How AI can help to understand the customer

Ahead of us is a significant change in the way brands use customer experience (CX).  We are already starting to see the switch from companies competing on price and product to competing on CX. But what exactly do we mean by CX? Gartner defines CX as a customer’s perceptions and feelings caused by the one-off and cumulative effect of interactions with a supplier’s employees, systems, channels or products.   

Previously, the communication flow between customers and companies was either in person, writing or via a telephone call to the support line. Now, there are increasingly more ways customers can interact with brands, and when they do, they expect a high-quality experience “on demand.” 81% of marketing leaders were expected to mostly or completely compete based on customer experience by 2019, as revealed in the 2017 Gartner Customer Experience in Marketing Survey.  

There are many tools already giving insight to CX, such as NPS and Customer Success Scores. However, when companies need to make quick decisions, real-time insights are what’s helping decision makers. Technologies such as AI are now gathering these insights by allowing companies to organize and categorize data based on business needs, helping to make sense of all these interactions.  

To understand the customer from a CX perspective, and give some real-world examples, we can filter down a myriad of AI technologies and categorize them into three buckets: 

  • Speech Analytics: understanding, interpreting and analyzing voice conversations. Example: understand sentiment, IVR systems.
  • Image: capturing, processing and analyzing images, photos and video. Example: customer patterns, social media image analysis. 
  • Natural Language Processing: analyzing human expression and emotion. Example: text, chatbot, email analysis.  

The below table shows CX use cases and examples of these AI technologies in action:  

Source: Gartner 2019

Are data scientists the only ones needing to understand these technologies? No, it’s extremely valuable to both marketing and CX teams to gain an understanding of these tools. Every company has unique needs depending on CX goals and business objectives. Teams need to make a well-informed decision and understand which tools are most useful to their business, which will essentially lead to more accurate decision-making and a customer-first approach.     

Now, are people rushing to adopt these new AI technologies for CX? In Gartner´s 2018 Enterprise AI survey, it was revealed that businesses that are already deploying AI, 26% are implementing it to improve customer experience. Although it may not seem urgent to start implementing these technologies right away, it’s important that businesses are aware and start to familiarize themselves with these AI applications.  

A good place to start is mapping out a customer journey and finding the ‘dark spots’. These are the areas that could benefit from deeper real-time insights, such as understanding the mood of a customer when they are talking with a chatbot. Having these insights will allow you to hand over the conversation to a human based on the customer’s emotion.  

Companies are dealing with an increasing number of interactions happening across multiple channels and devices. With customer expectations are at an all-time high, it’s not easy to connect all these touch points and deliver an excellent customer. AI can help provide rich insights allowing you to get faster, real-time understandings, and optimize the overall customer journey. 

Recapping the week at MWC19

Mobile World Congress (MWC) 2019, the world’s largest exhibition for the mobile industry, welcomed leaders from mobile operators, device manufacturers, technology providers, vendors and more.  

This year’s event saw a focus on two core concepts: 5G and Artificial Intelligence. It was said to be one of the most important events in recent times for the mobile industry. In the days leading up to the show, a warm buzz of anticipation filled the air as attendees were eager to hear about the new groundbreaking technologies. We were excited to be surrounded by leaders in the field and pleased to be a co-exhibitor for the Washington State Delegation of Commerce. 

With a large number of keynote presentations, panel discussion and exhibitors, there were many outtakes from the event. A hot topic that continued to emerge was AI bias. On day two I was able to discuss this topic with other like-minded people: Elena Fersman (Ericsson), Beena Ammanath (HPE), Beth Smith (IBM) and Kriti Sharma (Sage), who are all working towards an unbiased future for AI.  

We discussed ‘Democratizing AI and Attacking Algorithmic Bias’. The discussion of bias in AI continued throughout the event as many people came to speak with us about how to overcome this problem. If you missed this talk and want to hear more, see an edited version here.  

We also attended the Applied AI Forum: an exclusive conference that brought together telecom leaders, AI specialists, start-ups and academics, with an aim to spur debate and discussion on the practice of AI across the digital economy. Google and IBM Watson held an interesting panel discussion that explored ‘Applied AI: new trends and strategies’. In this forum, we were able to share lessons learned and discuss recent breakthroughs with both data scientists and global leads from several large enterprise companies.  

Another key highlight of MWC was our exciting hiring announcement! On the second day, we released our plans for the year: to double the size of our company by the end of 2019. With the rapid growth of AI applications seen across all industries, there is an increasing demand for high-quality data. And with this, our company is growing faster than ever. We are looking for more talent to join our team in Portugal, Japan, and the United States. See our careers page for more information.  

What a big week it was as we move into a new era of Intelligent Connectivity. A huge thanks to GSMA, a body representing the interests of mobile networks globally, and everyone we met at the event.  

We’d love to continue the discussions we had, especially around the topic on bias in AI. Reach out at pr@definedcrowd.com, we’d be glad to hear from you. We are already thinking about what next year might hold.   

Job description: talent for a smarter AI

We’re in a huge growth stage and are looking for talent to join our global team in Portugal, Japan, and the United States. Check out our careers page for current openings.

We accelerate the evolution of Artificial Intelligence initiatives by delivering high-quality training data to enterprise companies. We are investing heavily in our business and the people to make this happen. Over the coming months, we’ll be searching for professionals who are looking to embark on an exciting career while making a difference in AI.  

So, who are we? We’re an 80-employee startup based in Seattle, with offices in Lisbon, Porto, and Tokyo. Our CEO, Daniela Braga founded DefinedCrowd in 2015 to fill a gap in the market by offering high-quality training data to help machine learning products reach the market at optimal quality and speed. And with the rapid growth of AI applications and the high demand for this data, our business is growing so quickly that we are looking to nearly double our team by the end of 2019.

“We have a very ambitious goal –  to be the number one provider of data for AI in the world. This year will be crucial to achieving this goal, as we mature our product, grow our client base, and increase our partnerships with the companies that are leading the AI revolution.”

Founder and CEO, Dr. Daniela Braga

We are currently hiring positions in the following departments: Development, Product, Marketing, and Operations, with several openings for Software Developers (Frontend, Backend, and Full Stack), QA Automation Engineers, and Machine Learning Engineers. These positions are available within our four offices and will have an important role in the expansion of the company’s product: an all-in-one data platform.  

Earlier this year, DefinedCrowd was selected as one of CB Insights top 100 AI startups. Our client list includes many Fortune 500 companies including BMW, Mastercard, Nuance, and Yahoo Japan. We also have partnerships with IBM, Microsoft, and Amazon.  

“We are looking for the best talent to join our team in this exciting moment, and to be part of the construction of a smarter AI”

Founder and CEO, Dr. Daniela Braga

For anyone interested, make sure you keep an eye out on our careers page http://careers.definedcrowd.com, where there will be new jobs added throughout the year.  

100 most promising AI startups

日本語版はこちら

We’ve come a long way since forming in 2015. Starting out as a small team, we now have four offices worldwide – Lisbon, Porto, Tokyo, and Seattle – and continue to grow every day.  

Our unique platform has helped many successful companies feed their artificial intelligence applications with training data. Using human intelligence coupled with machine-learning, we deliver project-specific, quality-guaranteed data.    

Today, we’re proud to announce that DefinedCrowd is among CB Insights’ third annual list of 100 AI startups. A research team from CB Insights selected 100 startups based on the following factors: investor profile, market potential, partnerships, competitive landscape, and team strength. 

Source: CB Insights

Companies are categorized by focus area. These focus areas aren’t mutually exclusive and include core sectors such as telecommunications, government, retail, healthcare and enterprise tech sectors such as training data (where we sit), software development, data management, and cybersecurity. 

We are pleased to be among this group of incredible AI startups, selected from an extensive list of 3k+ AI companies, and look forward to seeing these companies grow.  

It´s been a great start to 2019. And, we´re very thankful to everyone who has helped get us here.  

Daniela Braga’s journey with DefinedCrowd

Earlier this week, DefinedCrowd was Featured in Jornal Económico, a premium financial publication in Portugal. We’ve translated the article from the original Portuguese for our English- speaking friends. Enjoy!

Original Article by
António Sarmento

Founded in Seattle, USA, DefinedCrowd is a startup specializing in training data for Artificial Intelligence. The company counts Amazon, IBM, and EDP as investors and clients. 

DefinedCrowd provides services so that data scientists can gather, structure, and enrich datasets for Artificial Intelligence, helping companies improve speed to market and the overall quality of their AI products. DefinedCrowd accelerates enterprise AI initiatives by combining machine learning technology with human-in-the-loop collection processes. Founded in August 2015 by entrepreneur Daniela Braga, the company is headquartered in Seattle, has R&D centers in Lisbon and Porto, and a sales office in Tokyo. 

Three months after its founding, the company opened their first R&D office at Startup Lisbon. Since then, DefinedCrowd has blossomed from an initial team of three employees to a workforce of more than 70 that is still growing.

In 2016, the company raised $ 1.1 million in seed funding, with investors such as Sony, Amazon Alexa Fund, Portugal Ventures, and Busy Angels.
In July 2018, DefinedCrowd closed a Series A funding worth $11.8 million, led by Evolution Equity Partners. EDP Ventures, Mastercard and Kibo Ventures joined as new investors, while Sony, Amazon, Portugal Ventures and Busy Angels bolstered their investments with additional capital for the data company.

“It is important to raise capital if we want to move fast, especially in the technological sector.”   

Daniela Braga to Jornal Económico

This influx of capital is being used to accelerate product development and accelerate team growth. Two-thirds of DefinedCrowd’s 70 employees work out of Portugal. The company expects to add 80 more team members by the end of 2019.

Over the past six months, DefinedCrowd has announced three partnerships: a formal designation as an Amazon Alexa Skills partner, a product integration with IBM Watson Studio; and participation as a featured vendor in Microsoft‘s co-sell program.

DefinedCrowd’s platform provides industry-agnostic data services and can support text, audio, and image annotation. The company’s clients span industries as a result: from Fintech, to Retail, Healthcare, Utilities, and the Internet of Things. Their client portfolio consists mostly of Fortune 500 companies, including BMW, MasterCard, EDP, José de Mello Saúde, SoftBank, Yahoo Japan, Randstad, and Nuance

DefinedCrowd’s goals are ambitious. The company aims to become the world’s number one AI data provider through expanding their client-base and forging new partnerships with industry leaders. 

With a degree in Portuguese Language and Literature, Daniela Braga has spent her career examining the rigorous use of language, the perfect foundation for her business. “We deal daily with data in 70 languages and dialects. Our clients need, at a minimum, native-level speakers and sometimes even require linguists or specialists in language sciences for all of them” says the entrepreneur.

After graduating with a master’s degree in applied linguistics, she went on to earn a PhD in Speech Technologies at the Faculty of Engineering at the University of Porto and taught at the University of A Coruña for two years before joining Microsoft (whom she worked for in Portugal, China and the United States).

After leaving Microsoft in 2013, Daniela moved to American company Voicebox. Simultaneously, she was invited to teach Data and Crowdsourcing for Speech Technologies at the University of Washington. It was during this time that she saw the gap between the Artificial Intelligence data scientists wanted to develop and the training data available to build it. She decided to found her own company as a result.

Waving a well-paid job goodbye, and with few personal resources, she started meeting with investors in Seattle, and quickly received an initial check: $ 200,000 in financing to start her business. A business that is now signing contracts with some of the largest companies in the world.

DefinedCrowd is in constant growth and employee numbers have been updated to reflect our current position.

The Machine Learning Lover’s Holiday Book List

In the market for some last-minute gift recommendations for a machine learning “geek?” (we use the term affectionately around here). DefinedCrowd’s got you covered with our “machine learning lover’s book list,” hand-selected by our ML Team. From the ins and outs of speech and language processing to broad-level theoretical overviews of the machine learning field, these texts cover the wide-ranging topics we discuss in our office every day. Enjoy! And happy holidays from all of us at DefinedCrowd.

Speech and Language Processing An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition by Daniel Jurafsky

Summary: In this excellent intro to speech and language processing tecnologies, Jurafsky presents an empirical approach that comprehensively covers language technology based on applying statistical and machine-learning algorithms alongside modern technologies. The book largely emphasizes scientific evaluation and practical applications.

Foundations of Statistical Natural Language Processing by Christopher D. Manning and Hinrich Schütze

Summary:
A fantastic introduction to statistical natural language processing that uses an analytical approach to cover a range of mathematical and linguistic foundations for NLP technologies. Foundations of Statistical Natural Language Processing proves further useful in presenting theoretical and algorithmic building blocks for NLP technologies.

Crowdsourcing for Speech Processing: Applications to Data Collection, Transcription and Assessment by Maxine Eskenazi, Gina-Anne Levow, Helen Meng, Gabriel Parent and David Suendermann

Summary: An essential read for anyone interested in learning more about crowdsourcing training data for speech models. Offers a comprehensive overview from the basics of setting up a task, to tips for task interfaces and methodologies for quality assessment.

Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville and Francis Bach

Summary:
This book offers a great introduction to what many consider the “Holy Grail” of Machine Learning.
Topics covered, range from mathematical and conceptual background to deep learning techniques. The “research perspectives” that book-end chapters with specific case-studies make Deep Learning a great resource for students and software engineers alike.

Deep Learning for Computer Vison with Python by Adrian Rosebrock

Summary: For those looking to master deep learning for image recognition and classification, Deep Learning for Computer Vision offers practical walk-throughs, hands-on tutorials and a direct teaching style. Useful for both beginners and for the seasoned deep learning pro looking to brush up on the fundamentals. 

Artificial Intelligence and the Future of Jobs

The growth and development of computer programs supported by artificial intelligence has led to intense debate around regulatory difficulties and because of the technology’s potential effects on employment. Are people’s concerns in these areas warranted?

From the earliest days of civilization, man, as a single thinker on earth, sought to reduce the need for physical work by inventing tools. First came the wheel and the transportation of food over farther distances with less manpower. We have been taught to evolve by creating more with less effort. For many, this was negative in the short term. Those who were once freight carriers lost their jobs. The wheel was invented around 3000 BC. You might think I´m crazy to start a technological discussion about this historical moment, but the historical reference is useful to be made in order to demystify the discussion, and to then further analyze the data we have. 

Moving forward some years later, at the start of the industrial revolution, millions of people protested in the streets of England and the United States against the introduction of weaving machinery. On the surface, the “destruction” of jobs seemed quite high. However, in truth, these jobs were never really destroyed, but rather professionally reformed. Factories, with a drastic boost in production, were increasing the salaries of those who adapted to the machinery while simultaneously reducing their overhead costs. Both countries’ wealth grew as a result due to increases in disposable income for families, and more jobs created to support the burgeoning count weaving and spinning industry. Indeed, the number of people employed in weaving jumped from 7,900 to over 320,000 after the invention of the weaving machine.  

Now after a little history revision, let’s return to the present. 

Recently PWC, one of the world’s largest consultants, launched a global study in which they estimated that artificial intelligence in the UK will replace 20% of today’s jobs within the next 20 years. However, they also estimated that artificial intelligence will create just as many jobs as it replaces. Sectors at high risk include law, finance, insurance, drivers and white-collar workers. Areas like education, science, information, communication and computing are among those that will be most valued in the future.  

Nowadays, from the moment we wake up and look at our mobile phones, until the moment we lay down and check our Facebook feed for the last time, we’re in constant contact with artificial intelligence that gives us the kind of information that allows us to make better decisions. We need to accelerate the transformation of educational systems, adapting them to the new realities of the fourth technological revolution with a particular focus on programming disciplines. We also need to find ways to support professional training programs that respond to the demands of the labor market. 

Ultimately, there is no future in which machines will be able to replace what binds human beings: creativity, intuition and love. At the end of the day, perhaps AI will make us even more human.