Jed has a passion for unlocking hidden value in data and for creating great data-driven software. When he’s not at his computer, Jed enjoys camping and riding horses, and you might find him doing one of those things in Washington, Idaho, or Northwest Montana.
Holding degrees in applied math from Harvard and the University of Washington as well as a Masters in Scientific Computing from Stanford, Jed has worked as a Software Engineer at Microsoft and was the first person to hold the title of Data Scientist at Salesforce.com.
Selling is hard. Clari makes it easier. As a team, we’re obsessed with creating software that sales people love to use. We apply Artificial Intelligence (AI) to solve some of the biggest challenges they face in navigating the critical Opportunity-to-Close (OTC) process. We know sales and obsess over how to apply data science and prescriptive insights to make sales teams more productive and successful.
Tell us about your role at Clari and your journey into Artificial Intelligence.
I am head of Data Science at Clari, which means that I get to take a lead role in shaping how we use AI and predictive algorithms to drive better decision making in sales.
My journey into AI began in graduate school where I took my first classes on the subject. I became fascinated by the idea that machine learning could be used to extract insights from data that would otherwise be inaccessible. I sought out data-related assignments in my subsequent work at Microsoft and Salesforce.
There was a small, home-grown search engine embedded in the product I worked on at Microsoft, and I got to refactor this, which taught me a lot about information retrieval and about the abstractions we use to represent text in machine learning problems.
A friend then recruited me over to Salesforce to work on a new recommendation engine they were building, and this was my first taste of using Artificial Intelligence and machine learning to produce really great results in the context of a user-facing data product. I then became the first official Data Scientist at Salesforce and led projects on user profile clustering, customer attrition prediction, business social network analysis, feature adoption analysis, and other topics. AI is absolutely magical when you get it right, and I can’t imagine a better field to be in.
How should product teams and customers differentiate between Data Science and Artificial Intelligence technologies?
I like Foster Provost’s definition of Data Science as the practice of extracting knowledge and useful information from data. Artificial Intelligence, of course, has always been about teaching machines to think. The two fields overlap when machine learning is used to extract insights from data, and this is the zone we operate in at Clari.
Note that, there are many areas of Data Science that do not involve Artificial Intelligence at all. This would include statistics and most traditional BI. It’s in applying machine prediction to new classes of business problems where the exciting new ground is being broken.
How deep is the B2B sales tech ecosystem into AI/machine learning?
We are in the early days of how AI and machine learning are impacting the way we work and how sales teams operate. This is a massive and highly transformative change in the way teams process information, collaborate and make decisions. We’re seeing a shift from historical analysis, reports, and queries to AI-based insights.
The shift is pushing practical information and predictions into the hands of reps, managers and sales execs at key moments across the sales process.
For instance, Clari can help identify risk and upside on opportunities and project how the quarter will end. This transforms not only the way sales teams work together to drive their strategies but also the ability of business leaders to drive the actions needed to hit their numbers. With AI, we’re also able to project the forecast into future quarters and suggest the required pipeline to achieve those projections.
The C-suite including COOs, CFOs, CMOs and CROs are now collaborating around the same set of data points and metrics in a far more integrated and productive way than ever before.
What are the core tenets of your AI and machine learning roadmap at Clari?
There are a few core elements to our product innovation and AI roadmap:
- Data: We apply AI against a range of important signals from the buying process. We’re collecting and analyzing not just CRM data but also rep and prospect activity data – all the emails, files and contracts that are flying back and forth, as well as the actual meetings that are taking place. We’re constantly adding additional data signals through a range of integrations with partners and application providers to give sales teams better insights and a better understanding of buyer behavior.
- Practical use cases: AI insights are packaged into a set of practical applications for sales teams to use every day as they sell, close and forecast their business. For example, we’re analyzing opportunity win/loss data to come up with a CRM score that ranges from 1-99, representing the likelihood of an opportunity to close. Sales reps and managers can use this score to identify risky deals and know where to focus. Because sales is so much an art, we’ve invested in making AI transparent, providing explanations to give sales teams confidence in the AI and drive adoption.
- Custom models: We know every organization is different, so we’ve designed our system to automatically build independent models for every company, type of business, product line, and territory, thereby accommodating the unique ways our customers run their sales teams. Part of this involves automatic segmentation via unsupervised machine learning models, followed by the application of independent models to the resulting segments.
What are your predictions for AI for Sales and Marketing?
AI is here to stay and will continue to become a more powerful force in all areas of business.
AI is changing the course of work in the enterprise, putting a stop to systems that create more work and can’t answer critical questions or predict outcomes.
AI is about saving real people real time. It’s about giving people answers they would need six months from now, liberating them from manual, mundane tasks and making their lives better. Sales and marketing are just the beginning: AI will make its way into finance, customer success, and support, impacting all revenue-generating functions and adding a level of efficiency and predictability never seen in the enterprise before.
How can AI/machine learning help to build better CRM and Sales Automation platforms?
AI can help you anytime you have to make the same type of decision over and over again based on the same type of information. For instance, if you’re a sales professional, you’ve probably become really good at quickly deciding whether or not to pursue a new sales prospect by looking up information about them from sources you’ve decided are reliable. This is an important part of your job, and you’ve made this evaluation thousands of times.
But because it’s a repetitive decision that you always make from the same information sources, there’s a good chance that AI can be trained to make the decision for you efficiently enough that you should immediately accept an automatically generated prospects list and just get straight to selling. CRM and sales are full of opportunities for AI to take over tedious classification tasks, freeing people up to be much more efficient and effective at their jobs.
CRM is going through a renaissance. The original premise of CRM was around a single unified view of the customer, and at that time it was reliant on manually inputting data. Now, data volumes are growing, and data is increasingly coming from multiple sources outside of CRM. This is fueling a wave of new innovation in the way data is collected, analyzed and presented. It’s driving the unbundling of core sales processes and the emergence of new sales intelligence tools that leverage CRM data in addition to email, calendar, and other data signals to drive better decision making in sales.
Who does it best when it comes to leveraging AI for marketing?
Without mentioning any specific companies, we’ve seen a few best practices that early adopters of AI in marketing are using to gain a competitive advantage. First, they’re using AI to intelligently adapt the content of their websites based on predictive analytics around who’s visiting and what their interests are. It feels like we’re just in the very early days of what can be done with AI-driven personalization.
Second, they’re using AI to provide a more conversational experience for web visitors (via chatbots). Finally, they are using AI to help prioritize account and lead outreach. This is not your father’s lead scoring; it’s a whole new generation of AI-driven prioritization technology that involves identifying the attributes of your best customers and contacts based on historical buying patterns and leveraging that to identify new, high-potential targets.
How does Clari make selling better?
We’re solving key sales execution and forecasting problems around productivity, pipeline visibility and forecast accuracy that are common for any sales team. By providing clear visibility into pipeline risk and upside, sales reps know where to focus, managers can immediately spot risk in the pipeline, and execs can forecast with confidence.
How do you consume all the information on AI and other emerging technologies for advertising and branding?
One of the best things about my job is that I am learning all the time, and there are always fascinating new developments happening in AI and related areas.
At the same time, it’s important to recognize that the Artificial Intelligence revolution that is transforming so many industries is really driven by a core set of proven algorithms that happen to be effective in many different contexts. I definitely keep my ear to the ground for the next new thing, but it’s a balance between keeping up with what’s new and making sure you’re taking advantage of proven techniques in as many places as you can.
Thank you, Jed! That was fun and hope to see you back on AiThority soon.