Descartes Labs launches its geospatial analysis platform

Descartes Labs, a New Mexico-based geospatial analytics startup, today announced that its platform is now out of beta. The well-funded company already allowed businesses to analyze satellite imagery it pulls in from NASA and ESA and build predictive models based on this data, but starting today, it is adding both weather data to its library, as […]

Descartes Labs, a New Mexico-based geospatial analytics startup, today announced that its platform is now out of beta. The well-funded company already allowed businesses to analyze satellite imagery it pulls in from NASA and ESA and build predictive models based on this data, but starting today, it is adding both weather data to its library, as well as commercial high-resolution imagery thanks to a new partnership with Airbus’ OneAtlas project.

As Descartes Labs co-founder Mark Johnson, who you may remember from Zite, told me, the team now regularly pulls in 100 terabytes of new data every day. The company’s clients then use this data to predict the growth of crops, for example. And while Descartes Labs can’t disclose most of its clients, Johnson told me that Cargill and teams at Los Alamos National Labs are among its users.

While anybody could theoretically access the same data and spin up thousands of compute nodes to analyze it and build models, the value of a service like this is very much about abstracting all of that work away and letting developers and analysts focus on what they do best.

“If you look at the early beta customers of the system, typically it’s a company that has some kind of geospatial expertise,” Johnson told me. “Oftentimes, they’re collecting data of their own and their primary challenge is that the folks on their team who ought to be spending all their time doing science on the datasets — the majority of their time, sometimes 80 plus percent of their time — they are collecting the data, cleaning the data, getting the data analysis ready. So only a small percentage of their work time is spent on analysis.”

So far, Descartes Labs’ infrastructure, which mostly runs on the Google Cloud Platform, has processed over 11 petabytes of compressed data. Thanks to the partnership with Airbus, it’s now also getting very high-resolution data for its users. While some of the free data from the Landsat satellites, for example, have a resolution of 30m per pixel, the Airbus data comes in at 1.5m per pixel across the entire world and 50cm per pixel over 2,600 cities. Add NOAA’s global weather data to this, and it’s easy to imagine what kind of models developers could build based on all of this information.

Many users, Johnson tells me, also bring their own data to the service to build better models or see

While Descartes Labs’ early focus was on developers, it’s worth noting that the team has now also built a viewer that allows any user (who pays for the service) to work with the base map and add layers of additional information on top.

Johnson tells me that the team plans to add more datasets over time, though the focus of the service will always remain on spatial data.

TV Time debuts an analytics platform for the streaming era

TV Time, the consumer app that helps bingers keep track of where they are with favorite shows and socialize with fellow viewers, is today expanding its business with the launch of an analytics platform called TVLytics. The new service will allow creators and distributors to tap into real-time data from across more than 60,000 TV shows. It […]

TV Time, the consumer app that helps bingers keep track of where they are with favorite shows and socialize with fellow viewers, is today expanding its business with the launch of an analytics platform called TVLytics. The new service will allow creators and distributors to tap into real-time data from across more than 60,000 TV shows. It will also offer other anonymized data collected from viewers, including things like on which platforms viewers watched, their favorite characters, bingeing behavior, viewers’ locations, anticipation from fans for new episodes, social engagement and more.

The data is pulled from the app’s community of around a million daily users from more than 200 countries who check in with the app some 45 million times per month. To date, TV Time has tracked more than 10 billion TV episodes, and has seen 210 million reactions.

TV Time began its life as a source for TV show GIFs known as WhipClip, but later pivoted to a social TV community after acquiring TVShow Time in December 2016. This proved to be a smart move on its part, as the company has grown to 12 million registered users (and growing).

The app’s core functionality is focused on offering TV viewers a place where they can follow shows and mark off the ones they’ve watched — something that’s especially helpful in the streaming era where people are often hopping from one binge-watching session to another, then back again, or are watching multiple series at once and need to remember where they left off.

In addition to being a utility for tracking shows, the app offers a community section for each episode where fans can post photos, videos, GIFs and memes, as well as like and comment on the content others share. Viewers can even leave video reactions about each episode, in a format similar to the “Stories” found on apps like Instagram or Snapchat.

TV Time also interjects questions of its own — asking about your reaction (good, funny, wow, sad, etc.), favorite character, device watched on and more. And it inserts its own polls in the middle of the fan discussion page, which ask about pivotal moments from the episode and what people thought.

With the launch of analytics, TV Time aims to make use of all this data by offering it to clients in the TV industry who are looking for more comprehensive viewership data for planning purposes.

Of course, TV Time’s data is not a Nielsen equivalent — it’s user-generated and self-reported. That means it’s not going to be able to tell content creators, networks, distributors and other clients how many people are watching a show exactly. Nor can it give a holistic overview of the show’s fan base. TV Time’s viewers skew younger — in the 18 to 34-year-old range — and only around 10 to 15 percent are based in the U.S., though that market is the fastest growing.

But TV Time can tap into the reactions and sentiments shared by a subset of a show’s most engaged fans.

Its paying clients today include a handful of TV networks, streaming services and talent agencies that have been testing the app in beta for around a month. They use TV Time’s analytics to help spot trends, develop and expand a show’s audience and make decisions about how to cast and market their shows. Some have also used it in advertising negotiations. Customers pay a flat annual subscription fee for access to this data, but TV Time won’t disclose exact pricing.

“We’ve been testing it to figure out which of the insights we’ve launched are most valuable. That’s how we landed on things like the completion rate, the binge rate, affinity reports, mobility scores and favorite characters,” explains TV Time head of Programming, Jeremy Reed.

The value offered by TVLytics data doesn’t just come from the data itself, but also how hard it is to collect. In today’s fragmented TV viewing ecosystem, consumers now watch across devices, and split their time between live TV, recorded TV, live TV delivered over the internet, subscription video services and internet video sites, like YouTube.

In addition, TV Time notes that, overall, the number of long-form shows on television has grown by 69 percent since 2012, with nearly 500 scripted original series airing in 2017, citing data from FX Research Networks. The majority of these scripted shows are coming from over-the-top platforms such as Netflix, Amazon and others. That’s a lot of TV content to keep up with, especially as consumers hop between devices — even in the midst of a single episode.

What TV Time does is keep all this viewing data together in a single destination, and can make connections about what viewers are watching across platforms — from TV to Netflix and beyond.

“With studios — they’re looking two years out in producing content. They start to see trends in types of characters, and certainly start to see the characters of this show resonate with the characters of this other show and start to see the overlap,” notes Reed. Plus, he adds, that overlap is “agnostic to platform.”

TV Time data is put to use for consumers as well, in terms of helping to recommend their next binge.

And now its community is demanding the ability to track movies, too — especially now that streaming services are backing their own feature films. Reed says this isn’t something TV Time has planned for the near-term, as there’s so much to do around episodic content — but that it’s absolutely “a never-say-never” kind of thing, he hints.

Santa Monica-based TV Time’s team of 35 is backed by $60+ million in funding, according to Crunchbase, from investors including Eminence Capital, WME, IVP, Raine Ventures and Greycroft, plus individual entertainment and media industry executives like Ari Emanuel, Peter Guber, Steve Bornstein, Scooter Braun, Gordon Crawford and Ron Zuckerman.

Outlier raises $6.2 M Series A to change how companies use data

Traditionally, companies have gathered data from a variety of sources, then used spreadsheets and dashboards to try and make sense of it all. Outlier wants to change that and deliver a handful of insights right to your inbox that matter most for your job, company and industry. Today the company announced a $6.2 million Series […]

Traditionally, companies have gathered data from a variety of sources, then used spreadsheets and dashboards to try and make sense of it all. Outlier wants to change that and deliver a handful of insights right to your inbox that matter most for your job, company and industry. Today the company announced a $6.2 million Series A to further develop that vision.

The round was led by Ridge Ventures with assistance from 11.2 Capital, First Round Capital, Homebrew, Susa Ventures and SV Angel. The company has raised over $8 million.

The startup is trying to solve a difficult problem around delivering meaningful insight without requiring the customer to ask the right questions. With traditional BI tools, you get your data and you start asking questions and seeing if the data can give you some answers. Outlier wants to bring a level of intelligence and automation by pointing out insight without having to explicitly ask the right question.

Company founder and CEO Sean Byrnes says his previous company, Flurry, helped deliver mobile analytics to customers, but in his travels meeting customers in that previous iteration, he always came up against the same question: “This is great, but what should I look for in all that data?”

It was such a compelling question that after he sold Flurry in 2014 to Yahoo for more than $200 million, that question stuck in the back of his mind and he decided to start a business to solve it. He contends that the first 15 years of BI was about getting answers to basic questions about company performance, but the next 15 will be about finding a way to get the software to ask good questions based on the huge amounts of data.

Byrnes admits that when he launched, he didn’t have much sense of how to put this notion into action, and most people he approached didn’t think it was a great idea. He says he heard “No” from a fair number of investors early on because the artificial intelligence required to fuel a solution like this really wasn’t ready in 2015 when he started the company.

He says that it took four or five iterations to get to today’s product, which lets you connect to various data sources, and using artificial intelligence and machine learning delivers a list of four or five relevant questions to the user’s email inbox that points out data you might not have noticed, what he calls “shifts below the surface.” If you’re a retailer that could be changing market conditions that signal you might want to change your production goals.

Outlier email example. Photo: Outlier

The company launched in 2015. It took some time to polish the product, but today they have 14 employees and 14 customers including Jack Rogers, Celebrity Cruises and Swarovski.

This round should allow them to continuing working to grow the company. “We feel like we hit the right product-market fit because we have customers [generating] reproducible results and really changing the way people use the data,” he said.