Focus on Allies - Learning Ally Skills at Keen IO

At Keen IO, we value introspection, continuous learning, honesty and empathy. In the spirit of those values, we are eager to learn how we can leverage our individual and collective privilege to work towards a more tolerant and inclusive world. As such, we teamed up with Trello to host an expert thinker in diversity and inclusion, Valerie Aurora, to share how we can all become better allies.

Most diversity and inclusion initiatives focus on changing the behavior of targets of oppression, rather than allies. This talk helped to explain why we should focus on changing the behavior of allies instead. It described about a dozen specific ally skills and talk about effective ways to develop your own ally skills, including attending the Ally Skills Workshop. We’re happy to share the recording with you.

We’re hopeful the skills Valerie covered will help to inspire our community members to use their specific constellation of privilege to identify opportunities to support, protect and amplify the voices of targets of oppression. We’ve already had several conversations internally about how we can do that, and have been thrilled to hear event attendees have been having similar conversations at their companies.

Valerie’s slides can be accessed here.

We’ll be adding the verbatim transcript from this talk shortly, so stay tuned! 

Sarah-Jane Morris

Community and Awareness

Lesson One

Person Working

As Keen IO’s Chief Data Scientist, I’ve worked with thousands of companies over the years. Today I’m sharing one of the most impactful lessons we’ve learned in building successful analytics integrations.

The lesson is: take some time to reflect on what you are trying achieve. Once you are confident about that, your decisions about what data to collect and how to use it fall more naturally into place.

Here’s a mini-worksheet to get you started:

  1. What’s most important to my company/team right now?
  2. What are our goals?
  3. How can data help us achieve those goals?

Write out your answers. Reflect back on them whenever you feel overburdened by the seemingly infinite possibilities for your data. Re-center and drive forward. You’ve got this.

By the way, we’re pretty helpful people over here at Keen IO. Maybe we’ve already solved a problem like yours and we can help you do it faster.

Michelle Wetzler

Chief Data Scientist

What we learned dogfooding our Native Analytics product

This week, we released Project Analytics – a brand new analytics dashboard displaying service level usage for our customers. This project is the beginning of a new effort aimed at bringing greater insight and observability of Keen IO service usage to our customers. Many of our customers rely on our platform to deliver Native Analytics to their customers, directly in their own products. Now we’re doing exactly that: analytics for your analytics. Pretty meta, huh?

Sample Keen IO project analytics console

This project was super fun for us to build and an opportunity for us to dogfood our recently released Native Analytics product. Read on for some juicy details on how and why we built this and some of the design challenges we faced along the way.

Why did we build Project Analytics for our customers?

First and foremost, our goal with this update is to improve the product experience by bringing more information to the surface of our web console. We have a clear vision for our apps and interfaces that leans heavily on native analytics powered by our own platform. Account usage information has also long been a common customer request that would land on our support and customer success teams. Baking these metrics directly into the product reduces both support ticket volume for this topic and the time spent tracking down this information for each customer.

This was also a chance to dog-food our own Native Analytics product in ways we haven’t had to yet. The insights we’ve taken from this project have already influenced future product development in big ways, for both our API and data visualization capabilities.

What were some of the challenges we faced while building this?

The design phase of this project was pretty intense! We spent a lot of time sketching and iterating – not just what you see now, but what is coming next. This project also coincided with a substantial visual refresh, several big performance improvements behind the scenes, and an active recruiting effort by the engineering team responsible for building this project. Coordinating a successful release around so many moving pieces proved to be a bit challenging.

These obstacles probably won’t sound too surprising or unfamiliar for anyone reading this; that’s just daily life at a growing company. The experience did reaffirm how important Developer Experience is for API-based services like ours. We put a lot of thought and care into how quickly developers can get up and running with our API and SDKs. We know our customers already have a long list of obstacles and blockers to deal with to get a project out the door. Integrating with Keen and getting data flowing in and out of your app shouldn’t be one of them.

How did we decide which metrics to show?

For this first release, we really had to resist the urge to go big, or to over-commit to granularity or complexity. This is just one small piece in a roadmap that that will unfold over time, and we know we have a lot to learn along the way. We give our customers the same advice: start small and move fast; experiment, iterate, and leave room to be surprised by what you learn. This lets you develop deep understanding and momentum at the same pace. You need both. Having one without the other generally doesn’t work out too well.

What’s next for Project Analytics?

Next, we’ll be diving deeper into project-level usage stats, data visualization, and data management, bringing more information and utility into the web console. Basic usage metrics and fixed dashboards like these are just the beginning. We believe the apps and interfaces of the future will be highly self-aware, educating users about usage behavior and opening up new feedback loops that encourage better decision-making with less effort.

Log-in to your Keen IO account to check out your project analytics or create an account to get started. We’d love your feedback. Just shoot us a note on Slack.

How Jawbone uses wearable sensor data to drive habit change

Think about every time you take a step, increase your pace, skip a heartbeat, roll over in the night. If you were to count each time, how many total actions would you have in a day, in a week, in a year?! How would you go about keeping track?

More importantly, how could all that data point the way to a happier, healthier life?

In this data and product design speaker series at Keen IO HQ, Sameera Poduri, Principal Data Scientist at Jawbone, describes how they harness wearable sensor data to help people make healthier choices every day. She covers critical considerations like:

  • What is the right architecture for streaming and processing data from a wearable device?
  • What do you do with the wearable sensor data once you have access to it?
  • How do you process and display personalized insights to each user?
  • How do you use the data to inform product features that drive behavior change?

Check out her talk below:

We love chatting about wearable data analytics strategy. If you’re interested in enhancing your wearable device analytics, reach out to us anytime.

Want to be notified about the next live event at Keen IO? Sign up below to get on the invite list.

Alexa Meyer

Growth and UX. Cheese chaser. Aspiring behavioral economist.

Webinar: Introducing Native Analytics

Thinking about introducing data views as a part of your product?

Native Analytics is a suite of tools that empower you to build and ship white-labeled analytics in your product, in record time. 

Peter Nachbaur, our Director of Product, hosted this 30-minute webinar walking attendees through examples of Native Analytics, its features, and how to get it deployed. 


Why Native Analytics?

A few reasons our customers choose to build Native Analytics into their products:

  • To quantify the impact and value of their product to their customer
  • To drive more sales by selling Native Analytics as a premium feature
  • To differentiate their product from competitors (or keep up with them!)
  • To deliver analytics features that customers have been requesting forever
  • To increase user engagement and get customers hooked

Do you have questions about getting started with Native Analytics? Get in touch!


TRANSCRIPT:

Peter:
All right, we’re going to get started with the webinar today about Native Analytics with Keen IO. We’re going to be talking about how to build and ship analytics features for your users, that’s what Native Analytics is all about. I’m Peter Nachbaur. I’m the Director of Product at Keen IO. Been with the company for a long time. I started out as the first engineer, then moved into an architecture role. Today I’m really focused on making use cases like Native Analytics as enjoyable as possible.

Today we’re going to kind of reiterate what Native Analytics is and make sure that you understand what we’re talking about. We’re going to go through some customer examples to highlight some exciting things the folks that we work have done, and then walk through a more specific example to understand the actual features that support Native Analytics and then we will get to questions at the end. Through the Go-to-Webinar interface you can put text questions in there and we’ll get to those at the end of the webinar.

As we start, what is interesting here is that for years Keen IO has built this really scalable and very flexible analytics platform. We can collect data from anywhere and we can help you query that data from anywhere. We’ve supported a number of use cases over the years, a number of different industries for people both working with their data both internally and working with their data externally, but today’s focus is on the Native Analytics use case, which is all about putting your analytics directly in front of your own customers.

This is something that is a little bit different than the internal analytics folks who we are generally familiar with. There’s different latency needs and different privacy needs, but there is also a really, really huge payoff. Rather than working with internal teams you are able to really directly increase the value of the product to your customers and as part of that you are going to be controlling the experience natively so that it kind of increases the perception of your brand.  You’re going to do all of this in a very quick iterative fashion without having to worry about the operational overhead. Keen’s Native Analytics is all about letting you focus on the product design space without having to worry about the broader data infrastructure challenges that we take care of behind the scenes.

Bluecore is one of our most interesting examples. They’re a customer experience platform. They help manage marketing automation campaigns for some really, really major brands; retailers like Staples, and Tommy Hilfiger. What they are able to do is have really fine-grained tracking of the products that they share. When someone is working with you, when Bluecore is working with you, they can say that they do something for you but it is immediately more valuable when they can prove what it does for you. They can get these numbers in front of you and show you the open rate, show you the conversion rate. That’s actually just the beginning. You can then take that data that they show you and more intelligently control the campaigns that you are running and get more out of the automation tools. So it really goes beyond proving what the product does and actually makes the value that much higher.

Pixlee is another interesting example. They work with major brands to get significant conversions from newer social media like Instagram. So they help people track conversions that are otherwise hard to understand and they are able to drive really significant traffic through these brands and in a way that is really valuable to them. But again, being able to actually prove that this is happening is a huge part of it. Being able to understand which pieces of content are most valuable helps these brands come back again and again, to really get more value out of Pixlee.

SketchUp is someone I started working with recently. I just got off a call with them and they do some really fascinating stuff. They are a marketplace for 3D sketches and designs for architects, for manufacturers, for homeowners who are looking to improve things. They are able to allow these designers to understand who is viewing their models and their sketches and sort of begin to create new sketches that will work with their audience even better, to be able to follow up with people who are engaging heavily with the content that is working well for them.


Finally, Mic.com is perhaps one of the most interesting examples on this list because they built this really fantastic tool to show the performance of the media that they are producing. Both the long form articles and videos but the primary audience for one of the tools they built is not just the publishers they work with but actually the internal editorial team, who doesn’t have to worry about the complexities of an analytics platform or a visualization tool, or having to know a whole lot about how to dig into data. All they have to do is go into this system that was built for them and they can consume and ingest the information that they need to do their job better, and that’s natively built into the internal product that the Mic team maintains.

So we are going to now peek under the hood a little bit about the pieces of functionality that we have built that enable this use case. As we do so we are going to talk about this upcoming company you may have heard about called Walruspring. It’s a pretty crazy thing. The ascension intelligent walruses are coming out with really interesting fashion designs that fit the walrus body type and this all coming out of my mind, of course. I haven’t cloned any walruses and given them superpowers, but you can start to understand the features we’re talking about within the framework of a marketplace where clothing designers are coming in on one end and walrus consumers are coming in on the other end and trying to figure out how to match jackets to their tusks.

When a company wants to do Native Analytics they have some shared requirements. Things they need to understand. They need to be able to have fast and reliable query performance because consumers or partners or publishers are not necessarily going to be patient in the way that internal analysts will be. The team that’s delivering this needs to easily manage this for each customer that comes on. In the past when people would try and showcase data to their own users it would be a very manual process - for each user they added, they would have to configure everything and really customize it and have a lot of overhead and headaches there. A lot of the ways that they would end up doing it, it would be very janky - it wouldn’t match the brand and wouldn’t be a seamless part of the experience. So when people are look for Native Analytics, they are looking to be able to have all of these capabilities.

The cool thing is that this is a lot of functionality that we’ve designed and built on top of the core horizontal platform. So as you think about some of these things that we’ve built, I’d be interested to hear how you might want to use them in different use cases beyond just adding analytics into your own application.  The main components here are being able to programmatically create accounts, to manage access, to introduce caching, to get good performance, to be able to embed the charts, and then to be able to fully customize and control the visualizations.

So for Walruspring, when they are onboarding new designers they’ve got a flow where you sign in, you’re introduced to the platform, you get a sense of the features and behind-the-scenes, they start managing your account information. As part of that, they can make a simple API call to Keen that says “Hey, we had this new designer sign up and here is the information about Mark, and we are going to create this project that is totally separate for him. His data will be isolated both for privacy and for performance concerns.” So when they then move on to the next stage to pick out what access Mark should have into the data, it’s really going to be only the data that his consumers are seeing. Once you have those specific projects you are able to then have custom API keys, and these keys can have a lot of functionality baked into them - filters, so if you want to control the exploration and experience that they get, if you want to have different roles, so maybe, within the Walruspring environment, there is a basic tier for designers and there is a more premium tier where they are able to have to have additional access. You can control that through a customer specific key. You can say “Well okay, they have now unlocked this additional potential.”

Additionally, on the flipside, as we’re collecting data as part of a platform, these custom keys can actually automatically add customer specific data and enrichment so that the application itself is worried about here is the core data model that is shared across all customers and then, depending on the key that is used to collect data, one designers data will be added or another designers data will be added. That then seamlessly flows into how you think about the data model. You’re able to have the information about the product, or about the behavior, viewing different pieces of clothing as well as the specific customer properties that let you both do the internal analysis that you have done historically, and then also be able to present that in a way that is meaningful to each individual designer.

So, the Walruspring team is at a stage where they’re creating projects on the fly as new designers sign up. They’re managing the privacy performance of that. They’re collecting the data. Now they’re able to sit back and say “From a product design perspective, what do our walruses need to make sure that they’re selling the most clothing”, (…) make sure they are understanding their audiences so they can go through this exploratory phase and get to that "Eureka” moment of thinking, “Well these are few queries that will really help the designers get the most out of the Walruspring platform.”

Once those queries have been identified, they can be optimized. They can be cached. These high value questions are then going to respond very very quickly, in very predictable fashion, so that the designers, when they are logging in, they’re not seeing really long load time or having a bad experience there, so that they can jump around, but their own exploration is not slowed down by the challenges of the data infrastructure. So caching is a really huge tool. Once you’ve done that initial design-level exploration to figure out what people care about, you can then productionalize individual queries or full dashboards to make sure that experience is perfect.

Once you’ve got those queries it really becomes a matter of embedding the analytics, and this is really what makes it a native experience. We’re not talking about really heavy weight iframes or stuff that has a logo or a brand - you have complete flexibility with these charts, so you’ve got a pretty decent design sense when things come out of the box. You’ve got a color palette that you think is pretty attractive but the thing is that it has nothing to do with your branding and as you are going from that internal use case where it’s really easy on the eyes but a little generic, you are able to then, in the JavaScript itself, control what visualization library you’re using, specific chart types, the size, and the color. We’ve actually seen folks really go the extra mile here, and produce visualizations that would be worthy of the front page of The New York Times. It’s really impressive stuff.

We have a link here that we can share later, we can share an example of all the different ways that the data can be visualized and the library can be used.

Beyond some of these core things about onboarding your customers, and controlling access, and managing performance, you can actually then expose some of the more raw functionality and let the customers dig into CSV files, to be able to pass those reports around but plug them into systems that they already have. So we see use cases where people are regularly sending exports to their users so that they can wire that into existing systems. They can then answer their own questions and really get the maximum value from that. And again, because of the custom keys that we discussed earlier, you can rest assured that the data they have access to is only the data that belongs to them. They are then able to do complex modeling on their own or you can downstream, use the raw data that we’ve collected to start adding more predictive analytics functionality or recognition functionality to, again, keep making sure that they get as much value out of the data as possible.

A final aside is that, along on this theme of natively and seamlessly matching your brands, you can make sure that the API endpoint itself takes advantage of a custom cname so that, in this case, you would be able to send data to api.walruspring.com and there would just be no external way to know that this is powered by Keen. We’re thrilled to be able to help folks with this use case in a white label capacity.

We’ll briefly just talk over this little demo site that we’ve got that shows the Walruspring experience. This first page is what the consumer walruses would be seeing, and they’re able to kind of take a look at all this ridiculously creative clothing that the walrus designers have put together. I think, of these, I’m really feeling the pajamas right now. It’s little chilly in the office and I wish I had some nice fuzzy stars.

To reiterate, this is the same data that you are probably already collecting today, you’re thinking about the flow, you’re able to think about what the product ID is, the product name, all that sort of core information; and as the consumer is progressing through the site, different events are being sent. All that information is being tracked. As they continue down the flow, they check out, more data is collected. When they purchase it’s a really big piece of data that is an important part of the flow.

As far as the consumer is concerned that is just the experience. The analytics is not detracting from that. They’ve gotten these awesome, super comfy button-up pajamas, for a really reasonable price, and they can go on with their lives. But then, internal to the product experience, a Walruspring designer is going to be able to login in and have this analytics tab, which shows them what their conversion rate is, what their repeat purchase rate is, and really help them understand, “Okay, for the different products that I’m offering I’m really seeing heavy conversion in this area but pajamas are somehow really popular and that’s a bit unexpected and we can actually maybe look into why that might be.” As a design team you think “Okay, well we should make a couple different types of pajamas and really capitalize on that.” That’s the kind of decision making the customers can feel really good about. So, not only are they getting value from all of Walruspring because it connects them to these consumers and they can put a product up on the website and get sales from it, but they can actually then have this additional information to get more in depth sales.

So, to reiterate, the Native Analytics is all about full control while still having a very simple integration and an integration where you don’t have to worry about the challenge of data construction, the challenge of scaling. You can focus on the really fascinating questions on what makes your customers happier, what makes your customers smarter. These are often very aligned with the metrics that help you grow your product internally, as a product team or as a marketing team. You’re already looking at some of this information in aggregate but you can slide around a little bit and put yourself in their shoes, or wear their tusks for a day, to understand, as they’re doing their job, and as they’re using your product, what will help them get the most out of it and what is a natural progression. So they’re logging in and they’re going to check on the functionality that you’ve already got, whether that’s marketing automation, or social media conversions, or walrus clothing. What’s going to be a natural part of their flow and is that a full-on dashboard? Is that just small pieces of data that are wired directly into other parts of their experience? It’s a pretty open-ended question. It’s something that I encourage you all to think about. It gives you a lot of flexibility to iterate quickly.

So, be happy to take some questions. Both about the specific features and details that we talked about there or the use cases that we’ve seen.

Author’s note: apologies for the echoes and inaudible moments in the recording during the Q&A, and the impact this had on our transcript!

Sarah-Jane:
All right. This is Sarah-Jane. We’ve got a couple of questions in through our question interface and we’re going to leave that open a little while, so if you have any questions feel free to drop those in. Just give me one second while I bring those up.

All right. So the first question is what other charting libraries can it support and can I use my own visualizations?

Peter:
Yes, absolutely. So, on that previous slide where we’ve been looking at the visualization code you can very easily toggle between charting libraries, Google charts, C3, D-3, those are all powered by our API. The raw JSON that is returned can be visualized by any system that you’re more comfortable with.

Sarah-Jane:
Great. Another question here. Can I update the metrics that I show my customers without coding once Keen is implemented?

Peter:
Yes and no. There’s a really cool way to be able to do that today and we’ve actually got some products available to make that even easier. But we have saved queries where as you’re doing exploration of the data, thinking about what’s valuable in terms of that  that “Eureka” moment I was mentioning, you can then save that actual query. You can actually tie that to a custom API key and say, “This key for Tommy Hilfiger only has access to these saved queries.” Maybe have a Tommy Hilfiger Average Purchase query. If you then make a chart for that and put the metric for that chart into your products you can go back and using our interface, change the saved query and where you have embedded and then you just naturally take advantage of the changes that you’ve made to that saved query.

Sarah-Jane:
Great. Got a question here. Is it possible to track location natively with Keen? Would you just send an event with the locational information?

Peter:
Oh yeah. You have a handful of different tools and put those into add-ons into these custom API keys and geolocation wise (…) because it lets you do mapping views of data to figure out by state, by zipcode, by country, where people are buying all this clothing, you can take (…) patterns,  you can take an IP address and can expand that out into geographic information. We can give you latitude and longitude so you can really map that info . We have some stuff in the backboard there that geographic… everything’s basically even more powerful.

Sarah-Jane:
Great. One more question here. What is the current timeline to get Native Analytics implemented?

Peter:
Oh, that’s a great question. So, Bluecore, which was one of the examples we talked about earlier, is probably the most impressive example. They were able to get that dashboard looked at briefly and up-and-running in less than 24 hours. It was actually just built by an internal team that they had. I know that the mic.com team was able to get that information to their editors as part of their workflow in a matter of days. Generally the answer that I give is tied to an individual company’s development workflow. So, if your team is pushing code to production in a matter of hours then you can absolutely start making these changes very quickly. (…)

Sarah-Jane:
Great. I believe we’re going to be answering question for a little bit longer. If there’s anymore… We’re going to be sharing the recording of this webinar, so if you have any further questions go through one of the channels listed on this final slide. For our Slack chat go to keen.chat and join us there with any questions you may have. Maybe check out our Github repos and, of course, tweet us any time and tweet us on our website with any further questions. Have any closing thoughts, Peter? I will hand it over to you… Oh, we have some more questions. One second, here… alright, what’s the pricing model? Is it by license or SaaS subscription?

Peter:
Yeah, that’s a good question. So, we look to understand your usage, unlike products like a marketing automation platform for example, where you’d be sending out a very discrete, understandable number of emails. With Keen, the data that you’re tracking… the volume can get very large. A whole bunch of data has allowed us to look at that but the impact in usage varies dramatically depending on whether you’re doing a couple queries a minute, or an hour, or a day for that internal use case that some of you are familiar with. On the Native Analytics side, what we like to understand to get a sense of usage is how many customers you’re looking to serve today, to ensure the number of queries you’re going to be looking to optimize and keep running so that they get that seamless cached experience. Because our core offering is a very flexible and very customizable API, then we’ll perfectly match your product needs to let you build exactly the analytics experience that you want. There isn’t really a one-size-fits-all, if you’re going to be querying across hundreds of millions of events and you’re going to be doing that thousands of times an hour then it’s going to be more expensive than if you only have a few million events and fewer customers asking questions across that data.

Sarah-Jane:
Great. Another question is, “Do you offer machine learning algos on the data and if not, how can one run a custom routine on your data”

Peter:
So, we don’t have API-level functionality around running machine learning today, although it is definitely part of our design and roadmap. So what we do to meet that use case is our solutions architects will sit down and take a look at the raw data that’s available and help put together the laws that make sense. Generally what folks want to be able to do is have offline classifiers that you’d be able to actually, as part of a clone, make online checks against that. We’ve got a handful of folks on the team, myself included, have done that a number of times. What did end up happening was largely downstream from the data production and analysis and Native Analytics features that we were talking about today.

Sarah-Jane:
All right. So that’s pretty much it. No more questions. Thanks so much Peter and thanks to everyone on the webinar today. As I mentioned, we’re sharing the recording and feel free to connect with us on our community channels. Have a great day.

Peter:
Looking forward to hearing more and let me know how I can help.

Sarah-Jane Morris

Community and Awareness

Introducing the Keen IO Community Code of Conduct

A few weeks ago we sent an email to the whole company introducing the Keen Community Code of Conduct. This blog post includes most of that email with a few more things added that we wanted to share with our community.

A few months ago the work began on the Keen IO Community Code of Conduct. We’re very excited to announce v1.0 of the Keen IO Community Code of Conduct is now public. 🎉

This Code of Conduct applies to all Keen IO Community spaces, such as the Community Slack group, open source projects, Keen IO meetups, Happy Data Hours, and more! It will be added over the next few weeks to different projects and other community spaces.

It is the product of many meaningful conversations and advice from many Keenies and other humans from outside of Keen IO. To anyone that contributed to this Code of Conduct, thank you. The process of creating a document like this isn’t easy, and we have so much respect for anyone who has done it before.

The Code of Conduct is a living document. This is only v1.0. It will grow and change with Keen IO and its community. This is why it is on Github. Issues can be created to help with revisions and updates. There is also a feedback form, which can be filled out anonymously. Feedback is always appreciated. It will also help guide training and more internal procedures for the Community Code of Conduct.

Lastly, we’re looking forward to making it even clearer to our community that we are dedicated to providing a safe, inclusive, welcoming, and harassment-free space and experience for all community participants, which will help grow our community in amazing ways. We hope this Code of Conduct clearly states what behavior is expected and not tolerated as well as establishes a path for community members to report possible incidents and seek help.

Please feel free to ask me any questions! I would be more than happy to have a larger conversation about it and its existence. 😀

Taylor Barnett

developer, community builder, and huge fan of tacos

Keen IO Secures 14.7M in Series B Financing

Press release originally posted to Business Wire

Keen IO Secures $14.7M in Series B Financing to Accelerate the Adoption of its Leading Cloud Analytics Platform and Intelligence API

Financing Led by Pelion Venture Partners with Continued Participation by Sequoia

Keen IO, the leader in cloud analytics, today announced it has closed $14.7 million in a Series B financing round. This funding brings Keen’s total capital raised to date to approximately $30 million and supports the company’s accelerating global growth in the enterprise.

Keen now provides service to more than 3,000 customers and recently crossed the 50,000 developer milestone. The Keen platform has been integrated across a wide range of categories, including mobile, gaming, e-commerce, media, IoT and retail. The company also recently launched Native Analytics, a comprehensive product offering enabling customers to white-label real-time analytics and custom visualizations at Keen IO scale.

“We are thrilled to announce this financing and welcome these visionary investors as partners to our team,” said Kyle Wild, CEO and co-founder of Keen IO. “Our original vision of making the world’s best data science tools available to all developers is continuing to bear fruit. We now see our platform taking hold as an ‘Intelligence API’. In our fast-moving, hyper-connected world, everything that connects to Keen’s API becomes more intelligent through the power of data science.”

Investors Align to Make Keen IO the Standard for Cloud Analytics

Keen IO’s Series B attracted a prolific group of investors. The round was led by Pelion Venture Partners, with continued participation by Sequoia Capital, who led the Series A financing. Several other institutional investors, including Rincon Venture Partners, Amplify Partners, and Rothenberg Ventures, also participated, joining original seed investors Techstars, 500 Startups, Heavybit, and Galvanize Ventures.

“Keen IO is providing the best of breed data and analytics platform for the API economy,” said Ben Dahl, partner at Pelion Venture Partners who will join the board of directors as part of this financing. “We see expansive potential for the impact Keen can have as it continues to scale its footprint from the mid-market to the enterprise, empowering its customers to have analytics as a core competency.”

Hewlett Packard Pathfinder joined the Series B as a strategic investor and partner.

“As part of Hewlett Packard Pathfinder’s mission, we invest and partner with innovative startups like Keen to help them scale while meeting the needs of our enterprise customers,” said Lak Ananth, managing director of Hewlett Packard Pathfinder. “Keen uniquely addresses the need for our enterprise customers to embed analytics effortlessly and ubiquitously into their applications.”

The Next Phase of Growth for Keen IO and the Intelligence API

The company hosts a highly scalable cloud API that allows companies to collect, enrich, pipe, store, and analyze custom event data. Customers integrate Keen into all parts of their internal business infrastructure, marketing pipeline, and customer-facing products.

“We are seeing accelerated adoption of Keen’s Intelligence API,” said Will O’Brien, COO of Keen IO. “We are experiencing rapid growth of new accounts and new industries, while at the same time our existing customers are finding new use cases and integrating Keen in every part of their digital ecosystem. We are honored to be a core partner to their amazing and innovative journeys, and this financing will enable us to meet the increasing market demand even faster.” Keen employs 50 people and is headquartered in San Francisco.

// About Keen IO

Founded in 2011, Keen IO (https://keen.io/) is the leading cloud analytics platform used by more than 3,000 customers to collect, enrich, pipe, store, and analyze custom event data at a truly massive scale. The company has raised approximately $30 million in venture capital from Sequoia Capital, Pelion Venture Partners, Hewlett Packard Enterprise, Rothenberg Ventures, and other institutional investors. Keen IO is a core platform to customers in a variety of industries, including media, e-commerce, adTech, gaming, IoT and retail. The company’s cloud analytics API queries trillions of events daily, with operational performance required by the enterprise. Keen hosts an open-source visualization library that is the largest of any private company with over 900 forks and 8,500 stars on GitHub.

To get started with Keen IO, visit keen.io or email us at team@keen.io

We are hiring! Read about our job openings: https://keen.io/careers/

Press contact:

Joe Volat

press@keen.io

(408) 645-0448

Will O'Brien

COO @ Keen IO

Announcing our Series B

Hi! I’m Dan, one of the co-founders of Keen IO. If you’re reading this, you might already have seen our official press release announcing the closing of our Series B round of financing.

My co-workers and I have been building this company for four and a half years. Like most entrepreneurs, we’ve poured our fair share of sweat and tears into this company. And, as our press release says, we’ve accomplished some amazing things. Through a combination of hard work and dumb luck, we’ve got over 3,000 customers. Our developer community has passed 50,000 members. We have some of the best investors in the world. And we’ve just raised another 14.7 million dollars.

These are all true things. Which is wonderful, and grand, and lovely.

But.

We have plenty of opportunities to trumpet our successes. I’d rather not contribute yet another “we raised money so we’re the best company that’s ever existed” blog post to the Valley echo-chamber. And right now I’m asking myself: “What do I want you, my kind reader, to know about Keen IO?” It’s not an easy question for me to answer. There are lots of facts that I want you to know.

If you’re our customer, I want you to know how appreciative I am. We wouldn’t exist without you. Full stop. You’re the reason we wake up in the morning (and sometimes the middle of the night). If you’re happy with us, I hope we continue whatever it is that makes you happy. And if you’ve got some frustrations, I hope you let us prove to you that we can fix what you’re frustrated about.

If you’re a potential customer, I want you to know what problems we can solve for you. I want you to make an educated buying decision in this increasingly noisy and complicated analytics landscape. I want you to solve the analytics or data problems you’re thinking about. And, in the best cases, I want you to use Keen and get promoted for doing so.

If you’re an existing investor, I want you to know how much you’ve taught us. We’ve come a long way since three best friends with a vague idea that building custom analytics solutions should be a lot easier. You’ve been an instrumental part of that. And, I hope that we’ve taught you a thing or two as well.

If you’re a potential investor, I want you to know that you’re not going to hear much from us for a while. Let’s talk in a bit. I’m looking forward to pausing VC conversations and focusing entirely on building our business. ;)

If you’re a curious reader, I want you to take something useful away from this. I hope I’ll make that happen for you!

Finally, if you’re one of my co-workers, I want you to know that you’re the special sauce. You’re the reason I love working at Keen. I want you to know how important you are to me, even if I don’t show it all the time. I want you to know I’m a big old introvert and me not hanging out at happy hours doesn’t mean I don’t think you’re amazing. I want you to know how grateful I am to work with you.

And that, I think, might be the key to all of this. For me, at least. Gratitude. It’s easy to lose sight of how grateful I should be that I can do the kind of work I do, with the people I do, and for the customers we have.

So let’s end on that note.

I’m grateful that we raised $14.7 million in our Series B from world-class investors.

I’m grateful that we will use that money to continue serving our amazing customers.

I’m grateful that we will continue to serve our customers by building innovative analytics products backed by really cool technology.

I’m grateful that we will continue to build a workplace that invites its members to bring their full selves to the office.

And now I’m grateful to gently close one chapter of Keen IO’s history and begin the next. I can’t wait to see what it brings.

Daniel Kador

Software engineer, entrepreneur, geek, all around okay guy.

Follow Stream to Cabin & a Native Analytics wonderland...

Yesterday we told the world about Native Analytics - the latest offering from Keen IO, which is helping developers and businesses embed analytics and deliver insights to their audience (customers, partners, etc) via custom visualizations within their user-facing products.

Today, we’re announcing a project in collaboration with our friends at Stream.

The project is called Cabin - cute, huh? Cabin offers developers a series of tutorials to teach you how to build an application using React and Redux. Each installment in the series is based on a unique technology (APIs, cloud-services, etc) that makes it easy for a developer to add meaningful functionality to her app. With the powers of each “component” combined, one can create a full-featured application with relative ease.

Where Keen fits into Cabin

One important aspect of Cabin is its analytics page, which displays stats like how many people have viewed your profile and the number of views for each image you’ve submitted to the Cabin community. They used features from Keen’s Native Analytics to power the stats page of the Cabin React example app. Here’s a screenshot of what it looks like:

How did this come to be?

Stream - the API for scalable feeds - asked us to join them and several other developer-tools companies to create Cabin, which is, at its heart, an tutorial-based educational experience to help developers learn how to build a feature-rich, scalable social network application.

We’re quite stoked for this opportunity to work with other great companies to support the growth and advancement of software development, while illustrating the powerful possibilities that open up with the combination of some of the world’s most advanced APIs and platforms.

If you’re stoked, too, head on over to Stream’s blog for the full tutorial. And go here to learn more about the Cabin project, other participating companies, and further tutorials for learning fun!

Tim Falls

community crafter and scarf enthusiast

Why We Built Native Analytics

Four years ago, my friends and I embarked on a quest to build a business. We were going to build an analytics API. We dreamed big. Our platform was going to be so elegant and so powerful that, basically, anyone building anything connected to the internet would want to send data there. It would help people understand. It would help them automate. It would help them explore. It would help them discover. It would be the nervous system for the next great version of the internet.

In those four years, we’ve made some progress :). We built a database in the cloud. We built an API that allows you to query it, in real time, from anywhere. We built charting libraries so that developers could paint their apps with pies and bars.

And the whole time, we watched, delighted and amazed, to see what developers would create with these building blocks we had fashioned for them. We beamed when they showed us their dashboards. We cried when a real life rocket scientist showed us data from the Mars rovers. We watched, and we listened, as we learned, as thousands of developers integrated Keen into their products and built their businesses.

One of the main patterns we noticed was that many people were using our platform not only to analyze their business data, but to display data in the applications they were building for their users.

I’ve always thought of our business as a very organic thing, a jungle with wide-reaching and thickening roots, spreading and collecting data from all over the earth (and space!). But there is so much more to it than collecting data. The most exciting part has been seeing how people have taken that data and reflected it back out into the world. To see those roots grow into trees, and branches, and a striking variety of leaves and flowers.

It was this common usage pattern, alongside the growing demand for analytics views in nearly every modern SaaS application, that inspired us to build a number of core features that make building analytics natively into your application even faster and more powerful.

Today we’re announcing Native Analytics, a core product on the Keen platform. I hope you’ll take a peek at our new landing page, our product overview, and possibly even drop a note (see chat button, bottom right!) to talk about building analytics into your product. We’ve learned a lot over the years and look forward to building and sharing with you as we continue to grow.

Michelle Wetzler

Chief Data Scientist