To our customers:

Aug 11th at 3PM a bug was introduced into the system that caused a spike in your numbers for your events. You can see this spike by graphing your information by hour and look at Aug 11th, 3PM.

We advise you ignore this number and subtract it from your actual days value. Unique counting appears to be correct which you should consider using instead.

If you do less than 1M events, it's likely that you were affected otherwise probably not.

Sorry for your inconvienence and if you have any futher questions please email us at: support@mixpanel.com

A/B testing is a powerful way to optimize your website for better user conversion to a specific goal you may have. There are a lot of tools out there that specialize in providing different ways to A/B test – some are pretty complex and others are relatively simple. The beauty about A/B testing with Mixpanel is that you don’t have to learn anything new in order to do it; all you need to know is standard event tracking with properties attached.

The Test

At its heart, A/B testing is just creating 1 variable with various values and running simultaneous tests to compare outcomes towards a specific goal. This standard way to A/B test can be easily done with Mixpanel using our Funnel Analysis, and segmenting with Super Properties. Let’s take a look at a simple example of a website with 2 different home pages and how they affect users through a 3 step signup funnel.

Above are the 2 different home pages. For the most part, they consist of the same content just organized differently. Each page has an event in Mixpanel called “viewed home page”, and depending on the version a user sees there will be a super property assigned with V1 or V2 as the value. A super property is extra information that you can attached to an event, just like a regular property. We use super properties here because, unlike regular properties, they only have to be registered once but will follow a user around wherever they go on your website. Once a super property is registered, every event that user does will have that super property attached to it. Here are a few lines of Mixpanel code that can be used to do this:

Note that here we are using the “register_once” method instead of “register” to call the super property. We only want each user to see one version of the home page no matter how many times they view it, and we only want to track this on their first visit. In the second line we’re tracking an event called “viewed home page” – this will be the first step in our funnel. Now all we have to do is create similar events for the rest of the steps in the funnel and then use the Funnel Builder to create it. Here it is, with a little bit of fudged data to show:

This is a fairly standard funnel. We can see the drop-off rates and the end completion rate. To us the A/B testing functionality, simply click on the “Property” (not shown) and select “Home Page”. This will show all of the values of the home page, V1 and V2 for this example, and the individual conversion rates for each:

 This gives us a breakdown on the performance of each home page down the entire funnel. The conversion percentage as well as the total number of people for each step is listed, and at the end the overall conversion for the home page. Not only does this give you outcome for the end goal, but you can see how each home page individually affects user interaction down every step of the funnel.

This is a very simple example of how to A/B test with Mixpanel. We could have added as many iterations of the home page as we wanted, and also had more or less steps in the funnel too. 

Understanding Global Implications of A/B Testing

A/B testing is a great way to measure one part of your website against one outcome. However, have you ever thought about how changing one part of your website, like the home page, affects the rest of your site altogether -not just sign ups? What if sign ups went up, but overall game playing went down?

Most A/B tests are very narrowly focused and don’t show you how conducting an A/B test globally affects your website. Mixpanel has developed a tool that can help you better understand how changing one aspect of your website affects user interaction across all of the events associated with it. We call it Tests.

Below is a screenshot of the Tests page from the above example. It shows the results for every event that the “Home Page” super property is attached to (“sign up” has been excluded here since users only do it once):

We can see in the middle column that the unique visitors for the events “Play song” and “Viewed home page” match up with the corresponding steps in the above funnel breakdown. What we can also now see is how many times, on average, visitors performed each event and the total number of events done – all in one simple page.

Here’s a closer look at the two left columns:

We can see that while V2 of the home page has better conversion rates down the entire sign up funnel, the average number of times users play and complete songs is higher for users that have viewed V1 of the home page – V1 appears to be associated with a higher overall user interaction than V2.

It’s interesting to see results like this with Tests, but keep in mind that there are definitely more experiments that should be done to really understand what is causing users in one group to behave differently than the other. Tests is meant to be a tool that can help you check your results across your entire website and uncover any surprising things that often do happen when running website experiments.

Stéphane Hamel  is involved in various research & development activities with the prime objective of making web analytics easier; creator of the popular Web Analytics Solution Profiler and concepts such as Just-In-Time Tagging and the Online Analytics Maturity Model. He was among the first to receive the WAA Certified Web Analyst title and was awarded the Web Analytics Association Leadership and Technical Excellence Recognition. Frequent speaker at the eMetrics Marketing Optimization Summit and other conferences, he is also a member of the International Institute of Business Analysis, on the board of directors and treasurer of the Web Analytics Association and plays an advisory role to a number of agencies and vendors.

In this intereview he explains how companies can use the Online Analytics Maturity Model to evaluate their current analytics efforts, and also touches on when and why a company should seek guidance from an outside consulting firm.

1) A lot of companies have an internal analytics team responsible for figuring out what to do with all of their data. At what point do you recommend a company with an existing team to seek out guidance from a consultant like yourself, and how do you begin the process?

If we were to do a classic SWOT analysis (wikipedia) (Strengths, Weaknesses, Opportunities, Threats) of most web analytics team we could find many factors playing in. In general, the picture looks like this:

• Strengths: An internal team is best positioned to build for the long term, understand the business, internal factors and realities specific to the company.
• Weaknesses: There are few qualified and experienced web analytics professionals and growing a team from the ground can be a long and tedious effort and in some cases, developing the skills and building the credibility internally is actually more difficult than bringing an external “expert” for a while.
• Opportunities: Shifting toward a web analytics career is a fantastic opportunity for employees and a positive cultural change for the business.
  
• Threats: Market conditions are largely in favor of anyone with decent web analytics experience. There is a real possibility skilled employees will seek for greater challenges or be tempted by more favorable conditions. The other big threat is change management - which is often poorly executed or simply ignored, leading to passive resistance and disbelief of analytics value.

Over the past two years I have studied the factors that makes some companies succeed at web analytics while so many others fail. The Online Analytics Maturity Model (OAMM) looks at the six key process area contributing to a successful web analytics practice:

1. management, governance & adoption,
2. scope - the size of the playing field,
3. objectives - how they are defined & managed,
4. team & expertise,
5. process - agile and optimized for problem solving,
6. and lastly, the tools, technologies and data integration.

After conducting an assessment - a simplified self-assessment is available on the OAMM website - we are presented with clear indications of strengths and weaknesses; the actual vs desired state. The goal is to keep a balanced approach in all six dimensions and gradually grow in maturity. This holistic approach is excellent to spark discussion with management, educate stakeholders and work on what needs to be improved in order to fully leverage web analytics potential.

In conclusion, I think external help can always be helpful - pending it is the right profile and the right approach. At the simplest, external help can offer a sounding hear to quickly validate a concept or objective, acute implementation skills and techniques, additional coaching & training, etc. More organisations are realizing web analytics isn’t one of their core skills and for some, the right answer might be to totally outsource the technology and analysis components to a specialized analytics agency.

2) If a company decides to get outside help, what kind should it be?

There are basically three major dimensions to web analytics, as showed in the picture coming from the OAMM workshop:

1. The business/marketing: defines the strategy and the goals, which are communicated as business requirements & objectives to the    technology side. If OAMM dimensions 1,2 or 3 are weak you might request help from a senior ebusiness strategist.
2. The technology: understand the capabilities & constraints of the medium, the architecture, and the data collection mechanism to supply the means, tools and data to the analyst. If OAMM dimensions 4, 5 or 6 are weaker, seek for more technical help and someone seasoned at agile development and methodologies
3. The analyst comes with his problem solving skills to do a synthesis of the data at hand and communicate back actionable insight & recommendations to the business. And lastly, if dimensions 3,4 or 5 are lower, you might want to look for someone with SixSigma or similar background.

3) Can you give a few examples of huge turnarounds you’ve seen as a result of OAMM improvements?

Many practitioners and agencies looked at OAMM and provided great feedback on what works and what needs to be further improved. I frequently mention a maturity model must be used and abused in order to live, adapt and improve over time. Let’s look at three classic examples:

1. Canoe.ca, a large Canadian media company had several good elements but lacked a cohesive approach to implement and manage web analytics on over 200 websites. When a senior manager joined the company and became the champion all key process areas significantly improved - reinforcing the first dimension of OAMM, Management, Governance and Adoption is the most important. More info is available on my blog.
2. The practitioner at an European car manufacturer conducted a self-assessment and used the “as is” and “to be” as a strong discussion point to demonstrate the issues the organisation was facing. The fact OAMM was developed by an independent consultant and is supported by academic background and methodologies makes it agnostic of any vendor or consulting agency bias. It is sometimes easier to convey a message when we can point out the exercise is based on a rigorous approach from someone external to the internal politics and influences - a great way to start the discussion with managers and stakeholders! More information about this case is available on my blog.
3. An agency wanted to start their own web analytics practice and I insisted to conduct a maturity assessment before starting off with one of their client. Even when faced with the harsh reality some people insist on ignoring it and I pointed out very weak areas that would likely make the project fail with their client. Sadly, this is exactly what happened - on the brighter side, it demonstrated the model is valid!

The common team in those examples: the tools aren’t really the issue - the ability to navigate internal politics and get decision makers on board is the real challenge.

4) How do you go about choosing the right analytics tools for your clients?
It becomes amazingly simple after conducting the OAMM assessment. When you spend some time thinking about the business goals and how they will be measured, when you look at the analytical process and available resources, and when you consider the scope of work, you can more easily identify which solutions will have the best fit. Of course, my own hands on experience and knowledge of the market makes it easier for me to point out solutions that will cater to the right maturity, market and vertical. I have witnessed many cases where totally unrealistic scope & objectives lead companies to chose high-end solutions only to fail a year later because they didn’t invest enough in the process and resources... and usually, practitioners and managers alike will point out to the tool not being “good enough” and seek another quick fix...

One of the area I’m looking into right now is to use OAMM to map out various vendors and agencies and highlight which key process areas they are the best at.

5) Any parting thoughts you’d like to leave our readers with?
There are really two school of thought: “web analytics is hard” vs “web analytics is easy”. My position: web analytics is not any harder than so many other things businesses have to deal with.

Depending on the source, technology related projects are unsuccessful between 60% and 80% of the time - CIO magazine states only 32% of the projects are delivered on time, on budget and with the required features. After all, Web analytics, and everything related to the Internet, involves technology.

Every analyst should print out the definition of “analysis” on Wikipedia and read it once per day: Analysis is the process of breaking a complex topic or substance into smaller parts to gain a better understanding of it. If web analysts can’t make their own job easier, do they really expect to be able to optimize much more complex issues?

We’ve noticed that a lot of you were tired of digging through all of your events every time you wanted to get to a frequently viewed graph. This was especially unpleasant if you wanted to share information with a colleague.  

Well, we’ve come up with a simple way that allows you to save and share the current state of your mixpanel report. When you’re at a graph that you want to save or share, just copy the current URL and treat it just like any other hyperlink:

The URL will change whenever you click a new item, whether it’s changing the graph type or comparing different properties to view.

Enjoy!

This post was originially written by Jeremy Richardson on Mashable

For a while, a big limitation of online optimization tools was their lack of real-time reporting. Google Analytics, the most popular analytics service out there, can easily take a full day before displaying your data. This was acceptable back when the web was static, but as websites become more and more dynamic, the rate at which we analyze and iterate based on our collected data has dramatically increased.

There are many industries where optimizing in real-time can have a large impact on overall business performance. Unfortunately, not all companies are aware of the potential value in tracking information in real-time. Let’s take a look at a few areas where real-time data processing is already making a big impact.

Content Websites

One area where we are seeing real-time analytics improve content companies is in article headlines. For most of us, an article headline is all we use to decide whether or not to read on, so having a good one is definitely important. The Huffington Post is ahead of the game here. They use analytics to run A/B split tests on their important articles – in real-time. The Huffington Post initially shows 2 headlines for the same story, after 5 minutes of testing they discard the less popular one.

Aside from A/B testing article headlines, real-time analytics can play a large role in organizing and prioritizing entire stories as they break. If publishers know the popularity of an article based on the number of reads, comments, tweets, or Facebook “Likes” in real-time, then they can make informed decisions about how to optimize their pages with the best content. As media companies continue to concentrate more of their business online, real-time data analysis will likely play a large role in shaping how key decisions are made in every aspect of the company.

Game Companies

Social gaming is a huge industry that can, and does, benefit from analyzing their data in real-time. It’s a very cut-throat business: Whenever a new strategy is successful, it’s immediately copied by everyone else. Analytics are necessary to keep an edge on the competition. Specifically, real-time analytics are important because social games often have a short lifetime, and the makers want to get as much value out of a single customer as possible. If they can’t iterate on a game for a whole day, and the average user only plays for 7 days, then that’s a large opportunity that is lost due to a lack of data.

A large gaming company that I’ve spoken with sees well over 30 million MAU’s and they analyze and make changes to their games every hour, every day. Real-time analytics gives them the ability to maximize their iteration and get the most out of each game they make. It’s a large part of why they’re so successful.

Live Streaming

In live streaming, a flash crowd is when a large group of people shows up and then disappears in a relatively short time span. Imagine how many people tuned in when Landon Donovan scored that epic goal to advance USA in the World Cup, for example. The amount of data here would have been huge if every network packet from every user was captured, and was likely too much information for most companies to capture, record and analyze in any timely and useful way. The huge amount of crowd behaviors would need to be observed in real-time.

An engineer at live streaming company Veetle told me they often need to reserve and allocate additional resources when a crowd is larger than they expect. For simple cases, like the season finale of Mad Men, there are simple allocation heuristics they use that automatically make up for a larger than expected audience. However, for extreme cases smart allocation is a difficult problem.

With something like the Donovan goal, they would have had to have a person observing the flux of people streaming the game, and manually decide what resources to properly allocate – something they would only be able to do with real-time analytics.

Instant Gratification Makes us Happier

Having data available in real-time simply makes you happier: The ability to set up and start tracking immediately gives you the peace of mind in knowing that everything is working smoothly. And, if something does end up being wrong, no time will have been wasted waiting and hoping that the data will eventually show up in a report somewhere. The joy that real-time brings to customers is evident: Analytics company Woopra flat out asked their customers why real-time mattered.

The responses Woopra received focused on the niche information that real-time data provided them. For example, one user wrote: “Just had a potential new customer on the phone, Woopra instantly shows me the 38 pages on our website they read before calling.” Another user found real-time data helped better filter the content he was producing, while for others the benefit was in identifying trends. “Real-time stats are great to understand upcoming trends. Helps to track user interest during different hours etc,” said one user in response.

[Full Disclosure: Woopra is a competitor to the author's company.]

Your Business in Real-Time

Being able to analyze your data in real-time has the potential to add a lot of value to your business, whether you run a hugely popular website like The Huffington Post or a small business. However, taking advantage of real-time analytics requires commitment – you have to be ready to act on the data as fast as you receive it. It’s possible to automate some of the work, but you usually need a human decision maker. This can be difficult to get correct but it can make your advantage even bigger once you succeed.

Jim Sterne is the founder of the eMetrics Marketing Optimization Summit and the Web Analytics Association. He’s also the author of the new book “Social Media Metrics: How to Measure and Optimize Your Marketing Investment”.

In this interview he addresses how metrics for social media are different from the web analytics that we’ve all become accustomed to and why understanding these numbers are crucial for your company’s growth:

What are the biggest ways social media marketing metrics differ from traditional analytics that measure core product performance? 

 Social media brings two new metrics to online marketing that we haven't worked with before. The first is influence. In social media, it's critical to know who the most influential people in your marketplace are. This is a challenge for every product or subject as the opinion of a relatively unknown individual thought leader can be much more powerful than a celebrity known to many. The second is sentiment. We now have the ability to listen in on an immeasurable number of conversations and we need to discern the tone of those conversations. Everything else we can measure has their roots in traditional advertising measurement or web analytics - What bearing do those interactions have on brand awareness and business outcomes?

What is the importance of knowing detailed user metrics, like the number of comments or ‘shares’ one makes, for understanding the effect of social media marketing? 

 Those metrics, coupled with number of readers/followers/fans/likers, etc., are the raw material of determining Influence. If one tweeter is only followed by a dozen others, but those others retweet everything that tweeter has to say and they, in turn, are followed by thousands, then the confluence of posts, comments, followers, retweeters, etc., helps marketing departments identify whom they should treat with special care.

If a company isn't already tracking their social media metrics, what are the first steps that should be taken to get them on their way to fully understanding the business value that their social media campaigns are creating?

 If a company isn't tracking their social media efforts, the first step is to really learn how to listen - starting with reputation management. While your bloggers and tweeters and campaign engineers are out there making noise, it would be Really important for your team to get a feeling for whether that noise is being well received or not. Companies need to know what the public thinks as an ongoing process and see if they are doing more harm than good with their efforts.

 Say a company already uses social media metrics and knows who their influencers are and what the general sentiment and awareness is around their brand - what are a few ways that this company can take advantage of having this information? 

 The next step would be to tie social media activities to business goals and outcomes. This is a bit more complex but central to assessing if any economical value is being generated. From there, it all becomes a matter of segmentation. Does this blogger have a bigger impact in times of crisis than that one? Does this sort of information get more attention and generate more engagement than that other sort? Can we effectively cool down a negative situation better with this approach or that one? Can we create more brand equity with posts or comments on other's blogs? Etc.

You're the founder and Chairman of the Web Analytics Association, which has been around for nearly 6 years now. Analytics have come pretty far since then; social media metrics being a good example of something that is relatively new. What is the biggest shift you see coming in Analytics over the next year or 2?

Over the next year or two, we're going to see analytics become more and more part of companies' standard operating procedures. "Show me the numbers" used to be only about sales or opinion polls. Now, it's going to be more about how the numbers related to each other.

The simplest analysis one can do is, "How does that number change when looking at this segment versus that segment?" Group A had a 45% response rate and group B had a 23% response rate. What does that tell us about the value of the target, the message, the medium or the offer? How can we leverage the differences to boost response?

Starting there, the world of analytics opens up to encompass everything from testing to personalization. It's getting the foot in the door and now that we have that toe-hold, managers are more readily willing to consider the power of complex calculations. They are more willing to experiment. Those who do, will see quick gains and reap the benefits of a sharp competitive edge.

Suhail touches on how to properly use analytics to grow your user base from 0-100MM users, and what aspects of analytics to focus on during the various growth stages.

This post was originally written by Tim Trefren on InsideSocialGames.

Because a range of our customers are social game developers, we can get a high-level look at trends they’re seeing in their Facebook applications. One of the big trends we’re seeing is that games are using tutorials to generate strong retention among new users. A related trend is that this initial retention is critical to the health of your game, in the weeks following launch. Here’s a closer look.

Impressive Results From Tutorials

One thing we’re seeing succeed is the tutorial-based signup process. A well-crafted tutorial removes all the ambiguity out of getting started and helps teach a new user how to play the game.

If you’re not familiar with this technique, the FarmVille signup process is a good example. FarmVille explicitly teaches you how to harvest, plow, and plant seeds with a 3-step tutorial.

Now that you’re familiar with the concept, let’s take a look at the data I’ve compiled from a number of games.

By The Numbers

The most impressive finding of this analysis is that individual steps in a tutorial convert at over 90% on average. Meaning, once a user has started a tutorial, they have a greater than 90% chance of continuing at each step.

This doesn’t include the first step, however – as you might expect, it’s harder to get users to start a tutorial than it is to get them to complete additional steps.

First step conversion rate: 71.4%
Additional step conversion rate: 95.06%
Overall completion rate: 37.9%

Many companies are now utilizing the tutorial technique, and it clearly deserves its popularity. Conversion rates of 95% are practically unheard of, but tutorials appear to be delivering these results.

An Interesting Trend in Visitor Retention

Another thing I noticed was a strong trend in retention behavior. There are some remarkable similarities in the *pattern* of visitor retention across games, despite the differences in the actual numbers.

Before I go any further, here’s a quick overview of the concept: Visitor retention is the percentage of visitors who come back and interact with an application after their first visit.

Visitors are chunked into groups—also known as ‘cohorts’—and then analyzed based on the the behavior of the group as a whole. The most common method is to group by visit date. For example, one group might consist of all the visitors who were first seen in the week starting May 3rd.

Once you have grouped your visitors, you can track them over the following weeks and see how many from each cohort return to the site.

Now let’s look at some actual retention numbers for a variety of different games. To compile this data, I first took a sample of the different social games using our service. Then I looked at the average week-over-week retention for each game.

Here’s a graph of the average weekly retention rates for the different games:

You can see that on the surface, the retention numbers are pretty different – some of these games have long-term retention rates close to 50%, while others rapidly approach 0%.

However, the interesting thing to note is that while the absolute retention rates are different, the pattern of retention is very similar across games. They all have a massive dropoff after the first week, with relatively flat retention in the following weeks. If you take a closer look, the ‘flat’ parts of the graph run nearly parallel, meaning they have very similar weekly conversion rates.

We can take a closer look by calculating the “conversion rate” – (e.g. week 3 divided by week 2, etc) between adjacent weeks. Here’s a graph with this transformation:

See a pattern? At the first point on the x-axis (Week 0-1), we can see that the initial conversion rate ranged from 1.76% on the low end to 62.83% on the high end. The interesting part comes later, though – no matter what the initial conversion rate between weeks 0 and 1, the following weeks convert at close to 80% across all of the games.

Basically, this means that once you’ve had a user for at least a week, they have an 80% chance of coming back each following week.

This suggests that your initial retention rate is critical, because once you’ve retained users for a week you are likely to keep them for quite a while. This behavior also raises another question: why do almost all of the games in our sample exhibit this behavior? Is it possible that this is just how social games work – retained users have an 80 – 95% chance of returning each week? If so, this could mean that the only thing you have control over is the initial retention rate. Time to write and polish your tutorials.

It’s been about two months since I joined Mixpanel as employee #1, and I thought it would be helpful to use my experienced so far to develop a brief guideline for what should be thought about before joining a very early stage startup.

A little bit of background

Although I’m currently at a web startup, I first moved out to California to do consumer product design consulting. After graduating and a short startup attempt in Boston, my fast approaching student loan payments convinced me to come out to CA and get a job doing what I’ve been learning about for the past four years: mechanical engineering. After a few interviews, I had secured the job of my choice at a nice consulting firm in Palo Alto – where I lasted a whole 6 months before making the switch to BizDev at Mixpanel (yeah, it caught me by surprise too).

To join or not to join?

 If you Google “joining a startup” you’ll get a barrage of results with reasons why to join a startup and also what to consider in the decision making process. I’m not going to focus on the “why” part here – for someone in their 20’s with few real responsibilities, there aren’t many reasons why NOT to join a startup as far as I’m concerned. What to consider, however, is a critical part in deciding whether or not a particular startup would be a good fit. If you focus on the wrong reasons then things can take a turn for the worse rather quickly. Looking back, here are the first three things I believe someone should think about before joining an early stage startup:

#1: Team

During the early days, especially, you’re going to be spending a large majority of your life with the people you’re working with. Making sure you get along with everyone on the team is crucial if you expect to make it big without killing one another. Your relationship with the rest of the team isn’t the only thing you should be looking at though; equally as important is how the founders get along with each other. Small arguments between partners are inevitable, but what’s important is that everyone involved handle the situation maturely, allowing business to resume ASAP.

Picture yourself and the rest of the team spending the next few years of your life together – if this brings any discomfort then this is a red flag.

#2: Expectations

After you’ve deemed team chemistry to be a fit, make sure everyone’s expectations are on the same page. There are a lot of reasons why people start companies – money, respect, impressing a girl – and the motives have a high impact on the work environment. Make sure you get an understanding of why the founders really started the company. If they did it to have a lavish lifestyle in a few years, then there will likely be a lot of friction when it comes to important company decisions down the road. Expectations for work intensity need to be discussed as well. Some founders have never heard of having a “work life balance”. If you aren’t prepared to dedicate your life to the company during the next year or so then don’t join that kind of team; it will definitely catch up with you in the long run.

This leads to another point that you should really understand your motives behind wanting to join a startup. Knowing this will better prepare you when you’re deciding what you want to dedicate yourself to over the next few years – whether it be work or play.

#3: Interest

The last factor I want to touch on is having an interest in what you’ll be doing. The only way you’ll be able to sustain the long hours that a startup demands is by enjoying what you’re doing. While there will always be small tasks that no one likes but have to get done, the main focus of your role should be something that really interests you; something that you want to master. One great thing about being one of the few people building a company is that you’ll wear many hats. This will help a lot in figuring out what you’re good at and the type of position you’ll eventually want grow into.

There are, of course, many other factors to consider when joining a startup – salary/equity, investors – but I believe the above 3 criteria should be examined in great detail (and met) before going ahead in the decision process. If you don’t feel like you have this solid foundation then it’s only a matter of time before you lose passion for what you’re working on.  At that point, it just becomes another J.O.B.

If you're looking to join a startup and want to talk to me in more detail about my experience so far feel free to shoot me an email at jeremy@mixpanel.com.

Also, Mixpanel is hiring! For more info about that go to mixpanel.com/jobs