What Is Product Analytics?
What Is Product Analytics?
What Is Product Analytics?
What Exactly Is Product Analytics? A Day in the Life of a Product Analyst What Exactly Is Product Analytics? If you search for the definition online and ask ChatGPT, you will probably encounter a set of quite different definitions! Somehow, in the data space, the debate about semantics of different job titles and domains never ends.
In the context of this course, we will define product analytics as using behavioral data to enhance cross-functional decision-making around product strategy, roadmap prioritization, and user experience, and to connect the dots between the product changes and business outcomes.
It is a pretty broad definition, but there are a few important elements to it:
Focus on supporting/enhancing decision-making, not on reporting metrics or producing ✨insights✨. Ultimately, to be able to drive lots of impact as a product analyst, it is important to operationalize your work in a way that is embedded in the product development cycle’s key decision points, ensuring that your cross-functional partners can confidently reason about the data and metrics themselves, and understand where decisions can be made without data support.
Cross-functional focus: While a product analyst’s main partner is usually a product manager, you will likely find yourself working very closely with product designers (to help them learn more about user-behavior patterns before they suggest product changes) and engineers (to refine data collection, investigate bugs, and review product performance and quality metrics), as well as other stakeholders.
Connecting the dots between product and business: It is important not only to understand users’ problems and needs, but also to trace the impact of solving these problems all the way to the business impact. This ensures the product team is contributing to the company’s bottom line in a way that can foster ongoing investment in product development.
It might be helpful here also to touch on the definition of a product. In the context of business and marketing, a product is an offering that fulfills a specific need or desire of a target market. A product can be physical — a cake is a product, so is a pair of shoes — but in this course we will be covering mostly software products like websites and apps, although some hardware products with a software component (like a connected fitness device or even a smartphone) could be good examples for our course, too. Sadly, a purely physical product isn’t able to easily generate enough data for us to perform analytics at scale!
AH, THE SEMANTICS! If you are looking for product analytics job opportunities, you will quickly find that not all product analytics job postings are created equal! In some companies, product analysts or product management analysts are more like junior product managers or product owners. In other companies, there is no ‘product analyst’ title, but there might be a ‘product data scientist’ or a ‘data scientist, product’ title.
Make sure to expand your search to adjacent titles (‘data scientist’, ‘data analyst’, even ‘growth analyst’), and read the scope in the job description. If you know that a company has a squad-based operating model within the product team, it is more likely that someone with a ‘data scientist’ or a ‘data analyst’ title reporting within the product or tech org would have product analytics as a part of their scope.
A Day in the Life of a Product Analyst
Product analytics is a fairly full-stack discipline, and the day-to-day can vary significantly depending on what stage the product development is in. Are we in the discovery mode, where we are trying to figure out what to build next, or are we about to launch a Very Important Experiment?
The key types of work that this role entails are (sorted from more tactical to more strategic 🔽):
Supporting the product team on product roadmap items: defining success metrics and data-collection requirements for new launches, supporting decision-making with behavioral insights during design and development, reporting on the launch, and synthesizing learnings and recommendations
Helping the team leverage data for prioritization decisions: projecting business impact and reach, estimating confidence as inputs for stack-ranking roadmap candidates
Defining and monitoring actionable product metrics to identify areas of opportunity
Deep-dive research to validate metrics definitions, hypotheses about the connection between specific user-behavior patterns and product outcomes, and impact opportunity areas
Supporting the team on the roadmap items is just one of the four bullet points above, but it in and of itself includes a variety of analytics tasks — research and analysis, metrics design, dashboarding, storytelling and influence — and calls for the analyst to be fully embedded with the product development process.
It is not uncommon for product analysts to also contribute to building internal or user-facing data products, such as by prototyping a recommendation algorithm or creating a predictive model for identifying candidates for a re-engagement email campaign (although this type of work is outside the scope of this course).