In October 1999, serial entrepreneur Mark Cuban set the Guiness world record for the single largest ecommerce transaction of all time. His purchase? A luxurious $40 million dollar private jet. This event caused many to speculate that the internet would eventually make salespeople obsolete altogether.
Two decades later, the increasing sophistication of artificial intelligence is further fueling these predictions. Toby Carrington, VP of Revenue Operations at Seismic, calls BS. “I don’t believe for a second that everyone will just buy everything online,” he says. “That’s just not how business is done. People still want to talk to people.”
“Today’s salespeople spend too much time doing grunt work—verifying faulty customer data, filing expense reports, searching for relevant content—and not enough time acting as true strategic advisors.”
While he’s resolute in his conviction that salespeople will never be fully replaced, he also sees massive opportunity for sales to modernize. “Today’s salespeople spend too much time doing grunt work—verifying faulty customer data, filing expense reports, searching for relevant content—and not enough time acting as true strategic advisors.” With the average salesperson frittering away 900 hours a year on admin, according to Forbes, today’s sellers are hardly operating at peak efficiency.
We sat down with Toby to talk about how he envisions the future of sales, how revenue leaders can distinguish between necessary integrations and ones that add unnecessary complexity, plus one secret to achieving excellent data quality that’s hiding in plain sight.
Nick: Paint us a picture—what does the future of sales technology look like?
Toby: The salesperson of the future will be fully enabled—maybe it’s in a wearable or a device that’s stuck in their ear—but they will have all the information that they need at their fingertips so they can be fully focused on adding value for customers. They know things about a customer’s business that the customer doesn’t even know because they’re able to bring together all of these data sources, internal and external.
I see sales technology following a similar evolution to Siri or Alexa. Earlier models didn’t even recognize your voice, but now you can ask virtual assistants to perform multiple complex commands. One day a salesperson will be able to wake up in the morning and have their digital assistant say, “Good morning, John. Here are the three meetings you’ve got today. I’ve already sent this case study to the Company X account. Chloe read it already, and she loves slide three—make sure you talk about it in your meeting. I’m going to turn my language to Japanese later on when you have a call with Japan. If you need any help with translation, let me know.“
There are some people who say that Rev Ops is just a fancy new name for Sales Ops. What would you say to those people?
No. [Laughs.] I mean, there’s always going to be the next trend people want to hop onto. Rev Ops has certainly taken off over the past few years, and the seniority of the position profile is increasing. But revenue operations really is a marked departure from what came before it. It’s about combining all of the go-to-market functions and operations into one cohesive engine.
Most growth stage companies have messily grown from the bottom up. Somebody decided at a certain point you needed a CRM system. Then someone else decided you need a MAP, or an LMS. They all get haphazardly layered on top of one another, with nobody managing the entire process from a holistic view. They’ve got the best intentions, but the road to bad data is paved with good intentions.
Why is revenue operations indispensable?
Because the customer journey is not a linear progression, and your organizational structure needs to account for this fact. A customer journey jumps all around the place and goes back and forth between funnel stages. No single department owns a lead. A lead is owned by a company and the way we take care of that person has to be in the entire company’s best interests, whether you’re in marketing, sales, customer success, or professional services.
Regardless of how well people are aligned or like each other, there will always be natural tension when you’ve got different department leaders responsible for various profit and loss metrics for the business. RevOps must be very strategic to make sure that those things are being done in the best way for the organization as a whole, and not being optimized on a lower departmental level.
Otherwise, departments end up inadvertently working against each other. More leads for marketing might mean more mess for sales to clean up. More deals for the sales team might foretell more churn for customer success. And then you’ve got a tragedy of commons where the business ultimately suffers for all these supposed “successes.”
How can large organizations make sure everyone is “speaking the same language” when it comes to data?
A data dictionary is an invaluable asset for making sure people have a common understanding, even of simple things like pricing naming conventions, capitalization of records. It might seem basic, but they definitely can take on a life of their own if they’re not put in a common language. The challenge is to find a way to do this without losing important nuance by standardizing on the lowest common denominator.
You recently acquired a company called Percolate. What’s the hardest part of integrating their tech stack into your own?
The most challenging part is differentiating between which integrations are adding value and which are just integration for integration’s sake. It’s tough because various system owners have strong opinions when they’re used to using the system in a particular way. As a leader, it’s my job to get everyone to snap out of their personal preferences and reorient around what’s best for the collective good of the company.
How do you make that distinction between essential integrations and extraneous ones?
Integrations need to be designed with a specific role in your organization in mind. Just because these two pieces of information can talk doesn’t mean anyone in the organization will care about the intersection. The litmus test might be asking: Does a seller need to know this information? Will this data help them when they’re creating quotes, creating opportunities, sending proposals to customers?
Other functions may be using the same systems, but they fundamentally care about different things. For example, marketing cares about analyzing ROI on content and orchestrating campaigns. Their tools need to be configured and integrated in a way that supports these use cases.
Revenue operations is obsessed with busting through silos, but humans as a species tend to be quite change averse. How do you approach change management as a leader?
Always lead with answering the question: What’s in it for me? If you can explain to an AE or a CSM why the change is in their long-term best interest, even if there’s a short-term adjustment period, then you can secure the buy-in you need to smoothly roll out a change.
I explain it to my team like this: You always want to be focused on delivering value to your organization, but what constitutes value is not stable. It changes over time. Therefore you have to keep changing to keep contributing the most you can.
For example, we recently rolled out a CPQ (Configure, Price, Quote) tool to streamline our process and replace the more manual approach we used in the past. I began that meeting by explaining how the tool improves the end result for each salesperson. They care about getting a quote out the door quickly. Using the new tool may take a little bit of overhead to learn, but with time it’s even quicker and it offers additional benefits to them, like having the contract documentation in one place.
What’s your biggest tip for revenue leaders who care about excellent data quality?
Rules, discipline, and constant focus. It’s that simple, and that hard. I can’t tell you how often I’ve heard people say, “Look, we’re going to have to spend the next two or three weeks cleaning up our data because it’s gotten out of hand.” You have to sit down and do the hard work. It’s not sexy. It’s the worst thing you can do—sit down and clean up all the data, create rules, make sure certain fields can’t be overwritten, create data dictionaries. It’s mind-numbing stuff, but getting it right is one of the most important things you can do to generate revenue for your organization.
About the author: Nick is a CEO, founder, and author with over 25 years of experience in tech who writes about data ecosystems, SaaS, and product development. He spent nearly seven years as EVP of Product at Marketo and is now CEO and Founder of Syncari.