Raviv Turner is Co-Founder and CEO at CaliberMind. On this episode, he and I discuss the challenges B2B marketers face in understanding the lengthy buyer’s journey. It’s long, it’s complex, there are more people involved, and there is a ton of data spread across both structured and unstructured sources.
Raviv’s career path includes being an entrepreneur, a product designer, a marketing technologist, and a Former Israel Signal Intelligence Community Officer. He applied the knowledge he learned about Big Data while working as an Intelligence Officer into his work building software products.
He explains how CaliberMind uses machine learning techniques to harness data across the broad spectrum of sources, resulting in improved sales pipeline and accelerated close rates. We also discuss why he believes that Artificial Intelligence (AI) is over-hyped and what marketers need to understand to take advantage of the power and potential of AI. This take-away alone is worth the listen.
Raviv sees AI more as augmented intelligence than artificial intelligence. He adds that it’s not going to replace marketers. Marketers can work internally with IT or with vendors to help deploy AI, but it’s not plug-and-play, as it’s often hyped up to be. CaliberMind uses AI for lead scoring, persona and content tagging, and dynamic segmentation.
Utilizing Data to Assist Sales
CaliberMind is an Integrated Customer Database Platform that marketers use to unify customer data from marketing, sales, and service channels to enable customer modeling and drive customer acquisition. We now have large amounts of data, which requires that we fragment the data to make it usable. Raviv says we collect so much data that it’s hard to piece it together and manage it all without fragmentation. Another reason for data fragmentation is tool fatigue with AI and machine learning.
Their B2B customers collect various types of data, starting with first party information, which is about their buyers and is managed within CRM systems and other platforms. Second party data is gathered from sources such as Google AdWords, while third party data is rented or purchased. This data is segmented into types including firmographic and account level data, down to technographic, intent, engagement, and psychographic data.
Raviv says psychographic data (how people think) is trickier since it comes from unstructured data. 80% of behavior data is unstructured, meaning it comes from email communications, sales calls, notes in the CRM, social media engagement, etc. Combining the structured, semi-structured, and unstructured data gives a selling organization a full view of prospects.
The ‘Data Science Hierarchy of Needs’
Gartner’s Analytics Continuum includes data on what happened (descriptive), why it happened (diagnostic), and what will happen (predictive). This can all be found in the market, but the one thing you won’t find is actionable data, or what Gartner calls prescriptive data.
The ‘Data Science Hierarchy of Needs’ starts with collecting data from CRM and analytics. Below you see that it’s similar to Maslow’s Hierarchy of Needs but applies to data analysis.
6 Use Cases of CaliberMind
Raviv explains that you have to “collect the dots before you can connect the dots.” After obtaining the needed information, CaliberMind helps B2B brands with the following six objectives.
Mapping the Customer Journey
The customer journey is from lead to revenue to advocacy, and not just lead to sale. To integrate, measure, and optimize the entire lifecycle CaliberMind creates system maps post-sale thereby giving the customer a better experience.
The buyer journey is a maze with 17 touch points in the typical B2B journey. Mapping out the journey allows for journey orchestration and the ability to accelerate the deal.
Automation
Their technology allows organizations to go after a specific attribute of the target customer by applying tags across various systems. Most segmentation is based on firmographics, so it often stops at the account level.
Behavior Analysis
Raviv reminds us it’s people buying and that two accounts never look the same. Dynamic segmentation based on needs and behavior help lead to determining micro-segments.
Psychographic Analysis
By analyzing what is said, prospects can be mapped against personality traits to match the content and channel that will work best for them. The information is then pushed back to the CRM.
Operationalizing Personas
A persona is similar to a segment and Raviv says they need to be actionable. After listening to customers to operationalize personas, CaliberMind then pushes the persona score to the organization’s marketing automation software.
Content Intelligence
On average, buyers have already conducted about 60% of their research when they contact a company. Through customer journey mapping, CaliberMind can recommend a particular piece of content to guide them along their journey.
Centralized Dashboards and Insights
The Customer Data Platform provides prescriptive insights to improve the buyer experience. Through integration CaliberMind allows the sales and marketing team to continue using their current CRM and Marketing Automation software.
Raviv recommends focusing more on the data. He says everything links back to data, and the biggest struggle for customers is “garbage data.” He expects to see re-architecture of data unification to overcome tool fatigue and large amounts of data.