It’s easy to be suspicious or hesitant about the rapid advances in technology, especially in the field of artificial intelligence. Many people worry about being replaced in their jobs by some type of technology, and for prospectors, the idea of artificial intelligence can seem like a direct threat to their jobs. But it turns out, AI is not the threat to prospecting jobs that it might seem. In fact, AI can actually be used to improve prospectors chances of getting great leads and closing deals.
Today’s guest is Robert Käll, the CEO and cofounder of a company called Cien. Cien produces an AI-powered sales productivity app. Far from taking opportunities away from prospectors, Robert explains how Cien can be used to empower prospectors and help them increase their opportunities and ability to close deals. Listen to the episode to hear what Robert has to say about AI and how it figures into the future of prospecting.
- Robert’s company, Cien, and how it got its name
- Why advanced AI technology doesn’t mean the end of prospecting
- How AI can affect pipeline quality
- How AI can be applied to the problem of incomplete or low-quality data
- The implications of using AI to help differentiate between good leads and bad leads
- How AI can help underperforming reps increase their sales
- The importance of routing tasks to people based on who can make the most out of that particular task
- Why it’s important to understand why some deals close and others don’t
- Why you shouldn’t assume that there’s a one-size-fits-all formula for closing deals
- How Robert’s app works with Salesforce
Marylou: Hi everyone, it’s Marylou Tyler. This week’s guest is going to be just a huge level of awareness for you. Why? Because if you have been reading the news about AI—Artificial Intelligence—the first thing we think about is, “Oh my gosh. My prospecting job is no longer.” I’m here to tell you that that is the farthest thing. Today’s guest, Robert Kall. His last name is K-A-L-L with two things over the A but is pronounced “shell.” He’s the CEO and co-founder of a company called Cien. I am excited about having him on the show today because his view with his take on artificial intelligence is all about human relationships and having you guys flourish in closing opportunities, closing deals. We’re probably going to focus more top-of-funnel today because that’s what I know but he can take it all the way through. If you want to know more information, I will put all of his contact information after we have our conversation so you can learn more about what they’re doing.
Welcome to the podcast.
Robert: Thank you so much, Marylou. Pleasure to be here. I’m a huge fan, read your book.
Marylou: Why did you use the word Cien as the name of your company?
Robert: Cien means 100. Many things good are 100. Getting 100 on your test, giving 100% effort, reaching $100 million revenue—all of those things are the things that you strive for in life and business. We also called it Cien because we wanted to have a global company. I’m calling today from Barcelona where we have our R&D center. We also have an office in Dallas and one in Miami. I really wanted to create a company that was a hundred times better than the previous companies we worked on before which were more in the SaaS business. This is what we called an AI first company.
Marylou: You heard my opener, those of us who studied this and love the top-of-funnel like I do. AI is looming thing over the horizon that it’s looked at in some ways as being negative for sales executives who develop business. You’re saying that that is the first thing from what you’re discovering and what you’re working on. Can you share some of the initiatives that your company is working on and why that’s important for us at top-of-funnel?
Robert: Sure, absolutely. Again, who wants to buy something for $100,000 from a robot? That’s just not going to happen. But at the same time, you want to—as a sales leader, as a sales executive, as a sales development rep, whatever your title is—have the most amount of information at your disposal as you can possibly can. You want to understand what really drives success for you as an individual and for the company as a whole. That’s the type of stuff that you can actually get with AI.
We have a very simple view of the world when it comes to sales. There are three components to it and those three components are you lead in pipeline quality, your rep attributes, how your reps are behaving, and then you have your macro factors. I can go in a little bit more detail on each one of those if you like.
Marylou: Yeah, that’d be great. Let’s start with the pipeline quality.
Robert: Everybody knows that there are good leads and bad leads. People have not until now spent a lot of time to determine exactly what causes good and bad leads. We’re drilling down—there’s a myriad of factors here. We’re drilling into two key concepts: one we call fit and one we call interest.
Fit is the stuff that are attributable to leading but tend to be not able to change. For example where they are located, what industry they’re in, the type of contact person you have, the title and so forth. If you have a bad fit lead, it’s very hard to turn it into a great deal.
On the other hand, the interest. Everybody knows that a warm referral is much easier than calling someone from a list. That is what interest really measures. Your level of awareness and expressed interest from a prospect. In some cases, leads come in warm with high interests and some cases, they come in cold.
The thing that is new about AI is that we can actually measure that and we can measure how it changes over time. Because a great sales development rep for example can bring up the interest a lot by providing an effective value proposition and meaningful engagements. Whereas of course if you’re not applying any effort, the interest is more likely going to stay very low, or if you have a high interest and you fail to follow up. These are types of things that in the past it was really hard to measure but with AI and the statistical methods that we’re using, we can actually get a very accurate measurement of this on a lead by lead basis.
Marylou: Let’s stop there for a second. One of the biggest issues that I also see is we may be able to collect the data in a rudimentary way, by no means is systematic or mechanized, but we don’t necessarily apply what we’ve learned into a repeatable process. Is AI designed to not only diagnose or find descriptively where we are, but is it making recommendations how we organize our sales conversation canvas, if you will, for a lack of a better term, in the next iteration of our conversation?
Robert: Yeah, absolutely. If you don’t mind, we’ll get into that in a second because I wanted to talk about the two outer […] and you see how they all fit together to create this—what you call—the canvas.
There are two other aspects that they are equally important. One, the rep factors. There, you have things like work ethic, a measurement of the applied meaningful activity that a rep is doing on a day by day basis. It varies a lot from rep to rep, it’s not just about calling 100 times. It’s about meaningful activities. We measure a lot of those things by looking at exactly what’s going on with CRM activities and so forth.
Another factor is product knowledge or industry knowledge. A buyer always wants to talk to someone that’s knowledgeable and has a lot of insights. Many times, especially when you’re ramping up a sales team, your sales team does not have those skills. That’s a really big factor.
A third thing we look at is things like closing ability. The ability to not just get a deal up to an opportunity stage but actually close the deal and moving it past the final hurdles.
The last thing that we looked at, we look at more data on an aggregate on an individual person is what we call team mood. There is the overall engagement to the company and the things that are attractors and boosters in terms of the things. Many organizations are […] one of two things. They may have a terrible commission system that no one understands. They may have a product launch that failed and so forth. We’re measuring all of that stuff for various services and so forth. We can actually plan that into the models that we’re creating about how your sales is working.
That was the people factor. Again, it sounds like how can we know all of those things? It’s actually possible to extract a lot of that from data that you already have in your CRM. It’s also possible to get some of that proof by asking people. People will answer that unless it takes two hours to answer the survey. Our service takes 10 seconds. Very, very easy question to answer.
The last thing that people can’t forget, I told everybody that I called from Barcelona and this is a story from my previous life. I had built up a high velocity sales team and we’re doing an acquisition and we had a large team here in Barcelona that we brought on and made our plans. We were doing fine in May, June, July, and then August came. We basically hit a big fat zero on our sales results. We had just not built in the things like seasonality intermodals and that is something that some cases are really affecting your ability to get to your number.
Another factor would be competition where your competition all of a sudden changes drastically. We are able to measure that in ways that you may not have been able to do before.
Again, those three things, your lead and pipeline factors, your people, attributes, and macro factors, they together make that environment. We call it the 360 Sales Environment. When you understand all three of those things, you can get to what you asked about before which is start modelling, what’s going to happen next. Then we could do the whole thing around what can you do differently to get better results? Because once you have it in an AI engine, you can test a million different combinations and it takes fractions of a second to test each one. You can find where what we call the low hanging fruit is. A lot of times, the low hanging fruit is around giving the right leads to the right people.
Marylou: Wow. Of course, next question is going to be more about the data itself. Everyone admits their databases are pretty dirty or they’re not necessarily enhanced with conversational data because of the burden it puts on reps to do what they think of as data entry. Is there a process that you like to see or does it not matter at all what the data is currently in a current situation? Or is this also an opportunity for companies to start collecting the meaningful data going forward?
Robert: The listener that listens right now says, “While I can’t use this type of stuff because my data is too bad.” I don’t know if you remember this song by Michael Jackson. I’m not going to sing it but it goes like, “You are not alone.” That’s exactly what’s going on. We have never encountered a company that has been proud of the data quality that they have in their CRM system. It’s just doesn’t happen.
The reason for it is very easy. For the most part, there’s not a lot of incentives for sales rep to put in 100% of their data in there. We don’t have to go into that now. It’s just a fact.
What we’re doing is we are applying AI to that problem too, kind of like when we started. We have some services around that. It allows you to understand what the true state of your data is because there are patterns. AI is essentially a pattern recognition machine. There are patterns to your inconsistency on your “crappy data” as well. We are applying those patterns and then we can compensate for a lot of things. We know that people are, for example, skipping opportunity stages because it takes too long to enter. They want to either sand bag or show more in a pipeline and so forth.
Marylou: Gaming the system is what I call it.
Robert: Here’s the thing, if you’re gaming the system in a consistent way, we will be able to […]. Same thing with things when people are putting in all their activities in a Friday afternoon, there are patterns to it. Same thing when people are missing a lot of information.
The last thing which is very common in most large enterprises is that you change methodology in your Salesforce or whatever CRM you’re using several times during the course of things. The data that you compared to a year ago is not apples to apples. You used to prospect using the lead module, now you’re prospecting using the accounts model because you moved to account-based marketing or whatever. Those are super common things and that’s where you can apply AI so you’re really getting apples to apples true sense of what’s in your database. That’s really a super important step that we spent so much time working through and finding solutions to. Once you have that, that’s when you can start making these types of predictions. Now, you have much greater confidence today than you had before.
Marylou: The other too, is, let’s face it, there are so many tasks out there in the world. We used to kid ourselves saying death by task. There’s so many tasks out there we think that would feed into an AI system really nicely to see exactly what’s being done and what’s not being done, what’s being followed up on and what’s not. There’s a plethora of records that are generated automatically by systems for activities that never get done.
Robert: There are so many tasks out there. Today, many companies are using automated solutions to record a lot of things. What we’re finding is there’s a bunch of problems in CRM data but there’s also a bunch of signals as we call it in the data science world. In other words, information that we can use like predictions.
Marylou: Right. All of you sitting in the audience, you are not off the hook with your dirty data when it comes to AI.
Robert: No, obviously, bad data is better. There’s no doubt about that but if we were saying, “Okay, only people with perfect data need to apply to use […].” We would have absolutely zero customers.
Marylou: It would be bird chirping in your waiting room, there wouldn’t be much going on.
Robert: Again, these are the types of things that are prerequisites. We spent a lot of time building technology and services around.
I want to talk a little bit about one thing that I think is really interesting and that is the value of a lead. I don’t know if you spend any time thinking about that. What is a good lead, what is a bad lead? How should you evaluate when you’re giving it to a sales development rep? How much do you value an opportunity when you’re handing it over to an account executive?
Marylou: Yeah, we think about that all the time. Even in Predictable Revenue back in 2011. There’s a chapter on the seven fatal sins, and of the CEO or c-suite director level, that was one of them. They don’t understand the value of a lead. That’s why Aaron, in the book in the intro, said he had a $5 million loan or VC funding for a company that went under because he did not know the value of a lead.
Robert: I remember that story. That was on the “aggregate” or “average” level. To some degree, getting that information is just standard record keeping and making sure that you understand how your […]. What our company is trying to do now is to bring it down to individual record levels.
Marylou: That’s great.
Robert: Of course, you can’t get an exact value for every single lead with limited data. But what you can do is to create a statistical value where you can significantly differentiate the value between a lead that is very cold and bad fit. It’s extremely unlikely to turn into a deal, and one that’s just warm, for example, a friend referral or something like that in a perfect company.
The difference in value between those is not a factor. It’s not double the value or something like that. It’s a factor of 10, or in some cases 100. There’s a huge difference in valuing those. The thing that oftentimes tricks up and causes a lot of discord in sales team is often people sensed about it but they have no way of measuring it.
That’s, again, where we are developing something we called Cien Value Chain. We are actually using AI to measure the true value of each and every lead, and each and every opportunity. That’s obviously not the value of the final deal, it’s a fraction of it, since only certain deals, certain leads become things.
The story I tend to tell is about Bob and Sue. This is how you can very easily improve yourselves partly if you think about it. You have Bob and Sue, both are pretty good sales people according to the thing. Bob sold $100,000 last quarter, Sue sold for $80,000. The only way of looking at it would’ve been that Bob is a better salesperson. But if you dig in and see the true value of the leads and opportunities that they’ve received, you can see that Bob received $50,000 worth of leads and opportunities and Sue received $20,000. She was able to add a lot more value for her time in the last quarter than Bob was.
If you want to, in your organization, maximize your overall self-productivity, instead of having that big discrepancy about the value given, even it out. That alone can give you 10%-15% increase in self-productivity. In most companies that we work with, getting self-productivity up just a few percentage points is a fantastic thing because it concentrates to the bottom line because they sell high gross marketing software products that progress, or something like that.
Marylou: Right. We spend a lot of time manually, a lot of guesswork, trying to maximize the return on effort. This is like, “Turn the switch on.” And it does it for you. To me, this is transformational to the operations of the sales team. For everyone to become more productive, even the skill levels, you’ve got the outliers and you’ve got the people who are in the bell curve. This would help even that out so you’ll have a more predictable pipeline and a better forecast, I would think.
Robert: Absolutely. Both a better forecast. Then, you can also start […]. What’s causing these “underperformers” to do it if they just started yesterday, it’s very easy. They don’t have their product skills. They probably don’t have a lot of leads received. They’ve been in the company for a year and a half and they are still not adding a lot value compared to their peers.
It’s about looking at work ethic, looking at the training that you provided them. Are they missing their coaching skills which would translate into being able to articulate things. Are they able to navigate? If you’re selling a B2B product oftentimes, it’s a big skill to be able to navigate the hierarchy.
One of the things we do a lot for our customers is to look at the stakeholders in each deal. Who have been brought in? Understanding their true seniority, their true job functions, and so forth. Then, we can many times find that the approach the company has taken up to now has actually been detrimental because they’re talking to the wrong people. We find that if they’re avoiding talking to certain people, at least avoiding talking to them until the last part of the deal, they are having much better success. It’s a freebie.
Avoid talking to the IT department if you’re selling a technology solution to a non-IT department. Stay colder until you absolutely have to. That is just the pattern that repeats itself over and over again. Because IT departments by their nature are risk-averse. For them, just every new solution that you’re bringing into the table is more risk, more headache. Whereas if you’re selling a marketing solution for example, the marketing leader is looking for it to solve his problem or her problem. Stuff like that would be things that you’re seeing over and over again.
Again, in each case, each company is unique and we do this analysis. When we create an AI first company, the whole idea is to provide that top of analysis within the box of the software as opposed to have to rely on super expensive analysts and so forth.
Marylou: Can you tell us a couple of stories that eventually wow-ed you when you started implementing some of it to clients? What was that wow factor when you went in and worked on a client, “fixed” them, and got them up and running?
Robert: Again, I may have used the term before, the low-hanging fruit. That basically comes to a very simple concept: don’t send great leads to bad reps because we call those reps the productivity traps. They, for whatever reason, lack that ability to turn them into good opportunities.
Again, it could be a myriad of reasons but many times it’s a combination of one or two factors. When you do that, you’re destroying value for your sales organization. At the same time, you may have what you call the anti-potential reps. When you have some great reps, that because of the territory they’ve been assigned to for example, they’ve been assigned to this terrible territory that just has way fewer opportunities and leads are much less valuable than […], they are hard workers. They have the ability to close deals and they’re doing great for their stuff. They’re receiving so little that they are essentially underperforming just by that factor. Just spending a little bit of time evening that out. You don’t do it with leads that you have or opportunities you have in the pipeline. You just do the new stuff as it comes in. That immediately can give you 3%, 4%, 5% productivity increase.
Marylou: That’s a lot.
Robert: That is a lot for these types of companies that sell high gross margin software or technology services.
Marylou: It’s like an intelligent routing versus a round-robin. What they do now is round-robin is more of an intelligent routing to the appropriate person who can handle the opportunity at a faster clip or with more finesse or whatever the requirements are to get that opportunity to close. I love that.
Robert: Thank you. Again, this is something that we can do in a very short period of time. All you need to do is start the day you realize that this is occurring and change your behavior a little bit. You don’t have to change anything else except this distribution that you have. Then you see that thing, then you go back and look at these people who are underperforming.
You do a deeper analysis on what’s going on with them, and in many cases, it’s just one or two factors that are not clicking with them. They kind of know it but now you can measure it and you can see it over there. You can make progress on it. If it is a training issue for example, “Let’s go back to school with those guys and figure out what is it they’re missing.” If it’s a work ethic thing, then it’s more around activity management and making sure that someone is checking in with them every single day, right?
Marylou: Right. Tell us a little bit about the playbook portion. I remember reading on your website that the playbook can be enhanced so that you know why deals closed. You may get role-based guidance on the activities that lead to revenues. That’s a big one for me. What is the perfect route or perfect path or the perfect map to get to close?
Robert: I’m going to start with just an observation and then maybe some people in the audience does not agree with me but this is just my observation. I’ve had the privilege to work with some really awesome sales leaders over the last 10-15 years. Every single time that I’ve done that, they have presented their playbook to me. One guy with a very big on training. One guy was very big on activity management. The third person, she was more concerned about the overall team culture, mood, and so forth. Each one of them had really good points. They knew how to effect that and they have plans in place.
The thing is when they come into a company, all three of them cannot be correct that one of them is the most important. In each company, in each situation, you have unique factors that if you just apply the playbook what you were successful in your last company, you might be missing some huge opportunities. That’s where we are coming in. We can tell you all […] things and many more factors, then we can tell you right now in this company, this thing is the most important thing.
You need to work on team mood, culture, and so forth because people are the solution and the motivator. In another company, it’s all about the product. The product is not doing what it needs to do yet so you have to kind of go back and ensure that that piece is more competitive.
The whole point here is there is not a one size fits all playbook for sales leaders to come and do. That’s the temptation that many people have that they are just going to apply like last time.
Marylou: Yeah, that’s great. We’re going to wrap-up here pretty quickly but I wanted to make sure that the audience understood that this is a solution that spans across the sales organizations. Sales leaders will benefit. Of course, account execs, developers, operations, and even marketing. We didn’t talk a lot about marketing because I don’t have that piece of the pie in my audience but I can imagine that lead generation would be something that would benefit from this type of system if put into place.
Robert: Just to talk two seconds about how marketing uses a system like us. Marketing and sales many times have different views of what has happened. I sat through so many business review meetings where marketing comes in and says they had a great month, they generated 1,000 leads, blah, blah, and sales come in and says, “We had a terrible month, our leads did not work at all.”
Marylou: The leads are crap.
Robert: Exactly, yeah. With a system like this, you can basically, in a more objective way, measure how these leads that you got in the last month are the same, or better, or worse, than the leads you had before, the quantity, and the aggregate value on them. That could really move a lot of disputes and objective things that causes conflicts between different departments and so forth. Just say, “Okay, here’s the facts. These types of leads, we have great success with these. These types of leads, we should look at why are we generating them? What are the sources of them? And perhaps do less of that. Then we can focus on other things.
We are not trying to solve everything. There’s a ton of great tools out there for marketers using AI software. We’re trying to help settle that little dispute that you many times see in businesses. When these leads that marketing has generated gets to sales, this is what happens. If you just change the mix a little bit there, you’re more likely to have success.
Marylou: Yeah. I think it takes that argument out of the picture, that sentiment of, “I believe it’s this way,” is emotion-based. We’re flipping that on its side saying, “Yup, here’s the facts of this particular lead and why it should have been successful.” That opens up a whole new conversation about, “Alright, what do we need to do to make it better so that we are talking those leads from marketing and processing them in a way that yields the results that we should be getting?” It changes the whole conversation from pointing fingers to one of more of working together, which is what we want, ultimately.
Marylou: This is so exciting, I love it. This is something that attaches. Is it an app that attaches to common CRMs? How was it rendered out in the world?
Robert: We are an app that right now works with Salesforce. Other CRM systems will be added later. It allows you to, on your phone, think about it like a Fitbit if you are familiar with the Fitbit app.
Marylou: Of course, I’ve got mine on right now.
Robert: Yeah, me too. Every morning I check my Fitbit to see what’s going on with my sleep, how many calories I burned yesterday, stuff like that. Same thing. When you wake up in the morning and check the Cien app, you can see what’s going on with my pipeline, what’s my end of month prediction, what’s my end of quarter. What are the key takeaways that I should do today? That’s different obviously if you’re a sales executive, if you’re a manager, or if you’re an individual rep. It gives you that stuff just on your phone in real time. It’s not some kind of business intelligence—super complicated thing. You have to go to three months of training to understand. It’s just right there in your app and you can take actions.
The cool thing about AI is that everytime it gets a piece of feedback, for example, sometimes we provide advice that does not make sense to the particular individual for a reason. All you need to do just like in the Pandora, for example, if you hear a bad song, you do the thumbs down. You do the same thing here, the AI learns from that and understands that, “Hey, whatever advice we provided here does not make sense to this person. It could be for whatever reason.” Then we can tailor that and build that in so that the algorithm continues to learn. That’s the big difference between the traditional way of programming and the AI way. New data makes the algorithm smarter and they continue to learn.
Marylou: Yeah, they continue to get stronger and stronger as it moves along in life because it’s collecting more data points and it’s with instructions in some cases. It’s outputting basically the behavior that you’re trying to achieve. I love this. This is great.
How do we get ahold of you? So cien.ai is the name of the website. How do we connect with you on LinkedIn? Are you on LinkedIn?
Robert: Yes, I am. I am always accepting inbound requests from sales professionals. My LinkedIn is https://www.linkedin.com/in/robertkall/.
Marylou: Robert Kall, everybody.
Robert: It’s okay if you don’t pronounce it correctly, I know it’s a weird thing. I’m there and I’m also on Twitter with the same name, @robertkall. Please connect with me. I love to have conversations. I’m calling right now today from Barcelona but we also have an office in Dallas. We’ve been spending a lot of time there in the Metropolitan Area in United States. If you’re interested in meeting up, just connect with me.
Marylou: Wonderful. I’ll be sharing all the contact information in the show notes for those of you who are driving and listening to this podcast. I really appreciate your time. For a data geek like I am, this is great stuff, I just love it. I really try to band-aid all of what you’re talking about and it just doesn’t come out the way you want it to. To have an app like this that helps get that kind of baseline started so we can focus on what we’re supposed to be doing which is having better conversations in any way, it makes our jobs so much more easier.
Thank you much for your time, Robert. I really enjoyed this a lot.
Robert: Me too. Thanks a lot, Marylou. Again, thank you so much for the books that you’ve written. They’ve been super helpful for me, personally.
Marylou: Oh wonderful. I’m so happy to hear that. Take care!