Optimizing Sales Using AI with Ellie Fields, CPEO at Salesloft

And I'm talking here about B2B sales typically, so companies like manufacturers that are selling to other companies, software companies, services, and so on. And so the seller is managing a lot of information. And, uh, they're not only managing information, direct interactions with that buying group, but that buying group is out there on the web doing things like engaging in a chatbot or going to a review site or any number of things that's signaling their intent or their engagement in the process.

So sellers have more information to manage them today. So the first thing that sales technology is helping sellers with is simply managing all that information, bringing together everything in one place. Uh, it sells off. We bring into an area called rhythm and we prioritize the most important actions based on how big an opportunity is or how hot lead is.

So really a lot of it is taking that administrative load of let me go over here and check this system or go over here and check that system. So that's the first major way. It's just reducing an administrative load. When you do that, sellers can use their time more effectively. And ostensibly you've hired people to sell because you think there's some kind of a human job in there, right?

I sometimes say if it could be e commerce, it would be e commerce at this point, but in a, in a large sale where the buyer may be Making a big bet on a product or a company. You want somebody who can form a relationship, establish trust, guide through complexity, help the buyer understand their own needs.

And so the seller can actually apply themselves to those jobs much more, um, much more completely if they're not running around looking at, um, various inboxes and things. And in fact, some analysts like Forrester have estimated that Today's seller spends about 75 percent of their time doing that administrative, that administrative stuff and about 25 percent of their time or a quarter of their time with, with actual buyers and that, that seems bad, right?

And meanwhile, buyers don't have a great experience. And so by bringing all this information together, you can do things like review a meeting later or even just get automated, uh, AI generated, automatically generated action items from that meeting. They go straight to the seller and help them send a follow up, for example.

And so, sometimes we think about what does a human need to do and what does a computer need to do, and we're getting to a place where the computer is helping more and more so that the human can do those human things. 

Richie Cotton: Okay, I love the idea of not doing administrative stuff. I don't think that's anyone's idea of like, a fun job.

Um, sorry to anyone, any administrators in the audience, but I think, yeah, most salespeople want to focus their time on actually spending time with customers and, and selling things. And the idea that just, there's so much information out there, you want to bring all that data together into some sort of coherent story, that also seems very important.

Ellie Fields: Yeah, 

Richie Cotton: absolutely. Okay, so, um, I'd like to talk about what constitutes success. So can you just talk me through what are the different metrics that sales teams use in order to measure success? I believe it's more than just like how much revenue they bring in. There's probably a bit more detail in there.

Ellie Fields: There is, but what's, what's really interesting about sales as a domain is, um, you do know what success looks like. And I, uh, you know, I, I say that because I spent many years at Tableau Software. And I'm a huge data geek and, um, And I think it's really interesting to work with domain specific data because you know what the goal function is in sales.

It is, as you said, revenue, but more specifically, it's things like, um, win rate, size of deal, cycle time, productivity by seller, renewal rates. There are a lot of secondary metrics. But really it's all about what deals are won and closed, how big they are and how long they take. Um, I say that it's exciting to have those metrics because it lets us do things with our data platform that really do go to those metrics.

Whereas if you're looking at a very generic problem, um, you know, stuff like for example, or Tableau where I used to be, you have, you have to design your system so that you can meet any kind of goal for anyone like, um, scientists. or sellers or, uh, people making things, all kinds of things. And so we can actually go farther in the data model, uh, being domain specific and knowing what the outcomes 

Richie Cotton: are.

There are also, it seems like there are lots of, um, measures of activity, like how many emails you send, how many calls you make. Um, do you have a sense of, um, how those tie into those, um, sort of harder metrics like the money metrics that you mentioned before? 

Ellie Fields: Yeah, I love that question because our industry is really separated those two in the past and the CRM has typically been the repository of outcome data.

So you can get outcome metrics like, like win rate or, um, you know, number of opportunities that, uh, that progressed to a certain stage. And then you've had systems that have activity metrics so you can get all kinds of metrics like how many activities happened and per salesperson and so on. And we've never been able to really bring those together.

Um, it's one of the things that we're doing now at sales loft in our data platform. And that's what I meant about knowing the goals for the domain. When you can relate activity to outcomes. All of a sudden you can ask questions like what activity is effective? What do sellers do that help progress a deal?

What signals out there with digital footprints that buyers are leaving are really impactful to the ultimate outcome and potentially intermediate outcomes. And you start to ask the questions, uh, that help you influence action. What activity should we do as opposed to what activity did we do? And and give me some aggregate metrics around that.

Richie Cotton: That's kind of interesting because I suppose from there you can get to decisions like should people be spending more time doing email outreach or should they be spending time on the phone or should they? 

Ellie Fields: Absolutely. And even more interesting questions like if we, uh, if we do outreach to customers with this kind of a message, uh, is it ultimately more effective than this other kind of a message?

And I'll give you an example of what I mean there. Um, you know, when I was, uh, when I was at Tableau, we would sometimes do outreach based on terms like big data. and business intelligence and so on. And occasionally we do things like Excel for steroids. And the first class of group of terms or content outreach had much higher ultimate deal sizes, right?

We're reaching different people who are more looking for a full BI deployment versus somebody looking for a tool to replace Excel. And so you can do those kinds of analytics at scale, knowing that what you're really looking for is ultimately those outcome metrics. 

Richie Cotton: It seems like descriptive, um, statistics are pretty easy to do, so you can quantify like how much was done, how much money was, um, was, uh, was won in the last quarter.

Going from that, uh, to, uh, predictive stuff, so, uh, getting forecasts of how much revenue you're going to bring, that seems a lot harder. So do you have a sense of, like, how you get to that, uh, predictive forecasting state? 

Ellie Fields: AI is a big part of it. I mean, there's, um, AI models that we, we and others have in our products to help you understand based on prior activity in your own business, of course, whether a deal is likely to, um, to get over the line to be, to be closed within the quarter.

So AI modeling is a big part of it. Another big part of it is actually, um, bringing data from all those interactions into the forecast. We tend to build our A. I. is building blocks, so we might have a seniority model to understand how senior the people you're speaking with are. We might have, um, any number of models, but we also have models that pull objections from your meetings.

Maybe you have 10 meetings over the course of a deal, and we're pulling from those those objections. And we know that about halfway through the deal, if you still have a certain kind of objection, it really puts the deal at risk. So we can start Incorporate those building blocks and those that information from the conversational from the conversational data into the A.

I. So really, a forecast is is trying to sum up everything that's happening across the business all at once. And by having all this data digitally, you can even inform it in real time and help help sellers forecast where their business is going to land. But the other thing when you talk about predictive is, um, It's very important not just to be able to know where you land, but to be able to take action to influence that outcome.

And we've watched dozens, hundreds of forecast calls at different customers, and the difference between a good and a bad one is in the bad forecast call, people are just trying to gather the information. Did that call happen? What did the seller say? Is that person involved in that deal? And ultimately, they kind of grind out to a number and they say, this is our forecast.

A good forecast call has all that information distilled and ready and the start of a forecast and the team is actually using that forecast to create a plan for action. We should prioritize this deal. We should prioritize that deal. Again, the computer has done the things like gather the information and distill it and the humans are doing things like strategizing.

And, uh, and by using all this digital information, you're able to support processes like that. Also, by understanding models of buyer journey, you can start to predict what, what works. But it actually all comes back to your earlier question. Can you associate activity to outcomes? Because if you can't, You're just guessing at what activity leads to the right outcomes.

Richie Cotton: Okay, um, I like the idea that, um, when you're in a meeting, it should be all about, like, what decisions can we do? How do we, um, set a good strategy? It shouldn't just be, let's try and, like, update the spreadsheet that we should have done before the meeting. 

Ellie Fields: Yes, and, and, and most, Forecast meetings are more like what you describe, let's just try to update the spreadsheet, sometimes in a meeting, and they are, as you can imagine, incredibly painful and hated by sellers because it becomes that kind of spreadsheet updating, information gathering kind of exercise.

Richie Cotton: Okay. Uh, so I also like your point that rather than having one sort of enormous model, that's just like, this is the, this is how we do a forecast. It's actually lots of smaller models. So maybe look in different stages. So you mentioned the idea of like, um, a seniority model. So I presume this is to decide whether someone you've come across is going to be a good lead or not.

Um, do you want to go into a bit more about how, how this works? Cause it's, it sounds really simple now, whether someone's senior or not, like most humans can do that, but doing it. programmatically, there's like there's so many different job titles around, it always seems like a difficult thing. Do you want to talk me through how those models work?

Ellie Fields: Yeah. Seniority is a model we've had around for quite a while in the system. And it, it looks at actually the, um, the email signature that comes back from a buyer. And we can kind of parse that and say, well, we know what a vice president is or a manager and so on. Um, I think the, uh, uh, the other interesting thing about, um, the way that, uh, we build up our models is it, uh, it is individualized to every customer, right?

Because customers have different selling motions. And so you want to be able to know, Hey, this customer is selling smaller products very quickly. This other customer is selling very large products, but a little bit more slowly. And so we, we kind of, uh, Um, use all of these component models as building blocks and then build individual models for every customer, uh, to get to that forecast, but also to understand other things about whether or not the activity that they're, they're working on is, is going to be effective.

I'll say that the magic link between activity and outcomes is people because every single activity is done by a person. And so if, uh, if the head of, uh, finance at your buyer hit your website. There was an activity there, which was a website view, but it was done by a person who was the head of finance.

And it's really important to know that, and then to associate that head of finance back to an activity, uh, sorry, back to an opportunity that is, is current. And so we associate all activities through people, and then attach them to opportunities if they're not already there. And this helps our sellers form a more complete picture of what we call the buying group, or the people at the buyer who are involved in the transaction.

What you find as a As a deal progresses is that that buying group gets a little bigger and a little bigger and it can tell you a lot like if you are bringing in that head of finance, it means you're probably looking for budget approval, which is a very good thing. You don't look for budget approval for something you don't intend to buy.

Um, so it's a, it's a really nice engagement and intense signal. 

Richie Cotton: So, uh, I'd like to talk a bit about, um, working with non technical people because it seems like sales loft is quite a technical data application that is designed to be used by sales people who. Maybe care less about the data, more about the sales.

So, just from a user interface point of view, how do you go about, um, communicating all these data insights, um, to an audience of salespeople? Yeah, great 

Ellie Fields: question. And, uh, yeah, sellers are definitely focused on the human aspects of things. That's, that's their job. It's our job to make it easy for them. to take the right action at the right moment and have the right context at the right moment.

We do that mainly by bringing everything into one orchestrated interface that kind of walks sellers around to different You know, if they need to update an opportunity, it takes them to that point. If they need to make a phone call, it'll pop a dialer with the number already pre populated, really taking some of those road steps out so that they don't even need to learn the navigation.

Uh, we spend quite a bit of time on design and get a lot of kudos. We actually won a design award for our rhythm product. Uh, we, we focus on it a lot because we know sellers are not there to learn new products. They're not there to learn an interface. They're there to get a job done. And so we try to walk them to the right action.

And then. Especially with AI, we have a principle where we always put explainability right with the action. So Richie, I might tell you your, your, your top task when you walk in the morning is to write back to a certain buyer. And that one has been prioritized over all others because that buyer is associated to an activity that is very large and about to close.

And so you really want to get back to that person right now. And then you might have another set of tasks below and it may be, another may be prioritized because it's a high intent lead, uh, and so on and so on. But. I think sellers deserve that explainability. They won't trust the system if they don't get it, and nor should they, frankly.

It also, we find, helps build intuition in newer sellers, whether somebody new to the industry who doesn't know, maybe you should call that person back because it's a big opportunity, or sometimes new to the company. Because these models are company specific, it can help you understand what's really important in, uh, in your own book of business.

Maybe in your last job, you didn't need to involve the head of finance, but in this job, it's a real deal gap if you don't. And so we can surface those kinds of things to sellers and give them that, that prioritized action to take with the explainability right there. 

Richie Cotton: All right. So the idea of explaining why, why someone's being given a task, uh, just seemed incredibly important.

I suppose, um, if a computer tells you to, or a bit of software tells me to do something, my instinctive response is going to be, well, why should I do that? It's like, you're on the bus with me. So, um, if it, if there's that, if there's a reason given then that it just seemed like it's going to make it a lot more effective.

And I think one of the big lessons 

Ellie Fields: of software and, and data in fact, is that we have to give. a really strong value to the end user. One of the things I started to believe in my last few years at Tableau is that data that was over there that you had to go get somewhere was not as valuable, especially for people like sellers who are really in the moment.

They're kind of executing with their buyers. You don't want to send them to a BI system to go get a dashboard. You want to get insight right at the moment they're taking action. And I think if you can give people those actions and that insight right in their workflow, and they can see that it is valuable, they can start to trust the system.

Now in sales loft, we also let people skip tasks and so on. So it's not, it's not a jail or kind of telling you what to do next. It's, it's a recommendation system and a prioritization system. And to the extent that it resonates with sellers and they can see, Oh, this, This is right in front of me. I didn't have to spend five minutes going to get the action items from the last meeting.

I could, I could just have them serve to me right in an email. They can start to see the value for themselves and see that they can spend time the way they prefer to spend their time. 

Richie Cotton: Yeah, I do like that. Like going and getting data is like, um, it's not the fun part of doing data analysis or working with data.

It's like, uh, trying to figure out where, where that data is. Okay, so, um, It seems like there's a lot of different teams around the company that are going to want to know about, um, sales, uh, data or sales insights. How do you, um, tailor that message to different teams? 

Ellie Fields: We do a few things. One, the, the, um, core interface that we use, this kind of action and insight all in one place is quite adaptable because you might have different kinds of actions for different kinds of people.

So a seller might have a number of engagement actions, right? Call this buyer. Um, reach out to this person, join this meeting, their manager might get a number of coaching actions, same kind of interface, but go coach Bob because he struggled with pricing conversations, um, or review this meeting because we think that, um, Kathy could use some, some coaching in it and, and you could score that meeting as well.

So you, you've kind of naturally got a place to drop different kinds of actions within the same interface. The other thing that we do on the back end is have a rich understanding of groups. regions, et cetera, and the ability to, to kind of orchestrate different actions for different groups. And so one great example of that is we have customers who say, um, you know, you can tell me when a buyer has attended an event.

And if it's for my small immediate business team, that's great because then the seller will reach out to that buyer and say, Hey, I saw you attended that event and great. And do you want any followup? And let's, let's talk, et cetera. For an enterprise group who's selling to much larger customers. the rep was probably there with them at the event.

And so if you send them a task saying, hey, follow up with your buyer about this event and ask them how it was, it'll look kind of ridiculous to that seller because they'll say, well, I was right there with them the whole time. And so you need to be able to, to change things like that, or by region, if there's different norms in different regions, you need to be able to change things, uh, to respect those kinds of 

Richie Cotton: differences across the team.

Okay, so you need some kind of level of customizability to make sure that the right tasks are being given to the right person. Right. 

Ellie Fields: And then some amount of analytics because everybody's always interested in analytics, right? How are things working? 

Richie Cotton: So, um, related to the design of Salesforce, one of the big problems a lot of organizations have is that data tends to be trapped in different places.

So you've maybe got Um, customer data in a, in a CRM and then your financial data somewhere else. And then, as you mentioned, like maybe there's some BI system somewhere that has some kind of different, um, information. So how do you deal with data being in different places? And are there any ways to make it easier for people to.

understand what's going on. 

Ellie Fields: I think in the industry, there is this old notion that you would get all your data in one place and then it would magically be done and things would be perfect. And I saw customers struggle with that for years and years and years. And I think the only way your data is ever in one place and done is if you're out of business and nothing's, nothing's changing anymore.

I think the modern way to look at data is to assume that you will have different sources of record. The CRM for most of our customers is the source of record of opportunities. Um, there are BI systems. We tend to be a source of record. for activity, but you've also got emails and calendars and other sources of record.

The trick is to bring that data to bear in the right workflow for the right person at the right moment. At Salesforce, we touch all different kinds of data. In fact, have a, an API endpoint where you can send any kind of data in. An example of that is if you know from your BI system that a customer's Stopped using your product, you might want to reach out and make sure that you're, you know, everything's okay there.

You're going to get a renewal. That's a very custom kind of data coming in. By no means do you want to take all your BI data and send it into Salesforce. Do you want to send a distilled signal into Salesforce? And so what the way we think about it, and this is, I think, the way modern systems think about it is get the right data at the right moment, at the right level of distillation or aggregation to help somebody take the right action with the right insight.

Um, that, that is a way that lets you keep your sources of truth, your sources of record where they live and where they should be managed, which is often in specific systems or in a place like a CRM or data warehouse, but still use it as needed. Um, I think, uh, if you ever hear from a vendor, you have to move all your data into my system for this to be workable.

That is a huge red flag that you should be, you should be wary of. It's probably not a good approach for your data. Uh, it's probably not going to help you. Uh, with your workflow, and it probably has something to do with the, the licensing of that, uh, of that system. 

Richie Cotton: Okay, that's really interesting. So, um, that does actually sound, uh, quite a reasonable approach is just keep the data where you think it should live, and then make sure that, um, all the different bits of software are connected via APIs or whatever, some kind of pipeline to make sure things are in the right, get to the right place.

And actually, um, we're bolted by APIs, it seems like. There's a lot of, um, engineering, like software engineering and data engineering terminology that's sort of appearing, uh, in, in sales platforms and sales technology. So one term I came across recently was the idea of revenue orchestration. Um, can you talk me through what that means?

Revenue 

Ellie Fields: orchestration is a little bit what I've been talking about. The ability to give the right seller the right action for the right buyer. At the right moment with the right message. And to do that, it takes all kinds of different systems. You need all this, all the digital information we talked about, the signals, the recordings of the meetings, the emails and so on.

You need to understand the models of buyer journey and segments and so on. And eventually what you need to do is be able to make sure that your small, medium business seller in Germany. is getting the right set of actions to reach his or her buyers that day with the right message. And that seems easy, but when you think about doing that across a large enterprise, and making sure you're queuing all those right things so that that person can take the action, manage their, manage their deals, reach out to the buyers and engage, whatever it is they may need to do, that they can do it.

exactly in that moment. And said more, uh, in a market view, revenue orchestration is a number of markets coming together. So there was something called conversations and revenue intelligence, forecasting, sales engagement, those markets, um, I believe were never distinct markets, which is why we've been kind of building this way for many years, um, into a, into a full platform.

Those markets were rather Uh, products or features in some cases that were sold as products and that caused customers to have to go to many different places to figure out what they need to do next and what the right insight was for that action. Revenue orchestration platform brings together that action and insight.

for the selling team all in one 

Richie Cotton: place. Okay, so it's really that, uh, what you're talking about, just like making sure that people get the right tasks given to them at the right time. 

Ellie Fields: Yeah, they have the right prioritization. 

Richie Cotton: Beyond the software side of things, are there any sales processes that need to change in order to make best use of data?

Ellie Fields: We just talked about forecasting where it's gone from this kind of very information gathering, frustrating prioritization exercise. Um, you could imagine that Uh, sellers shouldn't need to enter forecasts at all and that it's all automatically done in the background. I think that's something that they can change.

Um, sellers used to keep all kinds of actions and, uh, things that they needed to do in spreadsheets. I think we can and have automated a lot of that for them so that you can kind of get to that. Well, what do I need to do next? Um, conversational intelligence has been huge here. So a good practice by a seller, if you've ever bought something you may know, Um, Um, good practice by a seller is that if we've had a meeting and I'm a seller, I should follow up with you afterwards and say, Hey, Richie, great meeting.

Here are the actions. Here's what I've done. Here's our next meeting, et cetera. And, uh, it can actually take somebody 15 or 20 minutes to pull that together, just to write it all down and so on. With conversational intelligence, if we're. Recording the meeting, we can use AI to pull those action items out and put it into a follow up email and write that email for me, the seller, to send to you and then just give it to me to kind of get that last pass so at the very end I can say, and I loved your shirt.

You know, whatever it was, and personalize it a bit or check it or pull an action out and add one in if the AI didn't get it quite right. Um, so you still want that human in the loop. You don't necessarily want that follow up completely automated. But the time for the seller goes from 15 minutes to prep that and review the call and do all the things and write it, to maybe 30 to 60 seconds to give it a quick glance and add something in.

Richie Cotton: Yeah, definitely. Um, AI summaries of meetings are a complete game changer. Um, yeah, it's definitely one of the most useful, uh, things you can do with it with generative AI. Interesting that, um, yeah, yeah, you can't get AI to sort of trust the taste in shirts. You've got to add that bit as a human. You've got to add that bit.

You've got to 

Ellie Fields: add that bit. And the summaries and action items are one thing. I think if you go back to the domain specific data, we can also pull out from conversations, Um, And I'm doing this things like objections, right? And on a sales call, you might have what we call an objection. Whereas the buyer says, Hey, it's going to be too expensive.

Or, you know, this doesn't fit our needs or any, any number of things. Uh, seller wants to understand those objections and then handle them, address them, um, because we know what objections look like and sound like we know what competitors look like and sound like. Um, we can actually. Pull those out for the seller and say, here are the objections voiced in that last call.

And that's something, again, you wouldn't do in the field of science, because you don't have this concept of objections in science, but you might be looking for hypotheses or something else. 

Richie Cotton: And so, as well as changes to processes, um, do you feel like, uh, the greatest use of data is changing the sales role itself?

I think it absolutely 

Ellie Fields: is. I mean, the um, the concept that a lot of people have of the, the steak dinners seller who isn't, you know, isn't maybe adding a lot of value, but has that relationship and takes you out. I think that's, that's mostly gone. Relationships and trust still very much matter. But, um, but you form trust by building value for your buyers and you build value for your buyers by understanding what they need and helping craft shared value.

So I think what we're going to see in sellers is you still need people who want to connect with their buyer, right? It's still a very human endeavor. I think we're actually going to free sellers up to do more of that human work, to do more of the relationship work, the guiding work, because sellers in the last decade or so have been really pushed into this role of, um, to some degree, administrators, right?

Trying to check all the things to make sure they're doing all the right things in the deal. So much of that is going to be done automatically or, or we'll call the seller in at the right moment. And so their human skills are really going to come to the fore. And those are the ones that they're going to refine, but not in the, I'm going to take you for a steak dinner kind of way more in the, Hey, I've got some real value to add.

I'm going to help you. I'm going to guide you. Uh, we're going to form some trust here. 

Richie Cotton: Probably the fans of stage trick are going to be disappointed by this development. But for everyone else There's still stage tricks out there. There's got to be more than that. All right. All right. Uh, so, uh, you mentioned like the future of sales is going to be about just really like focusing on those personal skills.

Um, are there any, Data and AI skills you think that salespeople need? Absolutely. 

Ellie Fields: I think they need to, like all of us do, be able to absorb and, um, consume and really evaluate data and AI. Uh, we are beyond the age of big data, right? Everything is data now. And so we're going to have to use AI to get So that we're not sitting there reading the data all day long.

You need data in action and in workflow. And so sellers like all of us are going to have to become experts at evaluating, um, what's being served to them, putting it in context, understanding it, and applying it. I do not see sellers becoming prompt engineers. I think that we're going to quickly eclipse that stage and not expect sellers to just like we don't expect them to code, not expect them to learn some arcane prompting language.

We're going to get a lot of that done for them. So again, they can focus on, you know, how's my buyer? What do they need? Where are we at in the deal? 

Richie Cotton: All right. So, um, you think like having AI generated emails, that's going to be done for them. Um, they don't need to like stop writing a prompt. Please graph me an email to say, thank you for the customer.

Correct. Okay. So you mentioned the idea that, uh, just being able to understand, uh, all the data that's being thrown at them is very important. I suppose from there, what the salesperson really needs to do is get to a decision on what to do. Do you feel like, um, that sort of data storytelling component is also a useful skill for, uh, for salespeople?

Ellie Fields: I actually think the prioritization in many ways can be done for them. And again, sellers can skip or choose to do something else. We do that in our system. Prioritization is really a function of. Um, what is the opportunity on the other side and how far away are we from it? And so a, a great revenue orchestration system is one that not only gives you information, and says, good luck.

I hope you do something useful with that, but actually guide you to the right action based on that information. It gives you the context you need in that moment. I think one of the shifts that we're seeing as we're using data and AI more effectively is getting away from that idea that, hey, I've given you an aggregate metric, right?

I've told you one thing is bigger than another, or, or whatever that aggregation is, and I've said, good luck. I hope you can use that right now. We take people all the way to the point of action to the point where we can cue the email up for them written or, um, you know, have the dialer make the call or, or move the forecast along or whatever it is.

You have to think very deeply about when do you want that human in the loop and what is the role, the right role for that human at that moment, but prioritizing all of the action I think can actually be done for them. And in fact, we would hear from sellers a lot as we were, as we were investigating our rhythm system, which is our prioritization system, that the first thing they would do in the morning is they'd come in, check all their inboxes, check all the systems, and write down their to do list for the day.

And it would take them maybe an hour. And that was an hour they'd lost selling. And in fact, many buyers are more reachable first thing in the morning. So that first hour can be very, very important. But if you think about a seller sitting down to organize their day for an hour, it's a thing you probably don't want them to do.

And, uh, I think that's something a computer can, can mostly do for them. 

Richie Cotton: So that's interesting that, um, since a lot of the tasks are being, uh, for the sales people are being completely automated. Um, do you think we'll reach a point where you can completely, um, automate your sales team? You don't leave that human in the loop?

I do not think so. I don't 

Ellie Fields: think so because, again, you. If you're hiring sellers, you must believe there's something human there. And if the buyer is making a big purchase, maybe a bet their career purchase, right, or something really critical for the, for the company, they probably don't want to go on Amazon and buy it.

They want to know that there's a team behind it. They want to have, have the chance to ask questions. They want to form that trust and that partnership. When you see, uh, buying and selling happening in this way, it's really more of a collaboration. It's collaboration across the entire opportunity and sometimes across many opportunities for the life of the partnership.

And so you do need a human there. Um, when I say we're prioritizing tasks for sellers, I think we can do that prioritization and then call them in at the right moment. Right? And, and that's where that taste comes in about, well, when do you need a human? And when can you just do some of the things for them?

And of course, give them visibility, let them, let them know. Um, but, uh, but you don't need a human. I think we've, we've talked about, you certainly don't need a human to go through a call transcript and pull out action items anymore. Just completely can be done for them. Um, organizing, uh, organizing the list of actions they can take in a day can actually be done by a computer right now.

Um, but thinking deeply about that deal strategy is something that we still see sellers and their managers working on. And we want to leave them time for that so they're not doing the other things. 

Richie Cotton: Okay, I think a lot of salespeople are breathing a sigh of relief that it's not going to be completely automated.

And so think about deal strategy and the sort of the despite aspects of the customer relationship seem incredibly important for humans to do. Okay, and what's the role of management in this? Um, so if, if you've got software deciding like what all the different salespeople should be doing, there's less of that sort of directing orchestrating thing that managers do.

Uh, What should managers be doing in role where all that's taken care of? Great 

Ellie Fields: question. Managers have been one of the underserved, most underserved roles in sales. Um, they have a couple of roles in coaching and also managing the overall business. Coaching in particular has been hard for them because I have to go get all the data or watch a lot of calls to figure out what to coach on.

Um, with, uh, with the digital information we have, we can actually do that for them. We can detect when a seller, for example, has a consistent weakness in an area. Uh, and And help the seller directly, maybe by serving them a training video. But we can also give that information to the manager. And sitting and coaching another human is actually a great use of a human being's time.

If they don't have to go and pull all that information. And again, we've watched managers and talked to them a lot about coaching. And they'll say things like, I just don't have time to watch all the calls. I don't, I just can't do it. I have too many, too many people on my team. So you take that work off of them and you let them coach.

Likewise, I think managers are really good at different levels, frontline managers and up, uh, other leaders and looking at the business and saying, wow, we're, we're getting a consistently new objection across the whole business, or this talk track really isn't landing or, or, or, or, right. Or our buyers are changing in some way and we need to, we need to adjust that.

We need to, um, grow the skill of our sellers in a certain way. And so some of those bigger picture things are what managers actually want to spend their time on. And if you can, again. Get all the information, distill it, and get meaning to the managers. They can spend their time on those coaching tasks and some of the business, um, business influence, business trend kind of tasks.

Richie Cotton: Okay, so when you're, uh, listening, when you hear, um, this call may be wanted for training purposes, we'll probably get a bit more of that where the manager is actually listening and on the, on the call, um, if you could be trained. Yep. 

Ellie Fields: Or, or the manager is getting a playlist of a number of two minute segments of different calls, right?

Because listening to an entire call is not a great use of time. One of the things that, uh, that we can do in, um, in SalesLoft because we get this holistic view of the buyer and seller engagement is bring together everything about a seller, right? What their emails, how their emails are performing, how their calls are performing, their ultimate outcomes, objections and things that are happening in their meetings, and put that right in front of a customer.

A manager coach and so that manager coach can be working on distilling the information and imparting knowledge versus having to go work through all that information. 

Richie Cotton: Okay. Yeah, I can certainly see how like the first 10 minutes of a call where it's like, Oh, hello, how are you doing? That probably not great use of manager's time to listening on, but they're probably going to be some key moments where you want to know, um, is this conversation helpful or not?

Absolutely. All right. Uh, so, uh, Jeff, me. Examples of companies that have gone all in on making their sales teams more data driven and then they've seen a benefit. 

Ellie Fields: Yeah, absolutely. We've um, we have a number of customers across the board. I think we have a case study on our website, a great one about a company called Wrike that is using sales off across the board.

And they've seen some amazing results in growing their pipeline and ultimately closing, mainly because with all of this automation, they were able to get sellers to get back to buyers much more quickly. And, uh, there's been a lot of research and different companies have experienced that if someone is on your website, kind of looking at things right now is the time to contact them.

If you contact them tomorrow, they might be thinking about something else that's a little bit harder. And so getting that, that speed of, um, The speed and the closeness of that moment when the buyer is interested has been really powerful for Wrike. So that's, that's one example and definitely would encourage you to go check it out.

IBM is going wall to wall with SalesLoft and has seen similar results in their sales team as they've been able to do, be much more structured and data oriented in the way that they engage with, with buyers. And they've, they've seen, uh, they've seen results both in prospecting and closing that are quite significant.

Richie Cotton: That's interesting that, um, time to response is an important sort of, uh, metric for success there. And I know you've got, um, data from many, many companies like, uh, uh, so have you seen any patterns across, um, with sales off like across all these different companies? Like what other things like time to response make a sales team effective?

Ellie Fields: Uh, time to response often makes a sales team very effective being able to detect patterns in the data. So sometimes you'll see, um, a deal that was hot, have no activity for a couple of weeks and that usually signals a stall and it's usually detected now by a manager going through, going through, um, activity kind of.

Visually, um, being able to detect patterns like that is, uh, is it makes companies much more effective. Having sellers who can be more productive helps companies be more effective. When we see sellers being much more productive when they're using a system like Rhythm, because again, they're doing less administrative time and you just think about it.

If you have a hundred sellers and they're all spending 25 percent of their time with buyers. Um, and then they go to spending 75 percent of their time with buyers. It's like you've tripled the size of your sales team. So you get commensurate results with that as well. One of the other things we see is, is the time to close sometimes called cycle time, uh, can decrease rapidly if you're using revenue orchestration system because, uh, you're moving faster, you're responding to the buyer more quickly, and, and you're just close, you're, you're, you're cutting out some of that back and forth time.

Richie Cotton: Okay. So it seems like the big key is then just spend, you know, the As little as time possible on admin, have software do all that for you. And then you can spend more time on calls and responding to customers faster. 

Ellie Fields: Yeah, and get the insight so that when you make the call to the buyer, you know exactly what to talk about, what their objections are, and so on.

Richie Cotton: And are there any common mistakes that organizations make when they try to become more data driven with their sales teams? 

Ellie Fields: One is you mentioned it before, but the the idea that you need to get all your data in one place first is a huge pitfall that you need to move it all to your CRM or to some data warehouse.

It tends to become this morass of a giant project that blocks other progress. So I think one thing is definitely to, you know, Think about where you want your data to live and what kind of workflow you want to have. I think the second, uh, the second pitfall is sometimes trying to design to old processes.

Um, being, there, there are a lot of new processes in sales, a lot of new ways of thinking and doing things. And, um, being open to that, we see our best customers doing that, really pushing forward on what sales process looks like. Because honestly, a lot of older sales process just existed because somebody did it at their last job.

So this is an industry that is changing very quickly, using the data to understand what's working and looking at other best practices out in the industry can really take a sales team 

Richie Cotton: forward. Just to wrap up, is there anything that you're particularly excited about in the world of sales? I am particularly 

Ellie Fields: excited about, um, being able to get signals from all over the web in front of a seller.

So there's always been this gulf between marketing and sales. Marketing has typically been able, um, more been able to see some of these digital signals. Uh, we talked about them earlier, like you were on a review site or maybe you watched a video that somebody sent and sales has been a bit blind to that.

When you can combine digital and sales led activity, you can get this very complete picture of what's happening with the buying grouping and serve them much better. And so I'm, I'm very excited about that. And the, the way that that happens is through bringing digital signals into a sales platform like SalesLoft so that sellers can see all of that activity and get this complete picture.

Richie Cotton: Okay. Yeah. So someone posts on social media about something related to, uh, your company when they're in the middle of the buying cycle. And yeah, it's probably a good signal there. Uh, that's kind of interesting. Um, all right. And do you have any final advice for organizations that want to improve their sales team metrics?

Absolutely. 

Ellie Fields: I'd say, um, get out there. Look at what's out there. This industry is changing rapidly. Uh, there's a lot going on. There's a lot of new technology. And if you take a look at your seller's day, that's the most important thing. Watch your sellers sell, and if those sellers are not spending their time on what you call high value activities, it probably means, uh, you can do better.

Richie Cotton: Okay, uh, that's good advice. Get people like, uh, doing those high value activities, actually selling things. Yeah. Nice. Great. That's great. All right, uh, thank you so much for your time, Eli. 

Ellie Fields: Yeah, thanks for having me!