An effective analytics strategy can enhance efficiency and drive informed decision-making in every part of your company. Product, marketing, sales teams and beyond can check their gut instincts with the right approach to analytics. But implementing an effective analytics strategy can feel overwhelming for three main reasons: 1) companies don’t trust their data, 2) companies don’t have the right data to answer core business questions and 3) their data isn’t accessible to all their employees (not to mention analysts!)
This is where a well-defined analytics strategy comes into play. It helps you define what you should be measuring and how to measure it. Companies with a well-defined analytics strategy:
- Minimize decision-making bias, which leads to better decisions and faster growth
- Discover untapped growth potential by understanding how users interact with their business
- Correct inefficiencies and drive cost savings
- Detect emerging trends earlier
This article will show you how to define and implement an analytics strategy based on our work with over 850 companies.
Define Your Core Metrics
An effective analytics strategy will allow you to track and improve the exchange of value between your company and everyone who interacts with your data. For instance, when a Calendly user subscribes to their service, they receive the ability to schedule meetings with greater efficiency and grow their business more quickly. But there’s one additional factor that defines every meaningful analytics strategy.
It’s crucial to connect the subject of your analytics plan to real business goals. Data is only valuable when it helps you take meaningful action. Without a clear analytics strategy, you might wind up drowning in a sea of data without knowing how to use it.
This is why the first step of all Mammoth Growth projects is to understand the key business question you need to answer.
We always advocate that companies set a single goal, also called a North Star metric. That’s because setting too many goals can slow your progress just as much as failing to clarify your North Star metric in the first place. If your team members focus on multiple metrics, they can easily duplicate each others’ efforts or work at cross-purposes.
So how do you define your North Star metric?
This metric should focus on actions that deliver value for users (rather than vanity metrics), center on user value (rather than business value), and track leading indicators of success (rather than lagging indicators).
Once you define your North Star metric, you can build out the key drivers of this metric that each of your teams can improve. The North Star metric provides a clear vision for how to measure long-term success, while the drivers represent specific, short-term actions that drive long-term growth. Depending on the complexity of your business, you could go one level deeper to uncover the second-level drivers (i.e. the drivers of the drivers). Where you end up is a ‘Metrics Tree’ similar to the outline from Mixpanel below:
“If we improve this number will the product’s long-term performance improve?”
Let’s break this down, one level at a time:
- Your North Star metric should represent your mission and be understandable, simple, & inspirational
- Key drivers of the North Star metric should be clear, representative, predictive, sensitive, and include associated guardrails
- Guardrails are key to any metric rollout, so quality and safety don’t get compromised at the expense of metric growth
Building A Tracking Plan For Analytics
If core metrics define what you want to measure, the tracking plan helps understand how you are going to do it. It lays out the specific bits of data you need to track your core metrics and ultimately answer important business questions.
The key conceptual model you need to design consists of three areas: Events, Users, and Properties.
An event is a data point that represents an interaction between a user and your product. This could be anything from a user clicking a button, submitting a form, or even just landing on a page. Each event is a piece of the puzzle that, when put together, gives you a complete picture of how users interact with your product.
To make the most of events, it's important to define them clearly and consistently. For example, you might define an event as ‘User Created' or 'Subscription Started'. Using active verbs in past tense (Object + Past Tense Action) can help make your events more understandable and actionable.
It’s also important to consider ‘event depth’ - a single ‘Interaction’ event for all actions on your site will be too broad for analysts to understand. Going the other direction, creating an event for ‘Red Back Button Clicked’ is too specific and will lead to creation of an unnecessary amount of events. The right depth groups events by actions e.g. Form Submitted, with properties for specific forms.
The User represents the person that completed the action or event. This could be a logged-in user or an anonymous visitor. By tracking users, you can understand who your users are, how they behave, and what their preferences are. This can help you tailor your product to meet their needs and goals, ultimately driving user satisfaction and growth.
Properties add additional detail about both the User and the Event being performed. For example, for a 'User Created' event, properties might include whether they created an account with a single-sign-on provider, and if so, which one. For a user, properties might include demographic information like age or location, or behavioral information like their browsing or purchase histories. The key difference between the two is that user properties can change over time as the user’s behavior changes, but event properties represent a snapshot of a specific action at a specific point in time.
To make the most of properties, it's important to choose details that are relevant and useful. Avoid property names that are too generic (e.g. error, source), and ensure property names are consistent across events if they capture the same data. It's also best to start with a high-level event, such as 'Song Played', and then drill into this with contextual properties, such as 'Song Title', 'Artist', etc.
What’s Next for Your Analytics Strategy?
In conclusion, a well-defined analytics strategy is essential in today's data-driven world. It can guide your decision-making, uncover growth potential, and help you identify emerging trends. By defining your North Star metric, identifying key drivers of success, and building a tracking plan that focuses on user value, you can make informed decisions that align with your company’s goals. This analytics strategy is flexible enough to evolve with your business while enabling you to navigate all of your data with confidence.