AI News, Machine Learning Marketing – Expert Consensus of 51 Executives and Startups

Machine Learning Marketing – Expert Consensus of 51 Executives and Startups

Modern digital marketing offers a huge volume of quantifiable data for teams to work with, and marketing can be said to take precedent over other areas like customer service and business intelligence because of it’s direct tie to driving revenue.

To help you find the insights you’re looking for quickly, we’ve broken this content up into the following sub-sections: In each of the graphics below, you’ll see details about the exact question that we asked our participants, in addition to details about whether the questions were multiple choice (where choices were pre-defined), or open-ended (categorized manually by TechEmergence).

It took our team over two months to reach out personally to dozens of executives at AI marketing companies, and we gathered responses from over 51 total companies.

In the first graphic below, you’ll see a breakdown of background information about our respondents, including their price points, pricing model, estimated revenues (as of 2016), and information about the executive who filled out the survey (their job function, title, etc.).

This isn’t the most interesting or insightful chart in terms of assessing ML and marketing trends, but it is probably the most insightful chart with respect to understanding the machine learning and marketing landscape.

This may in part reflect the kind of companies who responded—there might be a tendency for smaller firms to respond more readily to requests from market research firms like us—but it seems much more likely that this is a genuine reflection of the state of the field.

The set of graphics below help shed light on these matters, highlighting the value propositions that these companies pitch and as well as the types of clients targeted for sales.

The above chart essentially distills which business challenges our sample companies address, based on their own 1-through-4 scoring of each individual challenge.

It seems quite rare that companies ever get the opportunity to sell directly to an AI or ML business unit, and we can presume that very few prospect companies have a designated AI department or task force at this point in time.

In the complete data set and the open-ended responses therein, it seems evident that eCommerce and online media allow for the constant creation of quantifiable data, as well as relatively easy streaming and storage of this data.

businesses find ways to streamline their data ingestion and digestion at scale, online business models will gain most of the fruit from AI marketing tech.

Industries with less volume of sales data, such as large B2B firms, should generally expect to gain less from ML than companies who can garner huge volumes of sales data.

technologies is indeed challenging. After hundreds of interviews with AI founders and execs, it’s clear that at the time of this original writing (early 2017), AI is seen as something for “early adopters.”

Canvas Ventures partner Ben Narasin recommended this exact approach. Featured below is a selection of direct quotes in response to this question: What is the biggest challenge of selling AI / machine learning marketing and advertising solutions today?

We suppose that few of our respondent companies will focus exclusively on targeting and selling to sectors that aren’t poised to reap significant benefits from and leverage AI heavily.

From the data above, it seems our sample companies believe that direct-to-consumer industries (as opposed to business-to-business domains, or public-private sectors like healthcare) will benefit most from AI marketing developments.

literally anyone under the sun can create an advertising-driven site, or an eCommerce store, but it’s much harder to write software or to create a one-in-a-million popular social media platform.

From our collected quotes and rankings, we presume that these businesses have much less quantifiable transaction data and/or that their sales rely much more on non-quantifiable human relationships, phone calls, trade shows, etc.

Featured below is a selection of direct quotes in response to this question: Which type of business do you believe to be most poised to profit from machine learning in marketing and why?

From the chart above, it seems that today’s AI marketing executives don’t believe that content generation and market forecasting have nearly the same profit potential as “Segmentation / Targeting”

It is possible that our sample companies may be developing analytics technologies specifically for recommendation or customer segmentation, which would mean that analytics alone is simply wrapped up or included under the guise of another application.

The last section in our machine learning marketing survey asked executives to predict when AI / ML would be ubiquitous in marketing technologies, even for small businesses.

6 Examples of AI in Business Intelligence Applications

Companies can now use machines algorithms to identify trends and insights in vast reams of data and make faster decisions that potentially position them to be competitive in real-time.

It’s not a simple process for companies to incorporate machine learning into their existing business intelligence systems, though Skymind CEO and past TechEmergence podcast guest Chris Nicholson advises that it doesn’t have to be daunting. “AI is just a box,” he says.

But as AI has gained momentum, prominent application providers have gone beyond creating traditional software to developing more holistic platforms and solutions that better automate business intelligence and analytics processes.

Each example covers the following: Based on our past interviews withs executives and investors in the field, we predict that business intelligence applications will be one of the fastest growing areas for leveraging AI technology over the next five to 10 years.

HANA takes in information gathered from access points across the business—including mobile and desktop computers, financial transactions, sensors, and equipment at production plants.

If your sales staff uses company smartphones or tablets in the field to record purchase orders, data from those transactions can be analyzed and understood by HANA to spot trends and irregularities.

At a conference hosted by SAP in 2015, then-CIO Karenann Terrell described why Walmart chose to use HANA in order to operate faster and control back office costs by consolidating the processes and resources needed to handle the work.

For example, if a factory manager has an application installed on their computer to monitor the equipment on an assembly line, data from a slowdown in production could be collected and processed through HANA.

It is possible for small and midsize companies, not just enterprises, to explore using this kind of technology in different segments of their business—if the solution can fit within their budget.

The anticipated benefits of using machine learning platforms for business intelligence include infrastructure cost reductions and operational efficiency.

They also projected an average annual benefit of $19.27 million per organization by using HANA, compared with average annual investment of $2.41 million over five years.

Domo, a fast-growing business management software company that’s raised over $500 million in funding, has created a dashboard that gathers information to help companies make decisions.

There are more than 400 native software connectors that let Domo collect data from third-party apps, which can be used to offer insights and give context to business intelligence.

Once these features are rolled out, expected in late spring 2017, the platform is supposed to issue new alerts and notifications for significant changes, such as the detection of anomalies or new patterns in data (similar to approaches used in cyber security already).

Detecting these changes and patterns is expected to fuel the predictive analytics side of Mr. Roboto and help companies predict the return on investment for marketing in real-time, customer churn, and sales forecasts.

“By launching Domo, we were able to quickly optimize and achieve an 80 percent growth in yield during our first quarter,” said David Katz, Univision’s VP general manager for programmatic revenue and operations.

There are numerous ways for machine learning to enhance applications, including those from Apptus, which offer recommendations on actions that companies can take to boost their sales channels.

For example, when a customer visits an online store that uses Apptus eSales and starts to type in search terms to look up products, the machine learning solution can predict and automatically display related search phrases.

Though the technology is still in its early days, Amr Awadallah, founder and CTO at machine learning and software company Cloudera, says deep learning is already adept at prediction and anomaly detection.

According to a study commissioned by Avanade, a survey of 500 business and IT leaders from around the world revealed that they expect to see 33% increases in revenue as a result of smart technologies.

So far, these use cases point to machine learning largely used in service sectors, such as insurance and retail, to address tasks related to customers, sales, and operations;

The increasing prevalence of sensors in machinery, vehicles, production plants, and other hard equipment spaces means physical equipment can be digitized and be monitored by artificial intelligence, a topic we’ve covered before in machine learning applications in industry.

Oil and gas, aviation, and other industries, for example, have been using General Electric’s Predix operating system, which powers industrial apps to process the historic performance data of equipment.

This is a potential threshold moment for business and industry, where machine learning might weave its way further into how operations are handled, the way decisions are made, and resources get managed.

10 Real-World Examples of Machine Learning and AI [2018]

Smart machines and applications are steadily becoming a daily phenomenon, helping us make faster, more accurate decisions.

As of 2017, a quarter of organisations are spending 15 percent or more of their IT budget on machine learning capabilities, and we expect the number of machine learning examples to rise in the near future.

With cloud computing offering organisations an unprecedented level of scalability and power, we’re finally at a point where machine learning can hit the mainstream and drive innovation in every sector.

Before we introduce you to 10 real-world applications of machine learning, let’s take a look at some of the more transformative machine learning examples in a three key industries: The transformative potential of machine learning is the driving force behind its popularity in the financial services industry (see graph below) and is the reason why the insurance sector is slowly moving into the digital age.

Machine learning can help banks, insurers, and investors make smarter decisions in a number of different areas: Examples of machine learning can also be found in the health and social care industry.

Having studied a facial dataset of 4 million Facebook users, DeepFace has become adept at recognising the nuances in human countenance across 4000 separate identities.

These deep learning algorithms help the app extract street names and house numbers from photos taken by Street View cars and increase the accuracy of search results.

Machine learning frees up more time for Google engineers, automatically extracting information from geo-located images and achieving an accuracy rate of 84.2 percent for some of France’s most convoluted street signs.

RankBrain now handles around 15 percent of Google’s daily queries, working out the intent behind never before seen searches much faster than the previous old rules-based system.

In 2015, they introduced asmart reply functionto Gmail to help users tackle their inbox, with 10 percent ofmobileusers' emails sent using this tool the following year.

But with machine learning and neural networks, PayPal is able to draw upon financial, machine, and network information to provide a deeper understanding of a customer’s activity and motives.

Machine learning is integral to this process, as the platform caters to more than 100 million subscribers While the finer details of Netflix’s machine learning algorithms are kept behind closed doors, Tod Yellin, the company’s VP of product innovation states there are two things that feed the neural network: user behaviour and programme content.

To do this the machine learning model must handle three distinct requirements: You know that one cheesy pop song you listened to that triggered numerous other cheesy pop recommendations?

If we look at the five current biggest companies in the global market, we see that every single one of them has embraced digital transformation and used technology such as machine learning to change the game for everyone else.

How to Apply Machine Learning to Business Problems

It’s easy to see the massive rise in popularity for venture investment, conferences, and business-related queries for “machine learning”

In this article, we’ll break down categories of business problems that are commonly handled by ML, and we’ll also provide actionable advice to begin a ML initiative with the right approach and perspective (even it’s the first such project you’ve undertaken at your company).

For illustrative purposes, it will be helpful to list a number of well-established business use-cases for machine learning so that you (the reader) can churn up your own application ideas:

If you have reams of business data from years ago, it may have no relevance today, particularly in fields where the basic business processes change drastically year-over-year, such as mobile eCommerce).

For example, if you run a door delivery service for pet supplies, and your app, prices, product offerings, and service areas have changed significantly over the last six months, you will need much more recent data to learn from than, say, a company selling homeowners’

While unsupervised learning (see glossary below) allows for a wide degree of applications in making sense of data without labels, it’s usually not advised for companies to “jump into”

One letter difference or one number difference could mean overpaying your bill by 10x the original amount (if the decimal was interpreted to be in the wrong place), or sending money to the wrong company (if an invoicing company name isn’t registered exactly).

As an interesting caveat, there is a San Francisco-based startup called Roger.ai which is aiming to use natural language processing and machine vision to real and pay bills, albeit it pulls humans into the loop before sending funds.

In order to gain additional perspective on the issue of “picking a business problem for machine learning”, we decided to reach out to our network of previous AI podcast interview guests for additional guidance for our business readers: Dr. Ben Waber —

CEO, Calculation Consulting: “The best problems are those in which there is a very large, historical data set that includes both rich features and some kind of direct feedback that can be used to build and algorithm that can be implemented and tested easily and will either decrease operational costs and /or increase revenue immediately.“

CEO, AGI Innovations Inc: (To begin, Peter quotes Dr. Robin Hanson, Professor at George Mason University: “Good CS expert says: Most firms that think they want advanced AI/ML really just need linear regression on cleaned-up data.”) “I think that most businesses don’t justify the investment in ML/DL (of course, ML means many things). Cutting edge stuff that everyone is talking about requires a lot of data and expertise, and is static – i.e.

This leads us into the second major section of this guide: In an off-mic conversation with Dr. Charles Martin (AI consultant in the Bay Area), he mentioned that many companies read about ML with enthusiasm and decide to “find some way to use it.”

Pick a business problem that matters immensely, and seems to have a high likelihood of being solved UBER’s Danny Lange has mentioned from stage that there is one thought process that’s highly likely to yield fruitful machine learning use case ideas: “If we only knew ____.”

could get in the way of developing an effective ML solution: Building a ML solution requires careful thinking and testing in selecting algorithms, selecting data, cleaning data, and testing in a live environment.

Data Science and BD&A, Computer Sciences Corporation: “The most common mistake that businesses make when using ML is that they think that an ML solution is a one-shot process: They send data to data scientists, and data scientists send back THE model.

Log everything, build storage and processing systems, ensure they are accessible, conduct deep analysis and as many experiments as you can on your product, build in intelligence into as much as your product as possible.

The consensus (in the limited number of quotes above, and from dozens of other conversations with business-minded data scientists) is that machine learning is not as much of a mere “tool”

Interested readers might benefit from our recent consensus of 26 machine learning / AI researchers where we asked: “Where should machine learning be applied first in business?” The infographic featured drives home many of the same points highlighted in this article.

The ultimate question for executives remains: When can we have (a) the resources required to invest in machine learning seriously, and (b) a legitimate use case that started from trying to find real business value, not from “trying to find a way to kinda use machine learning.”

Smart Implementation of Machine Learning and AI in Data Analysis: 50 Examples, Use Cases and Insights on Leveraging AI and ML in Data Analytics

Now that more companies are mastering their use of analytics, they are delving deeper into their data to increase efficiency, gain a greater competitive advantage, and boost their bottom lines even more.

Specifically, companies in the customer engagement space utilize AI and machine learning to analyze conversations, both those that end in a sale and those that don’t, and to automatically identify the language that typically leads to a sale or that predicts when a sale will occur.

To help your company understand how machine learning and AI in data analysis can benefit your business, we have rounded up examples of smart implementation, insights from the experts, and business use cases to give you the information you need to start using these types of advanced data analysis yourself.

Please note, we have listed our 50 examples of smart implementation of machine learning and AI in data analysis in alphabetical order to simplify your search process;

@wittysparks WittySparks is a blog run by creative minds who practice in a host of fields and write about hot topics in digital marketing, content marketing, business, and technology, among other fields.

In this marketing strategy article, Dan Shewan shares 10 examples of companies using machine learning in innovative ways, including image curation at scale, improved content discovery, and to leverage chatbots.

In his ThoughtSpot article, chief data Evangelist Doug Bordonaro explains that you don’t really need to understand machine learning, artificial intelligence, and deep learning to take advantage of them for your business.

Three key details we like from 5 Ways Machine Learning Can Make Your BI Better: @Medium @NathanBenaich In his Medium article, investor and technologist Nathan Benaich uses his expertise in AI and emerging technology to encourage readers to delve into machine learning.

Three key details we like from 6 Areas of AI and Machine Learning to Watch Closely: @TechEmergence Professionals who want to know exactly how AI impacts their industry look to TechEmergence because they provide original market research and media on AI.

In his TechEmergence article, Joao-Pierre Ruth explores six examples of AI platform providers that are holistic solutions for better business intelligence and analytics automation.

Their AI Business Use Cases share detailed glimpses into how machine learning makes it possible to automate common data workflow, detect objects by image, and understand text.

They refer to AI and machine learning as “the most important general-purpose technology of our era,” because the machine continually improves its performance without humans needing to explain how to accomplish all its tasks.

Now, they are shaking up the banking world by focusing on customer behavior and analytics with its in-house startup, Advanced Analytics, which features leading-edge AI and machine learning technology.

Subramanian reminds readers that machine learning provides enterprises with the framework, insights, and algorithms needed to ensure better predictive ability.

Three key details we like from Enterprises Approach to Machine Learning: @mitsmr MIT Sloan Management Review leads the way for academic researchers, business executives, and other influencers and thought leaders about advances in management practice, especially those shaped by technology.

Three key details we like from How 11 CIOs are Using Machine Learning to Boost Innovation: @HarvardBiz After Erik Brynjolfsson and Andrew McAfee published their HBR article arguing AI and machine learning will become “general-purpose technologies,” HBR senior editor Walter Frick sat down with Hilary Mason, the founder of Fast Forward Labs, to discuss how companies can put these technologies into practice and how to take advantage of them.

Three key details we like from How AI Fits into Your Data Science Team: @IEGroup InnovationEnterprise is the leading global voice in enterprise innovation, providing access to cutting-edge content across nine distinct channels.

In their perspectives report, that provide a comprehensive overview of AI and machine learning and examine how smart apps are impacting small businesses and the implications of the technology on small businesses.

In fact, the Accenture Artificial Intelligence Report predicts that AI may cause annual economic growth rates to double and boost productivity by nearly 40% by 2035.

Three key details we like from How Healthcare Can Prep for Artificial Intelligence, Machine Learning: @MaruitTech Maruit Techlabs is a professional team delivering end-to-end software solutions related to chatbots, mobile platforms, application development, and web analytics.

Their machine learning article explores how the technology boosts predictive analytics, yet only 60% of business leaders who cite growth as a key source of value from analytics have predictive analytics capabilities.

Lukas Biewald’s TechCrunch article asserts that machine learning is forcing massive changes in company operations and explores how businesses use machine learning every day.

Three key details we like from How to Use Machine Learning in Business: @BernardMarr Barnard Marr, bestselling author, keynote speaker, strategic performance consultant, and analytics, KPI, and Big Data expert, shares how Walmart uses machine learning, AI, IoT, and Big Data to improve performance.

Three key details we like from How Walmart Is Using Machine Learning AI, IoT and Big Data to Boost Retail Performance: @edgylabsdotcom Edgy Labs is comprised of a group of technologists and successful tech entrepreneurs who specialize in growth hacking, SEO, artificial intelligence, virtual reality, augmented reality, and the Internet of Things.

Three key details we like from How You Use Machine Learning Everyday and Business Will, Too: @Deloitte Deloitte is a global network of member firms that helps clients achieve their goals, solve complex problems, and make meaningful progress.

Here, Philipp Gerbert, Martin Reeves, Sebastian Steinhäuser, and Patrick Ruwolt share the findings of a report BCG conducted with MIT Sloan Management Review to determine exactly how businesses use AI and establish a baseline to help companies compare their efforts and goals with the technology and to offer guidance for future initiatives Three key details we like from Is Your Business Ready for Artificial Intelligence?: @BioStorage Denodo senior product marketing manager Saptarshi Sengupta wrote this article BioStorage Technologies to examine the ways in which machine learning and AI impact medical research.

They also share this article by Scott Hackl, global head of sales for Finacle at EdgeVerve, which presents his argument that banks and credit unions should use Ai and the power of advanced analytics in order to become agile and remain relevant.

She also addresses the ways in which the advanced technology can work for small businesses and investigates several services and products that make AI and machine learning accessible for those businesses.

In this article, they explore deep learning and machine learning and the ways in which Gartner predicts deep learning will be a critical component of demand, fraud, and failure predictions by 2019.

Three key details we like from Why AI, Machine Learning and Big Data Really Matter to B2B Companies: @salesforceiq SalesforceIQ delivers relationship intelligence technology to help companies save time and close more deals via smarter selling and better relationships.

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