30 Direct Mail Statistics Worth Knowing

Posted on  by Compu-Mail

It’s that time of year again! Once again, we’ve pulled together a brand new list of direct mail stats to show why direct mail is still the best medium for cutting through the clutter and getting your marketing message directly in the hands of your customers.

Direct Mail Still Gets the Best Response

  1. Direct mail household response rate is 5.1% (compared to .6% email, .6% paid search, .2% online display, .4% social media). This is the highest response rate the DMA has ever reported, since coming out with the Response Rate Report in 2003.1
  2. Direct mail median household return on investment is 29% (compared to 124% email, 23% paid search, 16% online display, 30% social media).1
  3. At 6.6%, oversized envelopes have the greatest household response rates over other mediums (followed by postcards at 5.7% and letter-sized envelopes at 4.3%).1
  4. At 37%, oversized envelopes have the greatest household return on investment over other mediums (followed by postcards and letter-sized envelopes at 29%).1
  5. The response rate for direct mail among people aged 18-21 years old is 12.4%.1
  6. The top response rate tracking methods are online tracking such as PURLs (61%), call center or telephone (53%), and code or coupon (42%).1
  7. For every $167 spent of direct mail in the US, marketers sell $2095 in goods.2

The Bottom Line – Direct mail has the greatest impact because it offers a tangible experience for the customer. Oversized pieces stand out the most.

Personalization Boosts the Response Even Further

  1. Adding a person’s name and full color in the direct mail can increase response by 135%.3
  2. Adding a person’s name, full color and more sophisticated database information can increase the response rate by up to 500% vs not doing any of these things.3
  3. Targeting customers on a 1:1 level increases response rates up to 50% or more.4

The Bottom Line – People are even more likely to respond to a marketing message when it feels like it was written just for them.

Click here to see how to use data to power up your customer acquisition

It’s a Multi-Channel World Out There

  1. The average person receives more than 2900 marketing messages a day.5
  2. It can take up to 18-20 touchpoints to reach a customer for the first time.6
  3. The average number of mediums used by marketers is 3.4% (up from 2.7% in last year’s study).1
  4. Only 11% of marketers are just using one medium.1
  5. Single media users are most likely to use email (54%) or direct mail (22%).1

The Bottom Line – Your customers are on multiple platforms. Are your messages clear and consistent across all of them?

Direct Mail and Digital Work Together

  1. 90% visit website first before calling.7
  2. 96% leave without making a purchase.8
  3. Direct mail with digital ads yield 28% higher conversion rate.9
  4. Marketing campaigns that used direct mail and 1 or more digital media experienced 118% lift in response rate compared to using direct mail only.10
  5. Website visitors who are retargeted are 70% more likely to convert.11
  6. 26% of customers will return to a site through retargeting.12

The Bottom Line – Direct mail response rate can be difficult to track, because not everyone calls in right away. Most customers head straight to the web to learn more about the product online, rather than calling to speak to a sales representative about the product directly.

It’s Easier to Nurture Existing Interest than Create New Interest

  1. It is 10X harder to create new interest than nurture existing interest.13
  2. It can cost as much as 5-12X more to acquire a new customer than retain an existing customer.13
  3. The probability of selling to an existing customer is 60-70%, vs. the probability to sell to a new customer at 5-20%.13
  4. A 5% increase in retention yields profit increases of 25-95%.14
  5. The average response rate for direct mail pieces sent to former customers of a given brand is 18.4 percent.15
  6. The household cost per acquisition for direct mail is $26.40 (compared to $10.32 email, $20.32 social media, $16.22 paid search, $24.75 internet display).1

The Bottom Line – Use direct mail to nurture existing interest, keeping current customers and prospects engaged and delighted. While the cost per acquisition is higher for direct mail, average response rate and median return on investment is competitive enough to make up the difference.

Success is in the Data

  1. 40% of a direct marketing campaign’s success is in the data.16
  2. In general, purchased lists have a margin of error as high as 20-30% for various reasons (people move, change jobs, get married/divorced).
  3. 73% of firms aspire to be data-driven but only 29% of firms succeed at turning data into action.17

The Bottom Line – It’s critical to keep an accurate, updated customer database list.
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Sources

  1. DMA Response Rate Report https://thedma.org/
  2. Print Is Big http://www.printisbig.com/
  3. Canon Solutions America https://csa.canon.com/
  4. Data & Marketing Association https://thedma.org/
  5. FireSnap https://www.firesnap.net/blog/why-inbound-marketing-has-become-so-popular
  6. How to Kickstart Your Next Omnichannel Marketing Campaign https://compu-mail.com/blog/2017/03/06/kickstart-omnichannel-marketing-campaign/
  7. How to Transform Your Website Into a Marketing Powerhouse for a Mobile World http://www.huffingtonpost.com/ernesto-sosa/how-to-transform-your-website-into-a-marketing-powerhouse-for-a-mobile-world_b_9141792.html
  8. Google Analytics https://analytics.google.com/
  9. Non Profit Pro http://www.nonprofitpro.com/article/doctors-without-borders-uses-remarketing-retargeting-extend-reach/all/
  10. Merkle https://www.merkleinc.com/
  11. Criteo http://www.criteo.com/
  12. Retargeting: The 10 Stats you Probably Didnt Know http://blog.wishpond.com/post/85825723836/retargeting-the-10-stats-you-probably-didnt-know
  13. Invesp https://www.invespcro.com/
  14. Small Business Trends https://smallbiztrends.com/
  15. USPS Household Diary Study https://www.usps.com/
  16. Above the Fold Magazine http://www.abovethefoldmag.com/?q=article/40-40-20-rule-marketing
  17. Forrester Global State Of Strategic Planning https://go.forrester.com/

Marketing Transformation and Artificial Intelligence

Artificial Intelligence and Marketing
Artificial Intelligence and Marketing

7 examples where artificial intelligence is transforming marketing:

1. Content curation

Predictive analytics allows Netflix to optimize its recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription.

Uniting information from diverse datasets is a common use of AI.

Under Armour is one of the many companies to have worked with IBM’s Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc.

The result is the ability for the brand to offer up relevant (personalized) training and lifecycle advice based on aggregated wisdom.

2. Search

In 2015, Google admitted it was using RankBrain, an AI system, to interpret a ‘very large fraction’ of search queries. RankBrain utilizes natural language processing (NLP) to help find relevance in content and queries, as well as better interpretation of voice search and user context (e.g. Google Now).

3. Predictive customer service

Knowing how a customer might get in touch and for what reason is obviously valuable information.

Not only does it allow for planning of resource (do we have enough people on the phones?) but also allows personalization of communications.

Another project being tested at USAA uses this technique. It involves an AI technology built by Saffron, now a division of Intel.

Analyzing thousands of factors allows the matching of broad patterns of customer behavior to those of individual members.

4. Ad targeting

As Andrew Ng, Chief Scientist at Baidu Research, tells Wired, “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.”

Optimizing bids for advertisers, algorithms will achieve the best cost per acquisition (CPA) from the available inventory.

When it comes to targeting of programmatic ads, machine learning helps to increase the likelihood a user will click. This might be optimizing what product mix to display when retargeting, or what ad copy to use for what demographics.

5. Customer segmentation

Plugging first- and third-party data into a clustering algorithm, then using the results in a CRM or customer experience system is a burgeoning use of machine learning.

Companies such as AgilOne are allowing marketers to optimize email and website communications, continually learning from user behavior.

6. Sales forecasting

Conversion management again, but this time using inbound communication.

Much like predictive customer service, inbound emails can be analyzed and appropriate action taken based on past behaviors and conversions.

Should a response be sent, a meeting invite, an alert created, or the lead disqualified altogether? Machine learning can help with this filtering process.

7. Image recognition

Google Photos allows you to search your photos for ‘cats’. Facebook recognizes faces, as does Snapchat Face Swap.

Perhaps the most exciting implementation of image recognition is DuLight from Baidu…Designed for the visually impaired, this early prototype recognizes what is in front of the wearer and then describes it back to them.

Artificial Intelligence Potentials in B2B Marketing – D&B

ai-dnb
AI Marketing

By Leslie Hancock
Founder & CEO
CreativeCafeHQ.com

I did an amusing and highly informal survey, asking various people in my life what they think about when I mention “artificial intelligence” (AI). Not surprisingly, most think of human-like robots or omniscient, self-aware computer systems as portrayed in The Terminator, Ex Machina or Person of Interest. There’s the unavoidable association with a dystopian future in which the machines wake up and decide humans are a blight that must be eradicated. Even luminaries like Stephen Hawking and Elon Musk have warned us of the potential dangers of AI. But the general public still seems to think of AI technology as something to worry about in the distant future, not something that’s already a factor in almost every industry and aspect of our everyday lives.

The truth is that AI is already here, and it’s pervasive. Though most AI tech is nowhere near passing the Turing Test and taking over the world, it’s absolutely already transforming the way we do business. Whether they consider AI technology to be a friend or a foe, many B2B marketing leaders see the potential—and the inevitability—of AI. In fact, most CMOs in five global markets believe artificial intelligence will surpass social media’s influence in the industry. Nearly six in 10 believe that within the next five years, companies will need to compete in the AI space to succeed.

The Dawn of the Cognitive Era

Joanna L. Batstone, PhD, Vice President and Lab Director at IBM Research Australia and Chief Technology Officer, IBM Australia and New Zealand, says those involved in serious information science understand the enormous potential of intelligent systems.

“Cognitive computing systems learn at scale, reason with purpose and interact with humans naturally,” Batstone explains. “They learn and reason from their interactions with us and their experiences with their environment.”

Intelligent systems can go beyond answering numerical problems to offer hypotheses, reasoned arguments and recommendations. However, Batstone reassures anyone who might still be nervous about the nature of AI.

AI Is as Much About “Nurture” as “Nature”

But don’t worry, AI marketing (AIM) isn’t putting human marketers out of work anytime soon. Instead, it has the potential to be the connective tissue among martech systems, augmenting humans’ ability to make sense of and take action on data.

Wayne Sadin, Chief Digital and Information Officer at Affinitas Life, says, “Humans are still very much driving the train. It’s just a much faster, more powerful train now.”

Sadin urges marketers to rely on machine learning to perform rote tasks (like watching social media posts) in order to free teams to do higher-level creative work. By interconnecting social data to web analytics to a CRM database to external data and more, B2B marketers can use AI applications to automate and personalize many interactions that used to be time-intensive and far less efficient.

For this reason, Sadin thinks of the “A” in “AI” as augmented instead of artificial. “AI is part of the trend towards ‘Augmented Everything’: brains (AI), muscles (robots), vision (AR/VR),” Sadin says. “It makes workers smarter, stronger and faster. It’s not some central overlord machine, though. It’s just smart people taking advantage of data that’s already there and intelligent martech that is getting more and more capable of adapting to changing customer behaviors and expectations.”Sadin cautions that AI has to be carefully nurtured and guided. It can only do what we tell it to do and learn what we tell it to learn. AI can’t connect data and martech systems on its own without a human telling the technology how to make the connections and what to do with the data it takes in. (Maybe Maciej Ceglowski puts it best: “I find it helpful to think of algorithms as a dim-witted but extremely industrious graduate student, whom you don’t fully trust.”)

To train AI apps to be genuinely useful and not just more chaos cluttering up the martech landscape, we have to tie machine learning to business goals and ethical standards and be very, very specific about the data we feed into the AI to train it. We have to set limits and maintain good data hygiene so the AI makes the connections we want and stays on track. Without reasonably clean, complete data, AI is just garbage in, garbage out. And without encoding careful ethical parameters to nurture the AI’s learning and development.

ai-friend-foe

 

AI Marketing Requires a Different Mindset

AI is already proving to be more of a friend than a foe to B2B marketers. To get the desired results, though, not only do marketers have to properly nurture AI technologies, we also have to embrace their potential—even when there’s the possibility that AI will eventually render our current jobs obsolete.

Paul Greenberg, independent consultant and author of the best-selling book CRM at the Speed of Light, points out that marketers have to embrace a very different mindset moving forward. Marketers are trained to target personas—representations of broad groups of people with similar characteristics and drivers—and produce content that appeals to them. But now customers expect an extremely high level of personalization and real-time interactions with brands across a multitude of devices and channels.

These technologies will nevertheless grow in popularity. Greenberg expects these applications to be acquired by, and absorbed into, big marketing clouds like IBM, Oracle and Salesforce, so you should expect to see them soon in your martech portfolios if you don’t have them already.

As for what we can and should expect AIM technology to do today and in the future, Greenberg points to one of his favorite marketing videos of the past few years, Corning’s “A Day Made of Glass.” This video was not produced using AI, but it’s the kind of thing Greenberg believes we can and should be leveraging AI to create. He says the Corning video brilliantly connects with consumers’ emotions to create a human-centric vision of the future with extremely broad appeal.

“AIM technology can already do this if we ask it to. It’s not way off in the future,” Greenberg says. “Intelligent systems can already make the connections between systems of record and systems of engagement to understand what would emotionally engage people. These technologies can independently test and learn from people’s reactions enough to change their approach and build highly personalized, relevant and contextual custom content for prospects and customers in real time.”

So is AI a friend or a foe to B2B marketers? The answer is yes. AI is what you make it. It all goes back to how you feed, nurture and train your intelligent systems. Just as you wouldn’t toss a five-year-old child into a mosh pit to learn how to dance, you can’t leave AIM technology on its own to learn from unlimited inputs and “dirty” data sources. If you train it well and use it to build interconnections across your organization and beyond, AIM can significantly augment your human marketing team’s capabilities to anticipate and connect with much larger and more diverse audiences over time.

Source: Artificial Intelligence Potentials in B2B Marketing – D&B

Marketing Transformation and Artificial Intelligence

Artificial Intelligence and Marketing
Artificial Intelligence and Marketing

Here are seven examples where artificial intelligence is transforming marketing:

1. Content curation

Predictive analytics allows Netflix to optimize its recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription.

Uniting information from diverse datasets is a common use of AI.

Under Armour is one of the many companies to have worked with IBM’s Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc.

The result is the ability for the brand to offer up relevant (personalized) training and lifecycle advice based on aggregated wisdom.

2. Search

In 2015, Google admitted it was using RankBrain, an AI system, to interpret a ‘very large fraction’ of search queries. RankBrain utilizes natural language processing (NLP) to help find relevance in content and queries, as well as better interpretation of voice search and user context (e.g. Google Now).

3. Predictive customer service

Knowing how a customer might get in touch and for what reason is obviously valuable information.

Not only does it allow for planning of resource (do we have enough people on the phones?) but also allows personalization of communications.

Another project being tested at USAA uses this technique. It involves an AI technology built by Saffron, now a division of Intel.

Analyzing thousands of factors allows the matching of broad patterns of customer behavior to those of individual members.

4. Ad targeting

As Andrew Ng, Chief Scientist at Baidu Research, tells Wired, “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.”

Optimizing bids for advertisers, algorithms will achieve the best cost per acquisition (CPA) from the available inventory.

When it comes to targeting of programmatic ads, machine learning helps to increase the likelihood a user will click. This might be optimizing what product mix to display when retargeting, or what ad copy to use for what demographics.

5. Customer segmentation

Plugging first- and third-party data into a clustering algorithm, then using the results in a CRM or customer experience system is a burgeoning use of machine learning.

Companies such as AgilOne are allowing marketers to optimize email and website communications, continually learning from user behavior.

6. Sales forecasting

Conversion management again, but this time using inbound communication.

Much like predictive customer service, inbound emails can be analyzed and appropriate action taken based on past behaviors and conversions.

Should a response be sent, a meeting invite, an alert created, or the lead disqualified altogether? Machine learning can help with this filtering process.

7. Image recognition

Google Photos allows you to search your photos for ‘cats’. Facebook recognizes faces, as does Snapchat Face Swap.

Perhaps the most exciting implementation of image recognition is DuLight from Baidu…Designed for the visually impaired, this early prototype recognizes what is in front of the wearer and then describes it back to them.

The rise of the chief marketing technologist | IBM THINK Marketing

marketingtechnologist

Source: The rise of the chief marketing technologist – IBM THINK Marketing

This article was written by Marco Antonio Cavallo from CIO and was legally licensed through the NewsCred publisher network.

Whenever we hear the word “digitalization,” we must understand that it is the sound of inevitability and irreversibility. The digital economy isn’t on the horizon anymore, it′s here and it is here to stay. It’s no longer a secret that the digital economy is changing the world at an unprecedented rate. Companies that are looking to succeed in this fast emerging new economy must transform themselves by reinventing their business models, strategies, processes, and practices, and that impacts on the roles of all of its employees, as well as bringing departments to work together, once everyone is more and more dependent of technology to function.

It’s no surprise that marketing is rapidly becoming one of the most technology-dependent functions across all businesses. Gartner has predicted that by 2017, a company’s chief marketing officer (CMO) would be spending more on technology than its CIO, and that is becoming more credible every day, as many CMOs have adopted technology in their everyday activities, showing that technology became the core of marketing nowadays. Every year, CMOs are globally directing their budgets to the usage of technology or software in many different marketing areas.

IDC Research has released a few predictions on how marketing will strategically use technology to accelerate client acquisition, brand awareness, to gather and analyze market and customer information and even to optimize its operational efficiency in order to generate more revenue for companies and be more accurate when directing resources, mainly by enhancing customer experience.

1. In 2017, CMOs will spend more on content marketing assets than on product marketing assets: For decades, the product launch has reigned as the kingpin content event. With a “bill of materials” stretching through multiple Excel pages, product marketing assets suck up a major portion of the marketing budget – and much of that content is wasted. The days of product content dominance are numbered. Product content will remain important, but it will take its place behind the content marketing assets matched to decision-journey stages.

  1. By 2020, 50 percent of companies will use cognitive computing to automate marketing and sales interactions with customers: A few leads go right to sales. But the majority need further qualification and extended nurturing. Companies will increasingly turn to smart systems that automatically assess and respond to buyers at the point of need. IBM recently added Watson to its marketing cloud offerings. The question is not when cognitive marketing will become mainstream – but rather, will anyone notice?
  2. In 2017, 20 percent of large enterprise CMOs will consolidate their marketing technology infrastructure: Marketing has been absorbing marketing technology a bite at a time for more than a decade. Many organizations now manage dozens (if not hundreds) of point solutions. Just as marketing environments are hitting the wall of this operational complexity, marketing tech vendors are building solid integrated platforms – able to be tailored through a partner eco-system. A fortuitous convergence of supply and demand.
  3. By 2018, predictive analytics will be a standard tool for marketers, but only a third will get optimal benefit: Early adopters of predictive analytics for buyer behavior report amazing results. The benefits come from the ability to discover hidden segments that have a high propensity to buy. Marketers can also better serve these segments with behavioral targeting. However, the majority of marketers face big challenges to achieving the benefits. Chief inhibitors? Lack of statistical skills, stubborn organizational silos that won’t integrate data, and a culture that resists truth when it goes against tradition.
  4. By 2018, 50 percent of CMOs will make significant structural changes to their “intelligence” operations and organizations: “Intelligence” as a capability is growing in importance in modern marketing organizations. Intelligence includes market intelligence (MI), business intelligence (BI), competitive intelligence (CI), and social intelligence (SI). In the past, these four functions were spread around the enterprise. Now, IDC sees more companies consolidating into a larger, single, intelligence group – often combining with intelligence functions from other areas like sales. The elimination of silos in this important area is a positive sign.

With that perspective, it is clear that technology has turned a black art into hard science. Marketing now must be well versed in customer data, analytics, mobile, social and marketing automation tools, and that requires new type of executive. The Chief Marketing Technologist is emerging at the center of this transformation as a part strategist, part creative director, part technology leader, and part teacher professional. Its mission is very clear: align marketing technology with business goals, serving as a liaison to IT, and evaluating and choosing technology providers. About half are charged with helping craft new digital business models as well.

The best CMTs are able to set a technology vision for marketing in the digital age. They champion greater experimentation and more agile management of that function’s capabilities, as well as act as transformation agents, working within the function and across the company to create competitive advantage and collaboration. It is not difficult to enlist some of the main reasons why this new executive has emerged:

  • Software became the chief means of engaging prospects and customers: A marketing team’s choice of software and how to configure and operate it, along with how creatively the team applies it, materially affects how the firm perceives and influences its audience and how the audience sees the firm.
  • Digital marketing and e-commerce skills: once those two methodologies increasingly augment or replace traditional touch points, the importance of mastering those capabilities grows. Digital marketing budgets are expanding annually at double-digit rates, and CEOs say that digital marketing is now the most important technology-powered investment their firms can make.
  • The rise in digital budgets: it is not merely a migration of spending from traditional to digital media. A growing portion of marketing’s budget is now allocated to technology itself. A recent Gartner study found that 67% of marketing departments plan to increase their spending on technology-related activities over the next two years. In addition, 61% are increasing capital expenditures on technology, and 65% are increasing budgets for service providers that have technology-related offerings.
  • Efficiently manage all this technology: there are now well over 1,000 marketing software providers worldwide, with offerings ranging from major platforms for CRM, content management, and marketing automation to specialized solutions for social media management, content marketing, and customer-facing apps. Relationships with agencies and service providers now include technical interfaces for the exchange and integration of code and data. And bespoke software projects to develop unique customer experiences and new sources of advantage are proliferating under marketing’s umbrella.

The reason why this is a growing role within companies is very simple. In this new digital economy environment, the CMO and the CIO must collaborate closely, although this executive-level cooperation isn’t just enough. A supporting organizational structure is also needed and vital for this collaboration to work properly. A company can’t simply split marketing technology down the middle and declare that the CMO gets the marketing half and the CIO gets the technology half. Such division might look good on paper, but it leaves yawning knowledge gaps in practice.

Marketing might not understand how to fully leverage what IT can offer, and IT might not understand how to accurately translate marketing requirements into technical capabilities. Instead, marketing technology must be managed holistically. In a virtuous cycle, what’s possible with technology should inspire what’s desirable for marketing, and vice versa. The right structure will help marketing become proficient with the array of software it must use to attract, acquire, and retain customers. It will help marketing leadership recognize how new technologies can open up new opportunities and allow marketing to deftly handle the technical facets of agency and service provider relationships in both contract negotiations and day-to-day operations.

The chief marketing technologist role itself is already an acknowledgement of just how important the marketing group is to driving revenue within the organization and, when properly resourced, how today’s marketing information systems are driving the current and future growth of the business. Only by bringing the CIO and the CMO together can the CEO have a complete picture of what insights must be acted upon quickly in order to establish or maintain the top market position. In a nutshell, the power comes from the intersection between marketing and IT.

Today, companies can no longer afford separate silos between marketing and IT. The rapid collapse of these silos means that one person must be able to converse seamlessly between both groups. While many CMOs are getting their arms around the technology side of their business, the natural evolution of this role is for the CIO to improve its marketing skills in order to grow into the Chief Marketing Technologist role. The faster we embrace these trends, the bigger the impact we will have on our bottom line. This is the imminent future of the industry, and it’s the reason chief marketing technologists will be in high demand within 2017 and in the years to come.

Will You Lose Your Job to Artificial Intelligence? Here’s What the Experts Really Think | Inc.com

Credit Getty Images

By Kevin J. Ryan is a staff writer for Inc. He has Kevin J. Ryan is a staff writer for Inc. He has written for ESPN The Magazine and the Long Island Press and contributed to Mental Floss. He lives in Queens, New York.

An insurance firm in Japan is replacing 34 claims adjusters with artificial intelligence. What does it mean for the future of work?
A few months into my time as an insurance claims adjuster, a customer called and said he thought his house was breaking apart. He’d heard what sounded like wood beams snapping in the basement, so he went downstairs and crept into the crawl space to investigate.

While he was lying in the dark surrounded by cement, he told me, he started to panic. It brought him back to years earlier when, as a firefighter with the New York City Fire Department, he served as a first responder on September 11, crawling through the giant blocks of brick and mortar that had collapsed hours earlier.

This revelation came 10 or 15 minutes into our conversation, after I’d gathered his basic information and logged the details of the case. He’d been friendly, even lighthearted (if a little excitable) for the first part of it. But recalling his moment of anxiety, his voice quivered.

To an extent, I was able to relate. My father was a captain in the FDNY on September 11. He, too, spent the hours, days, and weeks that followed at Ground Zero, digging around in the rubble and coming home covered in dust. I know that he, too, has painful memories stored away in the deep parts of his mind.

I relayed this to the man on the other line. We talked for a few minutes about that day and its aftermath, and then I brought the conversation back to what was going to happen with his claim. He asked if it was OK if he called back just to chat if he needed to.

A few hours later, he did just that. I don’t remember exactly what we talked about, but I remember he was calm, he used my name a lot, and he thanked me.

These are the experiences I’d argue that artificial intelligence cannot fully replace. Or can it?

Case in point: Fukoku Mutual, an insurance firm in Japan, is replacing 34 claims adjusters with A.I. According to a press release from the company, the system, which uses the technology found in IBM’s Watson, will be able to study factors including the length of a hospital stay, procedures performed, and the patient’s medical history to determine a payout.

Benefits of using the system, according to the release, include improving operating efficiency by 30 percent. Japanese publication The Mainichi reports that it will save Fukoku Mutual $1.2 million (140 million yen) in wages annually.

Fukoku is just the latest example of a company testing the promises of artificial intelligence. It’s clear that A.I. is getting increasingly sophisticated at doing what humans do–but more efficiently and cheaply. What’s less clear is whether those gains trump the huge implications it would have for the future of work.


A.I. gets smarter

There’s a big temptation for businesses to use artificial intelligence to shave off time and money wherever they can, but experts say that’s not the smartest use of the technology.

“I actually think that’s the worst reason to automate things,” says Josh Sutton, global head of data and A.I. at marketing giant Publicis-Sapient. “Over time, it’s a losing proposition. It’s a race to the bottom.” Improving the customer experience and creating new revenue sources are much better applications of A.I., he says.

In the case of Fukoku Mutual, the more noble objective is getting customers a decision–and a payout–faster, which the company claims the system will accomplish. The company I worked for, which was one of the largest insurance providers in the U.S., made this the No. 1 priority, citing evidence that customer satisfaction is most closely related to the speed with which a payout is made.

But, as adjusters, we were also trained incessantly on our bedside manners. Someone who calls a home insurance firm is usually in a vulnerable place–and sometimes in full-blown crisis mode–so it was essential that a touch of humanity was included.

The question, then, is whether computers will ever have an emotional intelligence interchangeable with that of humans.
“There’s no question in my mind that the technology is moving in that direction,” says David Schatsky, managing director and emerging technology analyst at Deloitte. “A.I. can already perceive and understand emotional cues, based on factors like tone of voice and word choice. And it’s getting better at projecting them back at users.” So what does this mean for the work force? Until now, many of the jobs that have been displaced by machines are of the manual-labor kind: bots that fulfill orders in Amazon warehouses, for example, and machines that move products along an assembly line. The assumption has been that those jobs that require more training would be safe for some time. Arguably, that’s no longer the case.

“We’re starting to see the effects of technology automating cognitive work–things we used to think only people could do,” Schatsky says.

A 2015 Forrester analysis predicted automation would replace 25 percent of all job tasks by 2019. Losing some specific roles, like the country’s 1.6 million truck-driving jobs, seems like a foregone conclusion with the rise of self-driving cars. (Recently, an autonomous tractor trailer from Otto completed a 120-mile beer delivery.) Other occupations are using A.I. in tandem with people: Lawyers use software that can analyze cases and search for relevant past rulings; pharmaceutical firms use algorithms to aid in drug discovery.

Some experts argue that even humans in some of the highest-paying roles could become redundant. Last year, a team of British and American researchers fed an A.I. system the details of a series of court cases. The computer reached the same verdict as the judge 79 percent of the time. Other jobs, like those in the medical field, could eventually be replaced by faster, more accurate machines.
“Many of us in the A.I. field believe that physicians will be replaced long before nurses are replaced,” says Andrew Moore, dean of Carnegie Mellon’s School of Computer Science. ”

The parts of medical care to do more with interacting with the patients, making them comfortable, and communicating clearly with them are going to turn out to be the things that only humans can do well. The diagnostics, coming up with a theory as to what’s going on, or what a good subsequent test would be–those are the things that look very promising for automation. Compared with human experts, computers are doing a very good job.”

What happens next?

If it’s cost effective, it’s hard to imagine companies passing up the opportunity to take advantage of A.I. That’s especially true for positions like customer service reps, which require problem solving and a strong knowledge of the company’s operating standards. “The cost of both finding and retaining people with that combination of skills is pretty high,” Moore says.

At the insurance company where I worked, for example, training took anywhere from six to 12 weeks, and the employees turned over at a very high rate–sometimes just months after the program, which included a weeklong trip to company headquarters, was complete.
Sourcehttp://www.inc.com/kevin-j-ryan/artificial-intelligence-replaces-insurance-workers.html

Marketing and Artificial Intelligence

Artificial Intelligence and Marketing

7 examples where artificial intelligence is transforming marketing:

1. Content curation

Predictive analytics allows Netflix to optimize its recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription.

Uniting information from diverse datasets is a common use of AI.

Under Armour is one of the many companies to have worked with IBM’s Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc.

The result is the ability for the brand to offer up relevant (personalized) training and lifecycle advice based on aggregated wisdom.

2. Search

In 2015, Google admitted it was using RankBrain, an AI system, to interpret a ‘very large fraction’ of search queries. RankBrain utilizes natural language processing (NLP) to help find relevance in content and queries, as well as better interpretation of voice search and user context (e.g. Google Now).

3. Predictive customer service

Knowing how a customer might get in touch and for what reason is obviously valuable information.

Not only does it allow for planning of resource (do we have enough people on the phones?) but also allows personalization of communications.

Another project being tested at USAA uses this technique. It involves an AI technology built by Saffron, now a division of Intel.

Analyzing thousands of factors allows the matching of broad patterns of customer behavior to those of individual members.

4. Ad targeting

As Andrew Ng, Chief Scientist at Baidu Research, tells Wired, “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.”

Optimizing bids for advertisers, algorithms will achieve the best cost per acquisition (CPA) from the available inventory.

When it comes to targeting of programmatic ads, machine learning helps to increase the likelihood a user will click. This might be optimizing what product mix to display when retargeting, or what ad copy to use for what demographics.

5. Customer segmentation

Plugging first- and third-party data into a clustering algorithm, then using the results in a CRM or customer experience system is a burgeoning use of machine learning.

Companies such as AgilOne are allowing marketers to optimize email and website communications, continually learning from user behavior.

6. Sales forecasting

Conversion management again, but this time using inbound communication.

Much like predictive customer service, inbound emails can be analyzed and appropriate action taken based on past behaviors and conversions.

Should a response be sent, a meeting invite, an alert created, or the lead disqualified altogether? Machine learning can help with this filtering process.

7. Image recognition

Google Photos allows you to search your photos for ‘cats’. Facebook recognizes faces, as does Snapchat Face Swap.

Perhaps the most exciting implementation of image recognition is DuLight from Baidu…Designed for the visually impaired, this early prototype recognizes what is in front of the wearer and then describes it back to them.

Data Driven V. Predictive Marketing: BEWARE JETSON’S MARKETING!

The Big Willowby: Charlie Tarzian

My son came to me one day in early December and said:  ‘Hey, Dad, let’s get Mom one of those robotic vacuum cleaners.  You know, the ones you switch on and they vacuum your whole floor!’  He could not contain his enthusiasm – this was going to be great – no one would have to vacuum our floors ever again!!!

So we went to Amazon (of course) and two days later our round disc of a maid showed up via FEDEX.

Come Christmas Day, the robot fully charged, off we went to the kitchen to marvel at what was certain to be a life changing event.  We turned it on and put it down on the floor and the vacuum swung into action.  It crossed the floor, sensed it was coming to a wall, made a pivot, chugged in another direction…and got stuck on the slight incline by the fireplace…then stuck again on the floor mat by the stove…then got caught between a chair and a table and went into a break dance that would make R2D2 jealous.

I bring this up because a colleague sent me this little snippet from the website of a Predictive Marketing vendor:

“Predictive Marketing doesn’t need to be a services heavy engagement to get you up and running. With CompanyX (name of company withheld) and our push button integration, we can integrate with your existing systems and build your predictive model in under a day. – See more at: http://www.companyx.com/what-we-offer/#sthash.vBzkU18n.dpuf

There you have it: Jetson’s Marketing – just push our one little button and off you go:  great leads, great meetings, great website experiences – in fact all your marketing/sales problems solved in ‘under a day’.  All that is left to do is fire your staff, except for that one person in charge of pushing the button when you run out of leads, meetings and website visitors.

Look, I know what I don’t know, but I can tell you this: whatever you’re thinking the new generation of transformative marketing is – one thing it isn’t is automated bliss.  It takes a fair sized village to make things happen.  And herein lies the huge disconnect between data driven marketers and the shiny new object called Predictive Marketing.  Data driven marketers know that data can and should be utilized across the marketing/sales continuum – but it is more about data orchestration than anything else.  Therefore, one button, add water and stir does not take into consideration any of the cause and effect across all the communications and transactive channels that marketers rely on.

Marketing is services heavy (sorry, Company X) because at the nexus of MarTech, AdTech and Sales Enablement sits quite a bit of cause and effect.  And unless you aspire and build towards using predictive data to positively impact all channels aligning as one – then what you are predicting is a very small sliver of what could be.  In other words, if the connectivity and synapsis among outward facing channels are not orchestrated and optimized using predictive data and you are not feeling good that all channels are working in sync – then how can you predict a scaled outcome?  The predictions you are making will reflect a small percentage of the whole – and so instead of widening your funnel and increasing your opportunities along every step in a buying journey, you are narrowing that funnel based on a flawed assumption that you are predicting against a full boat of reliable data.

On the other hand, Data Driven Marketing  sets up to be based on solving for the cause and effect of what is less than optimized (can anyone say, broken?)  It attempts to determine (not predict) what works and doesn’t and then – as a village – cohesively knits together a response to results that can be repeatable but certainly is not a just add water, one button pushed result.

So – are we starting to see a difference:  Predictive Marketing – a push button approach to a complicated set of executional events and response, or, Data Driven Marketing – a human driven (sorry robots!) approach to the cause and effect of humans communicating to other humans about things that may or may not be important to the recipient (we always hope for the former)?

What do you think?  We would love to know.  Have any stories to share – we would love to hear from you.

by: Charlie Tarzian, Founder, The Big Willow

Inside The Growing Social Media Skills Gap – FastCompany

Fast Company LogoBY RYAN HOLMES

On February 4, 2004, a handful of Harvard students logged onto a newly launched website called thefacebook.com. Just a dozen years later, some 2 billion people—nearly a third of the planet’s population—are social media users.

So if companies are having trouble keeping up with that pace of adoption, it’s no surprise. Businesses have overcome their earlier skepticism and raced head-on into the social arena, chasing the estimated three-quarters of consumers who now say social media influences their buying decisions. Nearly 90% of U.S. companies are currently using Twitter, Facebook, and other networks—all jockeying for their share of the estimated $1.3 trillion in value that social media stands to unlock.

There’s just one small problem: The contemporary workforce is woefully ill-equipped to help companies unlock it.

THE SKILLS GAP YOU HAVEN’T HEARD OF

While social media races ahead, formal training and education programs are lagging seriously behind. If that isn’t making headlines, it’s testament to social media’s comprehensive mainstreaming: “Facebook? I use that everyday. Who needs to be trained in it?”

Yet a meager 12% of the 2,100 companies in a 2010 Harvard Business Review survey said they’re using social media effectively. And more recent research by Capgemini and others show that confidence gaining only incrementally.

IN A SHORT TIME . . . SOCIAL MEDIA DUTIES HAVE BEEN RADICALLY DEMOCRATIZED AND DECENTRALIZED [WITHIN COMPANIES].

Reports of social media gaffes and blunders in the workplace are still routine. Meanwhile, the real price of the skills gap often goes unnoticed—billions of dollars in missed opportunities and lost revenue.WHAT’S BEHIND THE SHORTFALL

The clearest culprit is the breakneck proliferation of new platforms and features. Around a year ago, Snapchat was still a toy for teens to trade disappearing messaging; today it’s the latest way to reach young customers on their own turf. As more platforms incorporate more sophisticated features, even the most plugged-in users are struggling to keep up.

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At the same time, how social media is used in the workplace is fundamentally changing. Just a few years ago, social media in the office was the domain of specialized social media managers, the gatekeepers who owned a company’s public face on the leading platforms. In a short time, however, social media duties have been radically democratized and decentralized. The number of job descriptions on Indeed.com mentioning social media skills is booming: “[We’re] seeing this demand span many levels, from executive assistants to senior vice presidents,” Amy Crow, Indeed’s then communication director told Quartz a few years ago.

Since then, employees have been asked to use social media in ever more numerous and unfamiliar ways. The standard marketing functions are just the tip of the iceberg. Social tools are being used to streamline customer service, drive sales, improve HR processes, and build employee brand advocacy programs.

Meanwhile, platforms like Facebook at Work (in beta now and expected to roll out this year) and Slack (which boasts millions of users, from NASA to your corner coffee shop) are quickly changing how workers collaborate. By bringing social messaging inside the office, these technologies are breaking down silos and boosting productivity (although some disagree). Social media is no longer a discrete thing that certain people do in certain jobs, and more of an integral component of work itself.

BECAUSE SOMEBODY GROWS UP BEING A SOCIAL MEDIA NATIVE, IT DOESN’T MAKE THEM AN EXPERT IN USING SOCIAL MEDIA AT WORK.

But this approach only works if employees are on board and up to speed. “The real problem is that we expect people to know these skills without providing any training,” William Ward, professor of social media at Syracuse University, recently told me. Social media know-how isn’t something you just pick up as a casual user. And it isn’t just older employees who are in the dark—millennial hires need training, too.”

Because somebody grows up being a social media native, it doesn’t make them an expert in using social media at work,” Ward says. “That’s like saying, ‘I grew up with a fax machine, so that makes me an expert in business.’”

BRIDGING THE SOCIAL GAP

Fixing this social skills gap is no small task. In the long term, social media coursework is slowly being incorporated into university programs, and not just for students pursuing marketing and communications degrees. Here at Hootsuite, for instance, we’ve developed a social media syllabus that’s now being used in more than 400 universities around the world by 30,000 students. Programs like these offer a foundation of social media skills for the workplace and may one day be as commonplace as introductory college writing and computer skills classes.

But what about employees struggling right now with the growing demands of social business? The good news is that companies are beginning to acknowledge social media literacy as a critical job skill (just like Internet and basic computer literacy back in the day) and are starting to offer on-the-job training programs. Altimeter reports that almost half of the companies it surveyed are planning on rolling out some kind of internal social education program for employees, while overall spending on corporate training is on a serious upswing, rising 15% in the U.S. in a recent year to $70 billion.

The challenge, of course, is how to teach social media in such a mercurial environment. In the last year alone, for instance, we’ve seen the meteoric rise of “social video” and a whole new crop of one-to-one messaging apps, while Twitter has struggled to reinvent itself.

But few employees have time for in-depth courses or bootcamps. Ultimately, the right training solution needs to be on-demand and mobile-friendly. Currently, some of the bestpaid options are coming not from traditional educational sources, but from companies immersed in the social and digital media space, offering real lessons from the front lines. (Hootsuite’s own online course, Podium, is one free alternative, with 50,000 users and counting.)

TWITTER, FACEBOOK, INSTAGRAM, AND OTHER NETWORKS AREN’T GOING AWAY . . . [AND] SOCIAL MEDIA BUDGETS AT COMPANIES ARE EXPECTED TO DOUBLE IN THE NEXT FIVE YEARS.

Ultimately, though, any investment in upgrading social media skills in the workplace is likely to be money well spent. Twitter, Facebook, Instagram, and other networks aren’t going away. Social business has become business as usual. Indeed, social media budgets at companies are expected to double in the next five years.

To avoid throwing good money after bad, companies need to ensure that their employees actually know how to use new and emerging social technologies. Those that succeed in closing the social media skills gap will discover new ways to reach and retain customers, engage and recruit employees, and boost productivity. Those that fail will miss out on their chunk of a multitrillion-dollar pie, and might not be around long enough to regret it.

Google’s Artificial Intelligence Masters Atari Video Games

atari-brainDeep learning, one of the hottest topics today in artificial intelligence (AI), has taken another leap forward with DeepMind’s latest announcement.

Source: Google’s Artificial Intelligence Masters Atari Video Games

Think you’re good at classic arcade games such as Space Invaders, Breakout and Pong? Think again.

In a groundbreaking paper published yesterday in Nature, a team of researchers led by DeepMind co-founder Demis Hassabis reported developing a deep neural network that was able to learn to play such games at an expert level.

What makes this achievement all the more impressive is that the program was not given any background knowledge about the games. It just had access to the score and the pixels on the screen.

It didn’t know about bats, balls, lasers or any of the other things we humans need to know about in order to play the games.

But by playing lots and lots of games many times over, the computer learned first how to play, and then how to play well.

A Machine That Learns From Scratch

This is the latest in a series of breakthroughs in deep learning, one of the hottest topics today in artificial intelligence (AI).

Actually, DeepMind isn’t the first such success at playing games. Twenty years ago a computer program known as TD-Gammon learned to play backgammon at a super-human level also using a neural network.

But TD-Gammon never did so well at similar games such as chess, Go or checkers.

In a few years time, though, you’re likely to see such deep learning in your Google search results. Early last year, inspired by results like these, Google bought DeepMind for a reported $400 million.

Many other technology companies are spending big in this space. Baidu, the “Chinese Google”, set up the Institute of Deep Learning and hired experts such as Stanford University professor Andrew Ng. Facebook has set up its Artificial Intelligence Research Lab which is led by another deep learning expert, Yann LeCun. And more recently Twitter acquired Madbits, another deep learning startup.

The Secret Sauce of Deep Learning

Geoffrey Hinton is one of the pioneers in this area, and is another recent Google hire. In an inspiring keynote talk at last month’s annual meeting of the Association for the Advancement of Artificial Intelligence, he outlined three main reasons for these recent breakthroughs.

First, lots of Central Processing Units (CPUs). These are not the sort of neural networks you can train at home. It takes thousands of CPUs to train the many layers of these networks. This requires some serious computing power.

In fact, a lot of progress is being made using the raw horse power of Graphics Processing Units (GPUs), the super fast chips that power graphics engines in the very same arcade games.

Second, lots of data. The deep neural network plays the arcade game millions of times.

Third, a couple of nifty tricks for speeding up the learning such as training a collection of networks rather than a single one. Think the wisdom of crowds.

What Will Deep Learning Be Good For?

Despite all the excitement about deep learning technologies, there are some limitations to what it can do.

Deep learning appears to be good for low-level tasks that we do without much thinking. Recognizing a cat in a picture, understanding some speech on the phone or playing an arcade game like an expert.

These are all tasks we have “compiled” down into our own marvelous neural networks.

Cutting through the hype, it’s much less clear if deep learning will be so good at high level reasoning. This includes proving difficult mathematical theorems, optimizing a complex supply chain or scheduling all the planes in an airline.

Where Next for Deep Learning?

Deep learning is sure to turn up in a browser or smartphone near you before too long. We will see products such as a super smart Siri that simplifies your life by predicting your next desire.

But I suspect there will eventually be a deep learning backlash in a few years time when we run into the limitations of this technology. Especially if more deep learning startups sell for hundreds of millions of dollars. It will be hard to meet the expectations that all these dollars entail.

Nevertheless, deep learning looks set to be another piece of the AI jigsaw. Putting these and other pieces together will see much of what we humans do replicated by computers.

If you want to hear more about the future of AI, I invite you to the Next Big Thing Summit in Melbourne on April 21, 2015. This is part of the two-day CONNECT conference taking place in the Victorian capital.

Along with AI experts such as Sebastian Thrun and Rodney Brooks, I will be trying to predict where all of this is taking us.

And if you’re feeling nostalgic and want to try your hand out at one of these games, go to Google Images and search for “atari breakout” (or follow this link). You’ll get a browser version of the Atari classic to play.

And once you’re an expert at Breakout, you might want to head to Atari’s arcade website.