November 26, 2021 was a big day for us at SEC! It marked our first in-person event since our last time in London back in December 2019. Nearly 2 years later, we returned to the home of Big Ben to celebrate sales enablement, and the incredible value it provides to organizations.
In 2019 Arup Chakravarti of Elavon Europe took to our stage in London to discuss how to leverage artificial intelligence (AI) and machine learning (ML) in sales enablement.
Little did any of us know how the world, and sales enablement as a result, would change just a few months later.
To celebrate our return to in-person events, we caught up with Arup to discuss how AI and ML has changed over the past two, primarily-virtual years, and where its future lies.
- How AI has been utilized over the last few years
- AI and segmentation
- The benefits AI brings to salespeople
And more below:
Q: How have you been using AI over the past two years?
A: If we look at some of the technology advancements over the last couple of years, we've been utilising AI’s capability for more intelligent segmentation. On that basis then, we’re driving the next set of opportunities that are linked to certain groups of customers within our broader portfolio.
When I presented on this topic two years back in December 2019, that's exactly what we were doing at that time. We were taking our portfolio data, pushing that through, getting the data scored, and then taking a view in terms of where we should focus our effort and energy in terms of retaining customers and generating value in terms of total portfolio revenue by reducing churn.
We looked at where we could hold on to customers, and not have cancellations, and then after that time we also started to explore where we could start to drive more sales and increase cross-sell and upsell with new customers (as well as existing customers).
Those are the two primary areas where we'd utilise AI and ML and in terms of just being smart about which customer we should go for.
Q: How does AI and ML help when it comes to targeting customers?
Q: It’s like a segmentation play: you've got a whole bunch of customers, you know you could go after a good chunk of those customers at any one point in time, but you don't have as many resources in terms of personnel, or digital channels. You don't have an infinite amount of resources.
Therefore, you need to figure out how you utilize your resources in the most effective manner to get the best return that you can
Over the last couple of years, we haven't really strayed away from those two use cases at Elavon. We've worked towards making the AI and ML environment smarter, and smarter, and smarter in order to provide us with better predictions which give us better outcomes.
We’ve also integrated it into some of our other systems. Now we have integrations into our CRM system, which automates a lot of the data flow. So the use cases have remained, but we're still constantly improving the value that we're getting from the AI and the models.
A lot of the efficiency that we're now focused on is coming through from having more technical integrations across the different products and platforms that we've got, and that's where we focused as a business.
Q: What potential is there with these integrations?
A: If you cast your net out and look at where AI and ML has been over the last couple of years you can see that Salesforce has had for some time now, and they've been increasingly championing and marketing a product called Einstein Analytics, their AI product.
That's now embedded into the CRM
They've embedded it into analytics and embedded it into a range of core Salesforce solutions and platforms, which then allows for very smart and intelligent analyses and predictions of where a customer is at a given point in time, if that customer is appropriate for some type of outreach engagement, if it's appropriate to be talking to that customer regarding a range of solutions, and so on.
So there are solutions, such as Salesforce's Einstein which has capabilities that are embedded across its products that allows it to integrate, but at a more ramped up and more sophisticated level.
Q: What are the benefits of the AI integrations?
A: Fundamentally it’s a time-saver. At the end of the day a lot of what we do in terms of sales enablement is about trying to generate productivity by reshaping where a salesperson or an account manager is spending their time.
We know that one of the biggest productivity drags is when a salesperson or an account manager has to spend time doing admin, spend time in the CRM system, and has to spend time planning and researching
Now all of those are value adding activities, researching and planning for a conversation, particularly if you're at a stage in a negotiation where you want to move the negotiation from here to there. Of course planning, preparing, and reviewing are all important activities, and fundamental to a successful sales process.
However, think about a scenario, particularly with lower value, higher volume-type deals, where you've got more smaller to medium sized businesses you're trying to engage with, and you perhaps have a consistency in terms of the the profile of those customers.
Instead of necessarily spending time preparing for each and every individual customer, if we can send to the salesperson a list of customers that are in a way already appropriate to reach out to and engage with, we're saving that salesperson a significant amount of time.
They don't have to go and look at their portfolio, undertake some analysis, research, which customers they should go after, then do some further background checks and further homework just to make sure that they're prepared.
What’s happening is that we're already doing the analysis, because the AI models are doing the analysis and coming back with results that say: “Don’t focus on these 100 customers, just focus on these 20”.
Through the integration and some of the data enrichment that we have from, we're surfacing that up to the salesperson in the CRM, and giving them some clear rationale behind the: “Why these 20 customers and not these 80 customers”.
Whether it's industry, whether it's snippets of information, whether it's something that the AI environments picked up in terms of the wider data lake which it has surfaced out to say, “this has changed, or that senior leader has changed” or some other trigger in terms of the enrichment and information going back to the salesperson.
As much as possible, what we're doing there is reducing the amount of groundwork that the salesperson has to do, and in the process, ensuring that they get to spend more time in front of the customer.
Q: Are there any other use cases for AI which you’re exploring, or see on the horizon?
A: There are plenty of use cases. Within the sales enablement environment, if you look at the sales enablement tech stack, there's a lot that's happening.
So if you look at the stacks for big companies, it all starts with content management and content management systems that are AI powered. They’re designed to surface up the right message for a salesperson to be able to bring to the customer.
That's an opportunity to be able to understand what message a salesperson should take to a customer, and then you have the chance to analyse and understand if that message is resonating. You’re able to even get insights regarding how the buyer is engaging with that content, what's interesting the buyer, where the buyer is spending time.
It’s about being able to shape the insights in terms of what message is important for a particular buyer or particular buyer persona
Other parts which have definitely definitely emerged in the last couple of years are things which are essentially conversation intelligence. That's artificial intelligence applied to verbal conversations.
This is a part of the sales enablement tech stack where you can have, as a salesperson is presenting to a buyer, a recording of that interaction, whether it's phone based or video based. Not only is that interaction recorded, so it can be listened to at a future point in time, but the conversation intelligence bot in the background is also able to pick up on sentiment points from the buyer through that conversation.
It’s a really intelligent mechanism which allows you to understand what content you are serving up, and whether that resonates with the buyer, as you're having the conversation with the buyer.
Is the buyer saying certain things that sound positive and that sound like they're really engaged? Or actually they’re perhaps disengaged, or maybe providing some constructive feedback. All of these things are things that sentiment analysis and conversation intelligence bots can pick up on. So I think that the direction that a lot of the enablement technology has taken is incredible.
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