With the recent unveiling of Salesforce Einstein, Dreamforce 2016 was always going to be abuzz with talk of Artificial Intelligence. Now that Einstein has been formally introduced at DF16, you won’t be able to get away from the subject. So if you find yourself coping with all the hype by smiling, nodding and then frantically Googling AI-related terms, let us save you the trouble with everything you need to know about AI at Dreamforce.
Stop Googling – here’s a simple definition
Artificial Intelligence is actually a very wide field. It spans everything from simple algorithms programming machines to perform basic tasks, to what’s known as “deep learning” (sounds vaguely sinister, doesn’t it?) – essentially, enabling computers to teach themselves.
A conveniently-timed and highly informative Fortune article published last week provides a succinct definition of AI:
AI is, in the broadest term, applying to any technique that enables computers to mimic human intelligence, using logic, if-then rules, decision trees, and machine learning (including deep learning).”
“Machine learning” is “the subset of AI that includes abstruse statistical techniques that enable machines to improve at tasks with experience.”
“Deep learning” is “the subset of machine learning (hold tight, we’re into a sub-subset of AI now…) composed of algorithms that permit software to train itself to perform tasks, like speech and image recognition, by exposing multilayered neural networks to vast amounts of data.”
Now that’s out of the way, here’s what all of this has to do with the excitement around Salesforce, Dreamforce and AI in the B2B context.
There’s no question: AI is good for business
Getting straight to the point, AI makes for far greater efficiency. Computers can simply process much more information (and perform more manual tasks) in far less time than humans can.
So just as other industries have taken advantage of AI – from assembling cars and flying drones, to helping doctors save lives – the B2B world is finding its own uses for it too.
To borrow again from the Fortune article: Andrew Ng, chief scientist at Baidu Research, compared the onset of AI to the beginning of the internet – or even electricity.
“In the past, a lot of S&P 500 CEOs wished they had started thinking sooner than they did about their Internet strategy. I think five years from now there will be a number of S&P 500 CEOs that will wish they’d started thinking earlier about their AI strategy.”…
“AI is the new electricity… Just as 100 years ago electricity transformed industry after industry, AI will now do the same.”
There are many potential applications, but what Salesforce is tapping into with Einstein – adding a layer of AI into the Salesforce CRM – is the growing interest in and demand for lead generation tools like predictive analytics. (That’s another subject that’s bound to come up at Dreamforce, by the way).
To quote Leadspace’s VP of Product Travis Kaufman:
“Predictive Analytics adds a crucial layer of objective, pure data-driven insights and intelligence to inform your marketing and sales decision-making process. …[T]here’s no doubt that Predictive Analytics can process data, identify patterns and surface insights that human beings simply can’t do at scale.”
Predictive analytics is in fact a great illustration of the incredible utility of Artificial Intelligence as a whole. However, it’s really only scratching the surface.
Predictive analytics applies machine-learning (including elements of deep learning) to your CRM and marketing automation systems, to predict which leads are most likely to buy.
An effective predictive analytics platform will enable you to score, prioritize and segment your leads, accounts and contacts to a high level of accuracy, by measuring the past behavior and traits of your previous customers and leads. It will save your sales and marketing teams countless hours, and significantly increase your conversion rates.
More advanced predictive analytics platforms can continue to learn beyond that initial phase, gradually refining their model by identifying key traits which indicate greater propensity to buy. That will enable you to create an Ideal Customer Profile to prospect for net-new – cutting through the dead-end leads to pinpoint only the most promising ones.
It’s the difference between, say, rowing upstream (or through your sales funnel) and powering through on a speed boat.
So it’s hardly any wonder this kind of technology is quickly catching on. The prominence of AI at Dreamforce is just the latest illustration of this phenomenon.
Beyond predictive: Unlocking the potential of Artificial Intelligence in B2B marketing
However, as Leadspace CEO Doug Bewsher pointed out in a recent Demand Gen Report article, for AI to remain relevant it needs to evolve beyond predictive alone.
For a start, there are the obvious limitations – specifically, any predictive model relies entirely on the quality of your data.
“Data is AI’s Achilles’ heel… No matter how smart the algorithms, their effectiveness is crippled if you use them with the same old basic, static contact data in your CRM or purchased from traditional data vendors.”
So for example, tools like Salesforce Einstein definitely add value to your CRM, but you’ll still ultimately be limited by the amount and quality of data you have in your CRM.
Predictive models aren’t daunted by enormous volumes of information, of the kind enterprise-level companies handle, for example (think tens of millions and more). In fact, the more, the merrier; a wider sample of data will inevitably make for greater accuracy.
But on the downside, if you’re working with smaller, customized databases most predictive models will struggle to perform.
“B2B marketers need a complete solution that works across multiple channels, in their existing marketing stack,” Bewsher added.
But remember, even Artificial Intelligence has its limits
We’ve covered this before, but despite the (well-deserved) hype, AI has its limits, so it’s important to manage expectations.
For a start, we’re not quite at the sci-fi stage yet. AI can do a lot, but it’s still no replacement for human experience, wisdom and intuition. You can’t make sales with algorithms alone.
The solution to this challenge is fairly straightforward: combine the two.
Just add a human touch…
AI is an obvious, even critical addition to your marketing and sales stacks – but it’s there to optimize the potential of your reps, not replace them. As such, whatever AI platform you use it needs to be highly customizable and accessible. Black box models – that is, start-to-finish products which don’t allow for any adjustment or ad-hoc customization – will be have limited utility.
Every sales and marketing professional knows the importance of bringing intuitive human input into the mix whatever the quality of your data.
Just like you wouldn’t let your car drive itself (no, we’re still not quite there yet), you can’t let AI run your sales funnel for you.
At Leadspace we call this hybrid human/artificial intelligence approach “semantic modeling.”
Using the example of predictive analytics, it means maintaining a “white box” model, so predictive models can always be adjusted as appropriate, instead of taking a “fire and forget” approach.
The results speak for themselves, and provide endless deliciously large statistics for us to throw around in these blogs.
Learn more about AI at Dreamforce
You don’t need to wait to reap the benefits of Artificial Intelligence for B2B marketing. Leadspace’s more than 120 customers – including seven of the world’s 10 largest enterprise software companies – get that today.
If you’re at Dreamforce this year, visit our Leadspace Booth 2116 in the Expo Hall to learn more about how Leadspace AI can revolutionize your B2B lead generation.
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