Mixpanel (YC S09) Raises $65M to Build Predictive Data Tech(wsj.com)
wsj.com
Mixpanel (YC S09) Raises $65M to Build Predictive Data Tech
http://www.wsj.com/articles/BL-VCDB-16129
30 comments
100% agree with the theory of what you're saying, but in practice I've found that none of these new event-based analytics tools provide much value until you add custom attributes to the events. That is where you get the important slicing and dicing and segmentation.
Unfortunately, Heap's approach only captures the events automatically but can't capture the business-specific attributes that need to go on those events. Ultimately that will always require some human being to think about their business-specific problems.
Unfortunately, Heap's approach only captures the events automatically but can't capture the business-specific attributes that need to go on those events. Ultimately that will always require some human being to think about their business-specific problems.
(I'm a founder at Heap.)
This is an excellent point. Two things:
1. Empirically, we've found that our customers rarely fall back on custom attributes. The vast majority of queries run in Heap (>75%) operate on automatically-captured events. To me, this suggests we've either: 1) cut out a significant portion of implementation work, or 2) enabled analysis that was previously blocked by implementation work. If either (or both) are true, it's a win, and suggests that automatic event-tracking produces salient data out-of-the-box.
2. That said, some metrics are important and do require manual instrumentation. Coincidentally enough, we're about to launch a feature that solves the problem you mention and requires no extra implementation work. We're excited about it. Want to try it out on newrelic.com?
This is an excellent point. Two things:
1. Empirically, we've found that our customers rarely fall back on custom attributes. The vast majority of queries run in Heap (>75%) operate on automatically-captured events. To me, this suggests we've either: 1) cut out a significant portion of implementation work, or 2) enabled analysis that was previously blocked by implementation work. If either (or both) are true, it's a win, and suggests that automatic event-tracking produces salient data out-of-the-box.
2. That said, some metrics are important and do require manual instrumentation. Coincidentally enough, we're about to launch a feature that solves the problem you mention and requires no extra implementation work. We're excited about it. Want to try it out on newrelic.com?
We love Heap's retroactive analytics and use custom attributes pretty regularly.
I really wish they would invest in helping their users answer the "Why" question more. Why did users click that link 5000 times? Why is this funnel dropping off? Why did the time-to-first-interaction increase?
These are questions for the business, but I feel like Mixpanel could add so much more context. "We noticed that 'time to first interaction' has gone down with 'watched home page video'." That would at least be a clue.
MP has the data, but all of that analysis is manual (or was, last time I used it).
These are questions for the business, but I feel like Mixpanel could add so much more context. "We noticed that 'time to first interaction' has gone down with 'watched home page video'." That would at least be a clue.
MP has the data, but all of that analysis is manual (or was, last time I used it).
This is in essence the "killer feature" of the analytics world, and it is hardly an easy problem to solve.
Before Mixpanel can even attempt to solve that though, they first need to tackle multi-channel attribution. Right now GA and Adobe are the big players who tackle it, and GA only offers dynamic attribution (ie. the holy grail of attribution) in GA Premium (which costs $150k/yr).
If you want a standalone attribution platform you're left with options like VisualIQ, Convertro, Adometry, etc., all of which have recently been acquired by big players in the space.
So yes, I'd love to see Mixpanel answer the "why." However the first step is to truly answer the "where" question and until they add those tools, their model is still fully last touch, and therefore dated.
Of course even GA doesn't use its attribution modeling capabilities outside of the multi-channel/attribution reports, so they still have a ways to go too. The big difference is, those tools are available today for free.
That said, congrats to Mixpanel. They made huge strides in the event tracking approach that GA has since gone on to borrow, and that is what any modern web analytics platform looks at these days because it simply makes more sense.
Before Mixpanel can even attempt to solve that though, they first need to tackle multi-channel attribution. Right now GA and Adobe are the big players who tackle it, and GA only offers dynamic attribution (ie. the holy grail of attribution) in GA Premium (which costs $150k/yr).
If you want a standalone attribution platform you're left with options like VisualIQ, Convertro, Adometry, etc., all of which have recently been acquired by big players in the space.
So yes, I'd love to see Mixpanel answer the "why." However the first step is to truly answer the "where" question and until they add those tools, their model is still fully last touch, and therefore dated.
Of course even GA doesn't use its attribution modeling capabilities outside of the multi-channel/attribution reports, so they still have a ways to go too. The big difference is, those tools are available today for free.
That said, congrats to Mixpanel. They made huge strides in the event tracking approach that GA has since gone on to borrow, and that is what any modern web analytics platform looks at these days because it simply makes more sense.
Absolutely. It's a really hard problem. I think predicting the future data accurately is also really hard though, and I'm not sure how much value is there for end users. That said, I haven't built a successful company and they seem to know what they're doing.
A plug for a former employer but I'd like to add optimine, http://optimine.com/insight to your list of attribution platforms.
They've got an elegant and, as far as I know, unique modeling approach that avoids the first-touch/last-touch/path-analysis pitfalls.
They've got an elegant and, as far as I know, unique modeling approach that avoids the first-touch/last-touch/path-analysis pitfalls.
I've heard of them before. Seems like they are largely for bigger players with offline touchpoints. How do they do with online SaaS companies with sales cycles 30-90 days out and a variety of content efforts, social media, video campaigns, etc.?
Also, would I need an ad server in place to properly attribute display or can they handle that on their own?
Also, would I need an ad server in place to properly attribute display or can they handle that on their own?
I think you are right they are targeting large advertisers with significant spend.
It's a unique approach in that they don't need a lot of granular attribution data, but instead use higher level aggregate data. As a result it's easier to integrate with all kinds of media campaigns.
You need to be able to provide impressions over time for your early funnel ads and conversions over time for your late funnel touchpoints. They use some econometric time series techniques to analyze and estimate the impact so you can focus on whats working.
It's a unique approach in that they don't need a lot of granular attribution data, but instead use higher level aggregate data. As a result it's easier to integrate with all kinds of media campaigns.
You need to be able to provide impressions over time for your early funnel ads and conversions over time for your late funnel touchpoints. They use some econometric time series techniques to analyze and estimate the impact so you can focus on whats working.
Interesting, thanks for the details. Right now I'd give anything for a dynamic attribution solution that didn't cost an arm and a leg and could be used at the level of volume a SMB deals with.
GA's attribution tools are fun to play with, but at the end of the day they are still static models that don't evolve over time as they collect data.
GA's attribution tools are fun to play with, but at the end of the day they are still static models that don't evolve over time as they collect data.
Heap [1] actually does a good job of what you're asking for.
They have these flows that chart the user's path through your site, so you can see where, when and why they dropped off where and when they did.
1. https://heapanalytics.com/
They have these flows that chart the user's path through your site, so you can see where, when and why they dropped off where and when they did.
1. https://heapanalytics.com/
Isn't that what you being in your space is about? Understanding the "Why"? If someone else can do that automatically, you're no longer needed in that space.
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So, cognitive science? The most effective way to do this is user interviews as this is purely subjective. Hints only tell part of the story.
MP is only ever going to be able provide you with analytics from on your site, so it's going to be able to miss the greater context. The greater context of user interests would give the motivation of why someone's engaging with something or not.
I don't see how "why" can ever definitely be resolved. It's far too nuanced a question, but higher resolution data capture and analysis helps you understand it more easily, which seems to be the direction analytics is going.
The closest thing I've got to the "WHY" question is inspectlet. It's like looking over your user's shoulder. It's awesome.
Localytics does that pretty well. In their funnel view, you can see, for each step in the funnel, it shows a comparison of users who went on to the next step of the funnel vs. users who did not, and highlights a diff of their actions.
Really impressed with their openness on the terms of the deal. He disclosed valuation ($865mm-post) / dilution (7.5%) and their balance sheet ($22mm). Very impressive terms for that matter. Congrats on the round and success with the business.
Congrats to Suhail and team. They have done absolutely incredible work with Mixpanel, building the world's best analytics solution, starting from a tiny team and against stiff competition.
It's an interesting idea - but I'm skeptical that they can create a generalized data model to support predictions across a wide variety of industries or verticals.
2015 is so going to be the year of personalization and actionable analytics. Seeing this stuff everywhere.
Congrats to the Mixpanel team. They have always been very supportive when we've had questions. When we had a problem with a particular statistic a few months back, they actually came on-site to diagnose.
Kudos to the hardworking team. I'v always thought that Google would acquire them, but their commitment and goal driven startup means a lot to their clients at this point, which is awesome.
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Mixpanel is awesome and rightly so their product is awesome!
I noticed they mentioned Heap Analytics (https://heapanalytics.com/) as one of their competitors. We've been using Heap for over a year and it seems like the logical and magical next step in analytics. Mixpanel gave you smarter analytics on things you had the foresight to track, but Heap automatically tracks everything from the day you integrate it. That means you can get smart analytics even on things you didn't have the clairvoyance to start tracking 6 months ago, or didn't have the resources to insert tracking code in.
For startups, Heap's automatic and retroactive tracking is huge. It means we can iterate on product features and marketing/outreach schemes way more quickly while still getting insight into what's successful and what's not. It's not perfect--a couple times we've added special class names to our HTML elements so Heap can distinguish them, but that's still easier than adding manual tracking code--but it's a huge improvement over the old way.
I noticed Heap has a page comparing themselves with Mixpanel (https://heapanalytics.com/compare/heap-vs-mixpanel) but I don't see anything similar from Mixpanel's POV. I'd be curious to hear what Mixpanel's plans are in this area (automatic/retroactive tracking).