Fake It Until You Make It: The Secret to Winning in Sales with Artificial Intelligence (A.I.)
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Whatever you call it — predictive analytics, machine learning, or artificial intelligence, — you need to be doing it or at least say you’re doing it. I’m serious, if your product isn’t powered by artificial intelligence then get it on the road map and do what everyone else is doing — sell it like you already have it.
With recent announcements by large software companies that A.I. is the next big thing, we’ve officially crossed the metaphoric Geoffrey Moore chasm and A.I. is here to stay. It is still early but you will see A.I. infiltrate every industry and vertical in the coming years and months. Kevin Kelly in his book, The Inevitable, talks about A.I. and says,
“According to quantitative analysis firm Quid, AI has attracted more than $18 billion in investments since 2009. Last year alone (2015), more than $2 billion was invested in 322 companies with AI-like technology. Facebook and Google have recruited researchers to join their in-house AI research teams. Yahoo!, Intel, Dropbox, LinkedIn, Pinterest and Twitter have all purchased AI companies since last year. Private investment in the AI sector has been expanding 70 per cent a year on average for the past four years, a rate that is expected to continue.”
You get the idea and if you’re like me, you’re hearing references to A.I. sneaking into every company pitch regarding marketing, prospecting, content and messaging, pipeline management, quote and proposal, etc. etc. etc. People are talking about it but I’m not sure they know what it is, actually I know they don’t know what it is. At InsideSales Labs we’re in the middle of a research study asking executives what their biggest obstacles to using A.I. are and 1 in 6 leaders have said they “don’t understand what A.I. is.” Like every buzzword we hear and start using, we may not fully understand its meaning so let’s define A.I.. I’m probably a little bit biased on this one but I’m going to go with the definition from CEO of InsideSales.com, Dave Elkington, who said, “AI is using a machine to understand past behavior to first predict, then potentially alter future behavior to produce more optimal outcomes.”
Good, now that we have that out of the way, let’s discuss how companies can win with A.I..
The Math is Table Stakes
You’re going to hear some cool terms and see some pretty neat math equations when you start diving into the A.I. space but don’t be fooled by such conversations as the math is important but it’s not the differentiator some people make it out to be. Why is that? Because the math has been around for a long time. In 1959 two smart people by the names of Bernard Widrow and Marcian Hoff used neural networks to remove the echo from a phone line. Wow! 1959? The science of A.I. has been around that long? Yup. Now if you’re not familiar with neural networks like the rest of us, the simplest way to grasp the concept is to understand that they are computational models designed to work like the human brain. (See the picture to the right from Fortune to see how neural networks work.) They are designed and then trained to solve problems and are extremely effective at what they do but again they’ve been around solving problems for 58 years. That’s not to say there has not been developments in the core math as concepts like deep neural networks and random forest algorithms have evolved but in general, the core math has been around for a long time.
So, if it’s not the math what is it that makes companies win with A.I.?
The Data is the Differentiator
Data makes all the difference and there are two parts to data, quantity and quality. According to Inc, in 2016 Amazon.com achieved a remarkable feat of 636 sales per second. With this type of data Amazon can run all sorts of analysis and recommendations. How many of us can claim similar numbers, especially in the B2B space? In enterprise sales I’ve seen companies do thousands of deals a year but that’s not big data. So how do we solve this problem? Your own data is not going to be enough. Well, you can augment your data with other data sources to boost both quantity and quality. Here are a couple of options:
Mandatory Self-Declared Data
This type of data is available to the public in such forms as the census. The problem is that this data is not timely. The census is taken every ten years and as we volunteer data we usually give as little as possible which makes the accuracy of the data questionable.
Brute Force Data
Think oversees call centers pounding the phone to gather data. This is fantastic data but the problem is it’s not timely. It’s difficult to call millions of people and keep up with the real-time changes that are taking place.
Voluntary Self- Declared Data
This is social media data and it is truly unique and revolutionary. It’s real-time information about who we are and what we do. The question is, does it represent our life or a curated version of our life? I’d argue the latter. Let’s be honest, we don’t spend the majority of time sitting on the beach taking random pictures of our feet. Social media data has amazing timeliness but its accuracy is also limited.
Passive Crowd Sourced
This is where the money is and this is what the B2C companies have figured out. This is observed data that is captured in real-time. It’s gathered by passively observing what is happening and then it’s aggregated together across multiple entities. This is how enterprise sales wins with big data — by working together to supplement each other’s data. One large company may not have enough data by itself, but aggregated with other large companies it becomes extremely powerful. This is the power of InsideSales.com Neuralytics. It’s a big data play that passively crowdsources and anonymizes data to allow everybody’s data to work together.
Application is Key
One you have the math and the data figured out, it all goes to waste if you can’t solve real world problems. Let me give you an example. I recently bought a Lexus. I was told by the dealership that they would send me reminders based on estimated mileage so I would know when my car needed to be serviced. That’s something I really need as I’m always bad at remembering to service my car. The other day I received in the mail the reminder to service my car but two weeks later I’ve not done anything about it. That’s the problem with A.I. It’s often not consumable or actionable so it becomes a nice thing but not anything useful. If A.I. is not blended perfectly into the workflow of what people do then we’ll miss out on many of the benefits. The music in Pandora automatically has to stream, I can’t be forced to go and make a selection. I can’t press buttons to find a better route in Google Maps, I have to be automatically be reassigned to a better route on my way to work.
In our world of sales, it’s no different. Lead scoring is old news and many companies have built their own modules simply by throwing an apache spark on a hadoop cluster and putting some data in it. The challenge is when you do that and give a score to a rep they are going to do exactly what they did before. Why? They don’t trust the algorithm, they know better, and they are lazy. Therein lies the problem. We call it no-lift A.I.. A.I. is trendy and everybody is talking about it but you must be careful of falling down the hole of no-lift A.I. If you don’t want to fall for this trap, you have to be deep domain experts. Netflix is an expert at entertainment and how people want to consume it. Nest knows more about how people live in their home than anybody on the planet, Amazon knows your shopping behaviors better than you do and I’d argue we at InsideSales.com know more about how people sell than anybody on the planet.
In order to be successful at A.I. remember . . .
The math is table stakes, the data is the differentiator, and application is key.
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***This post was taken from Dave Elkington’s presentation at InsideSales.com’s Accelerate