What a difference a decade makes. Ten years ago, the booths on the Dreamforce show floor were little more than outposts for widget-makers but fast-forward to Dreamforce 2015 and one is struck by the number, variety, and size of the partner community. But that’s only part of the story, often out of sight is the sizeable display of talent that has consolidated around Salesforce from other industry sources.
A little more than ten years ago Salesforce was a precocious upstart vendor of SaaS computing and Siebel was the top dog—the first billion-dollar CRM company—and it held a large proportion of the available CRM talent. But at this year’s Dreamforce there were numerous Siebel alumni all drinking the Salesforce1 Kool-Aid.
Former Siebel EVP, David Schmaier, after a sabbatical from the industry, started Vlocity, a company dedicated to making vertical market apps for healthcare, financial services, and insurance. Vlocity takes a page from Veeva, a highly successful company in the pharmaceutical space started by Siebel alumnus Matt Wallach and Peter Gassner (Salesforce, PeopleSoft).
Anthony Lye, (Siebel, Oracle and others) now CEO of HotSchedules a cloud service application for the restaurant industry, was prowling the floor. Kevin Nix and Narina Sippy ex-Siebel stars are spinning up Stellar Loyalty. Steve Mankoff is now a general partner with TDF Ventures and was keeping tabs on some of his investments. And Bruce Cleveland, former GM of Siebel and now general partner with InterWest Partners was not seen but his presence was felt in companies as diverse as Aria and Vlocity.
The presence of so many old CRM hands concentrated as they are around Salesforce will likely help further accelerate the company’s growth—certainly the potential is there. The partner keynote delivered by EVP Tyler Prince, revealed a $135 billion revenue opportunity calculated by Salesforce over the next 5 years. Even if you discount that by a large factor you will still be left with a lot of billions. That’s one reason so many industry veterans are attracted to Dreamforce.
The companies in attendance have dramatically grown in stature over the last decade and the show floor included many public companies or future IPO outfits including in no order, Xactly, FinancialForce, Zuora, Vlocity, Apttus, Full Circle Insights and about 390 others. Many of this group rented storefronts around the Moscone Center to provide meeting space and hospitality to their customers and prospects. Most also sponsored big parties and scheduled user events coinciding with Dreamforce to further induce customers to attend. Apttus raffled off a Tesla, FinancialForce sponsored a scotch tasting (full disclosure: I tasted the scotch but did not win the Tesla).
At the same time, Salesforce was trying to get a few messages out so there was plenty of discussion of the new Lightning UI for desktops and laptops. Significantly, the UI was announced last year but only for mobile devices—a demonstration of the importance of developing for the small screen first these days. The company also announced SalesforceIQ a rebranded absorption of RelateIQ for SMBs and the enterprise. The IQ product is designed to capture inferential data and turn it into useful things like new meeting appointments and follow up actions without requiring the rep to manually enter the data.
To go with Lightning, Salesforce introduced an IoT cloud powered by Thunder, the company’s initiative to corral the billions of devices that will need cloud connections by 2020. There were also specific keynotes for every cloud in the company’s kit and those announcements were way too numerous for this piece. Fortunately they are all preserved on YouTube.
But the biggest bang comes whenever Salesforce assembles a gang of smart people to talk about the future. They don’t do it every year and perhaps that’s wise since major change of the type they like to discuss follows more of a punctuated course, like an EKG.
This time they had a lively discussion about what happens when Moore’s Law and Metcalf’s Law collide with business in a big way. That intersection is best explored at length in The Second Machine Age and Race Against the Machine both by Brynjolfsson and McAfee of MIT’s Sloan School and their ideas were referenced more than once. You may have read those names here a few times prior to this. The questions they ask, which we are still searching for answers to, are of the type, what happens when machine intelligence becomes good enough to begin replacing humans at knowledge work.
We’ve all seen automation replace rote manual activities in business thus boosting productivity. The standard explanation is that the human resources are liberated to pursue higher-level value-add. But the rise of the service economy with its lower wages and hard to find jobs suggests that the future might not be as rosy. What happens when “there’s an app for that” means a pink slip?
Happy outcomes don’t automatically happen but the track record since the Industrial Revolution suggests that not only do new jobs spring up but also new kinds of jobs; an easy example is the software industry analyst. No one I know went to school to become an analyst—I certainly didn’t. There is no room for complacency though. Machines are now capable of writing reports in reasonably good English (though doubtless without the same panache as yours truly). It’s different this time; replacing manual labor is one thing but replacing thinking is much different. It will be a very different ballgame as Jeremy Rifkin writes in The Zero Marginal Cost Society when everyone has a computer and a 3D printer. That’s something the Salesforce brains trust didn’t get to this time.
Deep futures aside, it’s inescapable that the next shift in the front office and the enterprise will be adopting many of the platform technologies displayed on the show floor in order to support more automated processes which are rapidly replacing the transactions we’ve grown to accept in many vendor-customer interactions. Process isn’t exactly a new watchword yet but vendors like Salesforce and others are delivering increasingly capable suites that will make a shift to process rapid once it officially starts. (It has started, you might now see it but you also don’t want to be the last adopter.)
Also, kudos to founders Parker Harris and Marc Benioff for putting themselves on the spot and taking on some tough issues like sponsoring a Women’s Leadership Summit. They sat down for some interesting dialog and hard questions from Kara Swisher, Co-Executive Editor, Re/code about how to provide better opportunities for women in the tech industry. It was not an easy discussion because if you watch the video, you can see everyone trying to puzzle it all out. But Benioff and Harris didn’t shrink from it and expressed a commitment to put the issue at the top of their agenda (heck the summit was their idea). Though more needs to be done, you can’t put Salesforce, even today, in the same category of many older tech firms and the presence of women in the conference was notable. Still we need more.
So to net this out, Dreamforce had its requisite cornucopia of products, announcements, and invention. But it also held out some provocative insights into the future of work and our society, two things that will drive demand for its products and services long after this year’s new wiz bangs are history. To me that’s why you go to Dreamforce.
I read two things recently that go together like peanut butter and jelly and they make for an interesting exercise. The first is a new report, “2014 State of Marketing” from ExactTarget, one of the Marketing Cloud components of Salesforce.com. The other is The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, by Erik Brynjolfsson and Andrew McAfee, two economics researchers from the MIT Sloan School.
This is the second effort by Brynjolfsson and McAfee in as many years and this book follows up with more detail last year’s effort: Race Against The Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. While the first book hints at the benefits of the digital age, the new effort is a cautionary tale about the downside of automation as machines replace people at routine tasks (and some that are not routine). But really the two should be considered one big volume.
The central metaphor is the chessboard and exponential growth, which the authors boil down to the notion that the future evolution of technology builds on past experience but with an exponential twist. For example, if you place a grain of rice on square one of a chessboard and double the prior amount on each subsequent square, very soon the pile is mind bogglingly large. By the time you reach the second half of the board there isn’t enough rice in the world to do the doubling.
Such is the power of exponential growth that it is hard to imagine the downstream effects once doubling begins and that is the point of other exponential effects in our lives such as Moore’s Law. Let’s save the truth squad’s report for another time, you know, Moore’s Law is not a “law” like gravity or anything close or that each time Moore’s Law bumps up against the ceiling, someone comes along to rewrite it and provide more head space. True laws don’t require rescue.
What started as a prediction about doubling the number of transistors on a silicon chip every 18 months has been transformed into a more general statement about the power of layering invention on top of invention, technology on technology in a very short time until you have something unrecognizable to the original generation of technologists.
In the world of digital data and automation driven by Moore’s Law, the second half of the chess board becomes a wonderland full of amazing machines doing smart things — cars that drive themselves is a favorite example. But the second half is also full of pitfalls. Machines can drive themselves, prepare tax returns autonomously, fabricate products, win Jeopardy!, and many more miraculous tasks that make us obsolete. But machines are terrible (still) at doing the simple things any 2 year old does not to mention complex communication, social-motor-cognitive things and other jobs that might only require a high school diploma. Teach a computer to tie your shoes and you will really have something.
The authors’ solution is not to fight the machines but to find ways to win with them. In a kind of 1 + 1 = 3 approach they make the interesting suggestion that a weak human with a good computer and a good process usually beats a really fast computer alone or even the computer plus a smart human. This might be the most important point in the book and it drives us right into the report and my point about marketing automation in general.
Applying it to marketing
The “2014 State of Marketing” document represents information gleaned from a survey of more than 2,500 marketers and covers future plans and approaches to marketing. What caught my eye is that in so many areas of marketing the demand for marketing technologies is saturating rather quickly. To a large extent companies have surrounded themselves with outbound social marketing tools and many are disillusioned for predictable reasons.
A summary page shows data about current and future use of specific marketing automation technologies. Many of them already have high participation rates like 88% say they use Email Marketing, and 81% say they already use Data and Analytics while 78% use Social Media Marketing, 64% use Display/Banner Ads, and 75% use Landing Pages. Indeed, it seems like the majority of outbound marketing tools have high adoption rates overall.
If I had to evaluate the state of marketing in line with this data and the MIT scholars, I’d have to say that we’re only at the 1 + 1 = 2 stage. We’ve got great machines and smart people using the combination of marketing hardware and software but I’d say that we still lack process. That’s because process is one of those things that comes along rather late in adopting new technologies.
The most natural way we adopt technology is to attach it to old processes. So we see from the report that organizations are sending out well over a million of emails per year — about half send less and half send way more. This says that the processes used in marketing have not changed much from the days of sending offers through the mail. Back in the day you sent mail because it was a cheap way to see what would stick. Today sending out massive numbers of offers isn’t just cheap, it is virtually free but it makes you wonder about all the discussion of how new technologies would enable us to hit the bull’s eye rather than relying on making random and rather poorly defined offers.
The processes now being used by many marketers are scarcely different from the old spray and pray approaches of broadcast media days. The whole point of adding a social element is to figure out which customers to invest marketing effort in but the cost of using social in broadcast mode is so low that few of us are bothering to think differently about our processes yet, or so it seems.
It all comes back to the hype cycle and the realization that there seems to always be a gap between introducing a new technology and having it achieve its greatest utility. The reason for the gap, which you can infer from Brynjolfsson and McAfee, is that it takes time for complementary technologies and processes to evolve once the primary innovation has occurred. In our experience those things come from ecosystems of other vendors who develop things and processes that optimally utilize the original innovation.
If you look at the data from the report you can clearly see that marketers have adopted modern outbound technologies sure enough but old processes are being maintained while inbound strategies play catch up. A telltale sign from the report is that only 37% of the respondents use lead scoring and while 30% say they’ll try it this year, a full third say they don’t plan to use it.
But lead scoring could easily be the poster child for process, for getting from old school broadcast marketing to the future. And who wouldn’t want that? Consider this. Your leads, even from a well-structured nurturing campaign, are a dog’s breakfast of potential deals that will never close and some that will if a sales rep picks up a phone. Wouldn’t you like to know which is which? Of course you would because leads are a perishable commodity just like the bananas in a grocery store. A lead doesn’t rot like a banana but it goes away because someone else is going to approach it first and that’s what lead scoring helps with.
This is all in line with other bits of data that I have collected suggesting that even with the analytics capabilities marketers now possess we still don’t collect enough of the right stuff. Specifically, we let sales off the hook too often by not demanding feedback on the quality of marketing qualified leads. How do you improve without it?
Also, we still don’t collect enough data about our marketing processes and this goes right to the heart of the 1 + 1 = 3 argument. By default we collect data about our programs such as costs and what’s harvested, i.e. leads, dollars, products. But what we fail to collect is data about our processes, specifically time. We often don’t timestamp marketing processes and without that simple extra data collection we give up knowing how long marketing opportunities stay in various states. Absent this basic data and the information derived from it we can’t easily know which programs work best and a whole array of other stuff.
This is getting long so let me net this out. We’re mid-way through the social revolution according to the data. But according to the book we haven’t yet figured out how best to use the stuff that we have. That knowledge is on the second half of the chessboard and it is only when we get to that point that we’ll see the big marketing benefits we’ve always been promised. You can call it a hype cycle if you want but from now on I’m going to call it the creativity gap.
Note: This blog was cross posted at the Lattice Engines blog.
I can’t tell you how many emerging analytics companies have contacted me since January. Every day it seems there is another company — smelling blood in the proverbial water — wanting to brief me.
I know why. Now that Big Data questions have transitioned from “how do we store all this stuff?” and “what’s valuable in this pile?” to “how can we slice and dice this raw material,” everybody wants a piece of the action.
I’ve been preparing you for this for the last couple of weeks. You may recall my recent post about the relentless doubling of Moore’s Law and a new book, Race Against the Machine that discusses how our world is changing now that we have significant computing power.
You may also recall my last post about how knowledge is doubling at a ridiculous rate — by 2014 it’ll be doubling every 11 hours, as forecast by IBM. It’s no surprise, then, that analytics companies are as numerous as chocolate bunnies at Easter.
Here’s the tricky part: A typical early market starts out with messaging about the technology. It lets you do this or that with the data. Later on the messaging includes real quantifiable benefits that you can literally take to the bank in the form of ROI.
Then the ultimate validation numbers start rolling in and they are usually in the form of double-digit percentage increases and they don’t depend on return on investment because they are so fundamental that they stand on their own.
From what I see, this isn’t going to happen for this analytics wave. I suspect that there might never be a roll out like that again.
Analytics have been around for a long time and there have actually been several false starts. There have been times when analytics looked like a sure solution to many company ailments.
Some analytics vendors did quite well in that era — keep in mind that SAS is a multibillion-dollar company and it is still privately held. Did I mention that it’s about 30 years old?
Analytics, however, failed to break through because there was always something sexier and easier to understand coming out at the same time.
Back then, analytics suffered from the reality that you needed a Ph.D. in statistics to really appreciate the stuff. Most companies have very limited supplies of doctoral candidates. They have many more sales people who need leads, so in the showdown between analytics and CRM, analytics always won.
Then you should consider ROI and what SaaS computing has done to it. SaaS turns big investments into small numbers, so the division calculating savings per dollar invested no longer gets the attention it deserves. That means ROI as a driver just isn’t what it used to be.
For analytics to be successful this time — and we really need it to be successful — several things have to fall into place.
It has to be dead solid easy for mere mortals to use analytics. That means each of us needs to up our game. The jobs of tomorrow will be based on the ability to make sense of metrics and probability. That’s not a hard lift.
We also have to get the messaging down, all the way to the fundamental numbers that define it as worth having — period. We need to do that post haste.
Most vendors I’ve seen don’t have that picture in mind yet. They talk about their products like they are ends in themselves, instead of being means to the ends of profit and cost abatement. The best way to get the messaging right is to quit talking about ROI and begin talking about metrics — specifically the metrics that are germane to my business processes.
By implication that also means getting comfortable enough with the idea of metrics — that each of us can easily come up with our own now and then because we are the ones closest to the reality that needs to be described.
Again, that means we both need to be able to punch up an analysis, but we have to be fearless enough to take command and roll our own. Analytics won’t be mainstream until you can do this.
I think some emerging vendors are going to do that and then we’ll have a horse race.
This reminds me of the early days of SaaS computing and CRM. Back then there were multiple companies bringing SaaS SFA to market. Most thought about SaaS as simply another delivery method. One company, however, saw SaaS as a revolutionary approach to software and knew in its bones that it would disrupt the whole industry.
Today there’s only one company from that era still standing and you know who it is.
Analytics is at a similar tipping point because it’s no longer the thing that the data scientists use to discover interesting things about the business. Analytics is a seminal technology that harnesses Big Iron to digest Big Data to give us the insights we need to compete.
As Brynjolfsson and McAfee write in Race Against the Machine, it’s one of the things that enable us to play on the second half of the chessboard. It’s what we have to do — because that’s inevitably where we are going.