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Oribi, an Israeli startup promising to democratize web analytics, is now launching in the United States.
While we’ve written about a wide range of new or new-ish analytics companies, founder and CEO Iris Shoor said that most of them aren’t built for Oribi’s customers.
“A lot of companies are more focused on the high end,” Shoor told me. “Usually these solutions are very much based on a lot of technical resources and integrations — these are the Mixpanels and Heap Analytics and Adobe Marketing Clouds.”
She said that Oribi, on the other hand, is designed for small and medium businesses that don’t have large technical teams: “They have digital marketing strategies that are worth a few hundred thousand dollars a month, they have very large activity, but they don’t have a team for it. And I would say that all of them are using Google Analytics.”
Shoor described Oribi as designed specifically “to compete with Google Analytics” by allowing everyone on the team to get the data they need without requiring developers to write new code for every event they want to track.

In fact, if you use Oribi’s plugins for platforms like WordPress and Shopify, there’s no coding at all involved in the process. Apparently, that’s because Oribi is already tracking every major event in the customer journey. It also allows the team to define the conversion goals that they want to focus on — again, with no coding required.
Shoor contrasted Oribi with analytics platforms that simply provide “more and more data” but don’t help customers understand what to do with that data.
“We’ve created something that is much more clean,” she said. “We give them insights of what’s working; in the background, we create all these different queries and correlations about which part of the funnels are broken and where they can optimize.”
There are big businesses using Oribi already — including Audi, Sony and Crowne Plaza — but the company is now turning its attention to U.S. customers. Shoor said Oribi isn’t opening an office in the United States right away, but there are plans to do so in the next year.
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Of the various channels available to growth marketers, podcast is among the most misunderstood.
Brands like Dollar Shave Club, Squarespace, and ZipRecruiter have deployed podcast advertising for user acquisition for years, but it’s still a channel that flies under the radar. We have managed tens of millions of dollars in podcast ad spend for challenger brands and market leaders alike, and are eager to share some tricks of the trade.
If you want to test in a channel where early adopters are being rewarded with both attractive CAC and scale, here’s what you need to know:
Dive deeper on podcast ads and other growth marketing tips with Extra Crunch’s ongoing coverage of growth marketing, where Right Side Up was recently featured as a Verified Expert Growth Marketer.
Podcast listeners are a sought after group – the audience trends towards educated, early adopters with a high household income. You can find this profile elsewhere, but what makes podcasts unique is that they are choosing to consume that particular content time and time again. The host becomes a trusted voice to deliver them not only interesting stories and banter, but information on companies as well.
Often podcast advertisers are newcomers or start-ups, and the podcast ad might be the first time the listener has heard about that company. Having the first touch with consumers be from a thorough, personal, and often funny host-read interaction is incredibly valuable and helps brands jump over the credibility hurdle. Compare that to an impersonal banner ad, and I’d choose a podcast ad every time. 
Even though the term ‘podcast’ was coined in 2004, advertising in the medium has exploded in the last ~5 years. The IAB has been tracking podcast ad revenue since 2015, when the entire medium generated #105.7 million in ad sales. It recently released its third study of podcast ad revenue, which estimated the US market at $479 million in 2018, with growth accelerating to a projected $1 billion+ by 2021.
Andreesen Horowitz did a great investor profile on the space earlier this year, with a helpful rundown of the holistic ecosystem, from hosting mechanisms and platforms to the pace of podcast monetization.
Historically, the medium has been dominated by a mix of comedians doing their own thing, radio entities simulcasting sports shows, and otherwise popular shows that had a devoted niche following relative to other mediums. Most advertisers bought podcast ads as an extension of their other audio acquisition campaigns.
Then Serial came along, in 2014, exploding into popularity and pop culture. They ran a MailChimp ad that had someone mispronouncing the name of the company as “MailKimp”, which was a funny inside joke for those in the know. Nina Cwik and David Raphael, co-founders of Public Media Marketing, explain the initial conversation around this now iconic spot.
“While discussing a launch sponsorship with sponsors there wasn’t a huge amount of interest in taking a risk on a new show even with the amazing This American Life provenance. MailChimp was committed to supporting Serial. The talented production team at Serial and This American Life created MailKimp and the sponsor was rewarded for believing in the show.”
Not only were they rewarded by being a launch sponsor of one of the most successful podcasts in history, but once Serial and the medium itself expanded, a loving impersonation of Serial host Sarah Koenig and the MailKimp joke eventually made its way into a Saturday Night Live skit. Serial also appealed to a female audience, helping to bring new listeners into the channel, and podcasters and advertisers followed.
Over the past 5 years, the space has diversified. We now see so many different shows with all flavors of true crime, news and politics takes that you don’t hear in the broader media picture, women talking to other women about literally everything, comedy and pop culture pods as diverse as Bodega Boys, Who? Weekly, and RuPaul: What’s the Tee with Michelle Visage, and a podcast to go with every reality and television show you can think of. There are too many shows to talk about; there are over 750,000 shows indexed by iTunes.
So how do companies start testing in podcasts? And how do they do so successfully?
We advise companies to start with a test spend that you consider meaningful in the context of your other customer acquisition efforts. Initial tests in the channel that are properly diversified typically vary from $50,000 to $150,000 in media cost. If the idea of a testing budget in the high five figures makes you gasp, don’t rush it. If you under-invest, you run the risk of a false negative, i.e. you didn’t spend enough to validate performance, or a false positive; when you buy tiny shows, one or two sales may pay back. If you make media decisions at scale based on that data, you may find yourself in deep water. If the risk of testing a new channel and having a dip in your CAC is too great, we recommend you exhaust other channels, like Facebook, before jumping into the podcast space.
Podcast offers advertisers a low barrier to entry. Creative production is limited to producing copy points for hosts to use as they record their ad reads. However, it is quite manual relative to digital channels, and can take weeks to put into place. Most purchasing is done through a show’s sales representation or network, via calls and emails, and set in advance (sometimes way in advance depending on inventory levels). It entails RFPing multiple network partners, doing research and outreach to independent shows, gathering rates and evaluating content, and finally making decisions based on budget and inventory availability. We often describe this as the media puzzle – making sure that the ideal shows, with favorable pricing are available when you want them to be. This can take time and some back and forth with your network rep to set in stone, so give yourself room to plan ahead.
We buy with a lot of direct shows, sales representation firms, and ad networks. We’re starting to see the beginnings of programmatic and exchange-based inventory become available, but it’s largely impression-based media, which isn’t yet a proven tactic that direct response-oriented advertisers can consistently use for customer acquisition. There are some managed service-like buying partners in the space, that work to varying degrees of efficiency for customer acquisition.
When it comes to choosing what types of shows to partner with, beyond budget and availability, it’s important to remember the obvious choice may not be the best one.
One of the most consistent, and pleasant, surprises in podcast advertising is how well shows that are seemingly unrelated to a product work well for customer acquisition. We’ve worked on products that had a primary target demographic of suburban moms, but guess what? Gamers want to stay at home and order snacks and food delivery, too; they have disposable income and are harder to reach via traditional channels.
If you’re advertising a product targeted to parents, you shouldn’t just test into parenting shows, you should also consider testing into shows with hosts who are parents, but have content not at all or tangentially related to parenting, like Your Mom’s House, with Tom Segura and Christina Pazsitzky. Sure, it’s a comedy podcast, and it’s NSFW (and hilarious). They’re also human parents who they do amazing reads, and their fans are legion.
Ryan Iyengar, CMO of HealthIQ, notes that “hosts with wildly different backgrounds were able to find a through-line to connect ad reads with their audiences, regardless of product line.” Of course, contextual advertising is worth consideration, and there are sometimes unique opportunities, but most successful shows aren’t a bullseye for content.
We’ve also seen the inverse, on contextual fit; food products can either do amazing or not well at all on food-related podcasts. If you have a food product with mass appeal, but one that (for example) many home cooks may already be familiar with, you may be better off doing just about any other popular genre of shows besides food.
Plus, these hosts are pros; they’ve been doing ad reads for everything from mattresses to meal kits for years. They know how to talk about your product in an engaging way.
Doug Hoggatt, the VP of Marketing at Betabrand, agrees, mentioning he would also coach new advertisers to “take the time to test across genres and hosts, you’ll be surprised at the results.” Iyengar is also the former VP of Marketing at ZipRecruiter; if you’ve ever heard a podcast, you may have heard the company advertised once or twice. He also notes, “[regardless of] content of the show, audiences can be interested in all sorts of topics, and are still potential customers. Yes, even hiring managers listen to comedy podcasts!”
Many business-to-business (B2B) advertisers do well in the channel, in part due to higher allowable CAC and high lifetime value (LTV). And the same point about show selection holds true for those audiences, as well. Visnick noted, “[HoneyBook] originally focused on testing industry-specific podcasts as those seemed to be the most natural way to target our prospective customers. We discovered that by diversifying our podcast mix into non-industry content we could still reach our target audience while also growing our reach and overall program performance.”
If we hear something that we think can help us at work, we’re amenable to that message, especially when it comes from our favorite host. Having an open mind to testing has helped so many advertisers unlock additional shows, and possible customers. You can take those insights back to other channels, too, and begin to integrate your campaigns and establish cross-channel frequency.
Pricing in the channel is unstable, and demand-based because inventory is finite; effective CPMs for host read, embedded mid-roll advertisements — by far, the most consistently performing ad unit for customer acquisition in the space — vary from $10 to $100. Yes, really.
Worrying too much about CPMs could mean that you’re leaving behind some of the best inventory in the space. So while it could make sense to cut higher CPM placements from a media plan, you want to be cautious. You could inadvertently cut out potential volume drivers or otherwise highly effective placements.
The listener is there for the hosts. They relate to them, laugh with them, or laugh at them. They come to expect a performance from them, and often that performance bleeds into the ad reads. Whether it’s a semi-NSFW jingle about MeUndies from Bill Burr, or Joe Rogan recommending his mind-blowing NatureBox snack combination, or Levar Burton delivering an oh-so soothing Calm read.
Alan Abdine, Senior Vice President of Business Development for Rooster Teeth, a network with geeky, gamer shows with a hint of irreverence, said “the best ads are the ads that are organic, natural, and originate from the voice of the show talent. When brands allow our hosts to be themselves, there are more opportunities for entertaining side stories and commentary related to the brand.”
He continues to say his “belief is that if an advertiser is willing to spend money to reach out audience, then let us be the experts on that audience and let us use our own voice to share their message and talking points! They will always get better results in that scenario.”
There is a certain special trust that goes into podcast ads. And to allow hosts to be themselves while also being a positive brand advocate often mean striking a balance between scripting and giving space. The most commonly purchased ad unit for customer acquisition advertisers is a host-read, embedded, mid-roll advertisement, typically :60 in length, but many hosts go over.
Overly scripting the copy can lead to an ad sounding inauthentic and infringe on their creativity. Kate Spencer, the co-host of Forever 35, notes that “often there are a lot of required talking points to hit in a short amount of time. We’re always happy to oblige, but I think it takes away from the organic and conversational nature of the ad, which is what makes podcast advertising especially unique. ”
On the flip side, not scripting enough could lead to a disjointed read where the host is trying to piece value props together on the fly. Nick Freeman, Chief Revenue Officer at Cadence13, explains that “some hosts do like the perfectly written out :60 script, while others like bullets they can riff off of.” Because podcast campaign test across multiple shows and personalities, it’s best to find a starting point in your copy where hosts can be guided, but not stifled. Freeman says “that doesn’t necessarily mean trying to make jokes for comedy hosts, for example, so much as it’s giving the hosts who do well with it the freedom to ad-lib.”
And for those that want to get a little more creative, the space is primed for custom integrations. Recently DoorDash partnered with Rooster Teeth for an ad on a livestream in celebration of a new game their studios were releasing. Since there was a visual element, DoorDash and Rooster Teeth partnered on a creative spin to the ad.
Instead of the typical copy, food would be delivered to the group of hosts while recording. Grant Durando, Senior Marketing Consultant at Right Side Up, works with DoorDash on their podcast campaign and stewarded this unique partnership. “[Rooster Teeth] approached us with the opportunity to engage with the live stream in a deeper way than just a regular podcast ad. It was definitely an unorthodox integration, but exciting to be in front of the right audience for DoorDash, at scale, and in a meaningful, memorable way. Many conversations about chicken nuggets later (which I never thought would be part of my job), Rooster Teeth and Vicious Circle delivered a superb ad experience, [integrating] multiple brand mentions and actually making DoorDash a part of the content itself.”
Zack Boone, Senior Director of Sales at Rooster Teeth, added there is, “nothing better than having clients that understand how impactful utterly stupid things like this can be for a brand.” DoorDash “[offers] industry-leading selection to our customers,” said Micah Moreau, VP of Growth Marketing at DoorDash. “It was incredibly effective to bring the DoorDash experience to life with Rooster Teeth in a highly differentiated, yet relevant way.”
Ads almost always end in some sort of call to action, like use the show’s promo code to save money, or visit a URL to get a free trial of a product for listeners of the show. It’s a way for shows to get credit for their listeners taking some sort of action, usually a purchase, related to hearing the ad.
And it’s how advertisers can figure out if their ad investments are paying back, too. Along those lines, Hoggatt was happy to see “how direct response the channel could be. I was surprised at the lift in site visits and follow-on orders that correlate so closely to when our podcasts drop.” Consumers have been conditioned to listen for that call to action at the end of an advertisement so we can measure a direct response in the channel.
That isn’t to say podcast advertising should displace a highly effective channel like paid social or paid search in your paid marketing testing priorities. We often ask advertisers information about their overall CAC or CPA from other paid marketing efforts like Facebook or Google advertising, and use that data to benchmark target CAC for podcast.
As a general rule of thumb, if you can’t make Facebook or Google work for customer acquisition at meaningful scale, think twice before you engage in testing podcasts at a scale meaningful to your business. But if you’re looking for demand generating channels, podcast is an excellent contender.
“The success we’ve seen from podcast advertising has proven that we can drive sales through paid media outside of “traditional” direct digital response campaigns,” said Visnick. “We’ve significantly grown our podcast budget every quarter since we started testing the channel and it’s now a core part of our overall acquisition strategy and an important part of our media mix.
Another challenge for advertisers that aren’t used to offline channels is managing indirect activity, also sometimes called breakage. It’s imperative to look at indirect activity to help triangulate response, as another way to get a false negative is to only look at direct response, i.e. direct redemptions of a promo code or sales from only users who visited the vanity URL.
A decent analog is like view-through conversions, but without the technology enablement. You can tell, via tracking, what actions site visitors have taken after exposure to ads on Facebook and Google, etc.
However, there isn’t a way for a consumer to tap or click on your podcast ad, so you don’t have a direct action correlated to ad download or exposure, nor can you track indirect activity (view-through) via pixels or other technology enablement. The aforementioned promo code/vanity URL combo is what generates that direct response.
To get around this breakage and triangulate a full response, advertisers commonly use a post-conversion attribution survey, colloquially referred to as a How Did You Hear About Us? or HDYHAU survey. This allows for a crude, but effective, translation of the impact that podcasts had on that user’s activity.
It helps you determine how much of the activity you’re capturing in paid search, for example, may have actually been driven by podcasts, streaming audio, or television. It’s self-reported data from users, sure, and it can feel a little shaky when you’re used to more precise digital measurement, but it’s how virtually every scaled advertiser in the channel has discovered a path to scale.
It also helps you determine benchmarks before you get into other channels, and can provide a solid look at multi-touch attribution if the survey is designed with best practices, and served to enough of the population to achieve stability.
We already talked about why, even though podcasts are digital audio, we can’t track conversions digitally (we know, it’s a little crazy). Unlike television, where you can use spot-based attribution, or radio, where you can achieve consistent ad exposure and but according to average quarter-hour (AQH) ratings, there’s a delay in both download of an episode and media consumption.
For advertisers, that means performance comes in over time, and it takes a minute to build reach and frequency (R/F). You may see very little activity for the first week or two of a campaign, and then as R/F builds and crescendos, you’ll see conversion activity catch up. That’s when you can start to get a solid picture of return on ad spend (ROAS); you should have structured your tests so you have a good sense of performance by the third or fourth drop with a show.
Looking at results sooner is possible but largely inadvisable. “Give it time,” says Dan Visnick, CMO at HoneyBook, “It can take a few weeks to see the impact from a single podcast, and months to build a strong portfolio.”
One of the biggest mistakes new advertisers in the channel make is getting a false positive, by testing into tiny shows that back out because 2 people bought their product, and then quickly scaling in the same genre only to find out that the content doesn’t scale.
False negatives are also common, when advertisers get cold feet in the first few weeks of an integration, and cancel shows after one ad insertion in a single episode. The channel requires diligence in testing, and if you have other business challenges to navigate, using digital growth channels can help iron out your messaging, landing pages, etc. before you launch offline channels.
Although you may have honed your messaging in other channels, you should expect to be flexible when it comes to podcast creative.
Positive signals in podcast campaigns can also indicate that other audio channels may be ripe for testing, which can help diversify your marketing mix and minimize the pressure on individuals channels. Hoggatt says his “success in podcast advertising proved that it is possible to invest in offline channels and find measurable success.”
SiriusXM and streaming platforms, whether pureplay like Pandora or Spotify, or aggregators like Westwood One and ESPN, are great next steps for advertisers who see the right signals in podcast. For SiriusXM, it’s a high household income audience that are used to paying for a subscription (any subscription model companies out there?), and streaming audiences are choosing to listen to their content, similarly to how podcast listeners choose their content. The podcast landscape is the perfect arena to play in to learn more about how your brand works in offline media and allows there to be a stepping stone into other mediums.
We know that podcast advertising can have a powerful impact on the marketing mix for companies of all sizes. As more and more players get involved in the space, it benefits all involved, from advertisers, to networks, to marketers.
It’s rare to have an opportunity to participate in a nascent medium, and be good stewards of one of the last remaining mediums on earth with finite inventory and listeners who actually respond to ads. And along the way, we hope to change the way people think about traditional offline media channels, like how they can be held to high growth performance standards, and where they intersect with popular digital growth tactics like paid social.
You’ll have to get creative, but with some trust and patience, and adherence to best practices, advertisers can reap significant benefits and customer acquisition, at scale, from podcast advertising campaigns.
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App Annie, a go-to source for mobile app market data and analytics, is expanding its platform with the acquisition of mobile analytics provider Libring. The deal will allow App Annie to present its mobile app market data side by side with advertising analytics data in order to paint a more complete picture of an app’s performance and revenue.
Already, App Annie customers leverage its platform to track key metrics related to their app’s growth and usage, like downloads, active users, retention numbers, demographics, rankings, reviews, competitive analysis and more. But the company said it heard from publishers and brands how it’s still difficult to analyze their user acquisition efforts, including their ad spend and related costs.
With the addition of Libring, App Annie is integrating adtech insights into its platform.
This includes the ability to combine the ad spend and monetization insights from more than 325 data sources, including Supply Side Platforms (SSPs), Demand Side Platforms (DSPs), app stores and analytics platforms.
This data is then presented in a single dashboard so it’s easier to understand critical metrics — like the customer acquisition cost, the lifetime value, the return on ad spend and the return on investment.
It’s ideal for larger organizations that have outgrown the spreadsheet, as it’s been sort of the App Annie of revenue aggregation, so to speak.
“The most successful companies find a way to capitalize on mobile, yet they have been struggling to maximize its value to their business,” explained App Annie CEO Ted Krantz, in a statement about the acquisition. “Today, this requires custom work to stitch together multiple point solutions, spreadsheets, business intelligence teams, agencies and consultants. We are committed to solving this by applying data science and machine learning to automate these composite metrics for brands and publishers,” he said.
The deal comes at a time when mobile ad spend is continuing to grow rapidly — it’s expected to double to $375 billion globally by 2022, the company noted. It’s now a massive part of the overall app industry, at triple the amount of consumer spending on the app stores.
As a result of the deal, Libring’s 30-plus employees are joining App Annie.
In the near-term, Libring’s current customers will continue to use its product as they do today.
But App Annie tells us there’s only some overlap between the two companies’ respective customer bases. For now, App Annie will work with its customers who want to purchase the new analytics service and find out what sort of enhancements they are looking for in an analytics solution. Libring’s customers can also purchase App Annie’s analytics, if they choose.
Later, App Annie will migrate the Libring backend to the same infrastructure provider the rest of App Annie uses, and will then integrate the front-end so customers can log in and visualize the new analytics and other market data together. More information about how this will all work will be shared when those tools are closer to being available, which is still several months from now.
Going forward, App Annie says its data science team will also offer predictive and prescriptive insights based on the new data.
According to Libring’s website, its customers included SEGA, Slickdeals, Reddit, Jam City, Wooga, EA, Zynga, Next Games, Meet Me, GameInsight, Deviant Art, Webedia, Ubisoft, theChive, saambaa, badoo, textnow and others.
App Annie declined to disclose the deal terms.
Related to the changes and expansion, App Annie also today introduced a new brand that features a gem logomark. The gem is meant to be a tribute to mobile gaming and the idea of “leveling up” while also a reflection of the value of actionable data, the company says.

The acquisition comes on the heels of several notable milestones for App Annie, including the launch of a product development testing ground, App Annie Labs; plus the addition of mobile web analytics in March — the same time when App Annie passed $100 million in annual recurring revenue.
The company is soliciting feedback about its plans for Libring and will post updates about the project on App Annie Labs, it says.
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Tableau was acquired by Salesforce earlier this year for $15.7 billion, but long before that, the company had been working on its fall update, and today it announced several new tools, including a new feature called “Explain Data” that uses AI to get to insight quickly.
“What Explain Data does is it moves users from understanding what happened to why it might have happened by automatically uncovering and explaining what’s going on in your data. So what we’ve done is we’ve embedded a sophisticated statistical engine in Tableau, that when launched automatically analyzes all the data on behalf of the user, and brings up possible explanations of the most relevant factors that are driving a particular data point,” Tableau chief product officer, Francois Ajenstat explained.
He added that what this really means is that it saves users time by automatically doing the analysis for them, and It should help them do better analysis by removing biases and helping them dive deep into the data in an automated fashion.
Image: Tableau
Ajenstat says this is a major improvement, in that, previously users would have do all of this work manually. “So a human would have to go through every possible combination, and people would find incredible insights, but it was manually driven. Now with this engine, they are able to essentially drive automation to find those insights automatically for the users,” he said.
He says this has two major advantages. First of all, because it’s AI-driven it can deliver meaningful insight much faster, but also it gives a more rigorous perspective of the data.
In addition, the company announced a new Catalog feature, which provides data bread crumbs with the source of the data, so users can know where the data came from, and whether it’s relevant or trustworthy.
Finally, the company announced a new server management tool that helps companies with broad Tableau deployment across a large organization to manage those deployments in a more centralized way.
All of these features are available starting today for Tableau customers.
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You may not be familiar with Kaltura‘s name, but chances are you’ve used the company’s video platform at some point or another, given that it offers a variety of video services for enterprises, educational institutions and video-on-demand platforms, including HBO, Phillips, SAP, Stanford and others. Today, the company announced the launch of an advanced analytics platform for its enterprise and educational users.
This new platform, dubbed Kaltura Analytics for Admins, will provide its users with features like user-level reports. This may sound like a minor feature, because you probably don’t care about the exact details of a given user’s interactions with your video, but it will allow businesses to link this kind of behavior to other metrics. With this, you could measure the ROI of a given video by linking video watch time and sales, for example. This kind of granularity wasn’t possible with the company’s existing analytics systems. Companies and schools using the product will also get access to time-period comparisons to help admins identify trends, deeper technology and geolocation reports, as well as real-time analytics for live events.

“Video is a unique data type in that it has deep engagement indicators for measurement, both around video creation — what types of content are being created by whom, as well as around video consumption and engagement with content — what languages were selected for subtitles, what hot-spots were clicked upon in video,” said Michal Tsur, president and general manager of Enterprise and Learning at Kaltura. “Analytics is a very strategic area for our customers. Both for tech companies who are building on our VPaaS, as well as for large organizations and universities that use our video products for learning, communication, collaboration, knowledge management, marketing and sales.”
Tsur also tells me the company is looking at how to best use machine learning to give its customers even deeper insights into how people watch videos — and potentially even offer predictive analytics in the long run.
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When Google announced it was buying Looker yesterday morning for $2.6 billion, you couldn’t blame some of the company’s 1,600 customers if they worried a bit if Looker would continue its multi-cloud approach. But Google Cloud chief Thomas Kurian made clear the company will continue to support an open approach to its latest purchase when it joins the fold later this year.
It’s consistent with the messaging from Google Next, the company’s cloud conference in April. It was looking to portray itself as the more open cloud. It was going to be friendlier to open-source projects, running them directly on Google Cloud. It was going to provide a way to manage your workloads wherever they live, with Anthos.
Ray Wang, founder and principal analyst at Constellation Research, says that in a multi-cloud world, Looker represented one of the best choices, and that could be why Google went after it. “Looker’s strengths include its centralized data-modeling and governance, which promotes consistency and reuse. It runs on top of modern cloud databases including Google BigQuery, AWS Redshift and Snowflake,” Wang told TechCrunch. He added, “They wanted to acquire a tool that is as easy to use as Microsoft Power BI and as deep as Tableau.”
Patrick Moorhead, founder and principal analyst at Moor Insights & Strategy, also sees this deal as part of a consistent multi-cloud message from Google. “I do think it is in alignment with its latest strategy outlined at Google Next. It has talked about rich analytics tools that could pull data from disparate sources,” he said.
Google Cloud CEO Thomas Kurian, who took over from Diane Greene at the end of last year, was careful to emphasize the company’s commitment to multi-cloud and multi-database support in comments to media and analysts yesterday. “We first want to reiterate, we’re very committed to maintaining local support for other clouds, as well as to serve data from multiple databases because customers want a single analytics foundation for their organization, and they want to be able to in the analytics foundation, look at data from multiple data sources. So we’re very committed to that,” Kurian said yesterday.
From a broader customer perspective, Kurian sees Looker providing customers with a single way to access and visualize data. “One of the things that is challenging for organizations in operationalizing business intelligence, that we feel that Looker has done really well, is it gives you a single place to model your data, define your data definitions — like what’s revenue, who’s a gold customer or how many servers tickets are open — and allows you then to blend data across individual data silos, so that as an organization, you’re working off a consistent set of metrics,” Kurian explained.
In a blog post announcing the deal, Looker CEO Frank Bien sought to ease concerns that the company might move away from the multi-cloud, multi-database support. “For customers and partners, it’s important to know that today’s announcement solidifies ours as well as Google Cloud’s commitment to multi-cloud. Looker customers can expect continuing support of all cloud databases like Amazon Redshift, Azure SQL, Snowflake, Oracle, Microsoft SQL Server, Teradata and more,” Bien wrote in the post.
Kurian also emphasized that this deal shouldn’t attract the attention of antitrust regulators, who have been sniffing around the big tech companies like Google/Alphabet, Apple and Amazon as of late. “We’re not buying any data along with this transaction. So it does not introduce any concentration risk in terms of concentrating data. Secondly, there are a large number of analytic tools in the market. So by just acquiring Looker, we’re not further concentrating the market in any sense. And lastly, all the other cloud players also have their own analytic tools. So it represents a further strengthening of our competitive position relative to the other players in the market,” he explained. Not to mention its pledge to uphold the multi-cloud and multi-database support, which should show it is not doing this strictly to benefit Google or to draw customers specifically to GCP.
Just this week, the company announced a partnership with Snowflake, the cloud data warehouse startup that has raised almost a billion dollars, to run on Google Cloud Platform. It already runs AWS and Microsoft Azure. In fact, Wang suggested that Snowflake could be next on Google’s radar as it tries to build a multi-cloud soup-to-nuts analytics offering.
Regardless, with Looker the company has a data analytics tool to complement its data processing tools, and together the two companies should provide a fairly comprehensive data solution. If they truly keep it multi-cloud, that should keep current customers happy, especially those who work with tools outside of the Google Cloud ecosystem or simply want to maintain their flexibility.
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Sumo Logic, a cloud data analytics and log analysis company, announced a $110 million Series G investment today. The company indicated that its valuation was “north of a billion dollars,” but wouldn’t give an exact figure.
Today’s round was led by Battery Ventures with participation from new investors Tiger Global Management and Franklin Templeton. Other unnamed existing investors also participated, according to the company. Today’s investment brings the total raised to $345 million.
When we spoke to Sumo Logic CEO Ramin Sayar at the time of its $75 million Series F in 2017, he indicated the company was on its way to becoming a public company. While that hasn’t happened yet, he says it is still the goal for the company, and investors wanted in on that before it happened.
“We don’t need to raise capital. We had plenty of capital already, but when you bring on crossover investors and others in this stage of a company, they have minimum check sizes and they have a lot of appetite to help you as you get ready to address a lot of the challenges and opportunities as you become a public company,” he said.
He says the company will be investing the money in continuing to develop the platform, whether that’s through acquisitions, which of course the money would help with, or through the company’s own engineering efforts.
The IPO idea remains a goal, but Sayar was not willing or able to commit to when that might happen. The company clearly has plenty of runway now to last for quite some time.
“We could go out now if we wanted to, but we made a decision that that’s not what we’re going to do, and we’re going to continue to double down and invest, and therefore bring some more capital in to give us more optionality for strategic tuck-ins and product IP expansion, international expansion — and then look to the public markets [after] we do that,” he said.
Dharmesh Thakker, general partner at investor Battery Ventures, says his firm likes Sumo Logic’s approach and sees a big opportunity ahead with this investment. “We have been tracking the Sumo Logic team for some time, and admire the company’s early understanding of the massive cloud-native opportunity and the rise of new, modern application architectures,” he said in a statement.
The company crossed the $100 million revenue mark last year and has 2,000 customers, including Airbnb, Anheuser-Busch and Samsung. It competes with companies like Splunk, Scalyr and Loggly.
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Like virtually every big enterprise company, a few years ago, the German auto giant Daimler decided to invest in its own on-premises data centers. And while those aren’t going away anytime soon, the company today announced that it has successfully moved its on-premises big data platform to Microsoft’s Azure cloud. This new platform, which the company calls eXtollo, is Daimler’s first major service to run outside of its own data centers, though it’ll probably not be the last.
As Daimler’s head of its corporate center of excellence for advanced analytics and big data Guido Vetter told me, the company started getting interested in big data about five years ago. “We invested in technology — the classical way, on-premise — and got a couple of people on it. And we were investigating what we could do with data because data is transforming our whole business as well,” he said.
By 2016, the size of the organization had grown to the point where a more formal structure was needed to enable the company to handle its data at a global scale. At the time, the buzz phrase was “data lakes” and the company started building its own in order to build out its analytics capacities.
Electric lineup, Daimler AG
“Sooner or later, we hit the limits as it’s not our core business to run these big environments,” Vetter said. “Flexibility and scalability are what you need for AI and advanced analytics and our whole operations are not set up for that. Our backend operations are set up for keeping a plant running and keeping everything safe and secure.” But in this new world of enterprise IT, companies need to be able to be flexible and experiment — and, if necessary, throw out failed experiments quickly.
So about a year and a half ago, Vetter’s team started the eXtollo project to bring all the company’s activities around advanced analytics, big data and artificial intelligence into the Azure Cloud, and just over two weeks ago, the team shut down its last on-premises servers after slowly turning on its solutions in Microsoft’s data centers in Europe, the U.S. and Asia. All in all, the actual transition between the on-premises data centers and the Azure cloud took about nine months. That may not seem fast, but for an enterprise project like this, that’s about as fast as it gets (and for a while, it fed all new data into both its on-premises data lake and Azure).
If you work for a startup, then all of this probably doesn’t seem like a big deal, but for a more traditional enterprise like Daimler, even just giving up control over the physical hardware where your data resides was a major culture change and something that took quite a bit of convincing. In the end, the solution came down to encryption.
“We needed the means to secure the data in the Microsoft data center with our own means that ensure that only we have access to the raw data and work with the data,” explained Vetter. In the end, the company decided to use the Azure Key Vault to manage and rotate its encryption keys. Indeed, Vetter noted that knowing that the company had full control over its own data was what allowed this project to move forward.
Vetter tells me the company obviously looked at Microsoft’s competitors as well, but he noted that his team didn’t find a compelling offer from other vendors in terms of functionality and the security features that it needed.
Today, Daimler’s big data unit uses tools like HD Insights and Azure Databricks, which covers more than 90 percents of the company’s current use cases. In the future, Vetter also wants to make it easier for less experienced users to use self-service tools to launch AI and analytics services.
While cost is often a factor that counts against the cloud, because renting server capacity isn’t cheap, Vetter argues that this move will actually save the company money and that storage costs, especially, are going to be cheaper in the cloud than in its on-premises data center (and chances are that Daimler, given its size and prestige as a customer, isn’t exactly paying the same rack rate that others are paying for the Azure services).
As with so many big data AI projects, predictions are the focus of much of what Daimler is doing. That may mean looking at a car’s data and error code and helping the technician diagnose an issue or doing predictive maintenance on a commercial vehicle. Interestingly, the company isn’t currently bringing to the cloud any of its own IoT data from its plants. That’s all managed in the company’s on-premises data centers because it wants to avoid the risk of having to shut down a plant because its tools lost the connection to a data center, for example.
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Enterprises now amass huge amounts of data, both from their own tools and applications, as well as from the SaaS applications they use. For a long time, that data was basically exhaust. Maybe it was stored for a while to fulfill some legal requirements, but then it was discarded. Now, data is what drives machine learning models, and the more data you have, the better. It’s maybe no surprise, then, that the big cloud vendors started investing in data warehouses and lakes early on. But that’s just a first step. After that, you also need the analytics tools to make all of this data useful.
Today, it’s Microsoft turn to shine the spotlight on its data analytics services. The actual news here is pretty straightforward. Two of these are services that are moving into general availability: the second generation of Azure Data Lake Storage for big data analytics workloads and Azure Data Explorer, a managed service that makes easier ad-hoc analysis of massive data volumes. Microsoft is also previewing a new feature in Azure Data Factory, its graphical no-code service for building data transformation. Data Factory now features the ability to map data flows.
Those individual news pieces are interesting if you are a user or are considering Azure for your big data workloads, but what’s maybe more important here is that Microsoft is trying to offer a comprehensive set of tools for managing and storing this data — and then using it for building analytics and AI services.
(Photo credit:Josh Edelson/AFP/Getty Images)
“AI is a top priority for every company around the globe,” Julia White, Microsoft’s corporate VP for Azure, told me. “And as we are working with our customers on AI, it becomes clear that their analytics often aren’t good enough for building an AI platform.” These companies are generating plenty of data, which then has to be pulled into analytics systems. She stressed that she couldn’t remember a customer conversation in recent months that didn’t focus on AI. “There is urgency to get to the AI dream,” White said, but the growth and variety of data presents a major challenge for many enterprises. “They thought this was a technology that was separate from their core systems. Now it’s expected for both customer-facing and line-of-business applications.”
Data Lake Storage helps with managing this variety of data since it can handle both structured and unstructured data (and is optimized for the Spark and Hadoop analytics engines). The service can ingest any kind of data — yet Microsoft still promises that it will be very fast. “The world of analytics tended to be defined by having to decide upfront and then building rigid structures around it to get the performance you wanted,” explained White. Data Lake Storage, on the other hand, wants to offer the best of both worlds.
Likewise, White argued that while many enterprises used to keep these services on their on-premises servers, many of them are still appliance-based. But she believes the cloud has now reached the point where the price/performance calculations are in its favor. It took a while to get to this point, though, and to convince enterprises. White noted that for the longest time, enterprises that looked at their analytics projects thought $300 million projects took forever, tied up lots of people and were frankly a bit scary. “But also, what we had to offer in the cloud hasn’t been amazing until some of the recent work,” she said. “We’ve been on a journey — as well as the other cloud vendors — and the price performance is now compelling.” And it sure helps that if enterprises want to meet their AI goals, they’ll now have to tackle these workloads, too.
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Today, many companies provide developer access to their services via APIs. Moesif, a San Francisco startup, wants to help these companies gain insight into their customer’s API usage patterns. Today, the company announced a $3.5 million seed round.
The investment was led by Merus Capital, with participation by Heavybit, Fresco Capital and Zach Coelius, whose investments include Cruise Automation, which was sold to GM in 2016 for $1 billion.
Moesif co-founder and CEO Derric Gilling says Moesif is akin to Mixpanel or Google Analytics, except instead of tracking web or mobile analytics, it looks at API usage. “As more and more companies are using and creating these APIs, there comes a point where you need to understand how your customers are using them, any problems they are running into and how do you actually decrease developer churn.”
Heat map showing API usage by region. Screenshot: Moesif
The company is aiming at two primary types of users. First of all, there are developers who can use the monitoring features to understand when there are issues with the API. These folks have access to the free tier.
Moesif also targets business units like product management, sales and marketing, which use the tool to understand who’s using the API, how often and, with machine learning, understand who is likely to stop using the product based on how they are using it. The tool can tie into other business systems like Mailchimp or a CRM tool to get a more complete picture of customers as they use the API.
The product was released last year, and Gilling says his company already has 2,000 customers, which includes both the free and paid tiers. He said they have had particular success with SaaS and fintech companies, both of which make heavy use of APIs. Customers include PowerSchool, Schwab and DHL.
While the company currently consists of two founders and one employee, flush with the seed investment, it intends to hire around 10 people in the next six months, including a VP of engineering, additional developers and sales and marketing folks.
Moesif was founded in late 2016, and the founders went through the Alchemist Accelerator last year.
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