Software consultancy ProductLed reports that data-driven organizations grow 30% more annually and have five to six percent higher output and productivity than less data-driven companies. So, it’s not surprising that from startups to enterprises and across B2C and B2B sectors, everyone wants a piece of the data-driven action.
But as ProductLed is quick to point out, being data-driven is only as good as your cultural commitment and the tools you use to turn data into valuable, directional insight. “Data is amassing in product-led growth (PLG) companies worldwide at an incredible rate––which has the potential to set new standards for data-driven innovation. But can company cultures keep up with this newfound potential? Product-led organizations often don’t know what KPIs to track or aren’t integrating their data in a streamlined way. Bottlenecks form, and hastily put together standard operating procedures are implemented.”
In our recent discussion of the product analytics maturity curve, we explored how and where good data can get caught in the product stack. Product Science is the most advanced step in the evolution of product data analysis, with the potential to streamline data from multiple sources to build robust context for making decisions that positively impact core business outcomes.
Chasing the promise of data-driven returns, businesses with deep pockets often build in-house product science teams to deliver actionable insights to boost the bottom line. For growth-stage companies (and anyone else hoping to reach these insights with more time and budget efficiency), the emerging market of Product Science as a Service may be the silver bullet.
What is Product Science as a Service?
Product Science as a Service (PSaaS) platforms combine the power of product analysts, ops, data science, and strategy into a single, automated tool to help product organizations separate signal from noise and scale pattern detection across multiple spheres of data (user behavior, customer account attributes, outcomes, and results, product changes) to create rich context and actionable insights without expensive machine learning and data science resources.
Whereas PMs today are left to recognize and triangulate patterns across these disparate facets of data manually, PSaaS automates pattern detection of high signal behaviors and changes, so product teams can quickly traverse the frontier of understanding, eliminate cycles of experimentation, and confidently place their bets on outcome-focused optimizations backed by quantifiable evidence.
The Need for Product Science as a Service
The growth stage is a critical inflection point in a company’s journey, especially venture-backed startups. It can determine the overall trajectory of the business. Small teams must often navigate a fast-developing, competitive market with limited resources––and often, the technology organization is forced to make blind decisions. Most product teams don’t have good visibility into how the product is driving value for the business, and adding to the frenzy, products themselves are dynamic and constantly changing. Most data analysis tools are designed to present data, not move at the speed of product decision-making.
This means that most companies lack a deep understanding of how users interact with their apps and products and, more importantly, how those patterns result in varied outcomes across the user base and the business itself. When outcomes are hard to measure, teams default to what they can measure: output. As a result, collaboration in the product stack has oriented around this need for operational throughput, resulting in many product orgs becoming susceptible to a “feature factory” culture. Time urgency must be balanced against “getting it right,” leaving PMs to juggle tedious and time-consuming experimentation, user research, cross-departmental input, and the sprint schedule.
These factors are all the more daunting, considering that the price of making a bad product decision can be debilitating. Sometimes the penalty is a failed venture. But the payoff for making the right product decision sooner rather than later can pave the way for sustainable growth and customer happiness.
Growth is no longer driven by sales and marketing alone; product and engineering have more commercial impact potential––and increasingly responsibility––than ever before. More than ever, businesses need the technology organization to help steer the ship. In fact, according to a 2020 Forrester report, 41% of business leaders find it ‘very or extremely challenging’ to turn data into business decisions. These teams need Product Science as a Service to create a reliable shortcut to good product decisions with real impact on business outcomes.
Legacy product analytics tools simply don’t cut it when it comes to helping product teams, and their leaders make optimal decisions that impact the business. This is because prevailing analytics solutions lack important business context and only utilize behavioral data. Missing data and context means product managers only get limited vignettes, leaving them to connect the dots manually. PSaaS takes an integration-first approach to provide greater visibility beyond behavioral data.
This data integration is key now that specialization has made it to the product field. Once upon a time, all functions fell under the product manager. Now there are specialized roles for ops, analysts, product owners, and growth. Multiple people and multiple data streams make it critical to establish a single source of truth. PSaaS is this unifying force that keeps teams aligned and working as one toward a common goal.
The Benefits of Product Science as a Service
Summed up in a single thought, PSaaS supercharges continuous product discovery to scale your best product managers with evidence-based insights, enabling decision-making at the speed and accuracy demanded in a modern, continuous delivery organization.
Product Science as a Service has six potential benefits that can catapult growth-stage companies to sustained success.
Operational efficiency
PSaaS allows you to spend less time context-switching between data sources, mining and querying raw data and dashboards in search of opportunities and testing hypotheses individually. As our VP of Engineering, Triet Le, explains, “Product ops is all about aiding the PM to be more efficient with their day-to-day work; this is exactly what Product Science as a Service does. The first thing a good PM will do is review the metrics reporting (asking what happened and what the effect was). Without PSaaS, you must talk to many people to get the answer––data scientists, engineers, and more. That costs a lot of time. PSaaS collapses all this by synthesizing data, making it easier and more accessible to get actionable insights.”
“Last mile” analysis
PSaaS doesn’t just distill data from multiple sources for you to interpret unassisted. It uses machine learning to detect patterns and synthesize vast amounts of product and outcome data where ‘aha! moments' hide, then surfaces those to you. Regarding Fuzy’s PSaaS software, “You can discover the most impactful patterns working backward from your desired outcome,” says Triet Le. “It's like having a squad of analysts always watching the data and helping you understand what's important to your priorities.”
Eliminating bias
Without PSaaS, you can’t know what you don’t know. In other words, decisions are often constrained by a chosen hypothesis rather than important unseen signals in the data. You may further introduce bias by unintentionally cherry-picking data that supports a hypothesis or making optimization decisions based on what can be measured rather than what should be measured. PSaaS uncovers invisible patterns without human bias, helping you identify everything that may impact outcomes, especially the unexpected.
Increased scientific integrity
PMs today lean on hypothesis testing to inform their decisions. But this lacks an important dimension to prioritize against: expected outcome impact measurement. This results in weaker prioritization of product decisions overall. PSaaS helps you more closely follow the scientific method to strengthen data-based decisions by automating predictions and observation. Predictions differ from hypotheses in that they’re anchored in some quantitative signal of the expected impact in the front end of the process. This approach reduces the risk of optimizing for velocity and output (using predictions to set feature prioritization and inform business goals). The observations PSaaS provides allow for continuous product discovery, collapsing the feedback loop so you can make better decisions faster.
Passive and incremental learning
With PSaaS, you can automatically monitor and close the loop on impact analysis and reduce repeat analysis, enhancing incremental learning within your organization. “PSaaS makes product knowledge shareable across PMs and the business at large,” says Triet of the passive learning benefit of PSaaS. “Fuzy eliminates the need for many emails and conversations around why decisions were made and what drives certain outcomes. All that knowledge is built into the app. It gives the business a single source of truth.”
Cost efficiency
The technical roles you need to hire to achieve the insights rendered by PSaaS are hard to hire and very expensive. PSaaS eliminates many of these costs, as well as onboarding and ramp-up time.
The standard functionality of Product Science as a Service
As you evaluate any product science tool, capabilities must span the full scope of the PM’s decision-making process, from discovery through validation:
Discovery
PSaaS must help uncover what you don’t know or haven’t considered investigating. Data scientists can spend months looking at data to tell you what you already know, but PSaaS promise that AI reveals hidden patterns and signals that help you build a better picture of what’s working and not working in your product.
Prioritization
PSaaS should give you a quantified way of understanding how product work impacts KPIs and OKRs, providing triage guidance on opportunities and optimizations to build next. Simply put, it reduces the guesswork on which levers to pull to deliver on business goals. This is especially important now as layoffs and rising costs continue to shrink teams and resources. You can no longer build 10 interesting ideas. You have to build only what’s most important to the end user and, ultimately, the business.
Validation
In many organizations, the product team ships, and often they're immediately on to the next thing—but that's actually the beginning of the journey. PSaaS should create an unbiased feedback loop on performance to help you understand the effects of that feature.
Below is a core capability checklist to help you evaluate the robustness of Product Science as a Service solutions:
Continuous Intelligence: automating analysis through machine learning techniques vs. traditional statistics
Predictions and trends
Impact analysis [observations]
Autonomous anomaly detection
ML pattern detection and drift monitoring
Data visualizations
Workflow: automating tasks done by the PM or product ops today
User and account-level analysis
Segment generation
Customer journeys of complex non-linear funnel analysis
Defining and monitoring progress against goals
Memorializing product knowledge (annotations, releases)
Integration With Third-Party Tools
Access to multiple data sources (user interactions, customer and contract, continuous release)
Integration with communication and the product stack (Slack, Jira, etc.)
Natural Language Query and Understanding
Conversational bot no-code AI
Generative data visualizations [prompt based]
Ease of Use
API integrations
Non-technical data onboarding (no instrumentation)
Auto in-app event classification
Identity resolution layer
Example Use Cases for B2B/2C Companies
Product ops automation / Virtual product analyst
Context-independent Product analytics
Decision science enablement
Root cause analysis
Continuous product discovery
End-to-end visibility to the product value stream (from discovery to prioritization to impact)
Exploratory Use Support
Generative Use Support
Self-service Use Support
Now that you understand Product Science as a Service, you may wonder how it might serve your company’s product and business goals. Fuzy can help you evaluate whether implementing PSaaS or making a product analyst hire would best meet your needs, and we’ll show you how PSaaS seamlessly works with your existing tech stack to supercharge your product analytics suite.
How does Fuzy’s Product Science as a Service platform work?
Fuzy’s platform accelerates product decision-making using mathematical rigor to connect in-app user behavior to business outcomes such as engagement, revenue, and retention without a lot of the overhead of headcount or manual instrumentation and works with your existing product analytics data.
