The Definitive Guide to B2B Data Analytics for PLG

Product Analytics

Early-stage startups must prioritize efficient data management to ensure long-term success. Consider switching from an SDK to a data ingestion solution at that stage, like Segment. This will give you greater flexibility when working with event data and allow you to stream it to other applications easily. Another benefit of using Segment is its flexibility. As startups grow and evolve, they may need to add or change new event-logging tools. With Segment, this process is simple thanks to its extensive library of pre-built integrations with hundreds of popular tools. Also, I suggest considering Jitsuand Rudderstack as an alternative to Segment. They are both open-source platforms that offer more affordable pricing options.

These tools have the advantage of being suitable for both early-stage companies and growth enterprises. They offer a range of functionality that can be scaled according to your analytical needs. These tools can be transformed into a comprehensive CDP or data ingestion layer, but I will share more below.

Although I assume your team is still small and may need a dedicated data analyst, assigning someone responsible for tracking plans is essential. This person should ensure that the data collected can answer critical business questions, avoid unnecessary rework, and empower teams to use the data effectively with a shared understanding and language. This best practice can increase trust in your product data and make it more useful across the organization.

Also, at that stage, consider using collaborative event management systems, such as Avo, to reduce the overload of data quality. For data to be trustworthy, it needs to be accurate, reliable, up-to-date, consistent, continuous, complete and attributed to (and representative of) the cohort, population, or problem being studied. The collaborative schema will make a big difference in the consistency and consumability of the data, which ultimately provides greater trust.

It's difficult, costly, and often impossible to change event-based data retrospectively - and that assumes you know there's a specific issue in the first place. Planning the data you want to track well is essential to avoid duplicating dev effort. These platforms provide a centralized platform for managing your event schema, saving time, and ensuring consistency across your various analytics tools. You can define your event schema once and automatically generate code snippets to instrument your app or website with tracking events. This makes adding new events faster and less error-prone, as you don't have to manually update each analytics tool every time you add a new event. It's essential to ensure that even when the product undergoes a significant redesign, the event tracking can leverage the original event names and properties logic.

Workflows

Disregarding what tools you choose, automation is your friend. Well-defined workflow automation improves communication among internal teams.

Maintaining good business operability and proper communication among the internal teams is vital. We temporarily solved the connection of all our back-office tools with workflow automation tools like Zapier, Integromat, and n8n to build the trigger-based data flows.

For reference sake, this is what our automation "create a new lead in close" looked like during this stage

I would not suggest staying long with the no-code workflow tools. With the increasing complexity and lack of engineering practices, you risk sticking with a spaghetti code that makes it hard to maintain. 🍝

Frame your metrics wisely

‎At this stage, all our metrics were split into three portions:

• Product metrics answer questions about the product itself. They are used to understand how the changes made have affected user behavior. I recommend investing time in researching your product’s activation metric. Make sure you ask customer-facing employees what they think activation should be. Support reps, sales reps, and others spend all day talking to your users and trying to get them to find some value, so their insights are helpful for this early list.
• Growth metrics answer questions about the business built around the product. To succeed in the competitive market of SaaS products, the key is to master the art of monetization. The ability to effectively generate revenue from your product sets apart the winners. It's not just about having a great product but also about finding the right pricing strategy that aligns with your target audience and their willingness to pay. Therefore, it's crucial to understand how the product acquires, activates, monetizes, and retains the account. The one who nails the monetization in SaaS products wins the market.
• Functional metrics help your teammates understand how well their team performs. Those metrics answer the question about sales pipeline coverage, the number of qualified leads, or the NPS from the customer care team.

Note: Usage creates revenue, but revenue does not create usage. As a result, the most critical metrics in creating growth are not your revenue metrics, so make sure all KPIs and everything needed structurally are covered by tracking. In this, I’d be a bit more holistic and proactive. Even if there are no people to look at it, at least cover the primary customer journey, funnels, etc.

At this stage, I would advise against investing in a data warehouse. Doing so would divert attention away from the product and require additional capacity for maintenance. Hiring a good analyst for an early-stage product is notoriously difficult unless you build an analytical tool.