Smart Machines in Marketing

Data & Artificial Intelligence

A New Era of Technology

We are on the cusp of a new era in technology where decisions and problem solving will be aided by smart machines that sense, learn, infer, and even think on behalf of humans. Conversational user interfaces, which rely on smart machine technologies to facilitate dialogue between people and bots, are garnering particular attention.

Technological advances in machine learning, algorithms, and artificial intelligence capable of suggesting—and even taking action—on behalf of humans is driving the trend. But integrating smart machines into the marketing organization will require an investment, a willingness to experiment, a moxie to fail fast and learn faster, and the drive to develop a continuous-improvement-oriented culture.

In this piece from Razorfish, we look at marketing functions poised for transformation in the world of smart machines, from product development and sales to customer experience automation and C-suite advice. In the not-too-distant future, CMOs will:

  • Learn from Smart Machines, which uncover patterns that can be used to predict behavior
  • Draw laser-beam marketing insights from mountains of complex behavioral analysis
  • Engage consumers in new and meaningful ways with predictive analytics

The trend

Today’s machines are made smart by drawing on neural networks, which have become increasingly deep with recent advances in hardware. Designed to model outcomes based on a broad and complex set of inputs, smart machines outperform humans in several areas, especially those that involve uncovering patterns that can be used to predict behavior. This is where their greatest value lies—in the ability to identify patterns their developers never imagined.

Smart machines include autonomous cars, robots, and other cognitive computing systems able to solve problems and make decisions without human intervention. Machines that “think” like humans will help solve huge problems, from curing cancer to climate change.
Economists call smart machines a general-purpose technology, or GPT. Such technologies impact a society’s economic and social structures with forces so disruptive that it eventually leads to the launch of an entirely new economic era. Examples include the Iron Age, the Steam Age, the Information Age, and now the Connected Age.

Why now?

In 1955, researchers theorized that the creation of human intelligence could be automated. Today, a perfect storm is converting such theory into practice with powerful hardware, advanced algorithms, and big data.

In hardware, new GPU chips (graphical processing units) are capable of modeling 10,000 times as many artificial neurons as those from 2007. By 2020, deep neural networks (DNNs) will be powered by hardware one thousand times more powerful than those today, enabling smart machines to be trained in minutes and seconds versus hours and days.

Smart machines draw upon deep neural networks, or DNNs (which house text, images, voice, and even video). DNNs are particularly good at weighing and analyzing large streams of data to identify patterns their developers never anticipated. In fact, the more varied the input stream, the better DNNs perform. It is the learning attribute of a smart machine that differentiates it from “unsmart technologies,” which always require specific instructions from a coder to take action.

Deep Neural Networks.
Deep Neural Networks (DNNs) are particularly good at weighing and analyzing large streams of data to identify patterns their developers never anticipated.

 

What Role Will Smart Machines Play in Marketing?

Smart machines will help CMOs cut through the noise

Smart machines promise to make the CMO’s life easier as they filter out mountains of noise to pinpoint that single piece of insight needed to make a more informed decision, whether it’s knowing which segments are growing faster than the economy, where to invest next, or determining the precise offer that will generate the desired customer response.

The ability of smart machines to draw laser-beam insight from mountains of complex behavioral analysis becomes increasingly valuable in a connected age where CMOs are bombarded with information, content, and endless decision-making challenges.

Smart Machines and a CMO.
Smart machines promise to make the CMO’s life easier by filtering out noise.

 

Smart machines are coming to a bank near you

Several banks are using smart-machine-enabled virtual assistants in a broad range of functions. For example, personal relationship managers at DBS Bank in Singapore use IBM’s Watson to quality assure their advice to private banking customers. Ally Assist, deployed by Allied Financial in the U.S., gives contextualized answers to an individual’s question, by voice or text. Ally also offers predictions and feedback to help individuals manage their cash flow and budgeting, based on real-time analysis of their own spending history. Ally also uses its knowledge of an individual’s unique behavior to know exactly when to alert the customer to make a payment.

Mitsubishi UFJ Financial Group in Japan offers virtual customer assistants, fluent in 19 languages, to help interpret a consumer’s emotional state as he or she is greeted (at the branch or online).

At the commercial Bank of Dubai, a virtual customer assistant named Sara is available 24/7 to help website visitors fill out forms and get up-to-the-minute answers to questions about saving and investing. And customers don’t even need a keyboard or a mouse to “talk” to Sara; rather, they wave at or tap on the screen to dive deeper into a topic or initiate a new one.

Smart machines are innovating insurance

In the insurance sector, smart machines perform cognitive tasks that usually require human intelligence—performing everything from routine chores to solving complex problems, often in real time. For example, Genworth Financial automates the underwriting of life insurance applications with help from smart machines. Underwriter guidelines are encoded into the system, and a self-evolving algorithm is used to optimize performance.

Life and P&C insurers that hire highly skilled professionals to sell complex products, provide actuarial pricing support, and manage claim investigations are good candidates for smart machines. Other insurance firms are using smart machines to inform their telematics solutions (which quantify the driving behaviors of the insured to compute equitable insurance premiums).

Hospitality is another early adopter

One hotel that is getting a lot of buzz for its smart machine deployment, specifically robots, is Japan’s Henn-na Hotel. “These robots will warm your heart,” says a happy traveler upon his initial stay. Henn-na, which translates to “strange,” certainly lives up to its name, but in ways that delight its guests. Travelers who have experienced the hotel find the robots (especially the dinosaurs) absolutely charming and fun to be around.

Hideo Sawada, the hotel’s owner and general manager, aims to make Henn-na the most efficient hotel in the world by supplanting 90 percent of a typical hotel’s human staff with robots. Given that labor is typically a hotel’s highest line item in terms of operating cost, he might just get there. Sawada’s other creative applications of smart machines include facial recognition, which grants entry into one’s hotel room. A robotic concierge answers questions, explains breakfast times and locations, and orders taxis. Rooms start at just $75-100 USD per night, but during peak times are auctioned to the highest bidder.

Hen-na Hotel Raptor Check-In.

Travelers at the Henn-na Hotel check in with a smart machine velociraptor concierge. Source: Wired

CMO advisory services

Smart advisors will help executives see information beyond their natural limitations and their inherent blind spots. Smart advisors will also help executives consider alternative decision paths—and provide greater insight into the implications of those decisions.

Unlike virtual personal assistants (which focus on observing and acting on behalf of users), smart advisors ingest large amounts of relevant material—manuals, textbooks, and even case law in a particular domain—to make strategic recommendations. Smart advisors mimic an understanding of the material by extracting ideas, concepts, and relationships between major points, drawing inferences from what they have processed.

Language translation

AI and machine learning are on track to become a major paradigm shift as computers feed on deep neural networks to surface a new type of insight that has been previously impossible to find. Add AI developments in speech recognition and the human-computer-interface will soon transform areas such as ecommerce into conversational commerce. 

But the real productivity boost, especially for global CMOs, could come from MIT research that automatically associates images with audio clips, accelerating language-to-language translation without the tedious steps of training AI systems on the correlation between two languages’ words. If you’ve ever been the victim of bad translation, you’ll especially appreciate the nuances that will become inherent to such systems. 

As David Harwath of MIT notes, "Because our model is just working on the level of audio and images, we believe it to be language-agnostic. It shouldn’t care what language it’s working on."

Regulatory guidance and advice

CMOs could also use a smart advisor to ingest all policy documents within the organization and reference materials from government agencies or from industry associations. This application of smart advisors will enable the CMO to evaluate the information and understand the impact of proposed regulation changes in a particular country. In a sales capacity, smart advisors could be used to audit a business proposal against the external client’s purchasing policies and external regulatory constraints.

Smart Machines Help Consumers Make Better Decisions

While progress has been made to deliver quality targeting and to develop business rules that match customer and product, most of these processes are fixed, failing to take advantage of the thousands of clues users provide as they browse and shop.

To remedy this, one high-tech firm partnered with Razorfish to deliver a shopping experience that fuses an intuitive understanding of the product catalog with the buyer’s real-time shopping activity. By harnessing machine-learning algorithms and natural-language processing, the system is able to categorize and determine relationships between a large selection of products, which are custom served to shoppers as they browse the site.

It’s easy to see the benefits of this sort of customization—this particular project drove a significant increase in conversion rates and incremental revenue. The ability to engage users in new and meaningful ways and generate unique customer experiences will make smart machines  the engines behind much of the digital landscape, from digital commerce to content and beyond.

Customer retention, product, management, and pricing

Predictive analytics helps marketers take smarter actions by understanding where and why customers defect. Because a reduction in defection by even one percent can have big impact on revenue or profits, predictive analytics in retention initiatives has been a priority investment for many CMOs over the past 10 years. Once a customer is lost, they are often lost forever. Fusing predictive analytics with smart machines will be extremely effective in spotting customer defection early on, offering suggested actions to marketers to help them keep a customer before they pass the point of no return.

Products are getting smarter by the day, from hearing aids that filter noise to driverless cars that adapt to specific road conditions. As every product becomes embedded with technology (that regularly reports its status and relationship with other products), machine learning will be applied to help extend its life, or provide its owners with advice on how to use it more appropriately. For example, smart shoes will let the runner know that his current style of running is putting him at risk for injury. Automobile drivers will get similar advice from their vehicle’s tires and other components.

Intelligent Cars.

Source: Wired

What’s next?

One of the areas to watch in applying smart machines to marketing is the prescriptive analytics discipline, where the result of an analytical exercise is a recommended course of action.

In fact, it’s the decision output of prescriptive analytics that differentiates it from the descriptive, diagnostic nature of predictive analytics. Many mature-use cases exist, such as making more informed decisions in cross-selling, database marketing, and churn management. But with smart machines, many new use cases will emerge. 

The real power behind prescriptive analytics aided by machine learning will be the ability to make far better decisions, whether highly strategic, tactical, or operational. Prescriptive analytics is particularly attractive in decisions designed to manage risk, increase margins, reduce costs, or optimize resource allocation. In finance and insurance, for example, use cases tend to cluster around customer management, loan approvals, and claims processing.
 

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Source: Wired

Recommendations

Dream big

Thus far, the IT disciplines have improved productivity by focusing on the automation of existing, manual tasks. While that is changing, particularly in the decision support role of analytics, IT is still largely seen as a means to get work done more efficiently and effectively. But now, emerging technologies are poised to help business do things it never imagined; things it never thought possible. CMOs should get their teams to think way outside the lines when considering the role of smart machines in their innovation programs.

Focus on tasks versus occupations

In some cases, smart machines will supplant an entire role, but even then, it does not indicate the death of that role’s associated occupation. In many of the industry examples provided in this paper, smart machines perform certain activities of an occupation, freeing the individual to deliver work of higher value. Bar code scanners and point-of-sale systems (POS) in grocery stores have reduced labor costs (as well as the cost of goods by consumers), but cashiers are still needed. POS innovation has also freed up workers to focus on promotions and other work that was not getting done due to lack of resources or cost.

Look beyond your sector

As obvious as “dreaming big” sounds, it’s not always that simple to implement. CMOs and their teams are tightly wedded to how things are done today; moreover, new ideas are often instantly rejected because they are challenged with culture, processes, and decades-old attitudes and constraints about what it means to compete in one’s chosen sector. For example, while the driverless car may seem irrelevant to your company, think about what it implies for your customers’ daily lives.

Robot.

Source: PC Mag

Maintain perspective

American artist and philosopher Elbert Hubbard once said, “While one machine can do the work of 50 ordinary men, no machine can do the work of one extraordinary man.” As extraordinary as smart machines are, like any technology, they offer business leaders an extra set of capabilities and tools to make better, more informed decisions. As noted by Gartner analyst Andrew Frank, “Data can play a leading role in developing strategy and bringing precision to execution, but it does nothing—absolutely nothing—to stir motivation and create the desire that makes cash registers ring. Data is important, but it’s content that makes an emotional connection.”

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