3 Fundamental Areas of Ecommerce You Should Never Skimp on

Business, Ecommerce, Entrepreneur
Image Credit: Shutterstock

PRATIK DHOLAKIYA
CONTRIBUTOR
Co-Founder of E2M

The writing has been on the wall for years now: Retail is moving, en masse, to the internet. Ecommerce sales in the United States have been increasing steadily over the past decade and are predicted to top $4 trillion by 2020.

Related: Ecommerce Basics: 10 Questions to Ask When Creating an Online Store

One of the harsh realities of this widespread shift in consumer behavior is that the very concept of competition has changed. Retailers are no longer just competing with stores near their geographic location. They are going up against everyone else throughout their industry.

What’s more: In the incredibly fast-paced nature of online browsing, potential customers are quick to jump to conclusions. With so many options for them to choose from, the first impressions that you, as retailer, give them, are everything. Oftentimes, it’s these (seemingly) small elements that play a huge factor in people’s purchasing decisions.

With this in mind, there are several crucial components which you must invest heavily in, to keep a competitive edge. Here are three of the big ones.

Visual design

All kinds of studies have been conducted on how quickly the human brain processes images. While the conclusions vary, one common one says that people process visual effects much faster than text. Therefore, it’s worthwhile to budget for high-resolution images.

In ecommerce, after all, presentation is everything. Regardless of where you place photos on your site, they will inevitably be the points to which people’s eyes gravitate. Right off the bat, these pictures should be captivating and provide an introduction to what your brand is all about.

Even more, those photos should clarify the visual hierarchy of your content, to persuade visitors to take further action; photos should never just be decoration. In addition to capturing attention, these images should help advance the navigation and flow of your ecommerce site to get consumers on the path to conversion.

If you’re not a professional photographer, there are plenty of resources online providing you high-quality images. You can browse the vast libraries of royalty-free stock photos to find visual effects that fit your exact needs.

Store functionality

Your home page is more than just the first impression of your online business. It is the face of your brand and the central hub for all of your operations. Going the cheap route here can quickly render the rest of your efforts pointless.

Related: Shopping Cart Throw-down: Which Ecommerce Platform Reigns Supreme?

Luckily, we live in a time when you don’t need to pay an arm and a leg to have a fully functional ecommerce homepage. DIY ecommerce platforms like Shopify will supply the critical functionality you need — such as a shopping cart, inventory tracking, payment records and hosting — while providing a mobile-friendly or multi-device storefront

For more complex or larger stores, bringing in a few expert developers may be necessary. And this can benefit the bottom line. “Online shopping behavior is truly unpredictable. It’s amazing how a minuscule tweak on an ecommerce homepage can result in a huge increase of sales,” says Tristan King, founder of Blackbelt Commerce.

Developers provide specialist services in the form of web development, UX consultancy and management for ecommerce sites built on Shopify. Such platform-specific specialization allows for the discovery of subtle changes that can help you grow sales to the next level.

Ultimately, the home page of any website should be designed in a way that encourages visitors to dive deeper into the store’s offerings — be they categories, products or the latest end-of-season sale. If you’re going to invest heavily in your online business, this is the place to do it.

Marketing copy

Ecommerce and content marketing go hand in hand, both on-site and off. Once you’ve wowed visitors with compelling images and assured them with reviews about reliable user experience, you’ve got to prioritize the written content that makes the sale. This is where you’ll truly provide value for your visitors.

By nature, the writing of marketing copy is a subjective art. However, there are many universal blunders that separate the good from the bad. On an ecommerce website, the need for topnotch writing is foremost, in terms of crafting product pages, blog posts, email blasts, information pages and checkout processes. In one way or another, all of this content factors into your sales strategy. The copy you use must be flawless, concise, informative and consistent.

If you’ve been in the game for a while, you probably know just how much content is needed for your ecommerce site. To get that content, owners, many times, must dedicate a team of writers to the task, or outsource it to locals in multiple countries when the product line happens to be international.

Regardless of content needs, the most important thing is that all of your content must reflect the brand persona you’ve worked so hard to define. Online shoppers are smart and critical beings. Even the smallest blemish in your copy can wreak havoc on your credibility.

In sum, brick-and-mortar businesses have been around for centuries. Ecommerce, on the other hand, is so new that there isn’t yet a specific playbook to ensure a fruitful outcome. There may never be; and even the savviest owners may be puzzled by their online sales results.

Related: The ABCs of Writing Great Marketing Copy

Yet, while there are no guarantees with ecommerce, it’s been proven time and time again that investing the proper resources into the above three areas can do wonders to set you up for success. You should get started doing that today.

Article source: https://www.entrepreneur.com/article/298375

 

5 things everyone gets wrong about artificial intelligence and what it means for our future

Artificial Intelligence, Technology, Business, Innovation
The humanoid robot AILA (artificial intelligence lightweight android) operates a switchboard during a demonstration by the German research centre for artificial intelligence at the CeBit computer fair in Hanover March 5, 2013. REUTERS/Fabrizio Bensch

Luis Perez-Brava, MIT Innovation Program

Artificial Intelligence, Luis Perez-Brava, Innovation
Luis Perez-Brava is a Research Scientist at MIT’s School of Engineering. Alex Kingsbury

There are a lot of myths out there about artificial intelligence (AI).

In June, Alibaba founder Jack Ma said AI is not only a massive threat to jobs but could also spark World War III. Because of AI, he told CNBC, in 30 years we’ll work only 4 hours a day, 4 days a week.

Recode founder Kara Swisher told NPR’s “Here and Now” that Ma is “a hundred percent right,” adding that “any job that’s repetitive, that doesn’t include creativity, is finished because it can be digitized” and “it’s not crazy to imagine a society where there’s very little job availability.”

She even suggested only eldercare and childcare jobs will remain because they require “creativity” and “emotion”—something Swisher says AI can’t provide yet.

I actually find that all hard to imagine. I agree it has always been hard to predict new kinds of jobs that’ll follow a technological revolution, largely because they don’t just pop up. We create them. If AI is to become an engine of revolution, it’s up to us to imagine opportunities that will require new jobs. Apocalyptic predictions about the end of the world as we know it are not helpful.

Common confusion

So, what may be the biggest myth—Myth 1: AI is going to kill our jobs—is simply not true.

Ma and Swisher are echoing the rampant hyperbole of business and political commentators and even many technologists—many of whom seem to conflate AI, robotics, machine learning, Big Data, and so on. The most common confusion may be about AI and repetitive tasks. Automation is just computer programming, not AI. When Swisher mentions a future automated Amazon warehouse with only one human, that’s not AI.

We humans excel at systematizing, mechanizing, and automating. We’ve done it for ages. It takes human intelligence to automate something, but the automation that results isn’t itself “intelligence”—which is something altogether different. Intelligence goes beyond most notions of “creativity” as they tend to be applied by those who get AI wrong every time they talk about it. If a job lost to automation is not replaced with another job, it’s lack of human imagination to blame.

In my two decades spent conceiving and making AI systems work for me, I’ve seen people time and again trying to automate basic tasks using computers and over-marketing it as AI. Meanwhile, I’ve made AI work in places it’s not supposed to, solving problems we didn’t even know how to articulate using traditional means.

For instance, several years ago, my colleagues at MIT and I posited that if we could know how a cell’s DNA was being read it would bring us a step closer to designing personalized therapies. Instead of constraining a computer to use only what humans already knew about biology, we instructed an AI to think about DNA as an economic market in which DNA regulators and genes competed—and let the computer build its own model of that, which it learned from data. Then the AI used its own model to simulate genetic behavior in seconds on a laptop, with the same accuracy that took traditional DNA circuit models days of calculations with a supercomputer.

At present, the best AIs are laboriously built and limited to one narrow problem at a time. Competition revolves around research into increasingly sophisticated and general AI toolkits, not yet AIs. The aspiration is to create AIs that partner with humans across multiple domains—like in IBM’s ads for Watson. IBM’s aim is to turn what today’s just a powerful toolkit into an infrastructure for businesses.

The larger objective

The larger objective for AI is to create AIs that partner with us to build new narratives around problems we care to solve and can’t today—new kinds of jobs follow from the ability to solve new problems.

That’s a huge space of opportunity, but it’s difficult to explore with all these myths about AI swirling around. Let’s dispel some more of them.

Myth 2: Robots are AI. Not true.A worker guides the first shipment of an IBM System Z mainframe computer in Poughkeepsie, New York, U.S. March 6, 2015. Picture taken March 6, 2015. Jon Simon/IBM/Handout via REUTERS A worker guides the first shipment of an IBM System Z mainframe computer in PoughkeepsieThomson Reuters

Industrial and other robots, drones, self-organizing shelves in warehouses, and even the machines we’ve sent to Mars are all just machines programmed to move.

Myth 3: Big Data and Analytics are AI. Wrong again. These, along with data mining, pattern recognition, and data science, are all just names for cool things computers do based on human-created models. They may be complex, but they’re not AI. Data are like your senses: just because smells can trigger memories, it doesn’t make smelling itself intelligent, and more smelling is hardly the path to more intelligence.

Myth 4: Machine Learning and Deep Learning are AI. Nope. These are just tools for programming computers to react to complex patterns—like how your email filters out spam by “learning” what millions of users have identified as spam. They’re part of the AI toolkit like an auto mechanic has wrenches. They look smart—sometimes scarily so, like when a computer beats an expert at the game Go—but they’re certainly not AI.

Myth 5: Search engines are AI. They look smart, too, but they’re not AI. You can now search information in ways once impossible, but you—the searcher—contribute the intelligence. All the computer does is spot patterns from what you search and recommend others do the same. It doesn’t actually know any of what it finds; as a system, it’s as dumb as they come.

In my own AI work, I’ve made use of AI whenever a problem we could imagine solving with science became too complex for science’s reductive approaches. That’s because AI allows us to ask questions that are not easy to ask in traditional scientific “terms.” For instance, more than 20 years ago, my colleagues and I used AI to invent a technology to locate cellphones in an emergency faster and more accurately than GPS ever could. Traditional science didn’t help us solve the problem of finding you, so we worked on building an AI that would learn to figure out where you are so emergency services can find you.

By the way, our AI solution actually created jobs.

AI’s most important attribute isn’t processing scores of data or executing programs—all computers do that—but rather learning to fulfill tasks we humans cannot so we can reach further. It’s a partnership: we humans guide AI and learn to ask better questions.

Swisher is right, though: we ought to figure out what the next jobs are, but not by agonizing over how much some current job is creative or repetitive. I would note that the AI toolkit has already created hundreds of thousands of jobs of all kinds—Uber, Facebook, Google, Apple, Amazon, and so on.

Our choice is continuing the dystopian AI narrative about the future of jobs. or having a different conversation about making the AI we want happen so we can address problems that cannot be solved by traditional means, for which the science we have is inadequate, incomplete, or nonexistent—and imagining and creating some new jobs along the way.

Luis Perez-Brava is the head of MIT’s Innovation Program and a Research Scientist at MIT’s School of Engineering. He recently published ‘Innovating: A Doer’s Manifesto for Starting from a Hunch, Prototyping Problems, Scaling Up, and Learning to Be Productively Wrong.’  

Open Innovation is Alive and Well in the Pet Industry

Pets, Innovation, Business
Image Source: Getty Images

By Stephen Key
Co-founder, InventRight      

 

It is an incredible time to be a product developer. Like solving problems? Enjoy being creative? Today, you do not have to start a business to launch an idea into the market. You can go the licensing route and begin receiving passive income for your creativity instead. That’s the beauty of open innovation, the increasingly widespread practice of companies looking outside their own walls for the best new product ideas. By licensing your idea to a company that has existing distribution and relationships with retailers, you can get to market fast. In that way, as a business model, licensing simply cannot be beat. Innovative companies recognize this — even ones with a long and storied history of research and development like GE.

The pet industry is particularly ripe for open innovation and licensing. According to the American Pet Products Association, 68 percent of U.S. households own a pet today. That’s a lot! Last year, Americans spent a record $66.75 billion on their beloved companions. During the recession, the industry barely took a hit. Thanks to the Internet, dogs and cats are more popular than ever. And there’s the well-documented trend occurring worldwide of young people increasingly referring to and treating their pets like surrogate children. There’s a huge opportunity here for people who are creative.

Which is why I traveled to the annual pet trade show SuperZoo in Las Vegas earlier this week to ask companies explicitly: What do you need from us? After licensing many of my own ideas for products, I know what open innovation looks like in practice. But since I’ve made it my mission to help other people license their ideas, I’m committed to going one step further. The show is not as large as the Global Pet Expo in Florida, but it was very well-attended, light-hearted, and a lot of fun. How could it not be, with dogs of different shapes, sizes, and colors running this way and that? Turns out, people who love their pets like to have a good time. The theme this year was “Better Together,” which spoke directly to the inclusive nature of the industry at large.

As is typical, some companies were receptive to open innovation and others weren’t. You can tell which are and which aren’t pretty easily. Companies whose products all share the same beautiful design aesthetic? Not a good fit. These companies aren’t really innovating. Their focus is on designing products that are extremely pretty to look at. Their in-house designers are tasked with creating the look and feel they want their brand to reflect. These companies do not look at outside submissions. They’re more likely to acquire a brand outright or bring in a designer they like to keep working with them.

But I also met CEOs like Tim Blurton of Hyper Pet LLC who embrace open innovation emphatically.

“We love inventors and people with ideas. And we love being able to work with them and take their ideas and make them marketable, so we both benefit,” Blurton said. “Bring us any ideas you’ve got! We’ll listen and see if we can work on them.”

For Dr. Steven Tsengas of OurPets, intellectual property is of paramount importance. His company has something like 170 patents to its name. Electronic pet toys are increasingly popular, he told me, as he pointed out several products of his that make use of Bluetooth technology.

For companies like Ethical Products Inc., which has been in business since 1952 and markets its products under the brand SPOT, working with inventors is a way of life. Ausra Dapkus, the vice-president of purchasing and product development who is in charge of working with product developers and inventors, described her role in the following way.

“I take their ideas and then communicate them to our factories overseas to bring those products to life,” she explained. “I try to translate their vision into something that can actually work in production, which can sometimes be a challenge… but somehow we always manage to work it out.”

At the Ethical Products booth, professional inventor Chuck Lamprey showed off one of his licensed products. (Full disclosure: I know Chuck because he was my student.) Since he began developing pet products seven years ago, Lamprey has since licensed about nine of his ideas, all of which are still selling on the market. At first, he told me he struggled to make a good first impression at trade shows because he’s shy. But in time, as his knowledge of the industry grew, he became much more comfortable approaching booths.

These days, he’s confident because he know he adds value. He walks up to companies he wants to invent for and says something along the lines of: “Hello, my name is Chuck. I have many products in the marketplace. If you have needs in a particular area, I would love to help you out.”

This year, several CEOs explicitly thanked him for stopping by and expressed how much they appreciated that he was paying attention to the industry and actually inventing for them.

“Repeat trade show attendance is very useful in that way. They get to know me and that I’m serious about this,” he explained. “What I want to do is add value. It’s not about me and it’s not even about the company. It’s about the consumer. What can I give to the industry?”

His attitude is spot on. You cannot merely submit your ideas for new products to as many companies as possible and hope for the best. Becoming a professional independent product developer is all about communication and the relationships you build. That’s why attending a trade show can be so effective. You’re able to introduce yourself face-to-face and shake hands.

But some of the companies I interviewed were frank with me. In principle, they loved the idea of open innovation. In practice, they were frustrated. They’d become wary of working with inventors because so many of them failed to do their homework. The ideas these companies had received didn’t take their brand into consideration whatsoever, so they had decided to stop looking at outside ideas altogether.

The benefits of open innovation are enormous, but sifting through submissions takes time. So does getting back to product developers about why their ideas aren’t a perfect fit, which is a crucial part of the process. When companies decide their limited resources are better spent elsewhere, we all lose out.

If you have an apple and the company you show your idea to is selling oranges, that’s not a good fit. Most likely they are not going to be interested. And in that case what you’re sending is basically spam.

There’s a balance to be struck. Licensing is very much a numbers game. You need to contact enough companies about your idea, not just the major one or two players. At the same time, firing your sell sheet off to every single company in an industry isn’t going to get you very far.

So please, check out the websites of each company on your list of potential licensees. What are they all about? Can you tailor your sell sheet to better fit the needs of their consumers in some way? (Sometimes this is as easily accomplished as using a different color.) Remember, there are actual people reviewing your submissions at these companies.

Many people were honest with me about the fact that selling pet products has fundamentally changed. Between Amazon and other online retailers, brick and mortar sales are simply not as important. Savvy Internet retailers like the startup Chewy.com, which was recently acquired by PetSmart, offer better customer service. Before you show your idea to a company, investigate how it sells its products. If you don’t understand the retail landscape, you’re at a disadvantage.

Some companies were very clear with me about letting inventors know they could already be working on something similar, which is why they won’t sign a non-disclosure agreement right away. That’s perfectly reasonable.

Like always, I kept my eyes out for simple products, which are my favorite. I was not disappointed. Catit’s Flower Fountain water bowl is the best-selling cat accessory toy on Amazon, I was told. It’s a compact water fountain with a flower design on top that enables you to adjust the flow of water to your cat’s liking. Simple, very cute, and with smart packaging to boot: By cutting out a few pieces, the shipping box is transformed into a toy. If you own a cat, you know how much they adore playing with boxes.

This is an exciting industry to invent for, truly. Who doesn’t love pets? Open innovation in the pet industry is a win for all of us.

Article source: https://www.inc.com/stephen-key/open-innovation-is-alive-and-well-in-the-pet-indus.html

The opinions expressed here by Inc.com columnists are their own, not those of Inc.com.
PUBLISHED ON: JUL 28, 2017

How Artificial Intelligence benefits companies and ups their game

Technology, Artificial Intelligence, AI
A file photo of workers at the General Electric Co. (GE ) energy plant in Greenville, South Carolina, US. GE uses machine learning to predict required maintenance for its large industrial machines. Photo: Bloomberg

Jayanth Kolla

After decades of false starts, Artificial Intelligence (AI) is already pervasive in our lives. Although invisible to most people, features such as custom search engine results, social media alerts and notifications, e-commerce recommendations and listings are powered by AI-based algorithms and models. AI is fast turning out to be the key utility of the technology world, much as electricity evolved a century ago. Everything that we formerly electrified, we will now cognitize.

AI’s latest breakthrough is being propelled by machine learning—a subset of AI which includes abstruse techniques that enable machines to improve at tasks through learning and experience.Although in its infancy, the rapid development and impending AI-led technology revolution are expected to impact all the industries and companies (both big and small) in the respective ecosystem/value chains. We are already witnessing examples of how AI-powered new entrants are able to take on incumbents and win—as Uber and Lyft have done to the cab-hailing industry.

Currently, deployed key AI-based solutions, across industry verticals, include:

Predictive analytics, diagnostics and recommendations: Predictive analytics has been in the mainstream for a while, but deep learning changes and improves the whole game. Predictive analytics can be described as the ‘everywhere electricity’—it is not so much a product as it is a new capability that can be added to all the processes in a company. Be it a national bank, a key supplier of raw material and equipment for leading footwear brands, or a real estate company, companies across every industry vertical are highly motivated to adopt AI-based predictive analytics because of proven returns on investment.

Japanese insurance firm Fukoku Mutual Life Insurance is replacing its 34-strong workforce with IBM’s Watson Explorer AI. The AI system calculates insurance policy payouts, which according to the firm’s estimates is expected to increase productivity by 30% and save close to £1 million a year. Be it user-based collaborative filtering used by Spotify and Amazon to content-based collaborative filtering used by Pandora or Frequency Itemset Mining used by Netflix, digital media firms have been using various machine learning algorithms and predictive analytics models for their recommendation engines.

In e-commerce, with thousands of products and multiple factors that impact their sales, an estimate of the price to sales ratio or price elasticity is difficult. Dynamic price optimization using machine learning—correlating pricing trends with sales trends using an algorithm, then aligning with other factors such as category management and inventory levels—is used by almost every leading e-commerce player from Amazon.com to Blibli.com.

Chatbots and voice assistants: Chatbots have evolved mainly on the back of internet messenger platforms, and have hit an inflection point in 2016. As of mid-2016, more than 11,000 Facebook Messenger bots and 20,000 Kik bots had been launched. As of April 2017, 100,000 bots were created for Facebook Messenger alone in the first year of the platform. Currently, chatbots are rapidly proliferating across both the consumer and enterprise domains, with capabilities to handle multiple tasks including shopping, travel search and booking, payments, office management, customer support, and task management.

Royal Bank of Scotland (RBS) launched Luvo, a natural language processing AI bot which answers RBS, Natwest and Ulster bank customer queries and perform simple banking tasks like money transfers.

If Luvo is unable to find the answer it will pass the customer over to a member of staff. While RBS is the first retail bank in the UK to launch such a service, others such as Sweden’s SwedBank and Spain’s BBVA have created similar virtual assistants.

Technology companies and digital natives are investing in and deploying the technology at scale, but widespread adoption among less digitally mature sectors and companies is lagging. However, the current mismatch between AI investment and adoption has not stopped people from imagining a future where AI transforms businesses and entire industries.

The National Health Services (NHS) in the UK has implemented an AI-powered chatbot on the 111 non-emergency helpline. Being trialled in North London, its 1.2 million residents can opt for a chatbot rather than talking to a person on the 111 helpline. The chatbot encourages patients to enter their symptoms into the app. It will, then, consult a large medical database and users will receive tailored responses based on the information they have entered.

Image recognition, processing and diagnostics: On an average, it takes about 19 million images of cats for the current Deep Learning algorithms to recognize an image of a cat, unaided. Compared to the progress of natural language processing solutions, computer vision-based AI solutions are still in developmental stage, primarily due to the lack of large, structured data sets and the significant amount of computational power required to train the algorithms.

That said, we are witnessing adoption of image recognition in healthcare and financial services sectors. Israel-based Zebra Medical Systems uses deep learning techniques in radiology. It has amassed a huge training set of medical images along with categorization technology that will allow computers to predict diseases accurately better than humans.

Chinese technology companies Alipay (the mobile payments arm of Alibaba) and WeChat Pay (the mobile payments unit of Tencent) use advanced mobile-based image and facial recognition techniques for loan disbursement, financing, insurance claims authentication, fraud management and credit history ratings of both retail and enterprise customers.

General Electric (GE) is an example of a large multi-faceted conglomerate that has adopted AI and ML successfully at a large scale, across various functions, to evolve from industrial and consumer products and financial services firm to a ‘digital industrial’ company with a strong focus on the ‘Industrial Internet’. GE uses machine-learning approaches to predict required maintenance for its large industrial machines. The company achieves this by continuously monitoring and learning from new data of its machines ‘digital twins’ (a digital, cloud-based replica of its actual machines in the field) and modifying predictive models over time. Beyond, industrial equipment, the company has also used AI and ML effectively for integrating business data. GE used machine-learning software to identify and normalize differential pricing in its supplier data across business verticals, leading to savings of $80 million.

GE’s successful acquisition and integration of innovative AI startups such as “SmartSignal” (acquired in 2011) to provide supervised learning models for remote diagnostics, “Wise.io” (acquired in 2016) for unsupervised deep learning capabilities and its in-house the data scientists, and of “Bit Stew” (another 2016 acquisition) to integrate data from multiple sensors in industrial equipment has enabled the company to evolve as a leading conglomerate in the AI business.

Industry sector-wise adoption of AI: Sector-by-sector adoption of AI is highly uneven currently, reflecting many characteristics of digital adoption on a broader scale. According to the McKinsey Global Index survey, released in June, larger companies and industries that adopted digital technologies in the past are more likely to adopt AI. For them, AI is the next wave. Other than online and IT companies, which are early adopters and proponents of various AI technologies, banks, financial services and healthcare are the leading non-core technology verticals that are adopting AI. According to the McKinsey survey, there is also clear evidence that early AI adopters are driven to employ AI solutions in order to grow revenue and market share, and the potential for cost reduction is a secondary idea.

AI, thus, can go beyond changing business processes to changing entire business models with winner-takes-all dynamics. Firms that are waiting for the AI dust to settle down risk being left behind.

The author is Founder and Partner of digital technologies research and advisory firm, Convergence Catalyst.

How to Become A 2018 World’s Most Innovative Company

innovation, fast company, business, tech
Image Source: FastCompany.com

 

Innovation is everywhere. So how do we cut through the clutter to name our annual Most Innovative Companies Top 50 and Top 10 industry lists?

Our team of dogged and dedicated reporters and editors spend months culling research on the world’s top companies. But this year—for the first time ever—you can submit your own organizationto become a 2018 Most Innovative Company.

Here’s how you can put together the best possible entry for our team of Most Innovative Companies editors. (And don’t forget to download our MIC special edition and how-to guide here).

  1. Identify Your Innovation Bucket
    Fast Company takes an expansive view of what constitutes innovation: Product innovation: We’re happy to celebrate a successful new entrant in the market that serves a previously unmet need, such as new lifesaving drugs from Gilead Sciences or Casper’s mattresses and bedding. Creative innovation: We gave the nod to the ad agency 72andSunny for breaking through the clutter with great work in a variety of media for clients ranging from Starbucks to Activision to Google. Sometimes, of course, an innovation hits several of these notes or belongs in a category we haven’t mentioned here. Business-model innovation: Warby Parker introduced try-before-you-buy to eyewear and has led the way in marrying real-world retail with e-commerce.
  2. Focus On A Project
    Tell us about a particular initiative. It’s not enough merely to state that your product or strategy is innovative. The key is to isolate the novelty in what you’re doing and delineate how and why it’s different from what’s come before.
  3. Be Concise, Yet Descriptive
    We are not accepting attachments of any kind, including presentation decks or visuals. The more detail you can provide in the space allotted, the greater the case can be made for your innovation. What makes you most excited when you think about what you’ve developed? Which of your features are your customers are buzzing about, either in communicating back to you or among themselves?
  4.  Share Your Completed Work
    If you’re an architecture firm, finished buildings will garner more attention than those in the planning stage. If you’re a pharmaceutical company, an FDA-approved drug matters more than a promising clinical trial. In-progress ideas will certainly be considered, but completing the work counts.
  5. Choose Your Strategic Weapon
    Technology is transforming every aspect of our world. How are you using it to get a leg up on competitors? Or perhaps good design is…Continue reading

Article source: https://www.fastcompany.com/40440722/how-to-become-a-2018-worlds-most-innovative-company