Build vs Buy Product Analytics: The Guide to Follow in 2024

Data has finally found its place at the centre of business decisions. It was long due! Today, regardless of your industry — whether you are in e-commerce, FinTech, or EdTech — you need to be deeply aware of your users’ needs and behavior to build a successful business.

This well-found reliance on data also extends to the world of product development, often getting termed the “product analytics” function. 

If your product team seeks to answer nuanced questions like “Which feature brings in revenue?”, “Which pages/steps cause drop-off?”, and “Which cohorts are performing the best?” — that’s a sign that it’s time to invest in product analytics. Even more so, if your product team is limited by analyst's bandwidth and is struggling with long turn-around time for data requests.

But first: before you set up this product analytics arm, there is one important decision to make: should you build your product analytics tool in-house, or should you buy an external tool? 

In this guide, we will help you navigate this choice between building vs. buying a product analytics tool, covering the advantages and challenges of both approaches. Let’s dive in!

‍

5 Factors To Consider While Making Build vs. Buy Decisions for Product Analytics Software

There are a bunch of points of view that you should consider while making this decision. We’ll cover 5 such prevalent factors that we have come across, and are worth considering:

1. The strategic point of view:

• Making a business case for this investment: Here, we’d encourage you to think about your company’s primary objectives and long-term goals. From a business and ROI point of view, how important is the investment in product analytics? What are you missing out on without it? This knowledge will help you make a strong and clear case and keep all the stakeholders aligned.
• Laying out your expectations and objectives: Beyond strategic alignment, defining your product analytics objective is essential. What kind of data insights do you need? How will they contribute to your business strategies?
• Identifying KPIs and tangible outcomes: How will you measure success for this initiative? Assess what your current KPIs are and how you’ll qualify for success. These measurable metrics will be helpful in evaluating the effectiveness of your chosen solution.

2. The infrastructure point of view:

‍
Most likely, your organization already has an existing technology infrastructure. It's important to understand the non-negotiables and limitations here to ensure the chosen analytics software can smoothly integrate with your current systems and tools. We’d recommend jotting down the rough map of your current stack to help you. The most relevant ones here will be your event instrumentation tool, data lakhouse/warehouse, and current analytics system.‍

3. The investment and resources point of view:

• Time and Cost: Determine how quickly you need analytics capabilities. Building may take longer than buying an off-the-shelf solution. Additionally, calculate the total cost of ownership for each option. Include development, maintenance, and licensing fees.
• Technical Expertise: Building and maintaining in-house software demands a certain level of technical expertise. Assess whether your organization possesses the necessary skills or if acquiring and retaining such expertise is feasible. Buying a solution may be more practical if your organization lacks the technical know-how. 

4. The data and compliance point of view:‍

‍
With matters of data, control, security, and compliance are critical and can often be a deal-breaker. Depending on how tightly regulated your industry is, you might need to prioritize this over other factors. We’d encourage listing out these requirements and ensuring that your analytics solution, whether built or bought, aligns with your organisation’s and industry’s data privacy, security, and compliance requirements.
‍

5. The features and needs point of view:

‍
This is where we talk about the end-user: your product team. List out your team’s needs, workflows, and objectives to access the features and capabilities of potential solutions. If your requirements are highly specialized, building in-house software or buying a highly customisable option might be the right approach.

‍

Build vs. Buy Product Analytics: Comparison 

In this table, we’ll do a side-by-side comparison between building vs. buying product analytics solutions. We’ll also go deeper into these individual approaches later in the guide. 

‍

Building a Product Analytics Tool In-House: Pros and Cons

Pros:

• Seamless customisation and more control: Building your own product analytics software allows for tailored solutions, ensuring that the features built align precisely with your team’s needs.
  
  
• Better control over security: In-house development often offers a better control over data security, which helps enterprises stay compliant with security regulations.

• No additional license costs: By building internally, you eliminate external licensing fees. This might mean more investment from a resources point of view, though.

• Easy integration with existing systems: In-house analytics tools would seamlessly integrate with your current data stack and ways of operating.

• No vendor lock-in: When you rely on third-party vendors, you may become dependent on their roadmap and pricing. Building in-house ensures you have full control over your software's direction and maintenance.

Cons:

• Extensive development time and resources: Building product analytics software can be time-consuming, diverting valuable resources from your core product and objectives.

• Need for ongoing maintenance and support: Once developed, your software will require continuous maintenance and support to fix bugs, upgrade security, etc., which needs ongoing resources and expertise.  

• Need for in-house expertise: Developing and maintaining analytics software necessitates a specialized skill set. Acquiring and retaining the necessary talent can be difficult, leading to quality issues, overruns, and delays. 

• Significant upfront investment: Developing in-house analytics software often entails a substantial upfront cost, which can be a critical financial challenge. This significant initial investment may strain your immediate budget.
  ‍
• ‍Distraction from the core product: Building in-house analytics software can divert attention from your core product development, potentially slowing down innovation and time-to-market.

‍

Buying a Product Analytics Tool: Pros and Cons

Pros:

• Fast time-to-market: Purchasing product analytics software provides a quicker route to implementation. You can expedite your analytics capabilities without the time-resource-intensive development process.

• Easy access to specialisation, support, and updates: Vendor-provided software typically comes with professional support and maintenance services, reducing the burden on your team. Additionally, this helps you resolve issues fast and stay up-to-date with industry trends and cutting-edge analytics capabilities.

• No need for in-house expertise: Opting for a product analytics tool brings a distinct advantage by eliminating the need for in-house expertise. This liberates your organisation from the resource-intensive burden of maintaining specialised skills in-house, allowing your team the time to focus on your core product and business.

• Cost-effective without an upfront investment: Buying software can be cost-effective in the long run, as you avoid the high upfront development and long-term maintenance costs associated with in-house solutions. Most products also come with a subscription, so you can opt-out whenever you want.

• Scalability and reliability: Commercial solutions are often designed to scale effortlessly with your growing data and user requirements. These solutions are purpose-built with scalability in mind, ensuring as your organisation and requirements grow, the software can seamlessly accommodate this growth. 

Cons:

• Potential limitations on customisation: Off-the-shelf software may have limitations in terms of customisation, which can restrict your ability to tailor it to your specific and unique business needs.

• Limited data control and security: Opting for a commercial solution may restrict an organization's ability to manipulate and manage its data effectively. This limitation can pose challenges especially if you operate in a highly-regulated industry.

• Data migration overhead: Migrating data to a third-party solution can introduce complexities. These challenges may manifest as potential disruptions during and after migration and resource strain when dealing with large data volumes.

• Vendor lock-in: You are locked in when your organization becomes overly reliant on a specific software or service provider, making it difficult to switch to alternatives. This dependency can limit flexibility and reduce control over operations. 

How Houseware's Warehouse-Native Solution Combines the Best of Both Worlds

For far too long now, picking a product analytics tool has meant having to compromise. There have been two options:

• Option 1: Build something in-house, integrate with your stack, keep control of your data and costs, while distracting resources away from your core offering
• Option 2: Pick a traditional product analytics tool, lose control of your data and costs, get while getting an external specialised tool to help your team

Houseware is the new option for you, combining the best of both worlds. Houseware works right on top of your data warehouse, like a specialised layer for product analytics and enables your team while ensuring you retain control over your data, compliance, and costs. What’s better is that there is no lock-in. You own your event instrumentation, your warehouse, and your stack. 

Let's revisit the comparison we had made, so that you can see things side-by-side.

‍

‍

‍