Raviv Turner and Nic Zangre
CEO & VP, Sales & Marketing at CaliberMind
On Predictive Marketing Technology
MTS: Tell us a little bit about your role at CaliberMind and how you got here.
Raviv: I’m the Co-Founder & CEO of CaliberMind. Our customers such as Citrix, Gusto, Datavail and others use us to integrate, measure and optimize the entire B2B customer journey. Think of us as the system of buyer intelligence that sits between your CRM & systems of engagement (i.e marketing automation, content marketing, video marketing, chat etc’); we map the buyers in each account to graph and orchestrates complex buying journeys. My personal background is in SIGINT (Signal Intelligence) and product design,
MTS: Given how quickly machine learning influenced predictive marketing strategies have been accepted, how do you see this segment evolving over the next few years?
Nic: When most B2B marketers use the word “predictive”, they’re usually referring to identifying ideal customers profiles and trying to predict which companies would/could be their future customers. There are some deeper applications for customer modeling — such as predictive personas. What if you would not only predict companies that are a fit, but also predict why an individual would be motivated to buy, be an advocate, etc. Also, sequence and content predictions will become possible, such as having your marketing automation tool automatically pick what should happen next.
Raviv: We’ve seen predictive B2B marketing fail in the past due to data quality issues. CRMs and marketing automation systems were never built to track buying signals. 80% of customer behavior data is unstructured, it sits in email communication, recorded sales calls, sales notes, and social feeds, you won’t find it in your CRM. Our customers come to us to map data between systems and make sense of it. Machine Learning and Natural Language Processing can help marketing and sales teams make sense of all that data to dynamically model buyer personas and map the customer journey.
MTS: What do you see as the single most important technology trend or development that’s going to impact us?
Nic: I’ve been fascinated by the evolution of the ‘Operations’ function at high-growth B2B organizations. MarketingOps inevitably hits an innovation ceiling due to red tape. A common example is key technical team members in large organizations not having appropriate system access. If you have a Salesforce Admin Certified member of the MarketingOps team, they need full administrator privileges in Salesforce, or else you’re disabling some of their capacity. By creating one consolidated RevenueOps team, organizational culture and data silos can be avoided. For this reason, the most innovative organizations are combining various Ops functions for synergy. At CaliberMind, we believe this evolution will continue and fold in Customer Success Operations. We’re calling this organizational structure “CustomerOps”. We’re already seeing some top practitioners and leaders have titles such as “GTM Strategy & Operations”– this synonymous with CustomerOps.
Raviv: AI is going to automate most technical tasks performed by marketing and sales such as: lead qualification, initial chat conversations/ discovery (bots), data analysis, content optimization etc’ but the business logic will always remain human, there will always be a marketers or sales rep in the loop to guide the machine and drive the creative, strategic and human aspects of the business.
MTS: What’s the biggest challenge for startups to integrate a platform like CaliberMind into their stack?
Raviv: There are two challenges we address: quality of data and talent – we generate buying signals from your unstructured data and work with our data partners to enrich the data you don’t have in your CRM. The talent challenge is most marketing teams don’t have or can’t afford data engineers or data scientists. To address this challenge we designed a Customer Data Platform that is driven by marketers. We also provide professional marketing data science services.
Nic: Again, I think the biggest to challenge to innovation and agility within an organization’s culture. Organizations that only evaluate the technology budget annually are their own worst enemy. Think of your tech portfolio like an investment portfolio. You have your “blue chips”, such as CRM and Marketing Automation, and Customer Support. But you should always aim to diversify and place some bets on emerging technology. Mergers happen, new features come out, and sometimes you learn about a vendor that existed for years that can cure some of your pain. Most good these days will let you do a pilot or ‘proof of concept’ before committing to an annual deal.
MTS: What startups are you watching/keen on right now?
Raviv: We love AI of course, I think AI has many more applications, some more mission/ life critical than marketing, of course. For example, I like how Aldoc brings AI to medical imaging analysis. In the marketing & sales space, Persado is leading the way in emotional intelligence and text analysis.
Nic: As a first-time product manager, I’m a big fan of non-relational database technology and DevOps at the moment. I have my eyes on companies like SlamData that allow you to access NoSQL databases. In the next few years, we’re going to see a lot more applications for this technology. Also, I see the companies like Segment leading the way to help B2B companies organize and wrangle all the web event data so as to make a tool like ours even more powerful.
MTS: What tools does your marketing stack consist of in 2017?
Raviv: We have a lean but powerful stack that consists of a CRM (Salesforce), MAP (Autopilot), CMS (Hubspot). We use Terminus for ABM, Clearbit for enrichment and Outreach.io for outbound. Our platform is built on top of Amazon Redshift and MongoDB using microservices. We also obviously drink our champagne, using our own platform for journey analytics and segmentation.
Nic: I’d add too that our marketing, sales, customer success and product team are extremely aligned. For example, we log customer tasks as well and marketing projects in Jira if there’s a technical component. We prioritize CustomerOps stories in the sprint just as we would a new product feature.
MTS: Could you tell us about a standout digital campaign?
We are huge proponents of using podcasts to grow our business, it helps us to develop strategic business relationships, creates strong content and brands our customers as thought leaders in their industry. We recorded close to 30 podcasts so far with B2B marketing and sales teams all leaders in their space, 20+ resulted in product discussions of using CaliberMind.
MTS: (Who was your target audience and how did you measure success) How do you prepare for an AI-centric world as a marketing and business leaders?
We manage the machines so they won’t manage us 🙂
How I Work
MTS: One word that best describes how you work.
MTS: What apps/software/tools can’t you live without?
Slack, Asana, Chili Piper, and of course, CaliberMind!
MTS: What’s your smartest work related shortcut or productivity hack?
Our inbound leads show up fully enriched in Slack for the sales team to respond immediately.
MTS: What are you currently reading?
MTS: (What do you read, and how do you consume information?) What’s the best advice you’ve ever received?
Your network is your networth
MTS: Something you do better than others – the secret of your success?
Hustle and persistence
MTS: Thank you Raviv & Nic! That was fun and hope to see you back on MarTech Series soon.
2x entrepreneur, product designer, marketing technologist and a former intelligence officer. I’ve led companies and software product teams from inception to $1M in SaaS MRR and beyond. I like building technologies that integrate themselves seamlessly into our lives, capitalize on our unique strengths, and amplify the best of human nature.
My voracious personal curiosity extends into areas as diverse as cognitive computing, neurology, psychology, sociology and anthropology, always with the goal of understanding why we do the things we do and what that means for marketing strategy. In addition to being the Co-Founder & CEO of CaliberMind, I’m also a mentor at Techstars and a regular speaker at marketing industry events.
There’s a major challenge B2B marketers are facing today — lost revenue due to a disconnected buying process.
Research shows that there’s an average of 6.8 buyers per committee, 60% of the journey happens online (before sales gets involved), and there’s an average of 17 touches during the sales cycle.
With this, comes a high cost: 70% of B2B content goes unused, sales cycles, 25% of Selling time is spent searching for data, and only 9% of MQLs convert to revenue.
B2B buyers experiencing these broken processes receive generic, impersonal marketing communications in spite of the technology advances recently made in Machine Learning and Natural Language Processing. Hyper-personalization at-scale is now possible. Furthermore, by the time a lead reaches sales, systems can reveal deep buyer psychographics and insights to the seller so that the buying process is frictionless as it can be.
CaliberMind is a venture backed, AI powered, demand marketing and sales acceleration SaaS founded by seasoned marketing technology veterans (AdRoll, FullContact, Kapost) that is pioneering the next generation of B2B sales acceleration software (a $12.8b market). Unlike existing sales acceleration solutions that focus on the selling team, CaliberMind enables the B2B buying team throughout their decision journey. Used by buyer-centric B2B Fortune 500 (Citrix) and mid-market companies (Gusto, Apto, Datavail and others) that have made the shift from rule-based marketing campaigns to machine-informed buyer journeys, CaliberMind recommends the right message to the right buyer persona at the right stage of the buyer’s journey.
The buyer data platform automatically derives a buyer’s journey from internal (CRM, MAP, web analytics etc’) and external data sources (social, intent, psychographics) it assembled. It works by analyzing the sequence of lead-to-revenue events for each account and finding the most common paths to sale using machine learning and human language analysis. It then recommends the next-best ‘buying play’ and pushes the data back to the CRM & MAP (no change in workflow). The platform is fully accessible by external systems via API and is structured to fully support demand marketers and revenue ops needs for campaign management, marketing analyses and buyer intelligence.