Welcome back to another exciting episode of Funnel Lab Fridays, where we dive deep into the challenges and innovations that data-driven marketers face today. I’m your host, Eric Westerkamp, CEO of CaliberMind. Joining me are two brilliant minds: Emma Phan, Director of Marketing Operations & Analytics at Maven Clinic, and Nolan Garrido, our very own VP of Engineering at CaliberMind.
Today, we’re tackling a hot topic that’s on everyone’s minds: Improving Win Rates with AI Summaries for Sellers. We’ll also touch on transitioning from MQLs (Marketing Qualified Leads) to MQAs (Marketing Qualified Accounts).
Setting the Stage: The Challenge of Modern Selling
Emma kicks things off by highlighting a universal pain point: “It’s really hard to get prospects’ attention these days.” With inboxes flooded and budgets tightened—especially in industries like healthcare—cutting through the noise is more challenging than ever.
Emma’s team at Maven Clinic is grappling with scaling their efforts sustainably while hitting aggressive growth targets. “We have to think about how to empower our sellers with the data and insights they need, without overwhelming them,” she says.
This is where AI comes into play.
AI in Action: Crafting Personalized Messaging at Scale
Emma points out that while many are dabbling with AI for crafting messaging—like using ChatGPT to whip up emails—the real magic lies elsewhere. “We don’t want to start sounding like everyone else,” she cautions.
Her focus is on aggregating data from multiple platforms—intent data, engagement metrics, seller inputs—and using AI to present it in a way that’s both useful and digestible for sellers. “Our sellers have so many things on their plates. We need to serve up the data that’s relevant without being overwhelming,” Emma explains.
Nolan’s Take: The Engineering Behind the Magic
Nolan jumps in to shed light on the technical hurdles. “As with most AI projects, the lion’s share of challenges is in the preparation of the data,” he says. CaliberMind deals with data from eight to ten platforms per customer, and getting all that information to play nice is no small feat.
He praises tools like Google’s Vertex AI for making it easier to handle large datasets. “They’ve made it straightforward to move data into AI models that can scale and deliver insights quickly,” Nolan notes.
Scaling Sales Efforts with AI
Eric asks Emma how AI can help scale sales efforts. Emma doesn’t miss a beat: “It’s time-consuming to do account research across eight different platforms. If AI can do that initial legwork, sellers can focus on strategy rather than sifting through data.”
By reducing the time spent on manual research, sellers can prospect more accounts in a day. “When it’s harder to break into accounts, you have to go after more to keep your numbers up,” she adds.
The Struggle is Real: Outreach Challenges
Eric acknowledges a sentiment many share: “Outreach is really hard today. No B2B company is saying all our outreach is generating inbound leads.” With prospects harder to reach, the need for targeted, personalized messaging is critical.
Emma agrees, emphasizing the importance of providing sellers with actionable insights. “Handing them a summary of what’s going on with an account helps them craft better outreach,” she says.
Security Concerns: Walking the Tightrope
Eric brings up a crucial point: security. “Legal departments are wary of AI tools due to data risks,” he notes.
Emma explains Maven Clinic’s cautious approach, especially given they’re in healthcare. “We keep our systems separate to avoid any PHI (Protected Health Information) exposure,” she says. They’ve also formed an AI group to monitor usage and ensure compliance.
She adds, “We’re very strict with our privacy policies. Any new pixel or tool goes through rigorous review.”
Navigating the AI Landscape: Vendor Choices Matter
Emma mentions that they’ve chosen to use Google’s AI offerings over others like ChatGPT for security reasons. Eric elaborates on this, highlighting that working with reputable vendors can alleviate many security concerns.
“When you deploy applications embedding these capabilities and design them properly, data management and control are relatively strict,” he says. Data stays within secure projects, and the AI models don’t use customer data for training.
Looking Ahead: The Ideal AI-Driven Future
Eric asks Emma to envision the perfect AI world a year from now. Her eyes light up: “Right now, the AI summaries are great for one-to-one account lookups, but I want to scale that.”
She dreams of summarizing data for lists of 40 accounts or more, understanding commonalities, and strategizing at scale. “We have different ways to approach prospects—brokers, consultants, resellers—and AI could help us decide the best route for each account,” she explains.
Emma also envisions AI helping to identify the next set of accounts to prioritize. “We set a target account list at the beginning of the year, but markets change. How do we get ahead using intent signals and engagement data?”
Nolan’s Perspective: Overcoming Technical Hurdles
Nolan addresses the challenges in scaling AI summaries. “One of the biggest hurdles is the limitations on the amount of data you can send into these models,” he says. Context windows vary by model, and sending large datasets can be costly and technically challenging.
He remains optimistic, though, noting that vendors are continuously improving their platforms. “As they increase data capacities and processing speeds, we’ll be able to implement these features more effectively,” Nolan assures.
AI Summaries in Action: Tool Time
Eric transitions to a live demo, showcasing CaliberMind’s AI summary capabilities. He pulls up an account and highlights how the tool provides a concise summary, including key contacts and recent activities.
Emma shares how her team uses similar features: “We recently identified new target accounts. Instead of sifting through tons of data, sellers can quickly see who’s engaging and how.”
She emphasizes the difference this makes: “Sellers have 50 things on their to-do list. If they had to look through 163 touchpoints manually, they wouldn’t do it.”
Beyond Charts and Graphs: The Future of Data Interpretation
Eric expresses a bold vision: “If I could get away from charts and graphs completely, I would.” He believes the future lies in systems that interpret data and provide actionable insights without overwhelming users with visuals.
Emma agrees, noting that AI could help analysts focus on insights rather than getting lost in data exploration. “It’s about taking data to insights. That would be a game-changer,” she says.
Wrapping Up: Embracing AI for Competitive Advantage
As the session winds down, Emma reiterates the importance of balancing caution with innovation. “We want to be compliant and treat people’s data well, but if we’re not doing this, we’ll be behind,” she asserts.
Eric nods, adding, “These tools are evolving rapidly. Organizations that leverage them effectively will have a significant advantage.”
Final Thoughts and Next Steps
Emma and Nolan’s insights highlight a pivotal moment in B2B marketing and sales. AI isn’t just a buzzword; it’s a transformative tool that, when used responsibly, can significantly enhance efficiency and effectiveness.
Eric concludes the session with a teaser for next week’s Funnel Lab Friday: “We’ll have Camela Thompson from RevOps Co-op and our own Tara Wildt from CaliberMind joining Doug Bell. They’ll discuss transitioning to engagement-centric marketing models. You won’t want to miss it!”
