7 frequently asked questions about Mixpanel, answered
From understanding the analytics landscape to mapping capabilities with KPIs and integrating new tools with your current stack, we know that choosing a product analytics solution can be overwhelming—not to mention, time-consuming.
After thousands of conversations with product teams evaluating Mixpanel, we sifted through the data and identified seven questions that come up again and again:
- How is Mixpanel different from marketing analytics tools like Google Analytics?
- How is Mixpanel different from business intelligence (BI) tools like Looker, Tableau, and Mode?
- How is Mixpanel different from codeless solutions like Heap and Pendo?
- What’s Mixpanel’s pricing model?
- How long does it take to implement Mixpanel?
- How can I maintain data integrity in Mixpanel?
- Is Mixpanel secure?
1. How is Mixpanel different from marketing analytics tools like Google Analytics?
We’re not here to declare a winner: marketing analytics tools are immensely valuable, and have an important place in the tech stacks of just about every company. But the teams and objectives it serves are fundamentally different from those of product analytics, which provide real-time data on user events, the ability to analyze the user experience more deeply, and easier connections to existing tools.
Here’s a summary of exactly what teams and objectives are best served by each:
- 📊 Marketing analytics (e.g. Google Analytics): Built for marketers who want to understand the first part of the user journey, and report on KPIs like acquisition and attribution. Since marketing analytics tools rely on anonymized traffic data as opposed to event-based tracking, most product teams using it in isolation will find it difficult, or even impossible, to get the answers they need. Marketing analytics help teams answer questions like: where are my users coming from?
- 📈 Product analytics (e.g. Mixpanel): Built for product professionals who want to understand specific actions that users take within a digital product, and report on KPIs like engagement and retention. With built-in capabilities like user cohort trends, customizable event tracking, and a powerful segmentation engine, product analytics solutions are designed with product innovation in mind. Product analytics help teams answer questions like: which features are the most popular, who are my power users, and what behaviors are tied to long-term retention?
With many marketing professionals already using Google Analytics, you’ve likely seen instances of product teams “making it work” with some fancy tag management—but there are serious drawbacks to this approach. Just take it from Personal Capital:
“When I joined Personal Capital five years ago, we were relying on Google Analytics. Unfortunately, our access to real-time data was poor and it wasn’t the best fit for what our team needed. A/B test results were often delivered on the following day, or sometimes, the day after,” says Vince Maniago, VP of Product Management at Personal Capital. “I often had to turn to an engineer or data analyst to pull the data that I needed, but that report could be dated by 24-72 hours.”
Learn more about the difference between marketing analytics and product analytics (and whether you need both) in this guest post by our friends at Semetis.
2. How is Mixpanel different from BI tools like Looker, Tableau, and Mode?
As we learned above, even the most powerful analytics solutions can fall short if their capabilities are misplaced. Today’s SaaS tools are thoughtfully purpose-built, and yet too many product teams suffer from not having access to the right kind of data—ultimately hurting product innovation and the end-user experience.
While BI tools are incredibly powerful for reports and visualizations across many datasets, because they’re built on top of a SQL database, routine customizations take expertise, and often, a lot of time.
When evaluating what tools are essential for your company’s stack, take a step back and consider how their capabilities map to key KPIs:
- 📈 BI tools (e.g. Looker, Tableau, Mode): Used to ingest and collate data from sales, finance, and other parts of the company in order to inform business strategy and tactics, and report on KPIs like ARR and revenue churn. When used exclusively by a product team, the need for technical expertise can be a blocker to getting the real-time data necessary to take meaningful action.
- 📊 Product analytics (e.g. Mixpanel): Used to track, analyze, and deeply understand user behavior within a digital product, and report on KPIs like engagement and conversion. With out-of-the-box reports for things like understanding user funnels, flows, and retention (to name a few), product professionals as well as data analysts can dig into user trends and self-serve answers to their questions without the need for a data expert.
For Uber, layering product analytics into their stack has empowered everyone across the product team to make decisions, and freed up resources on the data team. “Mixpanel made analytics self-serve for the product teams at Uber so anyone can answer questions they have on user conversion, retention, and activation that are locally relevant and optimized for that region,” says Ingrid Bernaudin, Product Lead for Driver Growth.
Learn more about the unique use cases for both product analytics and BI tools—as well as how they can play nice together in a stack—in this post.
3. How is Mixpanel different from codeless solutions like Heap and Pendo?
Once teams decide that a product analytics solution is the way forward, there are two common paths to choose from: codeless tracking and precision tracking. Ultimately, the answer to this question requires an understanding of the difference between tools that automatically track user events, and tools that require engineers to programmatically add tracking code to their product.
Consider this: Mixpanel previously offered an automatic event collection model before moving away from it entirely. According to Mixpanel’s VP of Product & Design Neil Rahilly, with a codeless implementation, “You’ll end up with limited and unreliable data, spend even more developer time and money trying to fix problems and inconsistencies, and expose yourself and your customers to major security and privacy risks.”
Let’s go over the key characteristics of codeless tracking and precision tracking (also known as implicit and explicit event tracking, respectively):
- ⚡️ Codeless tracking (e.g. Heap, Pendo): A type of automatic event tracking that collects a wide range of interactions within your product without the need to define events beforehand, storing them retroactively until you’re ready to analyze them. Though great for non-technical teams, codeless event tracking can result in messy or unactionable data, and is prone to breakage.
- 💡 Precision tracking (e.g. Mixpanel): A type of strategic data collection that requires teams to manually define events based on the goals and metrics outlined in their tracking plan. This kind of tracking is set up with the help of developers who instrument the necessary analytics within a product’s codebase, and—though it requires more up-front planning and resources—users reap the long-term benefits of a safer, more versatile, implementation capable of providing context-rich data that’s easy to manage and govern.
For MarineTraffic, the decision to go with a precision tracking tool was about building a foundation that could scale with its ambitions. “We were initially sold on the codeless implementation because it captured all events without us having to define them first, but this also turned out to be its greatest disadvantage,” says George Charikiopoulos, MarineTraffic’s Head of Business Technology. “We have a unique product, and the auto-tracked generic events made creating some reports very hard, and in many cases impossible.”
Dive deeper in this post about why a codeless implementation will let you down.
4. What’s Mixpanel’s pricing model?
We get it: whether you’re part of an early-stage startup or enterprise level company, conversations about analytics tooling don’t happen without careful consideration of costs.
In 2019, after realizing it was better for customers, Mixpanel moved from an event volume model to one based on Monthly Tracked Users (MTUs). The MTU model is a way to calculate billing based on the numbers of users—denoted in Mixpanel as a ‘distinct_id’—that perform a qualifying event across all projects each month. To get an idea of your MTU count, add your product’s Monthly Active Users to the number of anonymous visitors you get per month (some tools also call this metric “Monthly Unique Visitors”).
Here’s a quick primer on why we think the MTU model is the best choice:
- 🔮 It’s predictable: Simply put, customer growth usually aligns with revenue growth. If your product is seeing a steady uptick in active users (and therefore, data sent to Mixpanel), the MTU model makes it much easier to forecast price increases on a month-to-month basis.
- 📈 It’s scaleable: Compared to other models we assessed, MTUs have a fairly consistent distribution across companies of various sizes. We think they strike a balance between affordability for smaller customers and volume discounts for larger ones—and can grow comfortably with each.
- 💸 It’s aligned with value: We conducted regression analyses for just about every pricing model and metric (event and people profiles, data storage, data exports, etc.) out there to see which correlated most to revenue. MTUs won by a landslide. As a product-led company, we wanted to make sure that the success of our business was tied as closely as possible to the success of our users.
Mixpanel has three MTU billing plans to choose from: our free Starter plan, our Growth plan which includes 1,000 MTUs, and our Enterprise plan for customers with more than 25,000 MTUs (if you’re an early-stage startup, you might also qualify for our Startup plan, which includes access to advanced features and is free for the first year).
Learn more about the MTU pricing model and our decision to roll it out for all customers in 2019 in this post.
5. How long does it take to implement Mixpanel?
We learned above why code-based event tracking is the preferred model for teams who value clean, actionable data, and sound governance practices. Here at Mixpanel, our ultimate aim is to get customers set up as quickly as possible, and—based on a business’ unique needs and preferences—there are a few ways to do this:
As with most things, some upfront planning and strategy work here will pay enormous dividends in the long run. And if you’re eager to jump in, our sample datasets are a great way to get familiar with Mixpanel—no expertise or credit card needed.
To set you off on the right foot, let’s demystify the amount of time and effort required for a successful implementation of Mixpanel:
- 🚧 The shorter, TL;DR answer: Once teams have a tracking plan in place, it takes about 30 minutes to implement a single tracked event in their product. Since most customers send between 20 and 60 unique events to Mixpanel (per product), the total ‘dev hours’ required could range anywhere from 10 and 30 hours. Most customers have a clear roadmap for their Mixpanel implementation on or before day 10, and are completely finished by day 40.
- 💪 The longer, better answer: Though the above offers a crude estimate for the timeline you should expect, Mixpanel Sr. SE Tech Lead Aaron Krivitsky (AK) likes to walk teams through a three-step evaluation that takes into account things like data collection and storage, client- and server-side tracking, and leveraging Mixpanel SDKs versus your own.
Get familiar with the analytics implementation process with the help of this detailed post, which also covers options for importing data.
6. How can I maintain data integrity in Mixpanel?
In order for the data you’ve worked so hard to surface to be actionable, it needs to be both accurate, and easy to understand. For instance, when a PM wants to analyze all the ways a new feature is being used, is it clear what events they should pull into their analysis (based on their descriptions)? And, do these events accurately represent the user actions that happened in the product?
To build a data governance program that drives data-informed decisions both now and in the future, consider these recommendations from Mixpanel’s Meghan Swidler, former Implementation Manager, and current Global Partner Operations:
- 1️⃣ Choose an ‘owner’: Select an individual, team or council responsible for owning your analytics implementation. Then, create a centralized implementation spec for your product to document new events and properties. (Lexicon allows you to add event and property descriptions as well as other useful metadata that will appear directly within the Mixpanel UI.)
- 2️⃣ Document new events and properties: When new product features are launched, the product manager responsible should then submit a request to the data governance owner that outlines the success metrics they’ve established. That way, it’s easy to build out the events and properties required to measure progress against them.
- 3️⃣ Implement the new phase: From here, the data governance owner should work with technical leads to proceed with development, translating the events and properties in the implementation spec above into triggers within the product’s source code.
Learn more about data integrity and governance in this eight-step guide for getting your data house in order.
7. Is Mixpanel secure?
Increasingly, privacy and security are top concerns for businesses that use third-party tooling to optimize their user experience. If that describes your business, then you’ve come to the right place.
Security is not simply a “check-the-box” exercise at Mixpanel. Mixpanel’s full-time security team has years of experience at the U.S. Department of Defense, the Federal Reserve, Apple, and Microsoft, drawing on additional expertise from our engineering team, external security researchers, penetration testers, and auditors.
Mixpanel has developed a multifaceted security program for the explicit purpose of protecting customer data from unauthorized use and access, including compliance with the GDPR, the CCPA, and HIPAA. Our native SOC2, Type II attestation provides additional assurance of our commitment to data protection, as does our European Data Residency Program. Check out our Privacy Hub for more on how Mixpanel can help you maintain compliance with the latest data regulations and laws.
Want to learn more about using Mixpanel to build better products? Get in touch with our team here.