AR Data Sciences
MY Data Science
Updated: Nov 20, 2019
DATA CRUSHES THE COLD CALL
How technology is revolutionizing selling
(not to mention learning)
Regional Director of Sales, DreamBox Learning
I’VE WORKED IN sales in the public sector for more than a decade, starting at Hewlett-Packard selling the backbones of IT infrastructure to school districts, universities, and local governments, eventually transitioning into education technology working with Edmentum and now DreamBox Learning. I’m DreamBox’s Regional Director of Sales for a handful of states, including Arkansas, and I’m here to tell you that data science is transforming the nature of sales just as it’s changing all core industries themselves.
Those of us who sell for a living usually just adopt the latest technology without giving much thought to the big picture, but when you stand back and actually look at how our world has changed, it’s really remarkable. The old tried-and-true method was face to face: knocking on doors to try to establish connections, or networking at events—the latter being still very much the case. The difference is that now we add a layer of intelligence on top of our face-to-face meetings. We use data to target who we’re going to call on, and data to tell us why we’re going to talk to them. Today we have amazing tools at our disposal.
Marketo is one of the largest programs used in the marketing field, and now it has trickled down into sales to help us with sales intelligence. Let’s use cars as an example. Marketo tells us that our prospect—we’ll call him Jim—is looking for a new car. Jim went onto to Subaru’s website, just kind of browsing around, seeing what’s new, checking out the features. But Marketo doesn’t just tell us that Jim went to the Subaru website. It tells us exactly how long he was on that website, and where all he browsed. Then Marketo assigns Jim a score, which it updates according to what Jim does. If he returns to that website later, or if he independently does a Google search and clicks on a link, Marketo will track that information and adjust his score indicating how hot a prospect he is, how ready he is to buy that car.
That’s just the start of it. Marketo will then keep tracking Jim. Say his score is now 10 points; the goal then is to nurture him up to a 50-point threshold, which is when a sales professional will phone Jim and say, “Come on down, come check out our cars.” But between the 10-point time and the 50-point goal, we’re going to send Jim info about local events, along with pop-up testimonials that are relevant not only geographically, but specifically to Jim. For example, if Marketo identifies that Jim is a 55-year-old gentleman who’s looking for a stable car that he doesn’t have to get serviced all the time, and if we find that Jim currently owns a Ford, we’ll start popping up digital advertising to him that speaks to Subaru versus Ford and why he should move to Subaru.
So by the time I, as the sales rep, actually call Jim that first time, I know everything he’s already done. I know what he’s looked at, I know what’s important to him. If he spent three and a half minutes studying Subaru’s 100,000-mile warranty, versus 30 seconds checking out the new backup camera safety features, then I know that when I talk with him I should go in and say, Hey, let’s look at this warranty, let me tell you why it’s the best in the business. In other words, I’ll basically have my hook, line, and sinkers all set before I even enter into a conversation with Jim.
All these new tech tools obviously make for a more efficient sales operation. In a sense, we’ve seen the end of the cold call. We go into any conversation with a lot more intelligence now, and with questions we already know the answers to. That helps us lead the conversation in the direction we want and get that next step or that objective met much easier. But it’s not just to our advantage. For the prospect, too, this technology improves the quality of the conversation. Nobody wants to spend their time having to give some strange salesperson information that’s public and which the salesperson should’ve dug up ahead of time. People tend to find more value in talking with someone who already knows that information.
The goal is to go into a prospect meeting and have a real conversation, not conduct an inquisition. I’m often asked these days if all this technology changes the kinds of salespeople I hire. My answer is that when I look at sales reps, the first quality I look for is a positive attitude. So we’ve had success with hiring people who are just your traditional knock-on-the-doors, have-longstanding-relationships, have-coffee, see-them-at-Walmart people. Those are the ones everyone likes to do business with.
But, today, being somewhat tech savvy is absolutely part of the equation. Data allows us to take our expertise to the next level. If we can overlay those longstanding sales relationships with the available data pieces, we can maximize the impact on whatever our goal is, whether it’s financial or opening new doors, or gaining customer testimonials. What we’re doing today is combining what we traditionally know with the technology to analyze every aspect of our work to show that that traditional approach is still true but can be enhanced.
For example, it’s general sales knowledge, as well as basic human nature, that people love talking about themselves and what they’re doing, and for that reason sales reps should ask open‑ended questions to let the prospect or customer talk. If that happens, they typically have a really good feeling at the end of that meeting.
And one way we can confirm that that’s still an eternal sales truth is by analyzing what we call our “closed or lost pipeline.” That refers to opportunities we had to do business with a customer and we either won something—closed the deal—or lost the deal. With data, we can look at common traits with those closed‑lost lists to say: You know what, on these three phone calls we had, no next steps were really established. But look, in every one of those calls, Damon was talking 60, 70 percent of the time! We can extrapolate that from the rep level to the territory level, and then on to the organizational level—Look, we’re losing deals. And in a high percent of the deals we’ve lost this year we’ve found that in those calls our reps were talking 70 percent of the time. On the other hand, in the calls that resulted in winning the deal, our reps were talking less than 30 percent of the time.
That’s valuable data, but how do we put it into action? I typically do it by sitting my reps down and saying, “Look, the data speaks for itself. When you’re talking most of the time, your customers aren’t feeling like their objectives are being met, and it’s not a good use of their time—and in turn you’re losing deals.” The great thing about this is that it’s not just advice from a manager, it’s not personal bias, it’s not my emotional reaction. It’s data, and we can use it for habit creation—we want these best practices to become muscle memory.
In general, it takes 66 days for something to become muscle memory, regardless of what you’re doing. And with this data and these analytics tools, instead of having to think about every individual deal, we can look for common traits throughout the team and give our reps those answers. We look at who’s the most successful at what they’re doing and figure out how to replicate and scale those best practices and habits.
We have a way to analyze their email messaging, such as which subject lines resulted in the highest open rates. The people I talk to, principals and superintendents, get an average of 200 to 300 emails a day from companies trying to solicit their business, so how do you stand out among this field of just incredibly repetitive messaging, of constant reach‑outs to try to grab someone’s business? Fortunately, today there are programs that will actually track when an email has been opened, tell if the recipient clicked on a link, and show how long they spent after they clicked on that link. This is all valuable data, and it’s no longer siloed in one place. All of these tools are working together to give us a more comprehensive picture of where you are as a consumer, how ready you are to buy, and the content I need to bring up with you in order to advance the conversation or close the deal. Programs like Infor, Salesforce.com, Oracle’s Siebel, and other CRMs serve as a data house.
But I before I end this piece, I want to mention another side of how we use data to prepare generations for better outcomes by opening up future opportunities that may not currently exist. While I was asked to focus specifically here about how technology and data help us sell better and improve efficiency, there’s an even better story to be told about how data helps us equip students and educators to be successful. At DreamBox, we’re known for using artificial intelligence and machine learning to “learn how the learner learns.” By collecting 800 data points a minute, we can analyze and understand past and present data to provide in-the-moment adjustments to the content presented to the individual learner.
Nationally, according to a study from the Council of Great City Schools, we ask our K-12 students to spend somewhere between 20-25 hours a school year taking standardized tests. But what if we could give back that time for educators to use teaching their students? That question is what AI and machine learning are helping companies like DreamBox Learning answer. While a learner is working in DreamBox, performing tasks and using virtual-manipulatives to solve questions and explore possibilities, we’re collecting data points at the tune of 48,000 an hour, using past and present data to understand how an individual learns, what they should be focusing on next, and, maybe eventually, how they would perform on a test—without their ever having to take one!
That’s where we connect the past and the present to help predict the future performance of students. As for the learners, they’re enjoying themselves as they develop conceptual understanding and critical-thinking skills in a highly engaging, gamified, and age-appropriate learning environment on what has traditionally been the most challenging subject in K-12 education—mathematics. We then automatically adjust each student’s learning path, making this one less thing an educator has to do in an already busy day. So for educators who take the time to work in DreamBox, we present that interpreted data in the form of feedback, indicating where a particular student may need help and attention. In other words, we empower that educator to have an informed teaching moment with that student.
At DreamBox Learning, we improve student outcomes and extend equity in learning opportunities, regardless of a student’s zip code. Our mission is to change the way the world learns, and we’re doing that one learner at a time, providing students with engaging learning experiences in which every minute spent is productive for the student and empowering for the learning guardian. That’s what inspires me to wake up every single day with the goal of getting more kids on DreamBox.