A view on Analytics for early-stage startups Part I of II

The P.I.A.O. Framework: How to get started with Product Analytics

A startup’s product is the result of months and years of blood, sweat, and tears. The ability of a startup to find product-market fit is also a vital indicator of the startup’s future.

So, let’s say you are a founder that has developed their product to the point where it’s out in the market, users are trying it out, and you and the founding team are starting to see signs of traction. But how do you, as a founder, really know how your product is doing and whether users are using it as intended, which can have vital consequences for a startup’s business model?

In this deep dive, split into two parts, I break down how to use analytics to get the most out of your product as a founder, including the tools you use, picking the right metrics, and understanding where you are on the analytics maturity curve as much as the venture maturity curve.

I will be using the P.I.A.O. Framework, which stands for Plan, Implement, Analyse, and Optimise. I created this framework after working with over 15+ startups on implementing analytics and helping them get value from their data.

Part I of this deep dive will focus on Planning and Implementation, while Part II will cover Analyse and Optimise.

Let’s begin with Planning.

Planning

When I think of planning in the context of product analytics, there are a few key points I’d consider:

• Why is analytics important?
• Understanding where you are in your analytics journey.
• Business metrics and core metrics.
• Product metrics that align with business metrics.
• The core value (NSM) and time frequency.
• Choosing the right analytics tool for you.

Why is analytics important?

“For startups, product analytics isn’t just a “nice to have” tool in your stack of tech solutions that are supposed to help you grow. In my experience, it can sharpen your whole approach to product development and engineering.” — Tim Flack

What is product analytics?

“Product analytics allow companies to fully understand how users engage with what they build. It is especially useful for technology products where teams can track users’ digital footprints to see what they like or dislike and what leads them to engage, return, or churn.” — Mixpanel

Product analytics (PA) shows how users are interacting with the product. It’s more comprehensive and behaviourally inclined, while marketing analytics (MA) is more attribution-focused.

Why is product analytics important?

Building products that users love is one of the core fundamentals of achieving product-market fit and scaling your startup. You can’t know if users love your product without seeing the data or knowing if they are using your product.

Product analytics shows startups vital data that aligns with their business metrics and shows their customers’ behaviours—specifically, what users do instead of what they say they do. Understanding your customers and their needs through data is essential to building effective and beneficial products because it does two things.

It provides you with:

• A better understanding of the business
• Information to make better decisions

“When you’re a resource-strapped startup, using data to back up your product and operations decisions can be a lifesaver. Your decisions not only become easier to make, but they also have a better chance of buying you the extra runway to make plenty more.” — Tim Flack

Understanding where you are in your analytics journey

At Founders Factory Africa, we speak to our portfolio companies about the four different stages of the product analytics maturity curve a startup can find itself in.

• Stage 1: Measuring traffic coming to your website/product: The focus is on simple traffic data.
• Stage 2: Identifying acquisition channels and going deeper to know which channels generate conversions.
• Stage 3: Measuring engagement and conversion: A deeper analysis ranging from page view to user behaviour, so you learn about the specific actions users are taking on your product.
• Stage 4: Measuring the full customer journey: This is tracking all events alongside your users’ journey from awareness to conversion.

Based on our experiences within the African tech ecosystem, most startups on the continent are in Stage 1 or 2.

Business metrics/core metrics

Ultimately, all activities are geared towards improving core business metrics. In setting up your analytics dashboard, it’s important to understand that all events and sub-KPIs are functions of the overall business goal.

This is essential, as it gives you a game plan for building your data strategy. By knowing what decisions you want data to inform before you invest in data analytics, you can create a data strategy that fits your goals rather than the other way around.

Before diving into implementation, first outline the business goals and metrics that show traction for your product and identify sub-metrics that will act as leading indicators of the core business goals.

Mixpanel called this the Focus metrics. A focus metric is one or more primary metrics that show traction towards product-market fit or scale.

“Focus metrics should be the top priority, not the sole priority, and improving the focus metric should not be accomplished at the expense of harming other KPIs.” — Mixpanel.

Product metrics that align with business metrics

“Founder’s goals tend to take shape less as goals and more as grand visions for world domination. But the smartest among us knows that any plan to conquer the world must follow a plan to conquer the town, which is preceded by a plan to conquer the neighbourhood.” — Tim Flack

After highlighting your core business metrics—focus metrics — you need to highlight product metrics that align with your focus choices. For those core metrics, ask, “What are the contributing KPIs that will impact the focus metrics??

For example, if a product’s focus metric is weekly active users (WAU), a good product metric would be 7-day retention or activation rate to ensure you aren’t spending precious marketing capital acquiring new users who do not activate or leave after a day or two.

Example of a metric tree for a subscription-based video streaming platform

Aligning the core value (NSM) and time-frequency

When choosing between daily active users (DAU), WAU, monthly active users, and even yearly active users (YAU), a lot depends on what is most important to your business and the product’s sought-after strategic outcomes. While social media platforms such as Facebook are on one end of the spectrum, focusing on DAUs, platforms like Airbnb are typically more interested in bi-annual or yearly usage since that time period better describes how their target customers use their platform.

Understanding the frequency of use of your product can help you analyse effectively if your users are forming a habit around your product (stickiness) or not using the product as envisaged, with traction still in the distance. the frequency you’ve built it for.

You can identify normal user frequency by looking at how often your target market uses solutions similar to your product or by asking your potential and current users how often they have the problem and need a solution. For social media platforms, it can be those brief moments in transit to work, waiting in line for coffee, on their lunch break, etc. Most of these events happen daily, and the need to socialise happens daily.

Analysing your user journey

In the product analytics journey, we highlighted Stage 4, which is understanding the full customer journey and tracking events (in most cases) from sign-up to core value delivery. For example, you do a walk-through of the app and highlight key events that should be captured to paint a picture of how users are interacting and engaging with the product.

We’ve often seen the designed user journey for that “Aha!” moment, not a pragmatic or tested example of how users interact with the platform. Lab-style conditions are very different from those users experience in their daily lives. Hence, as founders, you need to be aware of confirmation bias when sketching out the customer journey and focus on data-driven decision-making. We will spend more time covering this later.

Choosing the right analytics tool to use

Here are some useful questions to ask when deciding on a tool for your product analytics:

• Where are you currently, and what stage do you want to get to in the analytic journey?
• What stage is your product at? Is your product live, or are you in waitlist mode?
• Do you have a comprehensive product, or are you just working with a landing page and a signup page?
• Are most of your services delivered offline or online? If offline, do you store it manually in your backend? Can you connect Metabase to that data source?

Some analytics tools to use

1. Mixpanel: MixPanel is an analytics tool that delivers meaningful customer insights to startups, big enterprises, and SMEs. Mixpanel has simple workflows, straightforward data connections, and valuable data reports that help businesses retain and engage target clients effectively.
2. Amplitude: Amplitude is a digital analytics platform that provides tools for product teams to measure and evaluate their applications. Amplitude helps predict what actions your customers will make, reduces conversion process friction, gives you visibility into your user journey and experience, and helps you build better products with analytics.
3. PostHog: PostHog is an open-source product analytics platform that helps businesses gain insights into how users interact with their websites or applications. It allows you to capture event data, analyse user behaviour, and make data-driven decisions to improve your product.
4. Google Analytics: Google Analytics helps marketers track impressions, measure ROI on their paid campaigns, and figure out their major traffic channels so that they can allocate marketing and growth resources appropriately. Although not a comprehensive product analytics tool, it can be used to get initial marketing insights.

Implement

“Product insights supercharge a product team and strengthen a fundraising narrative, making it easier to achieve product-market fit and increase the value of your business. But hasty implementations and over-instrumentation can negate these benefits and make your dashboards unintelligible.” — Kapwing Co-founder.

Creating the tracking plan

In the Planning stage, we covered identifying your core business metrics and aligning sub-metrics and product metrics to the core business objectives. In the implementation stage, we will learn how to create a plan that enables us to add events correctly via a tracking plan.

A tracking plan is a single source of truth that is used across your organisation to standardise how the venture tracks data.

“The tracking plan allows all stakeholders to collaborate on a single source of truth for analytics definitions. It keeps everyone in sync on what data to track, when, and why, and maintains a consistent schema across engineering, product management, data science, and other consumers of analytics data.” — Amplitude

It should be treated as a mutable document that is continuously updated with any implementation changes or notes that your team can reference.

A tracking plan should contain the following:

• Event name: This is the event to track.
• KPI: Quantifiable business metrics associated with the event being tracked.
• Trigger: To give the developers a description of where the trigger should be entered into the code.
• Properties: Attributes of the event, either describing the event itself or the users performing that event.
• Property type: This is also important for the developers as it describes whether a property is an event, super, or people property (more on this below).
• Developer notes: Any additional developer notes.

Mixpanel tracking plan template.

Source: Mixpanel

Event vs property

We’ve referred to events as the primary actions users take when interacting with the product interface and UX, with these being specific actions users take that lead to your core business metrics. Examples of events to track are sign-ups, playing music, making a purchase, adding to a cart, etc. While it’s important to track user activity on your product, some actions, like scrolling a page, might not be relevant to your core metrics.

“Properties are where most of the magic occurs, and they are critical to getting the most out of your data.” — CXL

“Properties are attributes that provide additional context around your users and the events they trigger”. — Amplitude

There are 3 types of properties to keep in mind: event properties, super properties, and user properties.

• Event property: Event properties are attributes of a particular event. For example, the event “Play music” can have an event property of “Song_title”, “Genre”, etc.
• Super property: Super Properties are a type of event property that you can register once to attach to every event you’re tracking automatically. While the event property “Song_title” might be only relevant to the “Play_song” event, you might want to have a super property of “UserName” as it relates to all events coming in.
• User profile property: User profile properties are attributes you define to describe segments of your user base, such as language preference, email, LTV, or geographic location. It describes the users with properties that can change over time.

Event Property vs User Profile Property

The user profile properties build up a continuing evolving picture for your users. As the user continues to interact with your product, their profile is constantly being updated and reflected in the user profile

To identify relevant events to track, it’s best to work backwards and look at your core business metrics.

• If your company’s goal is to become the biggest content-sharing app, you need to highlight what KPIs and metrics align with the goal, which can be engagement, or how many people are using the app over a certain period of time.
• Metrics: Weekly engaged users, new users onboarded, and activation.
• Events to track can be: what people are watching and what they do once they onboard.

Server-side vs client-side implementation

Two types of implementation can be done. The client-side implementation involves implementing the Mixpanel snippet on the front end using Javascript, and the server-side implementation involves implementing Mixpanel on the back end of your product.

90% of the implementations we’ve seen are done on the client side.

Troubleshooting

Once the implementation is complete, it is important to ensure that the data coming in aligns with your tracking plan and that you are collecting the right information. If you are using Mixpanel for the implementation, you can use the Lexicon section to see all the custom events implemented on your platform. You can also test the data coming in by taking actions and using the live view to confirm the data coming in. You can also use a third-party Chrome extension to track user activity.

That will conclude Part I of this two-part deep dive into the world of product analytics. Next week, we will focus on how to analyse the data and optimise for business growth using the “Analyse” and “Optimise” parts of the P.I.A.O. product analytics framework.

P.S. You can jump right to Part 2 of this framework here, where we talk about how to analyse and optimise your product to get you to Product Market Fit

Kate Victory-Edema is a Growth Marketer at early-stage investor Founders Factory Africa.