suffice it to say the impact is real, but User Research is not without its detractors. Concerns about timelines often push product teams to skip this step and resort to guesswork based on customer tickets or existing assumptions of customers’ needs.
That’s why we’ve built Looppanel — to bring artificial intelligence algorithms to bear on a research analysis process that requires people to manually extract and structure important information from hundreds of pages of transcripts or hours of video recordings. The process is tedious and time-consuming — often taking 30–50% of a researcher’s time on a project (a week or more for projects that typically span 2–3 weeks).
Imagine if an algorithm could help you find all the times a person complained about your software? Or all the questions that were asked in a usability test (usually indicators of poor UI design)? How about every time your competitor’s name came up and what the sentiment was? That’s pretty powerful data, especially when you don’t have to have a person manually create it.
Interested in learning more? Sign up for Early Access to Looppanel