We’ve spoken a bit about the millennial sales rep and how they’ve adopted Artificial Intelligence (AI) in their personal life. The main issue we are finding is that automation and AI are finding their way slowly in the professional lives of salespeople, however not many know how to make the most out of this technology.
On the latest Playmakers Podcast, we spoke about what is Artificial Intelligence, how it works, and why you need it as a sales rep to sell more and sell faster and smarter.
A Day in the Life of a Millennial Sales Rep
I’m a millennial, and I’m proud of it. We use technology in our day-to-day to do just about anything. Some people call us lazy, but I think we are just efficient.
We live and breathe technology and are very fond of AI and automation. Here’s just a few examples:
- We use the Nest smart thermostat to warm up our homes
- Virtual coaches for our running sessions like Vi
- We use apps like GreenBlender to order our smoothies in the morning
- Google Maps and Waze power up our navigation through the cities
- We listen to Spotify, using AI recommendations to organize our musical preferences
- We watch Netflix, which populates your list AI-recommended shows
- Shopping on Amazon, which uses AI to recommend similar products that other customers bought
While all this technology makes our lives significantly easier in our personal lives, in our professional lives something is amiss.
When we get to the office everything changes.
We get to deal with a CRM, which is basically just a large Excel spreadsheet, work performance reviews are wholly subjective, and automation is inexistent in most workplaces. Sales reps are assigned randomly to territories, and most of the times sales reps don’t know where to focus their efforts to reach their sales goals.
Millennials are all about efficiency, and if a system doesn’t work, it’s not going to be adopted.
In this podcast, I spoke about how AI works, and some of the ways we can get people starting to adopt A.I., because if it’s not adopted, it doesn’t matter – it’s not helping.
Why AI Matters in the Sales Industry
The latest CSO Miller Heiman study on quota attainment shows that we’re now at 53% quota attainment. I think there is a problem in sales, and I do think Artificial Intelligence can be an answer.
Here’s three quick takeaways to stew on as you think about incorporating A.I. into sales:
- Only show me what’s important
- My data is not enough
- What is in it for me?
Let’s start though with what AI is, because I have heard some use some of these terms interchangeably: , ‘Machine learning’, ‘Predictive analytics’, ‘Artificial Intelligence’, ‘Cognitive Computing.’ There is a subtle difference between them. However, for simplicity’s sake, I am going to use them synonymously in this podcast even though they’re not precisely synonymous.
It would be helpful to start with a basic definition. Applied to the sales industry, our basic definition is A.I. is using a machine learning engine to understand past behavior to first predict, then potentially alter future behavior to produce more optimal outcomes.
Only Show Me What’s Important
This is one of the principles of smart sales, and I call it the ‘Law of Prioritization’. We can see a 400% improvement in close rates over base when they focus on people who are most likely to engage and purchase from them. This is why it’s important to use Artificial Intelligence in sales: “Only show me what’s important.”
Let’s study the most common companies using AI to power their systems in the B2C industry.
Netflix, for example, has about 100 million subscribers and around 5,500 titles. How many of these titles does it show to any one user at a time? I counted the titles shown on my own Netflix system and I came up with 142 titles.
That’s just 2.5% of the titles available, and there’s a lesson to be learned here.
In sales, most companies will just randomly assign you a territory, ‘just pick some accounts and some contacts and get to work.’
Some organizations have taken it to a level two, and have scored their contacts based on their likelihood to purchase or how interesting the accounts are. However, you still have no idea of which leads you should be focusing on.
Companies that have made it to level three will only show what matters.
Why would you ever show leads which are not hot, not relevant, or do not have any chance of closing a deal?
Too much is just not good, so don’t do it.
Help me – help you – as a sales rep and just give me the contacts or accounts that I should go after. Give me some variety, but don’t make me go after all 5,750. It’s just too much.
With AI-powered systems, we’re seeing companies do this effectively.
My Data is Not Enough
The second principle of applying AI to sales is “My data is not enough.” Predictive analytics analyzes past data and returns predictions on possible future events. However, to do this, it requires large amounts of data. And this is one vital principle, because an AI engine is only as good as the data it analyzes, and our engineers have covered this in other articles.
Let’s look at how this principle translates in real life.
Remember Amazon? In 2006, Amazon had about 89 million customers. That’s a lot of customers. I don’t have that many. I don’t know if you do, but in the enterprise B2B space, we just don’t have that kind of volume. That’s big data.
B2B companies will close around 200 deals per year. I don’t know if this is big data, and enough to get a lot of insights, and that is part of the problem with B2B sales, and when it comes to AI. We don’t have enough data on our own.
How does Amazon builds its recommendation engine? It does this by analyzing users purchase history, items in their shopping cart, items they’ve rated and linked and what other customers have viewed and purchased.
The data from other customers is the cherry on top, and this last point makes all the difference. Can you imagine if it’s all based on things that I’ve purchased? AI couldn’t make a road map for the future, just based on that.
AI In The Sales Industry
Here’s this principle applied to the sales industry, hands-on.
We see this all the time: a sales rep calls a phone number, and the phone number is disconnected. Another sales rep in another location from another company goes to call that number. The system reads the data from the previous interaction and lets you know – “Hey, that number doesn’t work. Here’s another recommended number.”
It’s crowd sourced data, or using each other’s data to help move sales forward.
That’s the key of cross-company data. This where XANT specializes. The AI analyzes 110 billion sales or actions across 30 million companies. We have a decade ahead of everybody on AI in the sales database, and it’s the largest in the world.
Smart CRM is Not Enough
Now, how does that apply to sales? Again, we go to the concept of the three levels of sales organizations.
- Siloed Data: This is level one, and most companies have siloed data, so truthfully, the data doesn’t matter. They have email data, activity data, pipeline data, CRM data there. Everything is in disparate systems, so they can’t utilize the data at all for any sort of A.I. or big data recommendations.
- Smart CRM: Smart CRM is level two, where the system can analyze the internal data and come up with a few recommendations based on their scores. However, this still isn’t enough, because they are only crunching their own numbers. They can’t go deep with these recommendations, because they don’t have the final piece of the puzzle, like Amazon does – ‘what others have done.’
- Neurosales Data: This is level three of AI implementation, and it happens when a company has cross-company data that they can leverage to produce accurate predictions.
Neurosales data has three differentiating characteristics:
- It is global data specific to sales behaviors and outcomes
- It is behavioral data, not just observations – it shows which leads take you to success and which fail
- Cross-company data – the data is collected across companies and across industries, so it goes beyond the CRM
Lots of players in the sales industry have what is dubbed now “Artificial Intelligence.” However, it is the quality of the data which differentiates between the systems, and the predictions it makes.
Outcomes can be significantly different, when cross-company, external data is added to the analysis. And we know, that in the B2B space, even a difference of 10 percent in revenue can be pivotal to a company’s success.
What’s In It For Me?
Artificial Intelligence is not just some buzzword. It has applications in the real world, in the sales industry. And I’ll give you just an example. When it comes to lead scoring and A1, B3 or C1 targets, marketing might know what this means, but for the average sales rep it may be meaningless.
A 51 out of 100 score, what does that even mean? A higher close rate, or contactability rate?
We’ve played around with the concept, and it’s speaking the language of sales reps, which is money. I call it the ‘Why do I give a crap score?’ A “scheisse” score. To the sales rep, it translates into a simple concept.
If you dial your high priority accounts, it’s worth the company $10. And you can make 10% of that.
That’s one dollar for you. It’s something I can recognize. I’ve seen sales pros coach this in their business, and I have seen a 28% increase in revenue in one use case.
Here it is, in a nutshell, why you really need AI for sales. I can be real, and it can have incredible effects in the B2B sales space.
We need to adopt this technology, we need to use it to our advantage.
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