Author: Steve Lucas
Well hello everyone—it’s time for my annual predictions blog where I get to prognosticate a little about where I see technology going in the new year!
First, a brief reflection back on 2016, as it was a dynamic year for the technology market and a year of change for me personally as well. On the personal side, I left one of the greatest companies in the world, SAP, to join Marketo—an equally exciting company—as the new CEO. It’s been “insanely great” as Steve Jobs used to say! I love Marketo’s team, brand, product, and partners and can’t wait to see what we do together in 2017!
For everything else that happened in 2016, I think it’s best summarized as the “year where our collective analytics and predictive prowess failed us,” at least in the U.S. when it comes to predicting little things like the presidential election. While everyone was largely predicting a win for Hillary in a “landslide,” we saw the exact opposite. Even the great Nate Silver of fivethirtyeight.com, someone I’ve had the pleasure of speaking with on stage before, gave Trump less than a 30% chance of winning. In spite of nearly every predictive model, Trump went on to win the electoral college in relatively decisive fashion. Regardless of political affiliation, I think very few people saw that coming.
Which begs the question, if our predictive models are getting better all the time, why did they perform so poorly in 2016 relative to the U.S. election? There’s an article I recalled from 2008 in Psychology Today which asserts, if our behavior (as humans) is completely predictable, then it makes it easier for other people to take advantage of us. Ergo, our cognitive system is willing to pay a price in the short-term in order to be unpredictable. Safe to say that we humans were “unpredictable” in many ways in 2016 and most notably on election day in the presidential election.
That said, there’s no need to throw the AI baby out with the bathwater (artificial intelligence for clarity’s sake). We benefit greatly from analytics, computational insight, predictive models, and all the like in many ways, especially when predictability stands to provide a benefit or convenience for us. Like when Amazon knows which products you prefer based on past buying or browsing behavior. The point is that we are willing to behave predictably for the sake of convenience.
Which leads me to 2017.
Prediction #1: The Engagement Economy Has Arrived
I believe that we will see a dramatic acceleration of adaptive and intuitive applications and technology like never before. Regardless of the labels we apply (AI, machine learning, etc.) to the technology, things will just get smarter. The apps we use on our phones. The ads we see in those apps and online. The way we connect with the brands and companies we like. Frankly, the applications we use at work and play HAVE to get smarter or we will get bored and disengage…which is the kiss of death for a product or app.
To that end, I am declaring that this notion of the “engagement economy” will dominate discussions between marketers, sellers, and anyone else concerned with engaging with customers. This is a simple notion yet profound: The runaway effect of digitization is in overdrive, and the only way for organizations to “win” is to find ways to engage with people in an authentic and personalized manner—at scale. That seems contradictory, but it is absolutely achievable.
If I apply my reasoning to marketing automation technology, for instance, it means that companies will begin to look for “engagement platforms” instead of marketing automation tools. At its core, automation is about technology and systemization, while engagement platforms put people first—and build relationships with them through authentic and personalized experiences. An engagement platform is a necessity, not a luxury, in a world where everyone and everything are connected 24/7 via myriad devices and apps.
Prediction #2: Cloud Consolidation Will Accelerate
Every day, when I come in the office, I have no fewer than 20 (yes, 20!) different cloud applications that I will need during the course of the day. Most of these applications are from different vendors, and there is no integration other than single sign-on between them. This has the trappings of 1995 in the client/server era when companies had dozens of “critical” apps all built by separate vendors. This will not be tenable over the long haul. You’ll see best-of-breed applications in segments like CRM or marketing get bigger and broader. This will, of course, not be limited to one particular market segment in cloud—but I predict some big acquisitions in cloud computing in an effort to consolidate. By the way—full disclosure on Marketo…(wait for it)…. I think we can be a consolidator in marketing! One last thing, if you’ve purged the ‘90s from your brain or you were born during or after the 90s, take a stroll down “Enterprise Application Integration” (EAI) lane thanks to Wikipedia.
Moving on, I stated in the title of my blog that I view the two predictions above as “zero” risk propositions. I firmly believe that embracing more adaptive and intuitive systems, like smarter and automated ways to engage with customers, has little downside. Assuming we get privacy and security right, this is where all companies will invest forward. I also believe it’s not exactly epiphonic to state that companies want more/broader capabilities from strategic vendors. At Marketo, our customers are asking me daily to expand our capabilities relative to marketing and deliver an adaptive and intelligent “platform” for the marketer. We have been and will continue to do just that!
One simple way to add 7% to revenue growth: It’s a technology called account-based marketing (ABM), or as I like to refer to it, account-based engagement. It’s simple notionally, in that larger companies are moving away from a single decision-maker “model” for making strategic purchases in medium and large organizations to a “consensus” buying model with multiple decision-makers. It’s incredibly challenging for companies to engage with another organization with a large committee that inevitably has to arrive at a common consensus in order to make a decision. Think about it…when was the last time you sold something of value to an enterprise organization and only interacted with one person? This “model” is being formalized and procurement is sponsoring this behavior in a big way! This trend, consensus buying, will dominate decision-making in 2017—and I believe that Marketo has the answer for engaging and winning with our ABM technology.
Furthermore, with predictive profiling capability, technology like an engagement platform for the enterprise can enable organizations to take an anonymous IP address and transform it into a “digital persona” and profile on an individual in a matter of minutes. This, in turn, enables an organization to deliver authentic and personalized content via mobile apps, social apps, websites, email, and more…all specific to a market of one. Think of it as a snowflake model—no two people are the same, so why should the content you serve them be the same? The CEO, CMO, CFO, and CIO are paid to think differently, and there is no way your content or the way you engage these buyers (and more) should be generic. We can change that—and to be somewhat neutral on this, I believe account-based engagement will change how sellers and marketers collaborate to drive revenue, regardless of who the provider may be. This is THE next big trend in sales and marketing for the enterprise. Beyond that—organizations that have adopted ABM are already seeing an immediate uplift in revenue—7%+—by simply engaging better! To net it out, they win more.
2017 will undoubtedly be the year where we see applications deliver the human touch, at scale, far better than any human ever could. It’s time to lead the way in your industry and put AI to work!
I wish all of you an incredibly prosperous new year and look forward to hearing about your successes!
2 Zero Risk Predictions for 2017 was posted at Marketo Marketing Blog – Best Practices and Thought Leadership. | http://blog.marketo.com
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