Posted In:Marketing Archives - AppFerret
Coming up with original ideas to feed the ever-increasing content demands of your readers, subscribers, prospects, customers and social fans is hard work! The lack of a single customer view was cited as the top barrier to successful cross-channel marketing in a recent Experian study, and it’s contributed to the skyrocketing volume of content we need to reach various audience segments where and when they prefer to consume content.
We also need to consider the devices on which consumers will access our content. Is it mobile-friendly? Is it visible and legible on small screens? Do you have longer form content for those who need more information to make a decision?
Of course, you then have to think of content formats — whether the message you’re trying to convey will come across best in written form, audio, video, visually, etc.
To that end, I found this awesome list of content formats infographic to help marketers get inspired and get out of the original content creation rut. There are 44 different content formats in this visual, which you can keep as a cheat sheet and refer to when you need ideas.
It’s also helpful for repurposing content. Make sure you’re getting the most mileage out of your content by repurposing it for your different audience segments. For example, that webinar you hosted can be repurposed into a summary blog post. The images from the PowerPoint you used in the webinar can become standalone graphics that you can share via social media. You can release the audio portion only of your webinar as a podcast, perhaps with supporting collateral like an e-book.
Check out this list of 44 content formats you can use to add flavor and variety to your content strategy:
Original article here.
The Gartner hype cycle is one of the more brilliant insights ever uncovered in the history of technology. I rank it right up there with Moore’s Law and Christensen’s model of disruptive innovation from below.
Gartner’s hype cycle describe a 5-stage pattern that almost all new technologies follow:
- A technology trigger introduces new possibilities — things like AI, chatbots, AR/VR, blockchain, etc. — which capture the imagination and create a rapid rise in expectations. (“Big data is a breakthrough!”)
- The fervor quickly reaches a peak of inflated expectations — the “hype” is deafening and dramatically overshoots the reality of what’s possible. (“Big data will change everything!”>
- Reality soon sets in though, as people realize that the promises of that hype aren’t coming to fruition. Expectations drop like a rock, and the market slips into a trough of disillusionment. (“Big data isn’t that magical after all.”)
- But there is underlying value to the technology, and as it steadily improves, people begin to figure out realistic applications. This is the slope of enlightenment: expectations rise again, but less sharply, in alignment with what’s achievable. (“Big data is actually useful in these cases…”)
- Finally the expectations of the technology are absorbed into everyday life, with well-established best practices, leveling off in the plateau of productivity. (“Big data is an ordinary fact of life. Here’s how we use it.”)
It might not be a law of nature, but as a law of technology markets, it’s pretty consistent.
We hear a lot about the hype cycle in the martech world, because we have been inundated with new technologies in marketing. I’m covering a number of them in my 2018 update to the 5 disruptions to marketing: artificial intelligence (AI), conversational interfaces, augmented reality (AR), Internet of Things (IoT), customer data platforms (CDP), etc.
In marketing, it’s not just technologies that follow this hype cycle, but also concepts and tactics, such as content marketing, account-based marketing, revenue operations, and so on. By the way, that’s not a knock against any of those. There is real value in all of them. But the hype exceeds the reality in the first 1/3 or so of their lifecycle.
Indeed, it’s the reality underneath the hype cycle that people lose sight of. Expectations are perception. The actual advancement of the technology (or concept or tactic) is reality.
At the peak of inflated expectations, reality is far below what’s being discussed ad nauseum in blog posts and board rooms. In the tough of disillusionment, the actual, present-day potential is sadly underestimated — discussions shift to the inflated expectations of the next new thing.
However, this desync between expectations and reality is a good thing — if you know what you’re doing. The gap between expectations and reality creates opportunities for a savvy company to manage to the reality while competitors chase the hype cycle.
It’s a variation of the age-old investment advice: buy low, sell high.
At the peak of inflated expectations, you want to avoid overspending on technology and overpromising results. You don’t want to ignore the movement entirely, since there is fire smoldering below the smoke. But you want to evaluate claims carefully, run things with an experimental mindset, and focus on real learning.
In the trough of disillusionment, that’s when you want to pour gas on the fire. Leverage what you learned from your experimental phase to scale up the things you know work, because you’ve proven them in your business.
Don’t be distracted by the backlash of negative chatter at this stage of the hype cycle. Reinvest your experimental efforts in pushing the possibilities ahead of the slope of enlightenment. This is your chance to race ahead of competitors who are pulling back from their missed results against earlier, unrealistic expectations.
As close as possible, you want to track the actual advancement of the technology. If you can achieve that, you’ll get two big wins, as the hype is on the way up and on the way down. You’ll harness the pendulum of the hype cycle into useful energy.
P.S. When I program the MarTech conferenceagenda, my goal is to give attendees as accurate of a picture of the actual advancement of marketing technologies as possible.
I won’t try to sell you a ticket on overinflated expectations. But I will try to sell you a ticket on getting you the ground truth of marketing technology and innovation, so you can capture the two opportunities that are yours to take from the hype cycle.
Our next event is coming up, April 23-25 in San Jose. Our early bird rates expire on January 27, which saves you $500 on all-access passes. Take advantage of that pricing while you can.
Original article here.
Gartner’s 2017 Hype Cycle for Marketing and Advertising is out (subscription required) and, predictably, AI for Marketing has appeared as a new dot making a rapid ascent toward the Peak of Inflated Expectations. I say “rapid” but some may be surprised to see us projecting that it will take more than 10 years for AI in Marketing to reach the Plateau of Productivity. Indeed, the timeframe drew some skepticism and we deliberated on this extensively, as have many organizations and communities.
AI for Marketing on the 2017 Hype Cycle for Marketing and Advertising
First, let’s be clear about one thing: a long journey to the plateau is not a recommendation to ignore a transformational technology. However, it does raise questions of just what to expect in the nearer term.
Skeptics of a longer timeframe rightly point out the velocity with which digital leaders from Google to Amazon to Baidu and Alibaba are embracing these technologies today, and the impact they’re likely to have on marketing and advertising once they’ve cracked the code on predicting buying behavior and customer satisfaction and acting accordingly.
There’s no point in debating the seriousness of the leading digital companies when it comes to AI. The impact that AI will have on marketing is perhaps more debatable – some breakthrough benefits are already being realized, but – to use some AI jargon here – many problems at the heart of marketing exhibit high enough dimensionality to suggest they’re AI-complete. In other words, human behavior is influenced by a large number of variables which makes it hard to predict unless you’re human. On the other hand, we’ve seen dramatic lifts in conversion rates from AI-enhanced campaigns and the global scale of markets means that even modest improvements in matching people with products could have major effects. Net-net, we do believe AI that will have a transformational on marketing and that some of these transformational effects will be felt in fewer than ten years – in fact, they’re being felt already.
Still, in the words of Paul Saffo, “Never mistake a clear view for a short distance.” The magnitude of a technology’s impact is, if anything, a sign it will take longer than expected to reach some sort of equilibrium. Just look at the Internet. I still vividly recall the collective expectation that many of us held in 1999 that business productivity was just around the corner. The ensuing descent into the Trough of Disillusionment didn’t diminish the Internet’s ultimate impact – it just delayed it. But the delay was significant enough to give a few companies that kept the faith, like Google and Amazon, an insurmountable advantage when Internet at last plateaued, about 10 years later.
Proponents of faster impact point out that AI has already been through a Trough of Disillusionment maybe ten times as long as the Internet – the “AI Winter” that you can trace to the 1980s. By this reckoning, productivity is long overdue. This may be true for a number of domains – such as natural language processing and image recognition – but it’s hardly the case for the kinds of applications we’re considering in AI for Marketing. Before we could start on those we needed massive data collection on the input side, a cloud-based big data machine learning infrastructure, and real-time operations on the output side to accelerate the learning process to the point where we could start to frame the optimization problem in AI. Some of the algorithms may be quite old, but their real-time marketing context is certainly new.
More importantly, consider the implications of replacing the way marketing works today with true lights-out AI-driven operations. Even when machines do outperform human counterparts in making the kinds of judgments marketers pride themselves on, the organizational and cultural resistance they will face from the enterprise is profound….with important exceptions: disruptive start-ups and the digital giants who are developing these technologies and currently dominate digital media.
And enterprises aren’t the only source of resistance. The data being collected in what’s being billed as “people-based marketing” – the kind that AI will need to predict and influence behavior – is the subject of privacy concerns that stem from the “people’s” notable lack of an AI ally in the data collection business. See more comments here.
Then consider this: In 2016, P&G spent over $4B media. Despite their acknowledgment of the growing importance of the Internet to their marketing (20 years in), they still spend orders of magnitude more on TV (see Ad Age, ubscription required). As we know, Marc Pritchard, P&G’s global head of brands, doesn’t care much for the Internet’s way of doing business and has demanded fundamental changes in what he calls its “corrupt and non-transparent media supply chain.”
Well, if Marc and his colleagues don’t like the Internet’s media supply chain, wait until they get a load of the emerging AI marketing supply chain. Here’s a market where the same small group of gatekeepers own the technology, the data, the media, the infrastructure – even some key physical distribution channels – and their business models are generally based on extracting payment from suppliers, not consumers who enjoy their services for “free.” The business impulses of these companies are clear: just ask Alexa. What they haven’t perfected yet is that shopping concierge that gets you exactly what you want, but they’re working on it. If their AI can solve that, then two of P&G’s most valuable assets – its legacy media-based brand power and its retail distribution network – will be neutralized. Does this mean the end of consumer brands? Not necessarily, but our future AI proxies may help us cultivate different ideas about brand loyalty.
This brings us to the final argument against putting AI for Marketing too far out on the hype cycle: it will encourage complacency in companies that need to act. By the time established brands recognize what’s happened, it will be too late.
Business leaders have told me they use Gartner’s Hype Cycles in two ways. One is to help build urgency behind initiatives that are forecast to have a large, near-term impact, especially ones tarnished by disillusionment. The second is to subdue people who insist on drawing attention to seductive technologies on the distant horizon. Neither use is appropriate for AI for Marketing. In this case, the long horizon is neither a cause for contentment nor is a reason to go shopping.
First, brands need a plan. And the plan has to anticipate major disruptions, not just in marketing, but in the entire consumer-driven, AI-mediated supply chain in which brands – or their AI agents – will find themselves negotiating with a lot of very smart algorithms. I feel confident in predicting that this will take a long time. But that doesn’t mean it’s time to ignore AI. On the contrary, it’s time to put learning about and experiencing AI at the top of the strategic priority list, and to consider what role your organization will play when these technologies are woven into our markets and brand experiences.
Original article here.
Asked and answered. These real-life pitch questions from Steve Case’s Rise of the Rest tour can help give you an edge on your next pitch.
Pitch competitions are a reality of startup life, as common as coffee mugs that say “Hustle Harder” or thought leaders expounding on the need for “grit.”
Still, even the smartest entrepreneur isn’t always ready for what competition judges might ask. During Steve Case’s Rise of the Rest tour, a seven-city road trip across the U.S. highlighting entrepreneurs outside the major startup hubs, founders in Phoenix participated in their own mock pitch competition, allowing them to practice and polish their answers.
We’ve collected a curated selection of questions during the competition, some asked more often than others.
To prepare for your next competition, get prepping with these potential questions:
1. What’s your top priority in the next six months? What metric are you watching the most closely?
2. What’s your exit strategy?
3. How does your product/service work?
4. Who is your customer?
5. Do you have contracts and if so, how often do they renew?
6. Why is your team the team to bring this to market?
7. You say you’ll have 100 staffers in five years. You have six now. What will those new staffers do?
8. Why is your product/service better than what’s already on the market?
9. Who are your competitors?
10. Do you have a patent?
11. If you win the investment, what would that partnership look like?
12. You’ve secured a strategic partnership. Is that partnership exclusive? And if not, is that a liability?
13. What’s your barrier to capacity?
14. What’s your expansion strategy?
Pricing and revenue
15. How much of your revenue is from upsells? And how do you see that changing over time?
16. Everyone says they can monetize the data they collect. What’s your plan?
17. Can you explain your revenue model?
18. What’s your margin?
19. Are you charging too little?
20. Are you charging too much?
21. How will you get to 1 million users?
22. Is this trend sustainable?
23. What regulatory approvals do you need and how have you progressed so far?
Original article here.