In early June, at the invitation of the European Commission to Brussels (Belgium), I toured some fascinating AI and blockchain-based projects, which the Commission is funding. Across industrial sectors, from healthcare to energy, from construction to retail, engineers are creating new technologies with potentially disruptive implications for the current architectural order of the global economy. One of the technologies, an “AI doctor”, shows great promise for the future of healthcare in Africa.
The solution is called CareAi: an AI-powered computing system anchored on blockchain that can diagnose infectious diseases, such as malaria, typhoid fever, and tuberculosis, within seconds. The platform is engineered to serve the invisible demographic of migrants, ethnic minorities, and those unregistered within traditional healthcare systems. By bringing AI and blockchain together, CareAi uses an anonymous distributed healthcare architecture to deliver health services to patients anonymously. This makes it possible for these
In January of 2018, Annette Zimmermann, vice president of research at Gartner, proclaimed: “By 2022, your personal device will know more about your emotional state than your own family.” Just two months later, a landmark study from the University of Ohio claimed that their algorithm was now better at detecting emotions than people are.
We’ve all experienced some version of this problem: Ask “how many customers do we have?” and the marketing team provides one answer, sales a second, and accounting a third. Each department trusts its own system, but when the task at hand requires that data be shared across silos, the company’s various systems simply do not talk to one and other.
The problem arises because different systems employ different definitions of key terms. Thus, the term “customer” can mean a potential buyer to the marketing department, the person who signed the purchase order to sales, and the legal entity that it bills to accounting. Then people misunderstand the data and make mistakes. These issues grow more important as companies try to pull more and more disparate data together — to develop predictive models using machine learning, for example.
Specialized vocabularies develop in the business world every day to
Companies are using AI in all kinds of innovative ways to advance their businesses. If you’ve ever searched Netflix to watch a movie, AI (a recommendation algorithm) was no doubt used in your decision about what to watch. If you’ve shopped on Amazon, your decision about what to buy was also influenced by AI (via an association algorithm). If you’ve ever ordered an Uber, AI (a location algorithm) was used to have a car in your vicinity quickly. If you ever had a thought about a product or a vacation, and it seemed to suddenly pop up on your search page or in your email inbox, I can assure you it was based on AI (a classification algorithm) monitoring your online activity.
These same types of AI algorithms can be used to power any company’s decision-making process, helping you make better business predictions. Based
A quiet revolution is taking place. In contrast to much of the press coverage of artificial intelligence, this revolution is not about the ascendance of a sentient android army. Rather, it is characterized by a steady increase in the automation of traditionally human-based decision processes throughout organizations all over the country. While advancements like AlphaGo Zero make for catchy headlines, it is fairly conventional machine learning and statistical techniques — ordinary least squares, logistic regression, decision trees — that are adding real value to the bottom line of many organizations. Real-world applications range from medical diagnoses and judicial sentencing to professional recruiting and resource allocation in public agencies.
A recent survey by Deloitte of “aggressive adopters” of cognitive technologies found that 76% believe that they will “substantially transform” their companies within the next three years. There probably hasn’t been this much excitement about a new technology since the dotcom boom years in the late 1990s.
The possibilities would seem to justify the hype. AI isn’t just one technology, but a wide array of tools, including a number of different algorithmic approaches, an abundance of new data sources, and advancement in hardware. In the future, we will see new computing architectures, like quantum computing and neuromorphic chips, propel capabilities even further.
Still, there remains a large gap between aspiration and reality. Gartner estimates that 85% of big data projects fail. There have also been embarrassing snafus, such as when Dow Jones reported that Google was buying Apple for $9 billion and the bots fell for
Many efforts to apply machine learning get stuck due to concerns about the “black box” — that is, the lack of transparency around why a system does what it does. Sometimes this is because people want to understand why some prediction was made before they take life-altering actions, as when a computer vision system indicates a 95% likelihood of cancer from an x-ray of a patient’s lung. Sometimes it’s because technical teams need to identify and resolve bugs without disrupting the entire system. And now that the General Data Protection Regulation (GDPR) is in effect, businesses that handle consumer data are required to explain how automated systems make decisions, especially those that significantly affect individual lives, like allocating credit or hiring a candidate for a job. While GDPR only applies in Europe, businesses around the world anticipate that similar changes are coming and so are revisiting governance efforts.
Over the last decade, e-commerce has imposed a painful profit squeeze on big-box retailers, resulting in layoffs, store closings, mall reconfigurations, and even bankruptcies. With no reprieve in sight for retailers, the online world is poised to do the same to brand-name consumer products companies.
One of the core reasons that this is happening is that in addition to providing always-on, on-demand convenience, online retailers know so much more about their customers than their offline counterparts do. In fact, they have mastered the art of creating a direct connection to their customers, which in turn allows them to collect massive amounts of data about them. Then, by applying tools like artificial intelligence, the online retailers are able to create more-personalized customer experiences, fostering levels of satisfaction, connection, and customer loyalty that traditional retailers just can’t compete with.
And when it comes to consumer goods companies, that same artificial
It’s a good time to be a consumer. New digital business models have flipped the customer-brand relationship on its head. No longer do consumers need to do their own background research on a product or company to find what they are looking for. Instead, brands come to us. There are more options and more channels to get what you want than ever before.
That said, there is always a downside. This seemingly limitless digital economy has brought with it feelings of overexposure. No one likes to feel as if they’re being watched, yet with technology continuing to mature, we have found ourselves entrenched in a marketing machine that has become far too intimate for anyone’s liking.
The power of digital is so great that brands have started to abuse it. And with this abuse of power has come the erosion of the trust that once existed between businesses
The pervasive adoption of mobile devices has driven an explosion of contextual user information, including geolocation data, which has become a valuable resource for marketers. However, a lack of technical skill sets among marketers has made it difficult for them to use this data (when they have access to it) effectively. Plus, changing regulations mean it’s more important than ever for marketers to understand what data they have access to and how to properly leverage it.
When brothers Shep and Ian Murray cut their ties with Corporate America to start a little company on Martha’s Vineyard in 1998, their motivation was clear: “We’re making neck ties so we don’t have to wear them.”
Little did they know that the business they founded, Vineyard Vines, would become a darling of the fashion industry and a household brand name around the country.
Today, the company best known for its smiling pink whale logo offers much more than their signature neckwear. They manufacturer a full line of “exclusive, yet attainable” clothing and accessories for men, women, and children. That “little” privately-held business has grown tremendously since its launch and currently has more than 90 physical retail locations and a highly successful eCommerce business.
I met the team at Vineyard Vines while doing research about data-driven marketing technologies for my book, Marketing, Interrupted, and
Poor pricing practices are insidious — they damage a company’s economics but can go unnoticed for years. Consider the case of a major industrial goods manufacturer that was struggling with low profit margins, relative both to competitors and to its own historical performance. It traced much of the cause to a mismatch between its sales incentives and pricing strategy. The manufacturer was compensating sales representatives based solely on how much revenue they generated. Reps thus had little motivation to hit or exceed price targets on any given deal, and most were closing deals at the lowest permissible margin.
Like this manufacturer, many business-to-business (B2B) companies have a major opportunity to improve their standing on price. To help companies understand the state of pricing capabilities and how they figure into performance, Bain & Company conducted a global survey of sales leaders, vice presidents of pricing, CEOs, CMOs, and
“Almost everything we do is a recommendation.” That’s the essential design philosophy articulated by then-Netflix engineering director Xavier Amatriain five years ago, where personalizing and customizing choice is the coin of the realm. “I was at eBay last week,” he said at the time, “and they told me that 90% of what people buy there comes from search. We’re the opposite. Recommendation is huge, and our search feature is what people do when we’re not able to show them what to watch.”
Unlike search, recommendation systems seek to predict the “rating” or “preference” a user would give to an item, action, or opportunity. Thoughtfully managed, recommendations can prove far more valuable to marketers than for the customers they ostensibly serve. Recommendation engines not only generate useful data for analyzing customer desires; they can be harnessed to make tactical and strategic recommendations
Today, good marketing relies on having detailed and accurate customer data. And companies, not surprisingly, are eager to collect vast troves of it. For instance, Amazon continuously tracks the behaviors of its 100 million Prime members, an example of “first-party” data. And many companies have found that sharing their own customer information with other companies creates synergies for both parties, especially with the increasing availability of “internet of things” data (GPS sensors, smart utility meters, fitness devices, etc.). These are examples of “second-party” data. Finally, many companies supplement their first-party data with “third-party” data from companies like Acxiom, which collects up to 1,500 data points on 700 million consumers worldwide.
The potential to conduct effective data-driven marketing with these augmented databases is enormous. At the same time, concerns about customer privacy have never been higher because of numerous, widely-publicized privacy hacks such as the recent Facebook-Cambridge Analytica scandal.
A non-negligible percentage of customers who buy a new smartphone return it within the “free return” window. Many of these returners claim that the phone does not work correctly. However, the data clearly indicates that this is often not the real issue. The reality is that these customers simply don’t know how to use the smartphone well enough, and either do not realize it, or are not willing to admit it. So they return it — which makes a major profit difference for both the smartphone manufacturer and the service provider. For the latter, it could be on the order of thousands of dollars in lifetime value per customer (CLV).
We see a paradox in two important analytics trends. The most recent results from The CMO Survey conducted by Duke University’s Fuqua School of Business and sponsored by Deloitte LLP and the American Marketing Association reports that the percentage of marketing budgets companies plan to allocate to analytics over the next three years will increase from 5.8% to 17.3%—a whopping 198% increase. These increases are expected despite the fact that top marketers report that the effect of analytics on company-wide performance remains modest, with an average performance score of 4.1 on a seven-point scale, where 1=not at all effective and 7=highly effective. More importantly, this performance impact has shown little increase over the last five years, when it was rated 3.8 on the same scale.
How can it be that firms have not seen any increase in how analytics contribute to company performance, but
According to Constellation Research, businesses across all sectors will spend more than $100 billion per year on Artificial Intelligence (AI) technologies by 2025, up from a mere $2 billion in 2015. The marketing industry will be no exception.
AI holds great promise for making marketing more intelligent, efficient, consumer-friendly, and, ultimately, more effective. Perhaps more pointedly, though, AI will soon move from being a “nice-to-have” capability to a “have-to-have.” AI is simply a requirement for making sense of the vast arrays of data — both structured and unstructured — being generated from an explosion of digital touchpoints to extract actionable insights at speeds no human could ever replicate in order to deliver the personalized service consumers now demand.
When Naomi Simson founded RedBalloon, an online gift retailer that sells personal experiences, she was pioneering the category in Australia. With a $25,000 personal investment and a small office in her home, she began aggregating sales leads and aggressively acquiring customers through very traditional marketing means — like yellow page advertisements. It was 2001, and online advertising was at its nascent stage. Internet Explorer was the leading Internet browser and Google AdWords had only just recently launched. With a cost of customer acquisition of just 5 cents, Simson’s traditional approach to advertising was generating an impressive return on investment. RedBalloon was setting the pace for gifting experiences like outdoor adventures, wine tastings, concert tickets, and spa treatments.
By 2015, RedBalloon was delivering more than four million customers to businesses across Australia and New Zealand that offered “experiences.” Simson wasn’t overconfident, but at this point, she felt
In recent years, marketers have lived through the Era of Big Data, and the Era of Personalization, and now we are living through the “Era of Consent.” With the General Data Protection Regulation (GDPR) going into effect on May 25th, businesses will be required to protect the personal data and privacy of EU citizens. For marketers, this means updating your privacy policies, but more importantly, it means finding innovative new ways to connect with customers and gather consent to use their data in order to continue your “marketing relationship” with them.
Marketers across the European Union (EU) have been preparing for this new regulation for months. Yet the regulation impacts all companies globally, including those in the United States, that collect and manage data on citizens in the EU. Many global marketers are still struggling to understand what steps they need to take to