Along with PowerPoint presentations, fact sheets, and lots of business cards, the new must-bring item to a marketing-technology sales pitch might very well be the data scientist.
With the rise of big data, technology, and more tech-minded marketers, some say the pairing makes sense and increases the credibility of a pitch.
“Data scientists, at some level, command a leading customer-facing role,” said Brennan Hayden, EVP and COO at WDA, a Rocket Fuel company, who finds it ironic that data scientists haven’t always been part of the marketing technology sales team.
The best approach, he told CMO.com, is to give sales access to people who spend their days answering data questions for existing and potential customers.
Dstillery, a marketing-technology provider based in New York, encourages sales, account management, data science, and analytics to work closely together. The data science team is involved in sales pitches and meetings when more technical conversations are expected.
“As a benefit of participating in a sales pitch, our data science team is keenly aware of the new challenges, concerns, and requirements of our clients,” said Claudia Perlich, chief scientist at Dstillery.
Data scientists are also often pulled into the sales pitch at Adobe (CMO.com’s parent company), since customers are typically digital marketers who are “more about numbers these days than they are about creative,” said Anil Kamath, VP of technology at Adobe.
Pick And Choose
San Mateo, Calif.-based Conviva, another marketing-technology company, includes data scientists in its sales pitch as well--but only when absolutely necessary.
“Presenting data science is rather analogous to presenting technological detail,” said Simon Jones, VP of marketing at Conviva. “It’s fascinating to some individuals and mind-numbing to others. Situational awareness is the essence of the decision to send, or not send, data scientists into the presentation.”
Retention Science also is more selective as to when it pulls its data scientists into sales meetings—about 10% to 20% of the time, and only if it’s necessary to gain a better understanding of a more complex business problem.
“Sometimes, if the conversation gets too technical, it takes away from the goal of a sales meeting,” said Jerry Jao, CEO of Retention Science.
According to WDA’s Hayden, data scientists can help when a salesperson is first crafting the sales pitch, providing, for example, data for case studies, which are still a key component in selling marketing technology today.
“However else you involve your data scientists, involve them heavily in telling those stories, in bringing to life the case studies of your most significant wins,” Hayden said. “Those stories, more than anything, are the keys to your next big win.”
Data Scientist vs. Sales Engineer
Some companies are hiring sales engineers as a supportive sales role, with the notion that these individuals combine the chops of a data scientist with the finesse of a salesperson, Retention Science’s Jao told CMO.com.
But traditional sales engineers aren’t the same as having a data scientist at your disposal, Kamath explained. Sales engineers are very familiar with the product in terms of UI and features, but data scientists are actually building big data infrastructure and algorithms, which go into the products. A sales engineer might be sufficient, but it depends on how data-deep your product is.
“The kind of analysis needed to be done to communicate ROI is very different than what sales engineers are equipped to do,” Kamath told CMO.com. “When a client says, ‘I have a $1 million budget for this quarter, how do you recommend I spend it between these different channels?’ that’s when you need a data scientist.”
Mark Asher, director of corporate strategy at Adobe, told CMO.com that a data expert can explore several themes with potential customers that a typical sales engineer cannot. One is unlocking value from data, which could involve new forms of data storage, modeling, transformation, and structure.
Additionally, only data scientists have the true expertise to extract value from data locked up in 30 siloed places inside of a company, Asher said. And they can help companies better act on the insights gleaned from the data.
“Data folks do need help, however, to be productive in front of customers,” Asher said. “Those folks are usually mad scientists. They’re wicked-smart but terrible communicators. The account executive needs to use data scientists carefully because they can head off quickly into theoretical discussions that no one understands. Translating their domain expertise into something useful is an important part of shaping a data scientist’s role on a customer call.”
At Adobe, data scientists are split into two groups: behind the scenes and customer-facing. Both have strengths in working with large amounts of data and understanding the mathematical frameworks for building large, scalable big data products, but the customer-facing scientists also have the necessary communication skills.
“They need to be able to present information very well and in terms that a marketer or somebody without a technical background can understand,” Kamath said.
In the end, however, Conviva’s Jones said the entire sales team needs to be data-versed, understanding the potential of data and the ways in which it can be used to create value. They should be able to talk about data broadly to customers.
“But [sales] also needs to know the boundaries of their own capabilities and when it’s time to bring in an expert,” Jones said.