Words like change, transformation, and automation will always generate a wide range of reactions throughout the rank and file of an organization, and our reaction to these words are good indicators as to which group we belong to on the adoption curve.
Innovators and early adopters tend to be motivated by change and late stage adopters and laggards tend to resist it. Paradoxically, both groups have roles of equal importance in the user adoption process.
Knowing where users and people belong on the adoption curve and organizing them into user groups will enable a phased approach to managing the user adoption process. This approach to user adoption is preferred and will provide a more seamless diffusion of your innovation throughout each stage of the user adoption process. Furthermore, this will ensure that by the time it reaches the laggard group, the bugs will have already been worked out.
Successful innovations will reach a tipping point – which is the point that it is widely accepted and adopted by laggards. However, by the time that happens, count on the next innovation already being in play, and have a plan in place to repeat the cycle of innovation again.
Documenting business processes, conducting frequent business process reviews, building and running knowledge management and collaboration portals, establishing a talent management program, and investing in the professional development of your people will have a direct impact to continued success of any future business transformation initiatives in the future.
As companies transform, it is important to retain a high level of diversity across the organization. Take into account the tacit knowledge that could be lost when choosing to acquire new talent.
Make the language of change pervasive throughout your organization and create a business culture that is comfortable with change and ready to adapt to it.
On a personal level, be prepared for change, because change will happen. Invest time in your own professional development. Always be learning and be willing to step out of the comfort zone.
For more on this topic, I suggest reading the following two books:
A lot of small business proprietors are jumping on the blogging bandwagon and there is a very good reason for this. A blog is a really effective tool for your small business, if used properly. You’re most likely asking yourself if a blog can truly aid your business or if it is simply an additional […]
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 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.
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.
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.
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.
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?
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.
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.
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.
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.
Online advertising is changing with the change in user behaviour and the technology and so we witness so many ways the ads reach us. This change is obvious and advertisers must have go with the flow for better conversions.
IP targeting in online advertising is relatively new, but is believed to be highly effective practice to deliver the ads to specific audience. Using IP targeting ad delivery method advertisers can deliver their ads to the user of one specific network.
A step further, you can deliver your ads to specific buildings even. That’s what El Toro helps you to do.
Explaining the technical terms in non-technical way, let me tell what exactly happens. El Toro maps an IP address to the physical address, i.e. XXXX IP has been mapped to XXX Airport. So, an advertiser who wants to target travellers can send the targeted advertisements based on the publicly-available data available for that physical address.
As an advertiser, you do not need to know the IP details etc. All you need to do is to provide a list of physical addresses where you want your ads to be delivered and El Toro will place your ads on the devices that are using the IPs on that particular location.
The next question that comes in mind is, where the ads will actually appear. El Toro claims to have access to +1,000,000 websites to place ads which altogether make an inventory of 30-50 billion advertising impressions per day. El Toro also provides detailed reports on where your ads were served, how many impressions and click they received and much more.
We have already witnessed so many improvements in online advertising, and this one is certainly going to deliver some real value of money. If you want to check an alternative of PPC for your business, do try El Toro and keep us posted with what you get there.
El Toro in Their Own Words:
Surface your digital ads on every computer at a specific location using only its physical mailing address. By mapping over 160 million home and business IP addresses in the United States back to their corresponding physical mailing address, Eltoro.com can surface online display ads to almost any household or business using IP targeting technology.
What Brings El Toro to the Spotlight:
Targeting prospects right in the specific building, home, airport, university, or any other Internet-equipped location is something new and noteworthy. Certainly, a must try for all internet advertisers