“The fact is, an outstanding customer experience has always been key to success. Once sales owned that experience. You talked to a salesperson if you wanted to buy a new product. If you were lucky, they were knowledgeable and empathetic and helped you buy the best product for your needs. With the internet came a growing ability for early digital marketers to measure results and to own growth metrics like engagement and acquisition.
But people don’t want to interact with salespeople or marketing campaigns anymore—not at the expense of actually getting to experience the product they’re buying. To keep up with the market and get ahead of the curve, businesses must reshape their marketing, sales, and service strategies and fundamentally rethink the roles of their customer-facing teams.
Sales-led and marketing-led growth had their time. The future is product-led growth.”
How companies go to market has evolved, fundamentally changing the product landscape.
It’s a tense and exciting time for software. Many SaaS business leaders are still talking about KPIs, MQLs, and ROI and touting the power of a cloud delivery model, while their employees are quietly getting more done by directly downloading software like Dropbox for file sharing, Jira or ProductBoard for the roadmap and sprint planning, and Notion for internal documentation This try-before-you-buy model that delivers value to users before they even open their wallets is driving a fundamental power shift in the software industry–both on the buyer and business sides.
The lines of where growth comes from are blurring, and Product and Engineering organizations are demonstrating more influence on revenue than ever before. It used to be that Sales was responsible for business growth. In the on-premise era of the 1980s and 90s, salespeople closed deals with CIOs over a round of golf and filet mignon. In the 2000s, the cloud era ushered in marketing-led growth as inside sales teams and product marketers performed their demos for executives and team managers. Beginning around 2010, the B2B market started catching onto a B2C trend with a go-to-market scheme that has increasingly put power where you’d least expect it: with end users. The era of product-led growth inverts the historical paradigm as everyday workers discover and champion software solutions that have already proven useful to them.
OpenView, who coined Product-Led Growth, explains how we got here. “Infrastructure is far more elastic and scalable than ever before. And APIs and modular tools mean developers no longer need to hard-code every program from scratch. The efficiency gains passed along to customers mean that trying out a new product is cheaper than ever (usually free). Thousands of shiny new products are just a few clicks or taps away. The affordability and accessibility of software have fully democratized purchasing down to the end user.”
What is product-led growth, and how is it changing the product stack?
According to Pendo, “Product-led growth describes a business strategy that places a company’s software at the center of the buying journey—and often at the center of the broader customer experience. A product-led growth strategy counts on the product itself—its features, performance, and virality—to do much of the selling.”
In other words, PLG gives users a free taste of the product journey before they become paying customers. The business sees product engagement before revenue. And the benefit? “Companies with a product-led growth strategy can grow faster and more efficiently by leveraging their products to create a pipeline of active users who are then converted into paying customers,” says OpenView.
It’s worth noting, as OpenView points out in their guide to PLG: “Although it’s rightfully associated with viral, freemium, bottom-up distribution, product-led growth is more than a simple go-to-market formula…The consumerization of software means that end users now demand better experiences from the tools they use.”
How we build and manage products hasn’t yet caught up with this rapid shift.
As the try-before-you-buy heyday settles in, companies have rightfully realized that the next big challenge is understanding and delivering what customers actually want. For that, they need data. Enter the explosion of analytics tools delivering heaps of product data at your feet…but there’s one critical problem: There’s no good way to make sense of all the numbers.
The result is that product teams still don't know how their efforts drive business outcomes despite the number of hours put into instrumenting analytics platforms and pouring over user-behavior data to form, validate, and prioritize their experiments. Product Ops teams (if the organization is large enough to have one!) are manually piecing together information from user interviews and product analytics tools that lack insight into contextual business impact, and adding expensive, specialized analyst headcount—still to come up short on knowing where and why to focus their efforts and understand the payoff.
Product-led growth is spurring an evolution in how we think about and use product data.
Fully embracing the product-led growth model will require product teams and owners to cut through the noise and use data more efficiently and strategically. Simply put, product teams must turn their data into actionable insight. To build what matters, they must connect product data to business outcomes like retention, revenue, lifetime value, and engagement.
The product-led growth era is an exciting time for engineering and product people. Because the software experience is now the first thing users touch, products no longer support brands but are becoming the brand. That means understanding the connection between user behavior and business growth is mission-critical. Those who can quickly modernize their product stack will have a distinct advantage in the coming years.
The Product Stack: From Product Analytics to Product Intelligence, to Product Science
Every product leader knows the product stack is not complete without analytics. The term itself has become an industry buzzword, and hundreds of products and services have been developed to address various angles of the discipline. As a result, a clear maturity curve for analytics has surfaced: from product analytics to product intelligence to product science.
Product Stack Level 1: Product Analytics
Product analytics refers to the tools and processes that aggregate and report product data. Atlassian explains, “To get a quantitative understanding of what users do with your product, the first step is instrumenting it with product analytics. The idea is to fire an event for every user's action in your product to get an aggregated view of how many users use a feature and how often they use it.” But ultimately, this is an exercise in counting. Analytics help you turn raw data into information but can’t go further to help product leaders understand the significance of the information at hand.
In other words, product analytics are dashboard and data-heavy but fall far short when it comes to what Fuzy calls “last mile analysis,” which means you can't take informed action that you know is aligned with company and product strategy.
Product Stack Level 2: Product Intelligence
To deliver on the promise of product analytics, most engineering leaders turn to data scientists, who pull insights from product analytics' information. These professionals develop hypotheses and experiments to understand product performance better; their work is often called product intelligence.
Product intelligence is great at identifying cursory anomalies in user behavior and surfacing outcomes contained fully within the product (such as user retention, page-to-page conversion, and weekly active users). However, there are significant limitations. Product intelligence requires teams to cultivate the context for these insights manually, usually painstakingly connecting product analytics with Salesforce data. Entire departments are built around the problem of wrangling and interpreting vast amounts of disconnected data in dozens of reporting tools and dashboards. Ultimately, you hope you can poke at the data long enough to stumble upon the ‘Aha!,’ or inspiration strikes with just the right hypothesis––but this runs the risks of searching for needles in haystacks or else cherry-picking data.
More challenging still, product intelligence often cannot connect the dots to core business outcomes, as these lie outside the bounds of what product analytics tell us. That means it’s nearly impossible to tie product outcomes to business strategy––which is a huge missing piece of the puzzle.
The inability to connect product data (in the form of user actions) and outcomes data (in the form of revenue, customer/account, and survey data) may explain why 21% of products fail to meet customers’ needs.
Product Stack Level 3: Product Science
Product science is the last mile of impact analysis. It fully connects product data to business outcomes (not just product outcomes), including revenue, lifetime customer value, expansion and upgrades, retention, and churn.
Product science provides a deep understanding of how users interact with apps and how those patterns result in outcomes across the user base. As a result, it allows product leaders to make better decisions with more context (and without all the extra work). Research by the 280 Group indicates that “A fully optimized product manager could increase company profits by 34.2%.” And product science is how to optimize the product manager fully.
Stay tuned to the Fuzy blog to learn how to bring product science to your team––without any changes to your current product stack.