Episode 215: Arup Chakravarti on the Power of AI for Enablement

Sales Enablement PRO Podcast

Shawnna Sumaoang: Hi, and welcome to the Sales Enablement PRO podcast. I am Shawnna Sumaoang. Sales enablement is a constantly evolving space and we’re here to help professionals stay up to date on the latest trends and best practices so that they can be more effective in their jobs.

Today I’m excited to have Arup Chakravarti from Equifax UK join us. Arup, I would love to introduce yourself, your role in your organization to our audience.

Arup Chakravarti: Absolutely, firstly thank you so much for having me on the podcast. It’s a real pleasure to be here. A little bit about myself, I have been in sales enablement essentially for almost all of my career. I started off in maybe more of a sales operations stream and that’s again going back almost 20 years now working in financial services. I’ve always been in a B2B context, so therefore always been in that space where I’ve been very close to sales teams and account management teams and helped them become much more effective and productive in what they do.

Certainly 20 years ago there wasn’t really a sales enablement domain, I think sales operations was perhaps just starting out then, but you know, in terms of what that domain is today, if you think about it either its revenue operations or revenue enablement, it’s so much more sophisticated, it’s so much more mature and so much more complex. In many respects also so much more of a satisfying environment in which to work, say than 20 years ago. Over that period of time of seeing how the entire domain, the discipline has matured, as I said, how it’s moved from saying sales operations, which is you know, again 20 years ago maybe literally looking at things like sales incentive plans to doing sales performance to maturing into sales tools such as CRM etcetera. Of course, now, that whole space is such a strong blend of sales operations and sales enablement which naturally includes training and coaching and development. So that’s been my career pathway for actually all of my career, all in a B2B context.

SS: We’re excited to have you here Arup, we’ve worked with you numerous times over the years, so I’m so glad you’re able to join our podcast. Now in the past, we’ve talked about the power of leveraging artificial intelligence or AI to increase sales effectiveness. In your experience, what are some of the benefits of using AI and enablement?

AC: Grand, I’m glad you asked me that question. Let’s just cut it back to something that you know, why would you use AI, why would any organization use AI, and what value does it get from AI in different business functions and different business use cases? So fundamentally the value of AI is essentially getting the computer to process a huge amount of data and to process all of that data in a much more intelligent and frankly much more powerful than accelerated manner than any human being could do. AI in most applications, in most use cases artificial intelligence, machine learning, what it’s effectively doing is going through huge amounts of data and finding consistent patterns in those data and in the process of finding consistent patterns, flagging up those patterns for some type of decision making. At its simplest, even in the consumer world when you’re looking at things like Netflix, if you’ve got your Netflix account and Netflix throws up or Amazon throws up certain items that you might be interested in purchasing or certain movies or TV shows that you might be interested in watching what’s happening, there really is just a huge amount of data that are being processed data about yourself in terms of what you like to do, but data about similar profile consumers that are also looking at similar programs and then a decision is being made in terms of what you might like. That’s all in many respects that AI is doing right in the context of what we think about in most business use cases, it’s looking at a huge amount of data and then being able to pinpoint certain behavioral patterns in that data.

Within the context of enablement, especially revenue enablement it’s really powerful because essentially what it’s doing is it’s helping an organization and individuals in an organization be much more intelligent in the context of identifying some of their customers, their clients, whether it’s existing or prospective customers, that may be closer to making some type of buying decision. You’re looking at patterns of behavior either at the individual level or you’re looking at patterns of behavior within a firm level. If you think about buyers and buyers in the marketplace and you know this yourself, the way that enablement has changed the way that buyers purchase has shifted so fundamentally that now it’s so much less about the sales effort to the buyer, but so much more about how a buyer discovers a particular company, how buyer goes through that buyer journey, and how sellers are able to educate that buyer through that process. What AI does is kind of just help sellers within an organization just be much smarter about identifying which companies are closer to making a buying decision.

SS: I love that. What does it look like to embed AI into your enablement programs though, what are some of the key ways that you’ve implemented AI-driven programs?

AC: Yeah, so I’ll take that in two halves because in the second half you’re asking me how I implemented AI and you know, to be perfectly honest with you, I would love to have implemented a lot more AI. I think it’s a really exciting space. I think forward-looking companies that do implement AI in terms of their sales, and marketing processes, get a lot of value from it, and absolutely, I would love to have done some more. Let’s first talk about, you know, some of the use cases and some of the implementations that we see.

We sort of hear AI being much more prevalent in terms of some of the sales stack, in terms of some of the marketing stack, and how that’s helping, again, as I said, companies make much more intelligent decisions about which buyers they should engage with and when. We definitely see that in terms of conversation intelligence, I mean obviously, you know, some of the big names, they’re great companies because what they’ve been doing is clearly being able to build out capabilities where they can analyze unstructured verbal communications and in the process start to identify different types of sentiments and again, it’s just that process of if you can listen to that conversation, you can be intelligent in terms of how you analyze that conversation, you can get the machine the computer to flag up insights and behavioral patterns to you. It then starts to give you as a sales rep that capability in a company that’s selling to a set of buyers. It starts to give you a really clear indication in terms of which of your buyers are potentially closer to making a buying decision. So we absolutely see that when you see deals being tracked through CRM and through the pipeline, the revenue intelligence capabilities have AI that is analyzing again how that deal and information about that deal is being tracked. So it starts to again exhibit information about is a particular deal closer to being a converted close one or actually is there less confidence in terms of that deal coming to a successful closure. So those are sort of the areas where and when you look at revenue enablement, in particular, those are the sorts of areas where we’re starting to see AI getting embedded into a lot of that revenue value chain.

If you think about all of the different activities that a seller needs to go through to be able to prepare for, engage with, proposed to, go through a negotiation process, and again, capture information in their CRM system, capturing information across a number of different systems, utilizing sells enablement platforms to be able to access information and be much smarter in terms of their there they’re kind of they’re selling engagement, all of those areas are just becoming much more sophisticated in terms of utilizing machine learning artificial intelligence to be able to help automate a number of decisions to be able to help bring the information up to a sales rep and also to be able to help that sales rep understand how they’re engaging with the customer and the levels of kind of sentiment and engagement from that customer.

SS: Fantastic. One thing you’ve mentioned is the importance of essentially demystifying AI for enablement leaders. Why do you think some leaders might be apprehensive about leveraging AI? And what is your advice for demystifying AI for them?

AC: That’s a great question. I don’t know if enablement leaders are necessarily apprehensive about implementing AI, I think it’s just a case of not necessarily having a clear picture of what AI means and how it can deliver value. I think there’s also a certain confusion in terms of artificial intelligence machine learning, its association with data science, and having a very big data science function. I sort of see the deployment of AI into business processes, it really falls into a kind of two buckets for me, what I call the kind of the functional level deployment of AI or the kind of application-level deployment of AI and in the functional level deployment of AI, what you’ve got there is exactly that. You’ve got like very big organizations oftentimes banks because banks have been doing this to very big financial institutions. Banks have been doing this for a really long period of time. You get a whole bunch of really smart people, data engineers, and data scientists that know what they’re doing and know how to code the machine and code the data. Because it’s a bank they’ll have lots of on-premise infrastructures, lots of server power, lots of space that they can bring in a huge amount of data, and again, oftentimes because its banks, whether it’s credit card companies or mortgage companies or any type of financial transaction related businesses, they have a huge amount of information in terms of how people utilize their products, their financial services products. So they’re able to do a huge amount of analysis engineer that day to process that data have the smart guys, in terms of the data scientists looking at that and then being able to build out decision models in terms of is Arup going to default on his credit card is Arup looking like the type of person we want to be able to make a mortgage loan to etcetera. That’s kind of AI at the functional level. Utilizing a huge amount of Human Resources to build out, I got a very powerful and of course a very expensive function. So that’s what I call AI at the functional level.

A lot of big institutions are doing that, but you need to be a very, very large scale well established enterprise. Again, oftentimes banks are in terms of financial services to be able to have that type of a function. Whereas I think a lot of companies now are starting to realize that AI is now being embedded more into the application, that you can get it in all of those different capabilities. You don’t necessarily know how the AI is working 100%, it’s a little bit of a black box, but that’s okay if you know that you’re buying into one of those companies and you know that as you plug it in into your sales process, you plug it into kind of your sales enablement and engagement processes that you start to see the value, it starts to help automate decisions, it makes life easier for the sales rep, that’s the application level kind of deployment of AI.

If you talk about our enablement leaders going to apprehend and everyone nervous about engaging with AI I don’t think they are, I think it’s just a case of being able to realize that a lot of the AI though, that enablement leaders work with is already there, it’s already embedded into their application, it’s already embedded into the way that they’re kind of working. So the big challenge for enablement Leaders is if you have all of those applications, how do you ensure that in a way the AI across each of those individual platforms is working in as harmonized a manner as possible? I think again there are a lot of talks that’s been coming out recently about Frankenstein where you end up with too many kinds of different tools within your sales stack, they don’t necessarily fit together really well. The AI within each of those tools is kind of sending you up to different decisions and different kinds of insights that might not be harmonized. So whilst you’ve got all of this AI the challenge for an enablement Leader might not be the desire to utilize AI might actually be the sort of problem that AI delivers, because if you’ve got all of these applications, you may suddenly find that actually the decision that you’re getting from, it is not necessarily harmonized all the way through.

SS: I love that now, AI helps make predictions but it’s up to enablement teams to really utilize these predictions for success. What are some of the ways in which you’ve leveraged AI predictions to aid in decision-making?

AC: Thank you for asking that. I’d love to call out my time at Elavon, which is my most recent company. I joined Equifax about 6 or 7 weeks ago, so I’ve still yet to figure out where we have some of these opportunities and what we can develop and do, and perhaps what are some of the vendors and deployment capabilities we’re going to look at. More recently Elavon, I spent seven years there, and in the last three years looking at developing and building out a customer data platform capability with AI embedded into it. What we did with that was a really simple business retention use case, a kind of customer retention use case. Elavon merchant services is a payments processor. So our portfolio of customers is huge so we have in the region of 200,000 plus customers across all of Europe. So we have a very big portfolio of S&B customers that are remotely managed and we have naturally a very small proportion of account managers as opposed to the number of accounts in the portfolio. In fact, we’ve got about 50 to 60 account managers against a total portfolio in that S&B space, a total portfolio of more than 100,000 accounts.

You’ve got a very big portfolio and a very small team on a proportionate basis. So when it comes to saving customers when it comes to retaining customers, that’s the biggest challenge that the team had. In a lot of instances, they weren’t even necessarily speaking as proactively as they would like to an individual customer in that portfolio on a regular basis. So the challenge that we have is how do you then flag up customers into the team that could be at a much higher risk of canceling. Through that customer data platform, the CDP solution that we deployed, we were able to train that with the AI data with the huge amount of information that we had in terms of where we’ve seen retention, retention challenges, where customers had canceled, equally where customers have been going to cancel but we’d save them. We trained that entire environment. So effectively what we could start to do was about 3-4 months ahead of a customer potentially canceling. We were able to see some of the signals and those signals that were coming through would give us an indication that this customer is at risk of canceling.

So we did that and we did. Obviously no longer with them, but of course very proud of the team because we deployed that, and certainly through the course of 2021 we worked through a total list of about 7500 customers. About 50% of those customers were genuinely at risk of canceling. We caught those 50%. You’re talking about 3200 customers. We caught those customers 2 to 3 months ahead of canceling. And again, not my team, but this is the account management team, we facilitated their effectiveness, and we facilitated their productivity so they had the right conversations at the right time and were able to save about 80% of those customers, which is fantastic. So all in all, you know across the board, the contribution that my team made through that AI deployment through the customer data platform, the contribution that the team made was now $2 million through the course of 2021. So really pleased in terms of a simple use case like that, which is like how do you identify customers that are potentially going to cancel, be confident about that and get in front of that conversation before the customer does cancel.

SS: In your experience, what are some of the business outcomes that you’ve been able to correlate to your AI-driven programs?

AC: So again, in the context of say using something like a customer data platform and then utilizing that with insights in terms of what you should do, absolutely, it kind of goes in two directions. For me it’s the direction of how you generate more revenue through cross-sell, upsell, better engagement with the customer, or even deal conversion or how do you protect revenue by promptly identifying customers that are potentially at risk of canceling. So again not that we do not use this capability but you know again just through my reading and understanding of the marketplace.

I think a lot of what we’re seeing in terms of business outcomes has got to be and we were talking about it from an enablement perspective again we’re talking about revenue enablement has got to be that. It’s got to be like how do we help sellers be more confident, be focused and more productive and focused on the right deals at the right time and be more confident in that engagement so that they can increase their sales conversion rates, their win rates and AI should be able to help with that. The CDP platform that I deployed at Elavon offered up a 2 to 3 times stronger win rate, and a sales conversion rate than you would have on your average. We piloted that and it proved itself in that context on the flip side as well you should be able to then engage with customers that are potentially going to cancel so you want to hold onto and protect that revenue again, AI should be able to identify those customers before they go through that process, before they experience any dissatisfaction or any challenges and they threatened to cancel. AI should be able to help you get ahead of that so that you can protect that revenue.

SS: Fantastic. Now, the last question for you Arup. With AI technology and capabilities constantly evolving, what predictions do you have for the future of these tools and how they might continue to drive innovation and enablement?

AC: I think one of the biggest trends I think that we’ll see over the next 3-4 years is consolidation. I think there are a lot of applications and platforms out there. Clearly, there will be some consolidation, that happens all the time. Each of the different providers and players in the marketplace is just trying to identify which part of that revenue value chain do they not have in their mix today that they could stretch into, and is there a platform in that place? Oftentimes facilitated by AI the primary players will buy, so again, I think that consolidation of these different tools and capabilities so that the sales stack starts to become a lot more, for lack of a different expression, consolidated. That is the direction that it’s going and I think that the part of the challenge, again, that’s a double-edged sword, that makes it easier sometimes for a customer, a kind of a buying client in that space that’s looking for those types of tools, it makes it easier for buying clients to get to the right decision, but also you could end up with a loss of some of the sophistication and some of the kind of the features and the benefits and the quality of the capabilities in terms of the current context as it sits across all of these different providers.

So consolidation is a good thing in some respects for buyers that want to buy AI, want to buy this kind of sales stack and marketing stack, and want to buy that capability. It’s good because it simplifies the buying decision but also maybe, you know, again, as I said, I think maybe a bit challenging in terms of loss of features and benefits etcetera. So that’s number one, I’d say, absolutely consolidation.

I think number two, within that technology space, integration. So you want to see a number of the different key providers that are still big names that continue to have a market presence, looking to integrate more with each other. So this notion of frenemies, working closely with another provider in that space, in the sales stack and utilizing AI machine learning. Providers that are able to kind of work together and think about that sort of revenue value chain and being able to build out a kind of a coordinated comprehensive solution set. I think that the integration piece is going to be key and I guess part of that integration piece will be how do you get much more API kind of glue-based capabilities, as opposed to types of capabilities that help add your AI-driven sales stack into your core business platforms. That’s the space where I think a lot of things will start to evolve. I think you know in terms of AI it’s already driving a lot of sophistication. So you’ve already got speech analytics in terms of conversation intelligence, you have text analytics obviously you know, in terms of kind of revenue intelligence. Again all of that’s going on there, so the power of AI it’s really fantastic to know where it’s going. I think that the key will be how that all starts to come together in a more consolidated manner.

SS: Thank you so much, Arup. I greatly appreciated the opportunity to reconnect with you and have you share your insights with our audience. Thank you.

AC: That’s very kind, thank you very much for inviting me.

SS: To our audience, thanks for listening. For more insights, tips, and expertise from sales enablement leaders, visit salesenablement.pro. If there is something you’d like to share or a topic you’d like to learn more about, please let us know we’d love to hear from you.