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Many of todays marketing processes are powered by AI and machine learning. Discover how these technologies are shaping the future of customer experience.

By using artificial intelligence (AI) and machine learning (ML) along with analytics, brands are in a much better position to elevate customer service experiences at every touchpoint and create positive emotional connections.

This article will look at the ways that AI and ML are used by brands to improve customer service and support.

AI improves the customer service journey in several ways, including tracking conversations in real-time, providing feedback to service agents and using intelligence to monitor language, speech patterns and psychographic profiles to predict future customer needs.

This functionality can also drastically enhance the effectiveness of customer relationship management (CRM) and customer data platforms (CDP).

CRM platforms, including C2CRM, Salesforce Einstein and Zoho, have integrated AI into their software to provide real-time decisioning, predictive analysis and conversational assistants, all of which help brands more fully understand and engage their customers.

CDPs, such as Amperity, BlueConic, Adobes Real-Time CDP and ActionIQ, have also integrated AI into more traditional capabilities to unify customer data and provide real-time functionality and decisoning. This technology enables brands to gain a deeper understanding of what their customers want, how they feel and what they are most likely to do next.

Related Article: What's Next for Artificial Intelligence in Customer Experience?

Artificial intelligence and machine learning are now used for gathering and analyzing social, historical and behavioral data, which allows brands to gain a much more complete understanding of their customers.

Because AI continuously learns and improves from the data it analyzes, it can anticipate customer behavior. As such, AI- and ML-driven chatbots can provide customers with a more personalized, informed conversation that can easily answer their questions and if not, immediately route them to a live customer service agent.

Bill Schwaab, VP of sales, North America for boost.ai, told CMSWire that ML is used in combination with AI and a number of other deep learning models to support todays virtual customer service agents.

ML on its own may not be sufficient to gain a total understanding of customer requests, but its useful in classifying basic user intent, said Schwaab, who believes that the brightest applications of these technologies in customer service find the balance between AI and human intervention.

Virtual agents are becoming the first line in customer experience in addition to human agents, he explained. Because these virtual agents can resolve service queries quickly and are available outside of normal service hours, human agents can focus on more complex or valuable customer interactions. Round-the-clock availability provides brands with additional time to capture customer input and inform better decision-making.

Swapnil Jain, CEO and co-founder of Observe.AI, said that todays customer service agents no longer have to spend as much time on simpler, transactional interactions, as digital and self-serve options have reduced the volume of those tasks.

"Instead, agents must excel at higher-value, complex behaviors that meaningfully impact CX and revenue," said Jain, adding that brands are harnessing AI and ML to up-level agent skills, which include empathy and active listening. This, in turn, "drives the behavioral changes needed to improve CX performance at speed and scale."

Because customer conversations contain a goldmine of insights for improving agent performance, AI-powered conversation intelligence can help brands with everything from service and support to sales and retention, said Jain. Using advanced interaction analytics, brands can benefit from pinpointing positive and negative CX drivers, advanced tonality-based sentiment and intent analysis and evidence-based agent coaching.

Predictive analytics is the process of using statistics, data mining and modeling to make predictions.

AI can analyze large amounts of data in a very short time, and along with predictive analytics, it can produce real-time, actionable insights that can guide interactions between a customer and a brand. This practice is also referred to as predictive engagement and uses AI to inform a brand when and how to interact with each customer.

Don Kaye, CCO of Exasol, spoke with CMSWire about the ways brands are using predictive analytics as part of their data strategies that link to their overall business objectives.

Weve seen first-hand how businesses use predictive analytics to better inform their organizations decision-making processes to drive powerful customer experiences that result in brand loyalty and earn consumer trust, said Kaye.

As an example, he told CMSWire that banks use supervised learning or regression and classification to calculate the risks of loan defaults or IT departments to detect spam.

With retailers, weve seen them seeking the benefits of deep learning or reinforcement learning, which enables a new level of end-to-end automation, where models become more adaptable and use larger data volumes for increased accuracy, he said.

According to Kaye, businesses with advanced analytics also tend to have agile, open data architectures that promote open access to data, also known as data democratization.

Kaye is a big advocate for AI and ML and believes that the technologies will continue to grow and become routine across all verticals, with the democratization of analytics enabling data professionals to focus on more complex scenarios and making customer experience personalization the norm.

Related Article: What Customer-Centric Predictive Analytics Looks Like

AI-driven sentiment analysis enables brands to obtain actionable insights which facilitate a better understanding of the emotions that customers feel when they encounter pain points or friction along the customer journey as well as how they feel when they have positive, emotionally satisfying experiences.

Julien Salinas, founder and CTO at NLP Cloud, told CMSWire that AI is often used to perform sentiment analysis to automatically detect whether an incoming customer support request is urgent or not. "If the detected sentiment is negative, the ticket is more likely to be addressed quickly by the support team."

Sentiment analysis can automatically detect emotions and opinions by classifying customer text as positive, negative or neutral through the use of AI, natural language processing (NLP) and ML.

Pieter Buteneers, director of engineering in ML and AI at Sinch, said that NLP enables applications to understand, write and speak languages in a manner that is similar to humans.

"It also facilitates a deeper understanding of customer sentiment, he explained. When NLP is incorporated into chatbots and voice bots it permits them to have seemingly human-like language proficiency and adjust their tones during conversations.

When used in conjunction with chatbots, NLP can facilitate human-like conversations based on sentiment. So if a customer is upset, for example, the bot can adjust its tone to diffuse the situation while moving along the conversation, said Buteneers. This would be an intuitive shift for a human, but bots that arent equipped with NLP sentiment analysis could miss the subtle cues of human sentiment in the conversation, and risk damaging the customer relationship."

Buteneers added that breakthroughs in NLP are making an enormous difference in how AI understands input from humans. For example, NLP can be used to perform textual sentiment analysis, which can decipher the polarity of sentiments in text."

Similar to sentiment analysis, AI is also useful for detecting intent. Salinas said that its sometimes difficult to have a quick grasp on a user request, especially when the users message is very long. In that case, AI can automatically extract the main idea from the message so the support agent can act more quickly.

While AI and ML have continued to evolve, and brands have found many ways to use these technologies to improve the customer service experience, the challenges of AI and ML can still be daunting.

Kaye explained that AI models need good data to deliver accurate results, so brands must also focus on quality and governance.

In-memory analytics databases will become the driver of creation, storage and loading features in ML training tools given their analysis capabilities, and ability to scale and deliver optimal time to insight, said Kaye. He added that these tools will benefit from closer integration with the companys data stores, which will enable them to run more effectively on larger data volumes to guarantee greater system scalability.

Iliya Rybchin, partner at Elixirr Consulting, told CMSWire that thanks to ML and the vast amount of data bots are collecting, they are getting better and will continue to improve. The challenge is that they will improve in proportion to the data they receive.

Therefore, if an under-represented minority with a unique dialect is not utilizing a particular service as much as other consumers, the ML will start to discount the aspects of that dialect as outliers vs. common language, said Rybchin.

He explained that the issue is not caused by the technology or programming, but rather, it is the result of the consumer-facing product that is not providing equal access to the bot. The solution is more about bringing more consumers to the product vs. changing how the product is built or designed."

AI and ML have been incorporated into the latest generations of CDP and CRM platforms, and conversational AI-driven bots are assisting service agents and enhancing and improving the customer service experience. Predictive analytics and sentiment analysis, meanwhile, are enabling brands to obtain actionable insights that guide the subsequent interactions between a customer and a brand.

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4 Ways AI, Analytics and Machine Learning Are Improving Customer Service and Support - CMSWire

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Were in the midst of a data revolution. The volume of digital data created within the next five years will total twice the amount produced so far and unstructured data will define this new era of digital experiences.

Unstructured data information that doesnt follow conventional models or fit into structured database formats represents more than 80% of all new enterprise data. To prepare for this shift, companies are finding innovative ways to manage, analyze and maximize the use of data in everything from business analytics to artificial intelligence (AI). But decision-makers are also running into an age-old problem: How do you maintain and improve the quality of massive, unwieldy datasets?

With machine learning (ML), thats how. Advancements in ML technology now enable organizations to efficiently process unstructured data and improve quality assurance efforts. With a data revolution happening all around us, where does your company fall? Are you saddled with valuable, yet unmanageable datasets or are you using data to propel your business into the future?

Theres no disputing the value of accurate, timely and consistent data for modern enterprises its as vital as cloud computing and digital apps. Despite this reality, however, poor data quality still costs companies an average of $13 million annually.

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To navigate data issues, you may apply statistical methods to measure data shapes, which enables your data teams to track variability, weed out outliers, and reel in data drift. Statistics-based controls remain valuable to judge data quality and determine how and when you should turn to datasets before making critical decisions. While effective, this statistical approach is typically reserved for structured datasets, which lend themselves to objective, quantitative measurements.

But what about data that doesnt fit neatly into Microsoft Excel or Google Sheets, including:

When these types of unstructured data are at play, its easy for incomplete or inaccurate information to slip into models. When errors go unnoticed, data issues accumulate and wreak havoc on everything from quarterly reports to forecasting projections. A simple copy and paste approach from structured data to unstructured data isnt enough and can actually make matters much worse for your business.

The common adage, garbage in, garbage out, is highly applicable in unstructured datasets. Maybe its time to trash your current data approach.

When considering solutions for unstructured data, ML should be at the top of your list. Thats because ML can analyze massive datasets and quickly find patterns among the clutter and with the right training, ML models can learn to interpret, organize and classify unstructured data types in any number of forms.

For example, an ML model can learn to recommend rules for data profiling, cleansing and standardization making efforts more efficient and precise in industries like healthcare and insurance. Likewise, ML programs can identify and classify text data by topic or sentiment in unstructured feeds, such as those on social media or within email records.

As you improve your data quality efforts through ML, keep in mind a few key dos and donts:

Your unstructured data is a treasure trove for new opportunities and insights. Yet only 18% of organizations currently take advantage of their unstructured data and data quality is one of the top factors holding more businesses back.

As unstructured data becomes more prevalent and more pertinent to everyday business decisions and operations, ML-based quality controls provide much-needed assurance that your data is relevant, accurate, and useful. And when you arent hung up on data quality, you can focus on using data to drive your business forward.

Just think about the possibilities that arise when you get your data under control or better yet, let ML take care of the work for you.

Edgar Honing is senior solutions architect at AHEAD.

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Solve the problem of unstructured data with machine learning - VentureBeat

Equipping Financial Controllers with predictive capabilities, advanced analytics and ML will help them elevate their role from providing back-office support to business partnering.

The role of a finance controller is changing. It is expected that controllers will not only take ownership of the companys accounts but also drive strategic performance. Such change in role is further accentuated with the explosion in the volume and variety of data available with an organization. Furthermore, data landscape in organizations is becoming more and more siloed, complex and distributed. Given this shift in business dynamics, it is becoming extremely important to upskill on how advanced analytics, AI/ML techniques be leveraged to become an effective business partner driving performance in an organization.

Use-cases of AI/ML in Finance

Here a a number of use cases of how data science and ML techniques can be used in the business context to drive productivity and performance in the organization:

1)Identifying and preventing Revenue leakages: Revenue leakage is a major issue with many large enterprise and A/R leaders spent a substantial time and effort in preventing them. This could be due to multiple reasons viz. a process issue with disjointed systems, poor experience of customer, disputes, invalid deductions by customer with a relatively high volume and low value, auto-approved write-offs etc. Here, advanced analytics can play a significant role the root cause of such leakages and provide insights to the A/R team on actions that can be taken to prevent such instances.

For example, there have been instances where few customers use low dollar value deductions as a strategy to strengthen their cash flow. In such situations, it is difficult to track low-dollar value deductions as it is really a small number and is below the acceptable tolerance / threshold. This becomes a scenario of finding a needle in a haystack. However, when such deductions are aggregated at a customer level over a period of time, it can be truly amazing to seehow certaingroup of customers are actually using this strategy to cause a significant cash flow leakage for the company. To track such events, there are advanced clustering algorithms which can provide which customers are consistently using this strategy and can help the A/R team to go and recover them.

2)Identifying high risk customers and undertaking recommended actions for faster collection:For organizationshaving thousands of transactions across a large customer, it is really a difficult task to understand the behavior and financial stability of its customer due to which there are late payments or sometimes the receivables are written off. To avoid such scenarios, advanced classification algorithms can help detect such customer at risk and help organization to take pro-active steps to not only identify the customers but also reduce exposures to them over a period of time. In order to implement such smart solutions, it is really important to have the finance leader defining the key variables or data points needed to develop such classification algorithm which then the data scientist will use in its modelling. In other words, it needs a close co-operation between the finance leaders and Data scientist to model the key variables and scenarios.

3)Inventory Management: Inventory management is a major challenge in an organization. There are different categories of inventory Finished goods, semi-finished goods, raw material etc. and within each of these categories, there could be different types viz. slow moving, fast moving etc. The use of AI/ML can help manage inventory by revealing insightful information about Stock keeping units (SKU) and their associated variables such as minimum order quantity, lead times, replenishment frequency, and safety stocks. Using predictive capabilities, advanced classification algorithms can help to keep the inventory issues such as supply mismanagement, deadstock, and wastage under strict control.

4)Improving Cash Conversion / working capital: One of the significant benefits of AI/ML is the optimization of cash conversion cycles by optimizing the management of receivables, inventory and payables. This, in turn, helps the company to perform well on the cash conversion and significantly improve its performance on accounts receivables.

5)Intelligent Root cause Analysis: The use of AI/ML offers profoundly important information on various business scenarios that could possibly spring in the future as a result of changing business environments. Whether it is the use of predictive analysis, scenario modeling, or descriptive root cause, AI/ML can help financial controllers in understanding the main reasons why some of the product gained immense popularity while others fail to find favor with consumers.

There is little to doubt about the transformative power of AI/ML. These solutions can transform the role of financial controllers and can catapult their positions to one of strategic relevance to the company. That said, with a plethora of choices around, financial controllers should opt for holistic and comprehensive solutions so that the benefits of AI/ML solutions can be realized in a holistic manner.

Views expressed above are the author's own.

END OF ARTICLE

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How advanced analytics and machine learning are transforming the role of Finance Controllers - Times of India

Anima Anandkumar, Bren Professor of computing at the California Institute of Technology and senior director of machine learning research at Nvidia, has a bone to pick with the matrix. Her misgivings are not about the sci-fi movies, but about mathematical matrices grids of numbers or variables used throughout computer science. While researchers typically use matrices to study the relationships and patterns hiding within large sets of data, these tools are best suited for two-way relationships. Complicated processes like social dynamics, on the other hand, involve higher-order interactions.

Luckily, Anandkumar has long savored such challenges. When she recalls Ugadi, a new years festival she celebrated as a child in Mysore (now Mysuru), India, two flavors stand out: jaggery, an unrefined sugar representing lifes sweetness, and neem, bitter blossoms representing lifes setbacks and difficulties. Its one of the most bitter things you can think about, she said.

Shed typically load up on the neem, she said. I want challenges.

This appetite for effort propelled her to study electrical engineering at the Indian Institute of Technology in Madras. She earned her doctorate at Cornell University and was a postdoc at the Massachusetts Institute of Technology. She then started her own group as an assistant professor at the University of California, Irvine, focusing on machine learning, a subset of artificial intelligence in which a computer can gain knowledge without explicit programming. At Irvine, Anandkumar dived into the world of topic modeling, a type of machine learning where a computer tries to glean important topics from data; one example would be an algorithm on Twitter that identifies hidden trends. But the connection between words is one of those higher-order interactions too subtle for matrix relationships: Words can have multiple meanings, multiple words can refer to the same topic, and language evolves so quickly that nothing stays settled for long.

This led Anandkumar to challenge AIs reliance on matrix methods. She deduced that to keep an algorithm observant enough to learn amid such chaos, researchers must design it to grasp the algebra of higher dimensions. So she turned to what had long been an underutilized tool in algebra called the tensor. Tensors are like matrices, but they can extend to any dimension, going beyond a matrixs two dimensions of rows and columns. As a result, tensors are more general tools, making them less susceptible to overfitting when models match training data closely but cant accommodate new data. For example, if you enjoy many music genres but only stream jazz songs, your streaming platforms AI could learn to predict which jazz songs youd enjoy, but its R&B predictions would be baseless. Anandkumar believes tensors make machine learning more adaptable.

Its not the only challenge shes embraced. Anandkumar is a mentor and an advocate for changes to the systems that push marginalized groups out of the field. In 2018, she organized a petition to change the name of her fields annual Neural Information Processing Systems conference from a direct acronym to NeurIPS. The conference board rejected the petition that October. But Anandkumar and her peers refused to let up, and weeks later the board reversed course.

Quanta spoke with Anandkumar at her office in Pasadena about her upbringing, tensors and the ethical challenges facing AI. The interview has been condensed and edited for clarity.

In the early 1990s they were among the first to bring programmable manufacturing machines into Mysore. At that time it was seen as something odd: We can hire human operators to do this, so what is the need for automation? My parents saw that there can be huge efficiencies, and they can do it a lot faster compared to human-operated machines.

Yeah. And programming. I would see the green screen where my dad would write the program, and that would move the turret and the tools. It was just really fascinating to see understanding geometry, understanding how the tool should move. You see the engineering side of how such a massive machine can do this.

My mom was a pioneer in a sense. She was one of the first in her community and family background to take up engineering. Many other relatives advised my grandfather not to send her, saying she may not get married easily. My grandfather hesitated. Thats when my mom went on a hunger strike for three days.

As a result, I never saw it as something weird for women to be interested in engineering. My mother inculcated in us that appreciation of math and sciences early on. Having that be just a natural part of who I am from early childhood went a long way. If my mom ever saw sexism, she would point it out and say, No, dont accept this. That really helped.

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The AI Researcher Giving Her Field Its Bitter Medicine - Quanta Magazine

AUSTIN, Texas Sharing news articles with friends and followers on social media can prompt people to think they know more about the articles topics than they actually do, according to a new study from researchers at The University of Texas at Austin.

Social media sharers believe that they are knowledgeable about the content they share, even if they have not read it or have only glanced at a headline. Sharing can create this rise in confidence because by putting information online, sharers publicly commit to an expert identity. Doing so shapes their sense of self, helping them to feel just as knowledgeable as their post makes them seem.

This is especially true when sharing with close friends, according to a new paper from Susan M. Broniarczyk, professor of marketing, and Adrian Ward, assistant professor of marketing, at UTs McCombs School of Business.

The research isonline in advance in the Journal of Consumer Psychology. The findings are relevant in a world in which its simple to share content online without reading it. Recent data from the Reuters Institute for the Study of Journalism show only 51% of consumers who read an online news story actually read the whole article, while 26% read part, and 22% looked at just the headline or a few lines.

Broniarczyk, Ward and Frank Zheng, a McCombs marketing doctoral alum, conducted several studies that support their theory. In an initial one, the researchers presented 98 undergraduate students with a set of online news articles and told them they were free to read, share, or do both as they saw fit. Headlines included Why Does Theatre Popcorn Cost So Much and Red Meats Linked to Cancer.

Next, they measured participants subjective and objective knowledge for each article what the students thought they knew, and what they actually knew. Reading articles led to increases in both objective and subjective knowledge. Sharing articles also predicted increases in subjective knowledge even when students had not read what they chose to share, and thus lacked objective knowledge about the articles content.

In a second study, people who shared an article about cancer prevention came to believe they knew more about cancer than those who did not, even if they had not read the article.

Three additional studies found this effect occurs because people internalize their sharing into the self-concept, which leads them to believe they are as knowledgeable as their posts make them appear. Participants thought they knew more when their sharing publicly committed them to an expert identity: when sharing under their own identity versus an alias, when sharing with friends versus strangers, and when they had free choice in choosing what to share.

In a final study, the researchers asked 300 active Facebook users to read an article on How to Start Investing: A Guide for Beginners. Then, they assigned students to a sharing or no sharing group. All participants were told the content existed on several websites and saw Facebook posts with the sites. Sharers were asked to look at all posts and choose one to share on their Facebook page.

Next, in a supposedly unrelated task, a robo-advised retirement planning simulation informed participants that allocating more money to stocks is considered more aggressive and to bonds more conservative, and they received a customized investment recommendation based on their age. Participants then distributed a hypothetical $10,000 in retirement funds between stocks and bonds: Sharers took significantly more investment risk. Those who shared articles were twice as likely to take more risk than recommended by the robo-advisor.

When people feel theyre more knowledgeable, theyre more likely to make riskier decisions, Ward said.

The research also suggests theres merit to social media companies that have piloted ways to encourage people to read articles before sharing.

If people feel more knowledgeable on a topic, they also feel they maybe dont need to read or learn additional information on that topic, Broniarczyk said. This miscalibrated sense of knowledge can be hard to correct.

For more details about this research, read the McCombs Big Ideas feature story and watch the video explaining Broniarczyk and Wards work.

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Sharing on Social Media Makes Us Overconfident in Our Knowledge - UT News - The University of Texas at Austin