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Mailchimp wants to optimize your email campaigns using AI — here’s how

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Ben Dickson is the founder of TechTalks. He writes regularly about business, technology and politics. Follow him on Twitter and Facebook Ben Dickson is the founder of TechTalks. He writes regularly about business, technology and politics. Follow him on Twitter and Facebook
Earlier this month, Mailchimp released Content Optimizer, a new product that uses artificial intelligence to help improve the performance of email marketing campaigns.
Thanks to its vast trove of data, Mailchimp is in a unique position to discover common patterns of successful marketing campaigns. Content Optimizer taps into that data and uses machine learning models and business rules to predict the quality of email campaigns and provide suggestions on how to improve content, layout, and imagery.
This is not Mailchimp’s first foray into using AI for content marketing, but it might be its most impactful effort in the field. Leading the effort to develop Content Optimizer is John Wolf, Product Manager of Smart Content at Mailchimp. Wolf was the founder of Inspector 6, a startup acquired by Mailchimp in 2020. The technology and experience that Inspector 6 brought to Mailchimp played an important role in the development of Content Optimizer.
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In an interview with TechTalks, Wolf provided some behind-the-scenes details on the vision and development process of Content Optimizer and shared insights on how AI is changing the future of content marketing.

email

Like many products, the idea for Content Optimizer started with someone feeling the pain. Wolf spotted the need for AI-powered content marketing before founding Inspector 6, when he was Chief Marketing Officer at Intradiem, a software development company.
Like all companies, Intradiem needed great marketing content. But the process was difficult, and measuring quality and success was very subjective.
“The creative process was completely dominated by opinions with little data. It was very manual, very labor-intensive, lots of cycles to get the creative right, and I was thinking there just has to be another way,” Wolf said.
At the time, machine learning was starting to find real business applications in many sectors. So, Wolf started to think about using ML to optimize the creative process for content marketing.
“The idea was, what if we could use machine learning to understand marketing content? If a software can understand what story marketing content is telling and how it’s telling it, it can then correlate features with marketing outcomes and start to standardize and add data to much of the creative process and replace those opinions with data,” Wolf said.
In 2017, Wolf founded Inspector 6 with the vision of developing AI-powered content marketing. Inspector 6 became an AI platform that analyzes marketing content to deliver insights and recommendations for improvement.
Like all applied machine learning applications, marketing content optimization hinges on having large amounts of quality data. Accordingly, Inspector 6’s platform was successful in some areas and met challenges in others.
“Our largest customer was a very big multi-national consumer packaged goods company with 500 brands in 200 countries, and I thought they would have plenty of data to power my predictive models. And I found that it wasn’t necessarily true,” Wolf said. Providing value for small businesses was more difficult since they had even more limited data for training machine learning models.
Before Mailchimp acquired Inspector 6, the two companies entered a partnership. Mailchimp delivers hundreds of billions of emails per year, and through the partnership, the Inspector 6 team got to experience the advantage of training its models on the huge volume of marketing data that Mailchimp has.
“The amount of data and the breadth of data, everything from marketing content that is selling products and services to content marketing and newsletters and everything in-between, some very high-performing and some just as importantly low-performing—having that depth and breadth of data becomes the key component to delivering a solution like this,” Wolf said.
Recommendations also require context. An analyst would have to know the goal, business vertical, audience, and other information about a marketing campaign before providing feedback on its efficiency. Likewise, machine learning models require context. Fortunately for Mailchimp, the variety and number of customers it has provided ample data to train machine learning models that can perform well across different contexts.
“You take Mailchimp’s data with 360 billion emails a year that become our training set for this, but then you’d have to slice it by context to really be able to solve this problem,” Wolf said. “So basically, the 360 billion emails start to become a collection of many individual training sets that are context-specific.”

Mailchimp

The partnership between Mailchimp and Inspector 6 eventually turned into an acquisition proposal, which became a win-win situation for both companies.
Mailchimp’s data and infrastructure gave the Inspector 6 team the opportunity to expand its application to a wider base of customers
“Having gone through the experience of knowing how much data my predictive models were going to require and how much data Mailchimp had, I thought it could be really interesting to now solve this problem that I’m solving mostly for a handful of large multinationals for millions or tens of millions of small businesses, which is more aligned with my passions,” Wolf said.
On the other hand, Mailchimp got to boost its AI efforts by acquiring a tried-and-tested backend technology stack for predicting marketing campaign outcomes and a team of engineers who were focused on the intersection of marketing, computer science, and data science.
“Mailchimp has experience in all of those areas, but to have a focus on solving this problem at that intersection is what Mailchimp was most interested in acquiring from a talent standpoint,” Wolf said.
Inspector 6 had architected its technology as individual microservices on Amazon Web Services. The system ingests a marketing asset, and the individual microservices independently do their job to analyze it.
Mailchimp, on the other hand, uses the Google Cloud Platform. So, the services had to be transferred from one cloud platform to another. Fortunately, before the acquisition, Mailchimp had undergone a massive project to port all their data into Google BigQuery, a cloud-based data warehouse that makes it easy to manage large stores of information and use them in data analysis and machine learning pipelines. Mailchimp also uses other GCP products such as Dataflow, a streaming analytics service that creates dynamic views of real-time and stored data in very efficient ways.
This made it much easier to integrate Inspector 6’s services into Mailchimp’s cloud infrastructure.
“From a technology standpoint, we went from one collection of microservices to another, and that worked pretty well,” Wolf said.
Inspector 6’s microservices are an enabling technology. They are integrated into the backend of Mailchimp’s system and offered through frontend products. The services started with providing reporting services to Mailchimp but gradually developed into becoming a generator of content insights through frontend product groups. And Wolf’s vision of AI-powered optimization of marketing campaigns, which kicked off Inspector 6, eventually became Mailchimp’s Content Optimizer.

Mailchimp

Content Optimizer provides scorecards that reflect the overall content quality of marketing emails and the number of best practices in each analysis category, like skimmability and layout. All users can access the content scorecard. Premium users also get actionable recommendations to improve their content.
“Our north star in solving this problem is improving campaign performance. If we improve campaign performance across our user base by just 10 percent, that will create 190 million incremental online visits to our customers’ businesses,” Wolf said.
Naturally, machine learning is a key component of Content Optimizer. Behind the scenes, a pipeline of ML models goes to work to parse and analyze different parts of the marketing email and to predict its outcome.
The first batch of models extract the features of different elements of the content such as the tone of writing, the messaging, the layout of the marketing content, and the images used to tell the story.
These features become the input of the next series of machine learning models, which try to predict the outcome and quality of the marketing campaign. In some areas, Content Optimizer combines ML predictions with symbolic AI to provide recommendations that are more robust and understandable.
“Beyond machine learning, sometimes we use a hybrid of machine learning models and business rules to detect things,” Wolf said. “Sometimes we found that business rules are actually easier to maintain, easier to develop, and in some cases more accurate than machine learning.”
For example, a “call to action” is a key component of any marketing asset. Most successful call-to-action sentences start with a verb of a certain form. That business rule performs very well, the Content Optimizer team found. So, in this case, they use ML libraries to detect parts of speech in CTA text and feed the parsed data to a rule-based system that evaluates its quality based on static rules.

Glasses screen

While the machine learning models provide valuable insights, they can’t work autonomously yet. For the moment, Mailchimp uses human operators to make sure the output provided by Content Optimizer makes sense and would be in line with recommendations a creative director would make.
“We go through a traditional predictive modeling exercise, but then there’s a manual vetting process,” Wolf said. “That creates inefficiency in the process, but we feel it’s necessary at this point.”
There is some controversy around putting human operators behind AI systems. Sometimes, it’s called the “Wizard of Oz technique” or pseudo-AI. But in our conversation, Wolf was very transparent about it, and he believes that it will be an important factor in the success of the product. Moreover, the company is not outsourcing the task and is carrying it out entirely through internal resources.
“In the early days of applied AI, I think the risk of losing credibility with our users by making a recommendation that is just out of bounds and doesn’t make sense is too great that we want to be incredibly careful and sensitive,” Wolf said.
As the Content Optimizer gathers more data and feedback, the team will gradually finetune the machine learning models and figure out how to make them less reliant on human assistance.
“It takes time. It adds a labor-intensive element. But it’s an area we’re willing to put people against,” Wolf said.
It’s not guaranteed that the task will be fully automated. But at the end of the day, a machine learning product is, like all products, a tool to solve problems with better outcomes, at greater speeds, and at lower costs. If Content Optimizer helps Mailchimp improve the campaign performance of its clients in a statistically significant and cost-efficient way, then it is a successful product regardless of how much human effort it requires. A notable example in this regard is AdWords, Google’s online advertising platform and its greatest source of revenue. AdWords uses a combination of AI and human evaluation to make sure ads are relevant and compliant with the company’s policies.

Mailchimp

One of the key parts of the product management process is learning from users. After launching a product, your hypotheses will be put to test. You’ll usually find pain points that you had overestimated or overlooked and interesting use cases that you had not thought about.
For example, the Content Optimizer showed that in general, Mailchimp users did a better job at typography than the product team had initially estimated. They also found that many marketers struggle with writing simple and concise language.
“It’s almost like the collective system is the creative director for 14 million active users and you need to be the creative director for them,” Wolf said. “Sometimes they’ll surprise you with what they’re great at and what they’re still struggling with.”
One of the positive outcomes of the Content Optimizer, according to Wolf, is that marketers have already become comfortable with using the product.
“When you put a new product on the market, you expect a lot of questions about ‘What is this?’ and ‘How is it done?’” he said. But when people are using Content Optimizer, their conversations are more about marketing and less about the product, he says, which has been a nice surprise.
“If they go straight to the marketing conversation and what they’re going to do differently in the future, that’s the exact goal. They’ve taken the product and understood it really well,” Wolf said.
According to Wolf, his team will keep on expanding Content Optimizer to provide a wider variety of recommendations in tone, messaging, brand consistency, imagery, and other areas. The product will also expand from email marketing to other channels such as web pages and social media. The touchpoints will also increase in the future. For the moment, Content Optimizer is a reporting tool, but the team plans to also make it available as a real-time recommendation system that operates while users are editing their content.
Wolf is also interested in getting into computer-generated content in the future.
“Even the most sophisticated marketers in the world would love to spend less time generating content,” he says. “Everyone is familiar with cutting-edge copywriting generative models like GPT-3. Those are great. But how do you make sure they’re on-brand, on-message, and they’re optimized in the same way that we’re optimizing human-generated content.”
Generative models struggle with consistency and coherence when used in isolation. But Wolf believes that the combination of the Content Optimizer pipeline and generative models like GPT-3 can create immense value for marketers.
“Our customers spend 28 million hours a year writing copy alone, not even designing and sourcing images. We think with some technologies in the generative space, we can decrease that by 80 percent. That’s 22 million hours we can save our customers,” he said. “That to me is just staggering and it’s one of the things I found most compelling about selling my business to Mailchimp, just to be able to create value at that scale. We’re really excited about what the future holds and we’re really just getting going.”
This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech, and what we need to look out for. You can read the original articlehere.
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Tesla’s biggest Cybertruck competitor just rolled out the biggest IPO this year

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Rivian today showed that people “will come” even before you build it. Today, the electric truck manufacturer raised a massive $11.9 billion in its initial public offering (IPO). This is the biggest so far this year and the sixth-largest ever on a US exchange. It trumps Uber which raised $8.1 billion in 2019.
The company, founded in 2009, is now valued at more than $77 billion — nearly as much as traditional companies Ford and General Motors.
Shares were offered at $78 each — above the previously estimated price between $57 and $62, showing there’s a keen commitment to buy. Pretty good for a company with almost no revenue from actual vehicle sales. 
Can self-healing security software fight cybercrime?
I spoke to Maxim Manturov, Head of Investment Research at Freedom Finance Europe, just before the IPO, who explained:
Rivian’s two consumer products, the Rivian R1T and Rivian R1S received over 48,390 pre-orders as of the end of September, while its commercial vehicle, Rivian EDV, received substantial orders and investments from Amazon. 
By 2025, Amazon will own 100,000 EDV’s, with 10,000 to be purchased by the end of 2022.
Ford will be releasing its F-150 Lightning truck in 2022, and like the Rivian R1T, it also has a 480km driving range. But the F-150 is over $20k cheaper than Rivian. So far, the F-150 has secured 120,000 reservations. 
Further, Tesla’s cybertruck has a reported reservation backlog of a massive 1.3 million and will cost consumers between $40,000 and $78,000 — the latter with all the bells and whistles.
Besides news of a sexual discrimination lawsuit against Rivian last week, the company launched the Fleet section of its website on Friday. This is despite a previously exclusive deal with Amazon for van manufacturing. 
It also plans to sell the R1T electric pickup truck and R1S electric SUV in fleets with a corresponding fleet management platform called FleetOS and a charging infrastructure solution.
Of course, the challenge will be if Rivian can keep manufacturing up with an influx of orders, especially given the supply chain problems and semiconductor shortage affecting the whole auto industry. 
Rivian’s filing with the Security and Exchange Commission reveals worrying manufacturing numbers:
In the consumer market, we launched the R1 platform with our first-generation consumer vehicle, the R1T, a two-row five-passenger pickup truck, and began making customer deliveries in September 2021. 
As of September 30, 2021, we produced 12 R1Ts and delivered 11 R1Ts, and as of October 22, 2021, we produced 56 R1Ts and delivered 42 R1Ts.
These numbers are woeful considering that Rivian plans to deliver an order of 100,000 electric delivery vans in the next three years, with only 10 delivered by the end of 2021. 
Rivian only started delivering the R1T to primarily employees in September and plans to only make about 1,200 vehicles by the end of the year. 
According to the IPO filing, its plant in Normal, Illinois, can produce up to 150,000 vehicles annually. The company plans to ramp up to 200,000 vehicles by 2023.
The securities filing also revealed that Amazon holds a 20% stake in Rivian Automotive. The behemoth has more than $1.3 billion in Rivian to date. It’s likely they’ll honor these orders first. 
Manturov also said that brand recognition might prove to be a challenge for Rivian against its rivals: 
Tesla and Ford are both household names, with Tesla’s valuation skyrocketing from $73 billion in 2019 to three-quarters of a trillion today. As a result, they have a long line of loyal customers who trust their product. 
While Rivian follows Tesla’s approach in terms of advertising by shunning traditional advertising methods, Tesla’s charismatic owner, Elon Musk, keeps the company in the news, which explains why he has 60 million Twitter followers compared to Rivian CEO and founder RJ Scaringe’s 46,000 followers.  
Rivian hasn’t got a celebrity CEO. But today’s news shows a firm commitment to electric vehicle manufacturing. It’s a bonus for sustainable mobility.
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Top 20 Plugin Electric Vehicles In The World — September 2021

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Global plugin vehicle registrations were up 98% in September 2021 compared to September 2020, scoring a record 685,000 units (or 10.2% share of the overall auto market, the first time the global market share reached two digits). That’s a significant 16% increase over the previous record, set in June, and expect the two final months of the year to also become record months.
Fully electric vehicles (BEVs) represented 75% of plugin registrations in September, above the year-to-date tally (68%). In total, there were some 512,000 registrations of BEVs, or 7.6% share of the overall auto market.
With the YTD tally now above 4.3 million units (and at a record 7% share), and knowing that the last months of the year are traditionally strong sellers, we should be seeing the plugin vehicle (PEV) market easily surpass 6 million units this year, with the 7 million unit mark being a true possibility!
For comparison sake, 2020 ended with 3.1 million units registered. Not bad, considering the current chip shortage, eh?
While disruption is already happening in Europe and China, we should only see consistent disruptive levels on a global scale next year, which will probably get a boost from the US market as it goes into warp speed due to new incentives and the start of the ramp-up of several electric pickup trucks.
Having said that, December could be the first month to break one million units globally, as all 3 major markets (China, Europe, and USA) are expected to have record months.
The future will depend much on the development of the COVID pandemic, the economic recovery, and the chip (and battery) shortage, but whatever happens, expect plugins to continue increasing market share. Many legacy OEMs are now prioritising their plugin offerings over their fossil fuel models, because they need to have a foot in the door in the fast-growing plugin market now in order to assure their survival in a future BEV-based automotive market.

In the model ranking, it was a peak month for Tesla, and it shows, with both the #1 Tesla Model 3 and #2 Tesla Model Y hitting record months. The first hit 70,798 registrations in September and the second reached 68,469 registrations, but looking at the quarterly performances of each midsize model, which is the right approach to measure Tesla’s performance, there are significant nuances between the two. While the Model Y continues to increase its monthly average per quarter (~38,000 units), the Model 3’s third quarter performance (39,000) was its lowest since last year’s Q3. So, at this point, one can say that the sedan is now entering into its maturity stage, and Tesla’s growth prospects for the near future are mainly on the Model Y, which should regularly surpass the Model 3 soon — especially when we consider that the crossover is only now landing in Europe.

Off the podium, the rising Volkswagen ID.4 (13,138 registrations, a new record) was beaten once again by the surging BYD Qin Plus PHEV, which scored a record 15,164 registrations. With the BEV version of the midsize Chinese model also ramping up (it was #8, with a record 8,396 registrations), both versions counted together got an amazing total of 23,560 registrations. One wonders where and when the delivery ramp-up will end. Will the BYD midsizer be the first to run head to head with the Model 3?
Elsewhere, a mention goes out to several Chinese models hitting record scores — there were 12 Chinese models in September’s top 20, 6 of them with record scores. Besides the aforementioned BYD Qin Plus PHEV and BYD Qin Plus BEV, the #6 BYD Song Pro PHEV, #12 GAC Aion S, #13 XPeng P7, and #20 Letin Mango all hit new records.
Interestingly, if we count both the BEV and PHEV versions of the BYD Song Pro together, we get 14,791 units, which is above the #5 Volkswagen ID.4. So, basically, the closest competitors to both Tesla midsizers are not coming from Volkswagen but from BYD, in the form of the Qin Plus (Model 3) and Song Pro (Model Y). But more on this later….
In the traditional OEM camp, the highlights were the aforementioned Volkswagen ID.4, and VW’s lower riding #7 ID.3, with the hatchback scoring its best result in 2021 — 8,397 registrations. The #17 Hyundai Ioniq 5 continued to shine, hitting a record 6,754 units last month, while its Kia cousin, the EV6, had 2,823 registrations in its second month on the market, which means the sporty Kia is ramping up faster than its Hyundai stablemate.
Will both Koreans reach 5-digit scores soon? I presume there wouldn’t be a problem from a demand point of view, but as far as supply….
Outside the top 20, we should mention that there are 6 Chinese models in the following 10 positions, and 4 of them hit record scores (NIO ES6, Great Wall Ora Good Cat, Hozon Neta V, GAC Aion Y). So, soon we might have the majority of the top 20 belonging to Chinese brands. But more on this later….


In the YTD table, the climber of the month was the BYD Qin Plus PHEV, which jumped 4 spots to #5, with the BYD midsizer now set to go after the #4 VW ID.4. But if we were to add the BEV version volumes to the BYD sedan, the Qin Plus would actually have 89,177 registrations, which would grant it the 4th spot.
But there were other models climbing in the ranking, all coming from Asia. The GAC Aion S climbed to #10, while the Kia Niro EV profited from the slow months of the Renault Zoe and Hyundai Kona EV to jump two spots, into #12.
We have a new face in the top 20, with the XPeng P7 joining the table in #19. It’s the 10th Chinese model in the top 20, while below the #20 Ford Kuga/Escape PHEV, we should mention the rising #22 BYD Song Pro PHEV, which is some 600 units below the top 20 and should join the table next month. That would make it 11(!) Chinese models in the YTD top 20. But more on this (and dinosaurs) later….

In September, Tesla hit a record score of 143,143 registrations, doubling the numbers of #2 BYD, which nevertheless is continuing to ramp up production. In fact, BYD once again beat its own record, with 70,236 registrations in September.
With the seemingly never-ending record streak from the Shenzhen automaker set to continue, numbers could start to get close to Tesla’s own quarterly average, set at 80,433 units/month last quarter.
#5 SAIC also set a record in September, much thanks to export markets (it had 6,500 registrations last month alone). That, added to the continuing strong performances from SGMW, made Shanghai Auto one of the winners of the month.

Hyundai and Kia impressed in September, both with record scores, with the Ioniq 5 and EV6 providing that little extra volume to what are already very consistent lineups, so we might have these two aiming for top 5 presences soon.
But the main trend is the rise and rise of the Chinese brands. Besides aforementioned BYD and SAIC, 4 other automakers had record months in September, with #12 GAC, #14 Dongfeng(!), #17 XPeng, and #19 NIO all hitting record scores. That means that out of the 9 Chinese brands in this top 20, 6(!) had record months. These 9 brands made 30% of all plugin vehicles registered last month….
And there are plenty more Chinese automakers (Li Xiang, Hozon, Letin, Weltmeister, Chery, etc.) ramping up faster than the market average, which raises the question:
From a legacy OEM point of view (say, Volkswagen’s), what is more dangerous, one T-rex or a pack of 20 velociraptors?
Sure, it is easier to focus on the T-rex and make your moves according to what that Big, Bad Beast is doing, but they have to keep a close eye also on the fast-running velociraptors. While in isolation one Velociraptor doesn’t seem that threatening, as a pack, they are far more effective in gobbling up market share than the T-rex, as they can disperse throughout the market/territory in a way the T-rex can’t. After all, one can’t imagine the Tesla-rex going into the tight spaces of city cars, for example, while a pack of 20 velociraptors (or more) can easily spread into smaller groups and go after several categories/herds at once.
And while most of these Velociraptors are still contained in their native Jurassic Park, there is an opening in the fence and soon many will follow the lead of the early pioneers (SAIC, BYD, XPeng, NIO) and spread havoc on the legacy OEM domains. Even the T-rex will have to look out!
Editor’s note: Nice metaphor.


In the YTD table, the main news was that a rising BYD has surpassed SGMW and is now the new silver medalist, while SAIC climbed to 6th at the expense of Mercedes.
There wasn’t much else to report. Although, it should be mentioned that rising #10 Kia and #11 Hyundai are now set to catch #9 Audi by the end of the year, while #14 Peugeot has shortened the distance to #13 Toyota, and #17 GAC should surpass #16 Ford next month.
Finally, Skoda managed to retain the 20th spot, but it shouldn’t for long, as #21 XPeng is just 500 units behind it. With the current production ramp-up of the Chinese startup, we should see it join the table in October.


Looking at registrations by OEM, Tesla gained market share last month thanks to its end-of-quarter peak, but it also lost a bit of share compared to last quarter (15.2% in Q2, 14.7% now). Further, YoY, it has dropped 3 percentage points below its market share in September 2020 (18% then, 15% now), which goes inline with the current trend of a soft landing of Tesla into a long term 10–12% market share interval — according to our expectations.
Meanwhile, Volkswagen Group is 2nd at 12%, 1 percentage point less than it had a year ago. Still, the German conglomerate is by far the most important legacy OEM in the plugin business, and maybe the only representative of the Old Guard in the future Big 3/4 OEMs of an EV-based automotive business.
SAIC kept its market share compared to last quarter, ending Q3 with the same 11% it had in Q2, but its 3rd spot still feels like a win. After all, a year ago, SAIC wasn’t even in the top 5.
BYD jumped two positions from Q2 to Q3, now showing up in #4, with 8% share, a full 3 point jump over the 5% it had just three months ago. I believe that by the end of the year, SAIC and Volkswagen Group will have to defend their positions from an ambitious BYD.
Stellantis kept its 5th spot with 6% share (former #4 BMW Group was kicked out of the top 5). It is a strong candidate to become one of the Old Guard OEMs in a future B-League of the automotive business, but for that to happen, its American arm (ahem, pickup trucks) needs to start moving sooner than later.


If we exclude PHEVs and focus solely on BEVs, Tesla’s share increases, naturally. It is sitting at 22% share, 1 point less than in the previous quarter. It has lost 4 percentage points YoY (it had 26% share a year ago).
SAIC is the runner-up, keeping its 14% share of Q2 but winning a full 6 percentage points of share compared to a year ago (“You’re welcome,” says the Wuling Mini EV). Meanwhile, #3 Volkswagen Group has kept its 3rd spot, with 10% share, despite losing 1 point compared to the second quarter of this year.
Off the podium, BYD is in the 4th position, with 6% share, while in 5th we have a position change, with Stellantis losing that position to Hyundai–Kia. The Korean OEM benefited from the Ioniq 5/EV6 push to jump from #7 in Q2 to the current #5 position. However, YoY, the Korean group lost 2 percentage points of market share, dropping from 7% to 5%.
But the big tumble compared to September 2020 belongs to the Renault–Nissan Alliance. A year ago, it was in 3rd place, with 9% share, while now it has less than half that share…. Oh, how the mighty have fallen!
So, in short, changing from PHEV+BEV to just BEV doesn’t significantly change the overall trends, but there are differences. #1 Tesla and #2 SAIC benefit in terms of market share, while Volkswagen Group drops from #2 to #3 (replaced by SAIC). Also, more PHEV-dependent Stellantis gets replaced in #5 by more BEV-friendly Hyundai–Kia.
Always interested in the auto industry, particularly in electric cars, Jose has been overviewed the sales evolution of plug-ins on the EV Sales blog, allowing him to gain an expert view on where EVs are right now and where they are headed in the future. The EV Sales blog has become a go-to source for people interested in electric car sales around the world. Extending that work and expertise, Jose is also market analyst on EV-Volumes and works with the European Alternative Fuels Observatory on EV sales matters.

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SpaceX’s success is built on the bones of tiny birds

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There’s a bird killer on the loose, a vicious murderer with an insatiable bloodlust for endangered species. His name is Elon Musk.
The Tesla tycoon hasn’t been doing the dirty work himself, but Musk’s SpaceX is reportedly decimating birds around the company’s facility in Boca Chica, Texas.
The surrounding land hosts an array of endangered animals, including sea turtles, ocelots, and hundreds of bird species, including the federally threatened Piper Rover.
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Campaigners warn that SpaceX’s operations are destroying their habitats. According to an analysis by Coastal Bend Bays & Estuaries Program, the region’s Piping Plover population has shrunk by 54% in the three years since the arrival of SpaceX. 
“Boca Chica is incredibly important to birds,” said EJ Williams, the American Bird Conservancy’s (ABC) VP for the Southeast Region, in a statement.
“The SpaceX facility in Boca Chica is surrounded by federal and state public lands used by hundreds of thousands of individual birds of many different species throughout the year.”
Activists have raised concerns about the harm caused by rocket explosions, sonic booms, debris dropping from the sky, and increased traffic in the area. They also worry about the environmental impact of SpaceX’s plans to drill for gas in the area.
SpaceX is expanding its operations in Boca Chica, TX without a complete assessment of environmental impacts. The Federal Aviation Administration (FAA) is accepting comments on the project until next Monday, November 1st. Speak up now!https://t.co/TVundNiOeUpic.twitter.com/EAw09mCB5J
— American Bird Conservancy (@ABCbirds) October 26, 2021

Residents have also accused SpaceX of ruining the neighborhood. Musk, however, has previously appeared dismissive of the effects on their lives.
“We’ve got a lot of land with nobody around, so if it blows up, it’s cool,” he reportedly said in 2018.
Musk may strive to save humanity by colonizing space, but it’s a shame he has to destroy some species on Earth in the process. Flying on SpaceX rockets won’t feel quite as fun if they’ve been built on tiny bird skeletons.
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