Product Analytics helps product managers make the best decisions for their users, customers, and company. Effective product analysis allows product teams to build great products and accelerate business growth. Studies have shown that businesses who actively use analytics to aid their product development efforts experience higher growth than counterparts who don’t.
If you can’t measure it, you can’t improve it.
Peter Drucker
Product Analytics tells a product manager how users interact with your product. In fact, besides qualitative user research, there is nothing more user-centric than product analytics. A great product incorporates both to deliver the best experience for users and the business.
Implementing Product Analytics
It’s one thing to know about analytics, it’s another to actually implement it. It’s tempting to want to use only the default data from an analytics tool and call it a day. Unfortunately, taking this approach will come back to haunt you. As a product manager, you need to make sure your data is good and meaningful. Insights from your product are only as good as the data inputs. As the saying goes – garbage in, garbage out.
Making the most of your analytics begins with effective data management and PMs are responsible for that. It is good practice to capture proper metrics prior to development start. You can note relevant metrics to be captured as part of your user stories or you can create a separate ticket with all the data that needs to be instrumented. How you do this is less important, work with your engineering teams to figure out the best path to implementation.
Here are some recommended steps to take to ensure you’re instrumenting the right data and ultimately, achieving desired outcomes:
- Define Product Goals
- Set and Align on Key Performance Indicators (KPIs)
- Classify the appropriate data to track
- Pipe data into your product tool
- Observe end-to-end user journey and gather insights
Define Product Goals
In this post, I wrote about the importance of referencing company and product strategy for product prioritization. This is also applicable to product analytics. Product strategy evolves from your company’s mission and strategy. Your product strategy determines your product goals and roadmap. Your product goals should always aim to solve a problem or improve the experience for your target user. Having clarity on the goal makes it easier to set and measure the appropriate metrics that will deliver the most value.
Set and Align on Key Performance Indicators (KPIs)
Once you’re set on the goals for your product, the next step is to align on the Key Performance Indicators (KPIs). KPIs are performance metrics that evaluate the success or outcome of your feature or product. KPIs give you a sense of how close or far you are from meeting your product goals.
For instance, you could define a KPI as Number of Unique Blog Visitors per month. A target could be to increase that KPI by 30%. To achieve that target and see where adjustments can be made, I’d evaluate both the numerator and denominator in this equation – Total Number of New Visitors /Total Number of Visitors.
Be sure your team is in alignment with the KPIs before setting them in stone.
Classify the appropriate data to track
Once KPIs are set, data classification beings. It is important to categorize your data into events and attributes. Events are unique interactions or actions that users take within your product. Attributes (or properties) provide more details about the events, users, or both.
Once you get to this stage, it’s easy to lose focus and get overwhelmed with more data than is necessary. You should always aim to capture only the events and attributes that move the needle on your KPIs and product goals. Vanity metrics serve no one and waste valuable time.
As I mentioned earlier, you should classify the relevant data prior to development efforts. For a big project, I’ll capture everything in a spreadsheet and collaborate with the engineers on an instrumentation plan. For a simpler feature, I’ll make the request in the same user story ticket.
Below is an example of a data classification that I put together for an onboarding signup flow. To prevent duplicates, the event and property texts can be changed by the team as necessary.
Note: Exercise caution with the personal data you collect. In many cases, you don’t need to know and store sensitive user information. You don’t need their personal information to improve their experiences with your product.
Pipe data into your chosen product analytics tool
Once you’ve collected the appropriate data, you’ll need a tool for tracking and measurement purposes. It’s becoming the norm for companies to rely on third-party tools as opposed to an in-house analytics tool. However, there are situations where our data requires more customized analyses resulting in the development of an in-house solution.
There are a myriad of competent analytics tools in the market today, each with their key strengths. They compete on price, number of events, reporting, data modeling capabilities amongst others.
Some popular analytics tools include:
I often use 2 tools at a time – one for website traffic and quick highlights, another for funnel analyses. If you’re using more than 3, it might be time to address some technical debt and consolidate appropriately.
Observe end-to-end user journey and gather insights
With good data and the right tools, the fun begins! At this stage, you’re ready to begin analyzing your user’s interactions with your product. The insights gathered will lead you to track progress towards KPIs, segment users into cohorts, make product improvements, do some A/B tests and experimentations etc.
Whatever insights and decisions you make, be sure that they improve your KPIs, user or customer experiences, and drive business outcomes.
Key Takeaway
Product Analytics are critical for any digital experience and are necessary for product managers to be successful on the job. However, PMs should treat analytics as part of the overall inputs that go into improving your product’s experience for your users. It’s so easy to over-index on being so analytical that you can sometimes miss game-changing insights. Equally value both quantitative and qualitative insights if you want to build truly great products.
Remember, analytics tell you how your product is being used and not why it’s being used in a particular manner. Figuring out the why is your responsibility as a product manager 😃
Happy Analysis!!!
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