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How a testing model is driving SEAT and CUPRA’s search marketing performance

June 23, 2022 No Comments

“Will we ever be able to put search marketing strategy in the driver’s seat?” This is almost every search marketer’s dilemma as the community continues to remain at the mercy of Google’s algorithms and updates.

SEAT S.A, the Barcelona-based multinational automaker part of the Volkswagen group have innovated a testing model that is driving growth for its brands, SEAT and CUPRA in the European market. While SEAT is the young, cool and urban brand that offers cars with striking designs and several mobility solutions – CUPRA is an unconventional brand, which is defined by its progressive design and the performance of its electrified models.

How a testing model is driving SEAT and CUPRA’s search marketing performance

We spoke with Corinne Calcabrina, Global Media Manager at SEAT S.A, Sophie Santallusia, Global Paid Search and Programmatic Director, and Alejandro Sebastian, Global Search Team Lead at PHD Media Spain to discuss the ‘Performance innovation program’ (SEAT S.A’s testing model) and its value add to the businesses.

A fast-paced industry

Digital is a fast-moving sector and search is always reinventing itself with new formats and everchanging ways to create and manage accounts. The teams at SEAT and CUPRA had several pain points:

1. Staying on-top of all innovations and changes in the industry

“We needed to become first movers who actively capitalize on opportunities that appear. To ensure this our teams needed to take advantage of search space dynamics, apply best practices, and gain a technological and intelligence edge over the competition.”

– Corinne Calcabrina, Global Media Manager at SEAT S.A.

2. Improving visibility of the team’s hard work

“While we were putting all these efforts, we wanted to improve our team’s visibility. While we are busy becoming the best performing channel, always reinventing, working towards results and efficiencies, we often miss the glitter of other channels. Adding an official scope and framework means we get to report and showcase our achievements.”

– Corinne Calcabrina, Global Media Manager, SEAT S.A.

3. Maintaining performance and improving efficiency

“As the best performing channel on a last-click attribution model, we were also facing multiple challenges. The pandemic lockdowns and microchip shortages made search performance improvements a constant, ongoing must-have. This meant decreasing the cost per click (CPC) and improving the cost per acquisition (CPA) were always core reasons to develop such a testing model.”

– Corinne Calcabrina, Global Media Manager, SEAT S.A

Putting testing in the driver seat: The SEAT and CUPRA Performance innovation program

The SEAT S.A testing model, ‘Performance innovation program’ was designed to align with the inherent love for innovation that runs at the core of SEAT and CUPRA brands. The testing model was built centrally to maintain brand focus on the strength of paid search – improving cost efficiencies and accelerating performance.

Corinne and her team at SEAT S.A and their agency, PHD Media reviewed brand strategies for SEAT and CUPRA respectively, their performance, and local needs. They created a framework that provides structure, helps the brands expand their market share, and deliver central visibility on the testing results. They created specific testing roadmaps, based on quarterly goals that align with local markets based on their needs and strategies.

“We then applied our tests, sharing the hypothesis (highlighting results from other markets) of what we hope to achieve and then applying the test into the main strategy.

“We had a clear timeline and roadmap. We always test and learn. This allows us to have a specific position with partners, allowing us to always be part of the alphas and betas, testing new formats, always trying to improve results at the same time”, Corinne shared.

To facilitate consistency the SEAT S.A team organized tests throughout the year pacing one test at a time for an ad group or campaign to maintain efficiency and gain clear observations. The roadmap was created on these factors:

  • Priorities for markets based on the impact and workload
  • Changes that Google makes to ad formats or different features that it sunsets or iterates

The search marketing grand prix: data, automation, and visual optimization

SEAT S.A and PHD Media started differentiating strategies by keyword type and defined them for each ad group. Keywords were segmented based on brand and non-brand search, their role, and their respective KPIs. This data was then used during the auction bidding. Artificial intelligence (AI) was used to segment audiences and target ads that were top of the funnel. Comparative insights from these tests were later fed into the business to inform the direction of strategy.

To improve the click through rate (CTR) and lower CPCs, the SEAT S.A team focused on adding visuals to ads, improving ad-copies, and testing new extensions. They also decreased CPAs by using bid strategies and the system’s AI to get the best of their budgets.

To master their visual impact on audiences SEAT S.A used image extensions for each ad across all their campaigns. Google displayed these images based on multiple factors like clicks, content, and keyword triggers to optimize the best performing ones.

From a data point of view, in Search SEAT S.A used Google Search Ads (SA360) to manage and monitor their Google Ads and Bing Ads respectively. The data sets tracked all the core essentials of paid search:

  • Keyword conversion performance
  • Ad copies
  • Audience data through all the custom bidding options available in SA360

Outcomes

The ‘Performance innovation program’ model has helped SEAT and CUPRA achieve one of their best tests which catalyzed their search performance in terms of the cost per visit (CPV), one of their main KPIs that signaled top of the funnel conversions. The cost per visit (CPV) improved by 30% and cost per acquisition (CPA) improved by 37%.

SEAT S.A (SEAT and CUPRA) are now equipped with new ways to deduce and analyze conversions on a market-to-market basis.

Sharing intelligence across diverse markets

After completing the testing phase, the SEAT S.A team and their global partner PHD Media reported on results and observations. Sharing their learnings and insights with other markets has empowered other teams to benefit from the knowledge and expertise derived from the successful test prototypes. Focusing on components that drive results has allowed the teams spread across to be challenged and has facilitated constant learning while embracing changes and new features. The SEAT and CUPRA teams are now strongly positioned to outperform the competition.

Gearing up for a cookie less future

Going cookie less will bring challenging times and impact the search channel. SEAT and CUPRA plan to counter this with the use of Google Analytics 4 (GA4) to maintain performance and target the right audience. Opening up to new visual formats like Discovery campaigns and MMA/MSAN from Bing will also take an important place within search in the future, as the core of search might evolve with more automation, less granularity and control.

Greater focus on measurement and a privacy-first future

The team is testing ‘consent mode’ with GA4 and ‘enhanced conversion’ to estimate the attrition due to privacy guidelines. They are also focused on identifying and designing a risk contingency plan for the paid search elements that they won’t be able to test in the near future.

“We are testing all the new solutions and features that Google is bringing to the market in terms of privacy and cookie less capabilities. Particularly, our testing is focused on deploying the full suite of Google Analytics 4 (GA4), site-wide tagging, consent mode, and enhanced conversions.

Additionally, we are also testing new audience segments that GA4 allows within a privacy first ecosystem on our paid search campaigns. We are seeing some positive and promising results.”

– Corinne Calcabrina, Global Media Manager at SEAT S.A

SEAT S.A and PHD Media are actively focused on Google solutions for mapping markets and audiences that are privacy compliant and applicable for targeting segments.

They are also working towards gathering and connecting first party data like CRM audiences and customer match solutions.


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LOVE unveils a modern video messaging app with a business model that puts users in control

August 26, 2021 No Comments

A London-headquartered startup called LOVE, valued at $ 17 million following its pre-seed funding, aims to redefine how people stay in touch with close family and friends. The company is launching a messaging app that offers a combination of video calling as well as asynchronous video and audio messaging, in an ad-free, privacy-focused experience with a number of bells and whistles, including artistic filters and real-time transcription and translation features.

But LOVE’s bigger differentiator may not be its product alone, but rather the company’s mission.

LOVE aims for its product direction to be guided by its user base in a democratic fashion as opposed to having the decisions made about its future determined by an elite few at the top of some corporate hierarchy. In addition, the company’s longer-term goal is ultimately to hand over ownership of the app and its governance to its users, the company says.

These concepts have emerged as part of bigger trends towards a sort of “Web 3.0,” or next phase of internet development, where services are decentralized, user privacy is elevated, data is protected and transactions take place on digital ledgers, like a blockchain, in a more distributed fashion.

LOVE’s founders are proponents of this new model, including serial entrepreneur Samantha Radocchia, who previously founded three companies and was an early advocate for the blockchain as the co-founder of Chronicled, an enterprise blockchain company focused on the pharmaceutical supply chain.

As someone who’s been interested in emerging technology since her days of writing her anthropology thesis on currency exchanges in “Second Life’s” virtual world, she’s now faculty at Singularity University, where she’s given talks about blockchain, AI, Internet of Things, Future of Work, and other topics. She’s also authored an introductory guide to the blockchain with her book “Bitcoin Pizza.”

Co-founder Christopher Schlaeffer, meanwhile, held a number of roles at Deutsche Telekom, including chief product & innovation officer, corporate development officer and chief strategy officer, where he along with Google execs introduced the first mobile phone to run Android. He was also chief digital officer at the telecommunication services company VEON.

The two crossed paths after Schlaeffer had already begun the work of organizing a team to bring LOVE to the public, which includes co-founders Chief Technologist Jim Reeves, also previously of VEON, and Chief Designer Timm Kekeritz, previously an interaction designer at international design firm IDEO in San Francisco, design director at IXDS and founder of design consultancy Raureif in Berlin, among other roles.

Image Credits: LOVE

Explained Radocchia, what attracted her to join as CEO was the potential to create a new company that upholds more positive values than what’s often seen today — in fact, the brand name “LOVE” is a reference to this aim. She was also interested in the potential to think through what she describes as “new business models that are not reliant on advertising or harvesting the data of our users,” she says.

To that end, LOVE plans to monetize without any advertising. While the company isn’t ready to explain its business model in full, it would involve users opting in to services through granular permissions and membership, we’re told.

“We believe our users will much rather be willing to pay for services they consciously use and grant permissions to in a given context than have their data used for an advertising model which is simply not transparent,” says Radocchia.

LOVE expects to share more about the model next year.

As for the LOVE app itself, it’s a fairly polished mobile messenger offering an interesting combination of features. Like any other video chat app, you can video call with friends and family, either in one-on-one calls or in groups. Currently, LOVE supports up to five call participants, but expects to expand that as it scales. The app also supports video and audio messaging for asynchronous conversations. There are already tools that offer this sort of functionality on the market, of course — like WhatsApp, with its support for audio messages, or video messenger Marco Polo. But they don’t offer quite the same expanded feature set.

Image Credits: LOVE

For starters, LOVE limits its video messages to 60 seconds, for brevity’s sake. (As anyone who’s used Marco Polo knows, videos can become a bit rambling, which makes it harder to catch up when you’re behind on group chats.) In addition, LOVE allows you to both watch the video content as well as read the real-time transcription of what’s being said — the latter which comes in handy not only for accessibility’s sake, but also for those times you want to hear someone’s messages but aren’t in a private place to listen or don’t have headphones. Conversations can also be translated into 50 languages.

“A lot of the traditional communication or messenger products are coming from a paradigm that has always been text-based,” explains Radocchia. “We’re approaching it completely differently. So while other platforms have a lot of the features that we do, I think that…the perspective that we’ve approached it has completely flipped it on its head,” she continues. “As opposed to bolting video messages on to a primarily text-based interface, [LOVE is] actually doing it in the opposite way and adding text as a sort of a magically transcribed add-on — and something that you never, hopefully, need to be typing out on your keyboard again,” she adds.

The app’s user interface, meanwhile, has been designed to encourage eye-to-eye contact with the speaker to make conversations feel more natural. It does this by way of design elements where bubbles float around as you’re speaking and the bubble with the current speaker grows to pull your focus away from looking at yourself. The company is also working with the curator of Serpentine Gallery in London, Hans Ulrich-Obrist, to create new filters that aren’t about beautification or gimmicks, but are instead focused on introducing a new form of visual expression that makes people feel more comfortable on camera.

For the time being, this has resulted in a filter that slightly abstracts your appearance, almost in the style of animation or some other form of visual arts.

The app claims to use end-to-end encryption and the automatic deletion of its content after seven days — except for messages you yourself recorded, if you’ve chosen to save them as “memorable moments.”

“One of our commitments is to privacy and the right-to-forget,” says Radocchia. “We don’t want to be or need to be storing any of this information.”

LOVE has been soft-launched on the App Store, where it’s been used with a number of testers and is working to organically grow its user base through an onboarding invite mechanism that asks users to invite at least three people to join. This same onboarding process also carefully explains why LOVE asks for permissions — like using speech recognition to create subtitles.

LOVE says its valuation is around $ 17 million USD following pre-seed investments from a combination of traditional startup investors and strategic angel investors across a variety of industries, including tech, film, media, TV and financial services. The company will raise a seed round this fall.

The app is currently available on iOS, but an Android version will arrive later in the year. (Note that LOVE does not currently support the iOS 15 beta software, where it has issues with speech transcription and in other areas. That should be resolved next week, following an app update now in the works.)


Social – TechCrunch


Confluent’s IPO brings a high-growth, high-burn SaaS model to the public markets

June 4, 2021 No Comments

Confluent became the latest company to announce its intent to take the IPO route, officially filing its S-1 paperwork with the U.S. Securities and Exchange Commission this week. The company, which has raised over $ 455 million since it launched in 2014, was most recently valued at just over $ 4.5 billion when it raised $ 250 million last April.

What we can see in Confluent is nearly an old-school, high-burn SaaS business. It has taken on oodles of capital and used it in an increasingly expensive sales model.

What does Confluent do? It built a streaming data platform on top of the open-source Apache Kafka project. In addition to its open-source roots, Confluent has a free tier of its commercial cloud offering to complement its paid products, helping generate top-of-funnel inflows that it converts to sales.

Kafka itself emerged from a LinkedIn internal project in 2011. As we wrote at the time of Confluent’s $ 50 million Series C in 2017, the open-source project was designed to move massive amounts of data at the professional social network:

At its core, Kafka is simply a messaging system, created originally at LinkedIn, that’s been designed from the ground up to move massive amounts of data smoothly around the enterprise from application to application, system to system or on-prem to cloud — and deal with extremely high message volume.

Confluent CEO and co-founder Jay Kreps wrote at the time of the funding that events streaming is at the core of every business, reaching sales and other core business activities that occur in real time that go beyond storing data in a database after the fact.

“[D]atabases have long helped to store the current state of the world, but we think this is only half of the story. What is missing are the continually flowing stream of events that represents everything happening in a company, and that can act as the lifeblood of its operation,” he wrote.

That’s where Confluent comes in.

But enough about the technology. Is Confluent’s work with Kafka a good business? Let’s find out.


Enterprise – TechCrunch


Docugami’s new model for understanding documents cuts its teeth on NASA archives

April 13, 2021 No Comments

You hear so much about data these days that you might forget that a huge amount of the world runs on documents: a veritable menagerie of heterogeneous files and formats holding enormous value yet incompatible with the new era of clean, structured databases. Docugami plans to change that with a system that intuitively understands any set of documents and intelligently indexes their contents — and NASA is already on board.

If Docugami’s product works as planned, anyone will be able to take piles of documents accumulated over the years and near-instantly convert them to the kind of data that’s actually useful to people.

If Docugami’s product works as planned, anyone will be able to take piles of documents accumulated over the years and near-instantly convert them to the kind of data that’s actually useful to people.

Because it turns out that running just about any business ends up producing a ton of documents. Contracts and briefs in legal work, leases and agreements in real estate, proposals and releases in marketing, medical charts, etc, etc. Not to mention the various formats: Word docs, PDFs, scans of paper printouts of PDFs exported from Word docs, and so on.

Over the last decade there’s been an effort to corral this problem, but movement has largely been on the organizational side: put all your documents in one place, share and edit them collaboratively. Understanding the document itself has pretty much been left to the people who handle them, and for good reason — understanding documents is hard!

Think of a rental contract. We humans understand when the renter is named as Jill Jackson, that later on, “the renter” also refers to that person. Furthermore, in any of a hundred other contracts, we understand that the renters in those documents are the same type of person or concept in the context of the document, but not the same actual person. These are surprisingly difficult concepts for machine learning and natural language understanding systems to grasp and apply. Yet if they could be mastered, an enormous amount of useful information could be extracted from the millions of documents squirreled away around the world.

What’s up, .docx?

Docugami founder Jean Paoli says they’ve cracked the problem wide open, and while it’s a major claim, he’s one of few people who could credibly make it. Paoli was a major figure at Microsoft for decades, and among other things helped create the XML format — you know all those files that end in x, like .docx and .xlsx? Paoli is at least partly to thank for them.

“Data and documents aren’t the same thing,” he told me. “There’s a thing you understand, called documents, and there’s something that computers understand, called data. Why are they not the same thing? So my first job [at Microsoft] was to create a format that can represent documents as data. I created XML with friends in the industry, and Bill accepted it.” (Yes, that Bill.)

The formats became ubiquitous, yet 20 years later the same problem persists, having grown in scale with the digitization of industry after industry. But for Paoli the solution is the same. At the core of XML was the idea that a document should be structured almost like a webpage: boxes within boxes, each clearly defined by metadata — a hierarchical model more easily understood by computers.

Illustration showing a document corresponding to pieces of another document.

Image Credits: Docugami

“A few years ago I drank the AI kool-aid, got the idea to transform documents into data. I needed an algorithm that navigates the hierarchical model, and they told me that the algorithm you want does not exist,” he explained. “The XML model, where every piece is inside another, and each has a different name to represent the data it contains — that has not been married to the AI model we have today. That’s just a fact. I hoped the AI people would go and jump on it, but it didn’t happen.” (“I was busy doing something else,” he added, to excuse himself.)

The lack of compatibility with this new model of computing shouldn’t come as a surprise — every emerging technology carries with it certain assumptions and limitations, and AI has focused on a few other, equally crucial areas like speech understanding and computer vision. The approach taken there doesn’t match the needs of systematically understanding a document.

“Many people think that documents are like cats. You train the AI to look for their eyes, for their tails … documents are not like cats,” he said.

It sounds obvious, but it’s a real limitation. Advanced AI methods like segmentation, scene understanding, multimodal context, and such are all a sort of hyperadvanced cat detection that has moved beyond cats to detect dogs, car types, facial expressions, locations, etc. Documents are too different from one another, or in other ways too similar, for these approaches to do much more than roughly categorize them.

As for language understanding, it’s good in some ways but not in the ways Paoli needed. “They’re working sort of at the English language level,” he said. “They look at the text but they disconnect it from the document where they found it. I love NLP people, half my team is NLP people — but NLP people don’t think about business processes. You need to mix them with XML people, people who understand computer vision, then you start looking at the document at a different level.”

Docugami in action

Illustration showing a person interacting with a digital document.

Image Credits: Docugami

Paoli’s goal couldn’t be reached by adapting existing tools (beyond mature primitives like optical character recognition), so he assembled his own private AI lab, where a multidisciplinary team has been tinkering away for about two years.

“We did core science, self-funded, in stealth mode, and we sent a bunch of patents to the patent office,” he said. “Then we went to see the VCs, and SignalFire basically volunteered to lead the seed round at $ 10 million.”

Coverage of the round didn’t really get into the actual experience of using Docugami, but Paoli walked me through the platform with some live documents. I wasn’t given access myself and the company wouldn’t provide screenshots or video, saying it is still working on the integrations and UI, so you’ll have to use your imagination … but if you picture pretty much any enterprise SaaS service, you’re 90% of the way there.

As the user, you upload any number of documents to Docugami, from a couple dozen to hundreds or thousands. These enter a machine understanding workflow that parses the documents, whether they’re scanned PDFs, Word files, or something else, into an XML-esque hierarchical organization unique to the contents.

“Say you’ve got 500 documents, we try to categorize it in document sets, these 30 look the same, those 20 look the same, those five together. We group them with a mix of hints coming from how the document looked, what it’s talking about, what we think people are using it for, etc.,” said Paoli. Other services might be able to tell the difference between a lease and an NDA, but documents are too diverse to slot into pre-trained ideas of categories and expect it to work out. Every set of documents is potentially unique, and so Docugami trains itself anew every time, even for a set of one. “Once we group them, we understand the overall structure and hierarchy of that particular set of documents, because that’s how documents become useful: together.”

Illustration showing a document being turned into a report and a spreadsheet.

Image Credits: Docugami

That doesn’t just mean it picks up on header text and creates an index, or lets you search for words. The data that is in the document, for example who is paying whom, how much and when, and under what conditions, all that becomes structured and editable within the context of similar documents. (It asks for a little input to double check what it has deduced.)

It can be a little hard to picture, but now just imagine that you want to put together a report on your company’s active loans. All you need to do is highlight the information that’s important to you in an example document — literally, you just click “Jane Roe” and “$ 20,000” and “five years” anywhere they occur — and then select the other documents you want to pull corresponding information from. A few seconds later you have an ordered spreadsheet with names, amounts, dates, anything you wanted out of that set of documents.

All this data is meant to be portable too, of course — there are integrations planned with various other common pipes and services in business, allowing for automatic reports, alerts if certain conditions are reached, automated creation of templates and standard documents (no more keeping an old one around with underscores where the principals go).

Remember, this is all half an hour after you uploaded them in the first place, no labeling or pre-processing or cleaning required. And the AI isn’t working from some preconceived notion or format of what a lease document looks like. It’s learned all it needs to know from the actual docs you uploaded — how they’re structured, where things like names and dates figure relative to one another, and so on. And it works across verticals and uses an interface anyone can figure out in a few minutes. Whether you’re in healthcare data entry or construction contract management, the tool should make sense.

The web interface where you ingest and create new documents is one of the main tools, while the other lives inside Word. There Docugami acts as a sort of assistant that’s fully aware of every other document of whatever type you’re in, so you can create new ones, fill in standard information, comply with regulations and so on.

Okay, so processing legal documents isn’t exactly the most exciting application of machine learning in the world. But I wouldn’t be writing this (at all, let alone at this length) if I didn’t think this was a big deal. This sort of deep understanding of document types can be found here and there among established industries with standard document types (such as police or medical reports), but have fun waiting until someone trains a bespoke model for your kayak rental service. But small businesses have just as much value locked up in documents as large enterprises — and they can’t afford to hire a team of data scientists. And even the big organizations can’t do it all manually.

NASA’s treasure trove

Image Credits: NASA

The problem is extremely difficult, yet to humans seems almost trivial. You or I could glance through 20 similar documents and a list of names and amounts easily, perhaps even in less time than it takes for Docugami to crawl them and train itself.

But AI, after all, is meant to imitate and transcend human capacity, and it’s one thing for an account manager to do monthly reports on 20 contracts — quite another to do a daily report on a thousand. Yet Docugami accomplishes the latter and former equally easily — which is where it fits into both the enterprise system, where scaling this kind of operation is crucial, and to NASA, which is buried under a backlog of documentation from which it hopes to glean clean data and insights.

If there’s one thing NASA’s got a lot of, it’s documents. Its reasonably well-maintained archives go back to its founding, and many important ones are available by various means — I’ve spent many a pleasant hour perusing its cache of historical documents.

But NASA isn’t looking for new insights into Apollo 11. Through its many past and present programs, solicitations, grant programs, budgets, and of course engineering projects, it generates a huge amount of documents — being, after all, very much a part of the federal bureaucracy. And as with any large organization with its paperwork spread over decades, NASA’s document stash represents untapped potential.

Expert opinions, research precursors, engineering solutions, and a dozen more categories of important information are sitting in files searchable perhaps by basic word matching but otherwise unstructured. Wouldn’t it be nice for someone at JPL to get it in their head to look at the evolution of nozzle design, and within a few minutes have a complete and current list of documents on that topic, organized by type, date, author and status? What about the patent advisor who needs to provide a NIAC grant recipient information on prior art — shouldn’t they be able to pull those old patents and applications up with more specificity than any with a given keyword?

The NASA SBIR grant, awarded last summer, isn’t for any specific work, like collecting all the documents of such and such a type from Johnson Space Center or something. It’s an exploratory or investigative agreement, as many of these grants are, and Docugami is working with NASA scientists on the best ways to apply the technology to their archives. (One of the best applications may be to the SBIR and other small business funding programs themselves.)

Another SBIR grant with the NSF differs in that, while at NASA the team is looking into better organizing tons of disparate types of documents with some overlapping information, at NSF they’re aiming to better identify “small data.” “We are looking at the tiny things, the tiny details,” said Paoli. “For instance, if you have a name, is it the lender or the borrower? The doctor or the patient name? When you read a patient record, penicillin is mentioned, is it prescribed or prohibited? If there’s a section called allergies and another called prescriptions, we can make that connection.”

“Maybe it’s because I’m French”

When I pointed out the rather small budgets involved with SBIR grants and how his company couldn’t possibly survive on these, he laughed.

“Oh, we’re not running on grants! This isn’t our business. For me, this is a way to work with scientists, with the best labs in the world,” he said, while noting many more grant projects were in the offing. “Science for me is a fuel. The business model is very simple — a service that you subscribe to, like Docusign or Dropbox.”

The company is only just now beginning its real business operations, having made a few connections with integration partners and testers. But over the next year it will expand its private beta and eventually open it up — though there’s no timeline on that just yet.

“We’re very young. A year ago we were like five, six people, now we went and got this $ 10 million seed round and boom,” said Paoli. But he’s certain that this is a business that will be not just lucrative but will represent an important change in how companies work.

“People love documents. Maybe it’s because I’m French,” he said, “but I think text and books and writing are critical — that’s just how humans work. We really think people can help machines think better, and machines can help people think better.”


Enterprise – TechCrunch


Will the Clubhouse model work in China?

February 7, 2021 No Comments

On Friday just past midnight, I stumbled across a Clubhouse room hosted by a well-known figure in the Chinese startup community, Feng Dahui. At half-past midnight, the room still had nearly 500 listeners, many of whom were engineers, product managers, and entrepreneurs from China.

The discussion centered around whether Clubhouse, an app that lets people join pop-up voice chats in virtual rooms, will succeed in China. That’s a question I have been asking myself in recent weeks. Given the current hype swirling in Silicon Valley about the audio social network, it’s unsurprising to see well-informed, tech-savvy Chinese users start flocking to the platform. Demand for invitations in China runs high, with people paying as much as $ 100 to buy one from scalpers.

Many users I talked to believe the app won’t reach its full potential or even just find product-market fit in China before it gets banned. Indeed, a handful of well-attended Chinese-language rooms touch on topics that are normally censored in China, from crypto trading to protests in Hong Kong.

If it’s of any consolation, Clubhouse clones and derivatives are already in the making in China. A Chinese entrepreneur and blogger who goes by the nickname Herock told me he is aware of at least “dozens of local teams” that are working on something similar. Moreover, voice-based networking has been around in China for years, albeit in different forms. If Clubhouse is blocked, will any of its alternatives go on to succeed?

Information control

A direct Clubhouse clone probably won’t work in China.

A few factors dim its prospects in the country, which has nearly one billion internet users. The major appeal of Clubhouse is the organic flow of conversations in real time. But “how could the Chinese government allow free-flowing discussions to happen and spread without control,” a founder of a Chinese audio app rhetorically asked, declining to be named for this story. Video live streaming in China, for example, is under close regulatory oversight limiting who can speak and what they can say.

The founder then cited a famous online protest back in 2011. Thousands of small vendors launched a cyber attack on Alibaba’s online mall over a proposed fee hike. The tool they used to coordinate with one another was YY, which started out as a voice-based chatting software for gamers and later became known for video live streaming.

“The authorities dread the power of real-time audio communication,” the founder added.

There are signs that Clubhouse may already be the target of censorship. While Clubhouse works perfectly in China without the need for a virtual private network (VPN) or other censorship-circumvention tools (at least for the moment), the iOS-exclusive app is unavailable on China’s App Store. Clubhouse was removed there shortly after its global release in late September, app analytics firm Sensor Tower said.

Currently, in order to install Clubhouse, Chinese users need to install the app by switching to an App Store located in another country, which further limits the product’s reach to users who have the means of using a non-local store.

It’s unclear whether Apple preemptively delisted Clubhouse in anticipation of government action, given that any later removal of a major foreign app in China could stir up accusations of censorship. Alternatively, Clubhouse might have voluntarily pulled the app itself knowing that any form of real-time broadcasting won’t go unchecked by Chinese regulators, which would inevitably compromise user experience.

Entering China could be way down on Clubhouse’s to-do list given the traction it is gaining elsewhere. The app has seen about 3.6 million worldwide installs so far, according to Sensor Tower estimates. The majority of its lifetime installs originate in the United States, where the app has seen nearly 2 million first-time downloads, followed by Japan and Germany both with over 400,000 downloads.

Clubhouse elites

Clubhouse room hosted by Feng Dahui, a respected figure in China’s startup world. (Screenshot by TechCrunch)

The improbability of uncensored and open discussions on the Chinese internet may explain why the market hasn’t seen its own Clubhouse. But even if an app like Clubhouse is allowed to exist in China, it may not reach the same massive scale across the country as Douyin (TikTok’s Chinese version) and WeChat did.

The app is “elitist,” sort of like a voice version of Twitter, said Marco Lai, CEO and founder of Lizhi, a NASDAQ-listed Chinese audio platform. So far, Clubhouse’s invite-only model has confined its American user base largely to the tech, arts and celebrity circles. Herock observed that its Chinese demographics mirror the trend, with users concentrated in fields like finance, startup and product management, as well as crypto traders.

Even among these users though, there is the question of free time. The other night, I was up at midnight eavesdropping on a group of ByteDance employees. In fact, I’ve mostly been on Clubhouse in the late evenings after work, because that’s when user activity in China appears to peak. “Who in China has that much time?” said Zhou Lingyu, founder of Rainmaker, a Chinese networking community for professionals, when I asked whether she thinks Clubhouse will attract the masses in China.

While her remark may not apply to everyone, the tech-centric, educated crowds in China — the demographic that Clubhouse appears to be targeting or at least attracting — are also those most likely to work the notorious “996” schedule, the long hours practice common in Chinese tech companies. The type of “meaningful conversations” that Clubhouse encourages is desirable, but the app’s real-time, spontaneous nature is also a lot to ask of 996 workers, who likely prefer more efficient and manageable use of time.

Moderators may also need material incentives to remain active aside from the pure passion in connecting with other human beings. One potential solution is to turn quality conversations into podcast episodes. “Clubhouse is for one-off, casual conversations. Those who produce high-quality content would want to record the conversation so it could be for repeatable consumption later on,” said Zhou.

Chinese counterparts

In China, audio networking has played out in slightly different shapes. Some companies place a great deal of focus on gamification, filling their apps with playful, interactive features.

Lizhi’s social podcast app, for example, is not just about listening. It also lets listeners message hosts, tip them through virtual gifts, record themselves shadowing a host who is reading a poem, compete in online karaoke contests, and more.

Interaction between hosts and listeners happens in a relatively orchestrated way, as Lizhi’s operational staff design campaigns and work with content creators behind the scenes to ensure content quality and user engagement. Clubhouse growth, in comparison, is more organic.

“The Chinese products focus more on spectatorship and performance, not so much translating natural social behavior in real life into a product. Clubhouse features are simple. It’s more like a coffee shop,” Lai said.

Lizhi’s other voice product Tiya is considered a close answer to Clubhouse, but Tiya’s users are young — the majority of whom are 15-22 years old — and it focuses on entertainment, letting users chat via audio while they play games and watch sports. That also feeds the need for companionship.

Dizhua, which launched in 2019, is another Chinese app that’s been compared to Clubhouse. Unlike Clubhouse, which relies on people’s existing networks for room discovery, Dizhua matches anonymous users based on their declared interests. Clubhouse conversations can start and die off casually. Dizhua encourages users to pick a theme and stay engaged.

“Clubhouse is a pure audio app, with no timeline, no comment, et cetera,” said Armin Li, an expert in residence with a venture capital firm in China. “It’s a kind of casual and drop-in style for the scenarios where user needs are not clear like hangout or multitasking … Its high community participation, content quality, and user quality are unseen in Chinese voice products.”

The bottom line is: The conversations that happen on Chinese platforms are monitored by content auditors. User registration requires real-name verification on internet platforms in China, so there’s no real anonymity online. The topics that users can discuss are limited, often leaning towards the fun and innocuous.

Why do people in China join Clubhouse anyway? Some, like me, joined out of FOMO. Entrepreneurs are always scouring for the next market opportunity, and product managers from internet giants hope to learn a thing or two from Clubhouse that they could apply to their own products. Bitcoin traders and activists, on the other hand, see Clubhouse as a haven outside the purview of Chinese regulators.

Technical support

One thing I find impressive about Clubhouse is how smoothly it works in China. Even when a foreign app isn’t banned in China, it often loads slowly due to its servers’ distance from China.

Clubhouse doesn’t actually build the technology supporting its enormous chat groups that sometimes reach thousands of participants. Instead, it uses a real-time audio SDK from Agora, two sources told me. The South China Morning Post also reported that. When asked to verify the partnership, Agora CEO Tony Zhao said via email he can’t confirm or deny any engagement between his company and Clubhouse.

Rather, he emphasized Agora’s “virtual network,” which overlays on top of the public internet running on more than 200 co-located data centers worldwide. The company then uses algorithms to plan traffic and optimize routing.

Noticeably, Agora’s operations teams are mainly in China and the U.S., a setup that inevitably raises questions about whether Clubhouse data are within the scope of Chinese regulations, a possibility that the company flagged in its IPO prospectus.

With real-time voice technology providers like Agora, opportunists are able to build Clubhouse clones quickly at low costs, Herock said. Chinese entrepreneurs are unlikely to copy Clubhouse directly due to local regulatory challenges and different user behavior, but they will race to crank out their own interpretations of voice networking before the hype around Clubhouse fades away.


Social – TechCrunch


With $18M in new funding, Braintrust says it’s creating a fairer model for freelancers

October 3, 2020 No Comments

Braintrust, a network for freelance technical and design talent that launched over the summer, is announcing that it has raised $ 18 million in new funding.

Co-founder and CEO Adam Jackson has written for TechCrunch about how tech companies need to treat independent contractors with more empathy. He told me via email that the San Francisco-based startup is making that idea a reality by offering a very different approach than existing marketplaces for freelance work.

For one thing, Braintrust only charges the companies doing the hiring — freelancers won’t have to pay to join or to bid on a project, and Braintrust won’t charge a fee on their project payments. In addition, the startup is using a cryptocurrency token that it calls Btrust to reward users who build the network, for example by inviting new customers or vetting freelancers. Apparently, the token will give users a stake in how the network evolves in the future.

“Just imagine if Uber had given all of its drivers some ownership in the company what a different company it would be today,” Jackson said. “Braintrust will be 100% user-owned. Everyone who participates on the platform has skin in the game.”

And for companies, Braintrust is supposed to allow them to tap freelancers for work that they’d normally do in-house. The startup’s clients already include Nestlé, Pacific Life, Deloitte, Porsche, Blue Cross Blue Shield and TaskRabbit.

According to Jackson, most of the talent on the platform consists of career freelancers, but with many people losing their jobs during the COVID-19 pandemic, “we’ve seen an influx of talent coming looking to join the ranks of the freelancers.”

He added that the startup already became profitable after raising its $ 6 million seed round, so the new funding will allow it to build the core team and also bring in more work.

“We exist to help companies accelerate their product roadmaps and innovation, and this injection of funding will help us do just that,” Jackson said.

The new funding was led by ACME and Blockchange, with participation from new investors Pantera, Multicoin and Variant.


Enterprise – TechCrunch


New Relic is changing its pricing model to encourage broader monitoring

July 30, 2020 No Comments

In the monitoring world, typically when you spin up a new instance, you pay a fee to monitor it. If you are particularly active in any given month, that can result in a hefty bill at the end of the month. That leads to limiting what you choose to monitor to control costs. New Relic wants to change that, and today it announced that it’s moving to a model where customers pay by the user instead with a smaller less costly data component.

The company is also simplifying its product set with the goal of encouraging customers to instrument everything instead of deciding what to monitor and what to leave out to control cost. “What we’re announcing is a completely reimagined platform. We’re simplifying our products from 11 to three, and we eliminate those barriers to standardizing on a single source of truth,” New Relic founder and CEO Lew Cirne told TechCrunch.

The way the company can afford to make this switch is by exposing the underlying telemetry database that it created to run its own products. By taking advantage of this database to track all of your APM, tracing and metric data all in one place, Cirne says they can control costs much better and pass those savings onto customers, whose bills should be much smaller based on a this new pricing model, he said.

“Prior to this, there has not been any technology that’s good at gathering all of those data types into a single database, what we would call a telemetry database. And we actually created one ourselves and it’s the backbone of all of our products. [Up until now], we haven’t really exposed it to our customers, so that they can put all their data into it,” he said.

New Relic Telemetry Data. Image: New Relic

The company is distilling the product set into three main categories. The first is the Telemetry Data Platform, which offers a single way to gather any events, logs or traces, whether from their agents or someone else’s or even open source monitoring tools like Prometheus.

The second product is called Full-stack Observability. This includes all of their previous products, which were sold separately such as APM, mobility, infrastructure and logging. Finally they are offering an intelligence layer called New Relic AI.

Cirne says by simplifying the product set and changing the way they bill, it will save customers money through the efficiencies they have uncovered. In practice he says, pricing will consist of a combination of users and data, but he believes their approach will result in much lower bills and more cost certainty for customers.

“It’ll vary by customer so this is just a rough estimate but imagine that the typical New Relic bill under this model will be a 70% per user charge and 30% data charge, roughly, but so if that’s the case, and if you look at our competitors, 100% of the bill is data,” he said.

The new approach is available starting today. Companies can try it with 100 GB single user account.


Enterprise – TechCrunch


Best iPhone for 2019: Which Model Should You Actually Buy?

March 28, 2019 No Comments

Picking the right iPhone has become an increasingly difficult choice, but this breakdown should help you figure out whether you want an iPhone XR or another model, where to buy a case, and whether it’s a good time to purchase.
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Best iPhone for 2019: Which Model Should You Actually Buy?

March 28, 2019 No Comments

Picking the right iPhone has become an increasingly difficult choice, but this breakdown should help you figure out whether you want an iPhone XR or another model, where to buy a case, and whether it’s a good time to purchase.
Feed: All Latest


Boeing’s 737 Crash, Tesla’s Model Y, and More News This Week

March 18, 2019 No Comments

This week’s transportation news focused on two major stories: the investigation into the fatal crash of Ethiopian Flight 302 and Elon Musk’s reveal of Tesla’s new baby SUV.
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