eyeo’s Investment in the UK-Based AI Startup Ensures the Further Development of Its News-Rating Extension “Trusted News”

eyeo’s Investment in the UK-Based AI Startup Ensures the Further Development of Its News-Rating Extension “Trusted News”

eyeo GmbH, the company behind Adblock Plus, announced an investment and partnership with Factmata. As part of the investment, Factmata will take over further development of eyeo’s Trusted News browser extension. It will alert users to hate speech, racism, sexism, bias, sensationalism, one-sidedness or deceptiveness in news articles, using Factmata’s proprietary language-analysis algorithms. This investment furthers Factmata’s commitment to a quality media ecosystem.

The Trusted News extension was first released in beta in late 2018. The project was started by a small team of experts at eyeo who were driven to find a solution to the growing problem of fake news and hate speech online. When looking for a provider of filter data for such content, they found the ideal partner in Factmata.

Trusted News uses a simple approach: leaning on website blacklists from independent fact-checking organizations to generate its “fake news” ratings. Content based on facts and backed by primary sources is rated as trustworthy. If content contains politically-biased views that are not backed by facts or contains heavy elements of humor or exaggeration, it is rated as biased or satire. Misleading or false headlines for the purpose of enticing readers to visit a website purely for traffic or revenue are labeled as clickbait. Content that deliberately delivers threats to your computer or personal safety is marked as malicious.

“Just as eyeo and Adblock Plus are protecting users from harmful, annoying ads, the partnership between Factmata and Trusted News gets us one step closer to a safer, more transparent internet. Content that is harmful gets flagged automatically, giving users more control over what kind of content they trust and want to read.” said Till Faida, CEO & Co-Founder, eyeo.

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Factmata’s strategy with Trusted News is to involve a community in the process of critically thinking about online content, as well as to assist its users in the process of judging articles. To start with, this might include an integration of Factmata’s API to detect if a page’s content might be sexist, racist, threatening, or politically propagandist. Users should be able to agree or disagree with judgements, as well as add their own reasoned opinions to the system. Community monitoring systems will be put in place to ensure no obvious gaming or abuse of the system, as well as tracking what Factmata deems as the “relevance” of the user in assessing the content.

Using Factmata’s unique “expert in the loop” approach to training machine learning algorithms, Trusted News and Factmata will be in a unique position to build a reliable, trust-able rating system for any piece of online content using an advanced, scalable AI that becomes more representative of what the crowd thinks over time.

“Our goal is to build a fair, explainable, open approach to rating content online, and not judge something to be of low credibility just because we don’t agree with the views of the website. We believe there are reasonable bounded indicators of good quality, balanced, trustworthy journalism. With enough time and training data, a well-built AI should be able to automatically detect writing that strays from these bounds, whilst leaving the final evaluation and critical opinion to the reader,” says Co-Founder and CEO, Dhruv Ghulati.

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Factmata, started in 2017 by researchers Dhruv Ghulati, Sebastian Riedel, and Andreas Vlachos, was one of the first global companies to tackle fake news online. Factmata raised funding from major internet pioneers such as Mark Cuban, Craig Newmark, Sunil Paul and Biz Stone. Since then, it has developed AI software which can accurately score content for aspects like political bias, hate speech, racism, sexism, toxicity, obscenity, threats, insults, and clickbait. Other characteristics it has been developing in stealth include the detection of deceptive language, sensationalism, one-sidedness, other forms of identity hatred such as transphobia, bot-generated content, and potentially false claims and rumours.

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TechBytes with Matt Harada, GM Data, Sovrn Holdings

TechBytes with Matt Harada, GM Data, Sovrn Holdings

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Matt Harada
GM Data, Sovrn 

[/vc_column_text][/vc_column][vc_column width=”3/4″][vc_column_text]AI is a potent tool to combat the Fake News menace. Publishers are looking for Marketing Technologies that can solve this challenge. The recent Sovrn-Factmata partnership is seen as a credible technology integration to develop an advanced machine learning platform that can tackle fake-news. Matt Harada, GM Data, Sovrn reveals the fascinating aspects of their partnership with Factmata, using AI to combat Fake News and the growing role of Audience Data and CDPs.[/vc_column_text][vc_column_text][/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space height=”20px”][vc_column_text]Tell us about your role at Sovrn and the team/technology you handle. How do you work with data to make adtech platform better?

As GM Data, my role at Sovrn is all about data – but I’m also responsible for the DSP and agency relationships. I initially came to Sovrn to build standalone data products and we’ve been successful at that. For example, we work with publishers to monetize their email to cookie ID linkages.

However, it quickly became apparent there are huge opportunities to apply the data we process as part of our advertising exchange to improve the exchange itself. So I also took on the role of building the demand side of our exchange business to apply data optimization principles to the exchange. For instance, the natural language processing we do for page categorization to support our data products can also be used to enrich the bid requests we send to our DSP partners – thereby increasing yields for our publishers. Another example is our use of machine learning to predict the fair market value of requests. This feature in our exchange helps advertisers buy ads at fair prices in a first-price auction. We are also using related systems to predict the bidding behavior of our DSPs to send them only the requests they are most likely to bid on – reducing their infrastructure costs at the same time as improving our publishers’ yield.

We’re always on the look-out for further ways to use data to the benefit of all users in our exchange.

Tell us about your partnership with Factmata? How do you intend to fight against fake news?

We were Factmata’s first client. We are fully committed to the fight against fake news and were impressed by Factmata’s innovative use of technology to develop an anti-fake-news, advanced machine learning platform. We already do a significant amount to filter good quality publishers before they join our portfolio of 25,000+ sites. Today only 1 in 100 sites make it through our 25 step process. Since early May, we have been working with Factmata to continue this effort, by building new whitelists of inventory that are free of false or extreme content.

What role does AI play in this battle against fake news, and making the adtech ecosystem more productive?

Fake news is a difficult problem to combat, but in principle we’re able to use a lot of the same techniques that we use elsewhere: natural language processing to bring in the meaning of pages of content, training AI models with large data sets, and utilizing AI to identify patterns it finds to distinguish fake news from verified content. Our partnership with Factmata will further help us to enhance our quality media ecosystem and offer protection against deceptive digital content.

How do you see programmatic advertising technologies evolving with maturity of Audience and Customer data platforms?

The hot topic that I’m focused on – related to audience targeting – is how programmatic evolves through the mounting privacy regulatory pressures. In spite of predictions that GDPR will drive spend to shift to more contextual targeting, we still see massive premiums for consented reader targeted impressions over non-personalized ad opportunities. The response from the adtech industry has been fairly uninspired. I’d like to think that by the time California’s regulations come into effect, we can collectively embrace the benefits of respecting privacy concerns in digital programmatic advertising. I’m hoping the industry will accept the old way won’t work anymore and will start embracing a more privacy-friendly approach rather than pushing the law to the limits, and often beyond.

Not too long ago, data platform maturity meant centralizing disparate data sources and behaviors. These would be modeled into the interests, intents, characteristics, and identifiers of a massive scale of consumers. That information would be available to advertisers for use across the independent web to the benefit of the many small publishers and businesses contributing to the improving efficiency in the messaging.  It is going to be an even more difficult journey to achieve this long-term vision outside of Google, Facebook, and Amazon when current privacy trends provide relative strength to their grip on consumer data.

How do you empower content marketing teams to work more efficiently with videos, and cross-platform advertising formats?

The high CPMs in the video advertising space drew in a lot of businesses that provide little or no true and sustainable value to the publisher and advertiser. So we are highly cautious with every video product we work with to ensure we can provide both publishers and advertisers a very high quality and brand safe solution.

We’ve embraced Google’s Exchange Bidding product to work with more publishers. It is early days for us but we are excited by this partnership, and we are exploring several other approaches to provide the quality experience that has been lacking in a lot of earlier approaches. All of these relationships include screening and monitoring process even more rigorous than our display screening procedures.

What makes ad blocker a top-trending disruption in adtech? How can programmatic advertising formats help overcome the challenge with ad blockers?

Ad blocking is a ‘poison the well’ problem. It just takes one, high latency, high ad clutter, malvertising ridden site to drive a reader to install an ad blocker. Then that ad blocker starts picking away at the business of all online publishers – even the disciplined good sites – that the reader visits, forever. There are lots of approaches to mitigate the threat to the legitimate publisher but all of them are fighting against basic economic principles – this could be described as the tragedy of the commons. Even with all its market power, Google’s fight against bad ads can only go so far to stamp out the bad ad experiences that drive readers to install ad blockers.

At Sovrn, we support the fight against bad ad experiences. We have also worked on many approaches to help our publishers mitigate the loss of revenue from ad blockers programmatically, and they are all very difficult to pull off well. There is a difficult technical challenge of staying one step ahead of the ad-blockers. However, the bigger challenge is that the data-focused systems programmatic buyers use to make buying decisions break down in the blocked web. Buyers don’t see the blocked traffic as valuable and until they do, programmatic solutions to ad-blocking will be sub-par. Therefore, we recommend that publishers take non-programmatic approaches to mitigate ad-blockers such as subscriptions, whitelist messaging, etc.

Where do you see online advertising moving with better data regulations and hygiene?

There are lots of techniques to improve the reliability and effectiveness of data used in online advertising.  I think the issue we face is that the economic drivers in the industry often aren’t aligned with good data practices. Third party data providers have to focus on scale over precision all too often.  And first-party data can be highly precise but may not have the reach to meet many business goals.

The IAB’s Data Transparency Standards Proposal seems like a well-intentioned attempt at creating an objective and standardized quality component to data segments. That should help advertisers move beyond the scale of the segment as one of the few evaluation criteria available to them. Unfortunately, the proposal is both a heavy lift for data businesses to follow and may still not be enough to move advertisers. It may still be easier for advertisers to run test campaigns on multiple segments to pick the best performers rather than do the harder work of evaluating the sources of the data. I applaud the effort, and I wish I had a better suggestion of how to make quality data practices a better driver of segment use, but I think we have a way to go still.

Thanks for chatting with us, Matt.

Stay tuned for more insights on marketing technologies. To participate in our Tech Bytes program, email us at news@martechseries.com[/vc_column_text][/vc_column][/vc_row]

Interview with Dhruv Ghulati, Founder and CEO, Factmata

Dhruv Ghulati

[vc_row][vc_column][vc_row_inner][vc_column_inner width=”2/3″][vc_empty_space height=”40px”][vc_single_image image=”60018″ img_size=”400×400″ alignment=”center”][vc_empty_space height=”20px”][/vc_column_inner][vc_column_inner width=”1/3″][vc_empty_space height=”35px”][vc_wp_text]“We need to clean up the ecosystem so that consumers want to engage with online content and can easily find trustworthy content.”[/vc_wp_text][/vc_column_inner][/vc_row_inner][vc_row_inner][vc_column_inner width=”1/4″][/vc_column_inner][vc_column_inner width=”1/4″][easy-profiles profile_twitter=”https://twitter.com/dhruvghulati” profile_linkedin=”https://www.linkedin.com/in/dhruvghulati/”][/vc_column_inner][vc_column_inner width=”1/4″][/vc_column_inner][vc_column_inner width=”1/4″][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column width=”5/6″][vc_column_text]Tell us about your role at Factmata and how you got here. What inspired you to start the company?

I’m the Co-Founder of Factmata, an artificial intelligence startup solving the problem of factual misinformation on the internet. I like open information, transparency, the future of the web, artificial intelligence and building highly technical data products.

I started my career as an investment banker, then transitioned to a product manager role at a startup working on machine intelligence for web data. In 2015 I co-founded my first company, weave.ai and went through the Techstars business accelerator programme. After that, I was inspired to go back to university and study for a Masters degree in Computer Science. Immediately after completing my degree I was selected to join the 7th Cohort of Europe’s top accelerator, Entrepreneur First, which specializes in building deep technology companies from scratch.

Each stage of my career contributed to my belief that artificial intelligence (AI) will be the great general-purpose technological advance of our time. In November 2016, fake news was starting to become a problem and I could see the potential to use AI to solve the problems it was causing for any brand seeking to build real trust with customers and readers on the internet – Factmata was born in December that year.

What’s the most fascinating aspect of leading a tech-based media innovation company?

For me, the most fascinating parts have been what I have learned about myself. Luckily, from my investment banking years, I already knew that I was able to put in long hours and from my Masters degree I knew I could be innovative and solve problems with technology.

What leading a startup tech company has brought me is a deep understanding of how I can enable others, and pass on the joy you get from building something truly ambitious and challenging with a strong mission

The other thing that I have found wonderful is how supportive and open the investment community can be if you have a good idea. Typically, when someone invests in a startup they either know the founders or have had a “warm intro” to them. When I sought investment for Factmata I drew up a list of the people I most wanted onboard, people with varied experience who would each bring something different to the business. One of those people was Mark Cuban. I didn’t know him and I didn’t know anyone who did – so I emailed him and can now proudly say that he’s one of our founding investors.

Is machine learning the ubiquitous technology for every media company?

There’s no doubt that machine learning is spurring the media industry forward but it’s not the answer for every business. Machine learning has three main purposes: automating back-end processes, mining data, and understanding human behavior. All of these are important to media companies but whether or not machine learning can help a media company depends on their current set up and processes.

In order to evaluate whether a business is ready to embrace machine learning you need to look at whether you have the right people in place to implement and use the technology, what time and cost savings you will derive from the technology and understand what you want to get from the technology.

The reason machine learning works for many media companies is that they typically have tech-savvy staff who are used to integrating technology e.g. customer relationship management (CRM) platforms and data management platforms (DMP)s, and their business goal is to reduce the costs of running accounts. This aligns well with the benefits machine learning can bring to a business.

What is clear is that machine learning is going to be soon not just a differentiator but a must have technology for every business to be thinking about the business process optimization side of things. Where it still is a unique selling point today is in customer understanding – truly getting a sense of why your customers or users do something and what they really want from you.

Why is Fake News the biggest disruption for media companies?

Fake news is a huge problem because when a brand’s ad is displayed alongside junk content or fake news, consumers lose trust in the brand. When a brand crafts an advert with an inauthentic, biased message, consumers lose faith. If the brand produces content marketing that is not fact checked or well researched, it will suffer in the long run. Another issue with fake news is that it’s not a problem that can be solved overnight because of the volume of content that is created and shared online every day. Currently, it’s impossible to sift through all the junk that is out there at a rate that is sufficient to stop ads appearing against misinformation. As well as this, there is no right or wrong answer to Fake News, as it is sometimes subjective. The best that a machine learning algorithm can do is be built in an unbiased, effective, and explainable, interpretable manner.

Content producers largely rely on ads to generate revenue and there are some that will go to great lengths to increase the number of ads they can show a day in order to maximize revenue. On the whole content producers are good, they want to create good content that people want to read and they employ highly trained journalists to produce great, credible, accurate content. However, there are some people out there who don’t care about the accuracy or credibility of their content and these are the ones that spoil it for everyone else.

All of this has contributed to a waning of trust in the media industry which is the problem we have to solve – we need to clean up the ecosystem so that consumers want to engage with online content and can easily find trustworthy content. This will increase brands’ trust in online advertising as a way to engage their target audience in a safe, relevant environment where ultimately the most attractive customers from a viewability, purchasing power perspective actually reside

How can marketing teams leverage authentic Influencers as the ‘focus group’ for branding and social media promotions?

Any marketing campaign must start with knowing who your customers are and understanding their behavior. If you can establish that your customers use social media and are engaging with influencers you can look at building a campaign. The goal of any influencer campaign should be to develop your brand image, raise awareness of your products or services and drive sales.

One mistake brands often make is to try to work with influencers who have the greatest number of followers. The problem with this is that your target audience is likely to be a small percentage of those followers and you run the risk of seeing huge reach but low returns. In many cases, the best thing to do is to seek out niche influencers for specific use cases. The future of cult follower building is going to be about what Chris Messina calls “relationship design”, or the ability to have one on one, real talk customers with your real users across multiple platforms all at once.

Identifying influencers with a strong passion shared by their audience and whose values fit squarely with your brand is key. To do so, check their content, their network, their statistics and their audience demographics. An integrated solution like Lefty can provide you with this information

Tell us about the core tenets driving your company goals in a tech-heavy ecosystem?

Factmata is a group of entrepreneurs trying to solve a challenging problem with an amazing mission.

Our goal is to provide media buyers with a way to decide whether a particular website is the right place for them to show their ads.

We’re not in the business of trying to remove content or remove opinion from the internet. We’re here to flag potentially sensitive or biased content by providing brands and media agencies with a quality score which indicates the likelihood that a page contains misinformation. Media buyers can then use that score to decide whether they want to take the risk of advertising on that site or not.

We want to solve this problem in a scalable way and the only way to do that is via artificial intelligence and automation. Furthermore, we will remain outside the media or fact checking world so our technology can operate in a fully independent way. Another key component is the way our platform is built – using feedback and third party annotation provided by expert journalists, rather than our own potentially biased instruction. We call this community-driven explainable AI.

Given the changing dynamic of social media intelligence and audience analytics, where do you see Factmata fitting into a CMO’s tech stack?

Right now, Factmata’s core use case is going to be to help CMOs in the contextual brand safety space i.e. preventing placements of ads on low-quality content that harms reputation e.g. fake news, misinformation, sexism, controversy and hyperpartisan content. However, later down the road, we want to be more proactive with CMOs, to help them take stands proactively on subjective issues. For example, we are developing tech to allow brands to positively target pages which are pro-gun control, or against issues like nuclear warfare.

What are your thoughts on the future of ‘AR/VR/Video’ cloud marketing in 2018-2022?

Augmented reality and virtual reality are really interesting areas at the moment because we’re starting to see real-life use cases that go beyond being gimmicky. Only time will tell how widespread AR and VR become because at the end of the day it depends on user adoption. If consumers do not embrace the technology as a way to engage with brands then the advertising budgets won’t shift in that direction.

I think we will see more brands starting to experiment with AR and VR over the next few years, retail, automotive and travel brands are the likely contenders as their products are typically aspirational rather than transactional and long-term brand awareness plays a huge role in their marketing.

What startups in the technology industry are you watching keenly right now?

Fact-checking and fake news are hot topics right now, and a number of startups have emerged in these areas. Some companies appear to have similar ideas and are using AI, while others have gone down the blockchain route. Not all are our competitors, and we reach out a lot to those we think we might want to collaborate with.

We also keep an eye on startups developing technology that may help us develop and grow our own company. I am particularly interested in startups developing decentralised versions of existing successful consumer models, i.e. tokenized economies. For example, there are companies doing this in insurance, retail, travel and more. I like the idea that you raise investment from your users. They fund your development, and if it doesn’t go well, they don’t do well but they have split their risk with other users too. It seems a lot fairer because the incentives are aligned fully on product development rather than on other things like hype, reputation, ability to fundraise and have good contacts etc. I see less of the potential of chatbots than I did before and search, but am actively also looking at startups disrupting healthcare, in particular women’s health. The market for products fully focused on women is huge and very untapped.

What marketing and sales automation tools and technologies do you currently use?

We work with Streak to help us manage our sales pipeline. It’s a very quick and easy tool to customize the columns that we want to measure success against. It’s connected directly within our email system so we can see the current status and notes.  We can also output sales reports that are shared with the management team on our progress through the sales stages.

For marketing, we use a range of tools and databases, such as Meltwater,  to help us identify influencers and audiences talking about fake news and misinformation. We also use social media management tools such as Hootsuite to manage our various channels, including Twitter to Facebook.

Could you tell us about an outstanding digital campaign/ customer success story at Factmata?

We’re still in the beta testing phase with our early adopters so I can’t name any of our customers as they may be working with our competitors! Our beta phase has gone very well though, for example, we’ve successfully identified misinformation online that other technologies have failed to find. We’re confident that we’ve already helped our clients avoid brand safety scandals because we’ve seen the pages online where their ads would have appeared using their existing technology partners.

One recently quite successful digital campaign for us was on our B2C side, with our news platform briefr.cc. We have managed to grow our waiting list for the product substantially using referral techniques on a dedicated landing page, as well as subscriptions to unique news and journalist related newsletters which have had email open hit rates of 10-15% compared to the industry average of 3%.

How do you prepare for an AI-centric world as a business leader?

Factmata is an AI technology, so you could say I’ve been preparing for years! The advice I would give to others is never to lose sight of your business goals; all businesses need to embrace change but if AI steers the business away from its core purpose you run the risk of failure.

AI capabilities are expanding rapidly and have the power to revolutionize how many businesses operate. With AI you can do things faster, better and cheaper which is exciting for any business leader. However, it’s of utmost importance to fully evaluate where AI can fit into your business model. For example, if you run online advertising campaigns, it’s obvious that AI can improve the efficiency of your media buying by analyzing data in real time and optimizing where you buy ads and how much you pay for them. With other types of AI, it can be harder to understand where they will drive business improvements.

It’s also really important to talk to other people in similar situations and learn from what they have done (or not done). AI is a complicated ecosystem with many potential pitfalls if you get it wrong. Talk to specialists, research success stories and failure in AI and go to events that showcase AI technology and best practices.

If you decide that AI can improve your products or services then embrace it as long as it doesn’t harm your business values.

How do you inspire your people to work with technology?

Factmata is an advanced natural language understanding business so the people who tend to want to come and work with us are typically pretty technical, to begin with. Then there’s the fact that we’re a startup so everyone who works at Factmata is very driven to work as effectively as possible to accelerate the growth of the business.

We’re so lucky that there are so many technologies that enable people to do more with less time and the people at Factmata have been quick to adopt various collaboration tools. I actively encourage my team to always find ways of making their lives more productive and testing new tools all the time for admin they do. However, people have to do what works best for them. If someone is more comfortable writing their daily ‘to do’ list on paper I’m not going to stop them if it works for them. If I see someone struggling with something that I believe can be automated, I’ll show them how to use the technology but I won’t force them to change. You’ve got to let people be individuals.

One word that best describes how you work.

Scientifically.

What apps/software/tools can’t you live without?

Slack is brilliant for cutting down on emails and communicating with everyone in your team quickly. You can share documents and easily comment without getting lost in email chains.

I also use ToDoIst for my lists because of its amazing cross-platform functionality across all operating systems and web too, as well as even a Chrome app version. Other examples are Streak, Airtable, Revolut, momondo (for flights), Citymapper, Grip (for conferences) and Evernote.

What’s your smartest work related shortcut or productivity hack?

I wouldn’t call it a hack, but I think it’s important to set aside a block of time each day for deep work. There is so much going on and it’s easy to get distracted, but you need to be able to focus and think about your daily and longer-term goals. I prefer to do this in the mornings.

The other two tips I would recommend is using Franz, which puts your FB messages, and multiple Slack workspaces all in one desktop app, and most importantly calendly for scheduling. Calendly itself saves me at least 3 hours of thinking time a week, easily.

What are you currently reading?

I am reading Sapiens at the moment. It is incredible. I’ve used it in several talks I’ve given because what it talks about regarding human society and how we congregated as hunter gatherers vs. farmers relates a lot to Facebook and how we think about social media filter bubbles.

What’s the best advice you’ve ever received?

Don’t think about what you want to be, but what you want to do. It prevents you from being a poser, and being always objective in building a product or creating things rather than talking about it.

Something you do better than others – the secret of your success?

I don’t like to think I do anything better than anyone else, or if I do, it’s because we can’t all be good at everything. If I had to pick one thing out of all the factors that have contributed to my success I would say it’s self-belief. You’ve got to be your biggest believer because there will be times when you will feel alone as an entrepreneur and those are the times when self-belief will push you to do things you might not achieve otherwise.

Tag the one person from the industry whose answers to these questions you would love to read:

Keith Weed at Unilever

Thank you, Dhruv! That was fun and hope to see you back on MarTech Series soon.[/vc_column_text][vc_empty_space height=”30px”][/vc_column][vc_column width=”1/6″][/vc_column][/vc_row][vc_row][vc_column][vc_tta_tabs][vc_tta_section title=”About Dhruv” tab_id=”1501785390157-b58e162d-0ae25a4b-c27aca64-108e51b0-80edaf37-bd3d357a-6c46d712-3b68db8f-23cb1e1d-4907″][vc_column_text]Dhruv is the Co-Founder of Factmata, an artificial intelligence startup solving the problem of factual misinformation on the internet. He likes open information, transparency, the future of the web, artificial intelligence and building highly technical data products.[/vc_column_text][/vc_tta_section][vc_tta_section title=”About Factmata” tab_id=”1501785390320-2d44fa50-740c5a4b-c27aca64-108e51b0-80edaf37-bd3d357a-6c46d712-3b68db8f-23cb1e1d-4907″][vc_column_text]Factmata

We are a London based startup developing cutting-edge community-driven AI Brand Safety solution for advertisers. Our goal is to reduce online misinformation & abusive content from the internet.[/vc_column_text][/vc_tta_section][/vc_tta_tabs][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space height=”20px”][vc_separator color=”black” style=”shadow” border_width=”10″][vc_empty_space height=”20px”][vc_row_inner][vc_column_inner width=”1/4″][mnky_heading title=”MarTech Interview Series” link=”url:http%3A%2F%2Fmartechseries.com%2Fcategory%2Fmts-insights%2Finterviews%2F|||”][/vc_column_inner][vc_column_inner width=”1/2″][vc_column_text]

The MTS Martech Interview Series is a fun Q&A style chat which we really enjoy doing with martech leaders. With inspiration from Lifehacker’s How I work interviews, the MarTech Series Interviews follows a two part format On Marketing Technology, and This Is How I Work. The format was chosen because when we decided to start an interview series with the biggest and brightest minds in martech – we wanted to get insight into two areas … one – their ideas on marketing tech and two – insights into the philosophy and methods that make these leaders tick.

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