Your call reports can tell you which campaign made the phone ring. The conversation itself can reveal what the buyer needs, which questions your content has not answered, and whether that call became a worthwhile opportunity.
In A New Place To Look: Where Your Next AI Citations & Clicks Come From, CTM’s Lisa Salvatore, Sr. Manager of Integrated Marketing, and Brian Barranger, Sr. Account Executive III, explained how to put those insights to work across marketing, sales, and product teams.
Their starting point was not another dashboard. It was a shared definition of lead quality and a repeatable way to learn from the conversations a business is already having.
Define Lead Quality Before Counting Calls
Marketing can celebrate a rising lead count while sales struggles to find suitable prospects. Salvatore described that disconnect as the MQL treadmill: without agreement on quality, the two teams optimize toward different goals.
Her recommended first step was to have both teams review a representative group of calls. Look for the traits of a qualified conversation: a relevant need, a service the business can provide, buying intent, or an appointment booked.
Turn that agreement into scoring criteria that can be applied consistently, manually or with automation. The score is useful when it informs a decision, such as refining campaign targeting or coaching call handling, rather than becoming another number to report.
Turn Customer Questions Into Useful Search Content
Calls and transcripts capture how customers describe a problem in their own words. Salvatore’s conversation-to-content workflow uses repeated questions and objections to guide a content roadmap:
- Recurring questions can inform FAQ pages and answers on relevant service pages.
- Common objections can reveal the need for comparison content or clearer trust-building explanations.
- Customer phrasing can help shape headings, descriptions, and ad copy.
The task is to answer those questions accurately, not simply copy phrases onto a page. Salvatore cautioned that matching customer language can support discoverability but does not guarantee an AI recommendation. Credibility and brand recognition still matter.
Webinar clip: Pair customer call phrases with lead quality to see which keywords generate useful opportunities.
See the full on-demand clip at 11:54.
The approach also extends beyond phone calls. Chats, texts, and form submissions can supply customer language. In the Q&A, Salvatore advised looking for the differences in what people ask about your own pricing, services, and offering, rather than assuming competitors’ FAQ pages should all look alike.
Connect Marketing Sources With Qualified Outcomes
Barranger illustrated the difference between an attribution label and a useful customer insight with a pest-control call. Knowing that someone called about pest control is less specific than knowing they needed help with ants in their kitchen and booked an appointment.
That example connects three questions:
- Which marketing source generated the call?
- Was the caller a qualified opportunity for the business?
- What happened after the call was answered?
Looking at those dimensions together can distinguish a high-volume campaign from one that produces useful opportunities. It can also separate a targeting problem from a missed opportunity in call handling.
Webinar clip: Compare campaigns by the qualified calls they produce, not call volume alone.
See the full on-demand clip at 22:37.
In a separate example, Barranger described analyzing 29 CTM lead-call summaries. A recurring theme was prospects’ difficulty connecting marketing investment with opportunities and revenue, not merely a request for tracking numbers or recordings. He used that limited set of conversations to illustrate how customer needs can inform positioning and sales discussions.
Give Ad Platforms And AI Tools Better Inputs
Conversation scoring can help identify eligible qualified outcomes to return to advertising platforms through supported conversion workflows. Barranger’s point was to provide a more useful signal than treating every phone call as equally valuable.
He also emphasized validating the scoring criteria and testing before making major budget changes. More data is not automatically better feedback.
For content creation, call insights can supply context that a generic AI prompt lacks: the service a customer needs, the objection they raised, or the question they keep asking. A recurring concern can become material for a landing page or a messaging test.
“And the AI helps us produce and explore ideas faster. But the customer conversations are what make those ideas actually relevant.”
— Brian Barranger (Watch The Webinar, 29:51)
Barranger followed that distinction with a reminder to review and validate what gets published.
Route Demand Signals To The Teams That Can Act
Requests for an integration, a missing feature, or a service the business does not offer can be lost when they stay with the person who answered the phone.
The presenters recommended tagging recurring requests and compiling them for the teams responsible for product, services, and planning. Frequency can help distinguish a repeated customer need from a memorable one-off conversation. It does not mean every request automatically belongs on the roadmap.
Salvatore also suggested tracking relevant topics over time and pairing them with their originating campaigns or channels. An increase in requests is a signal to investigate and validate, not a reason to treat a small sample as a market-wide forecast.
Handle AI-Agent Calls As A Different Signal
One audience question asked how human calls compare with AI calls made to a business. Barranger said both can be tracked and analyzed, but they do not necessarily carry the same intent.
A person’s call may reveal buying urgency and objections. An AI agent may be checking hours, pricing, or availability on someone’s behalf. He recommended tagging those interactions differently rather than automatically treating every information request as a direct sales lead.
Another Q&A theme was ownership. Barranger recommended involving leaders from the relevant departments so that qualification criteria and the intended uses of the data reflect shared business priorities.
Start With One Repeatable Workflow
The presenters’ framework moves from capturing and attributing conversations to transcribing, scoring, analyzing, integrating the findings, and repeating the process. The full webinar’s examples and Q&A show how those stages connect.
You do not need to tackle every use case at once. Choose the area that would make the biggest difference for your team, then make the first pass concrete:
- Agree on qualification. Review a representative sample with sales and marketing, and document the traits of a useful opportunity.
- Organize the conversations. Tag recurring needs, questions, objections, and requests alongside the call’s source and outcome.
- Choose one application. Use a recurring question to improve a page, test a more specific message, or route a repeated request to the relevant team.
- Validate before scaling. Check the scoring and resulting content, and test conversion feedback before making major spend changes.
- Repeat and compare. Review how topics and qualified outcomes change over time, and use the findings to refine the next round of work.
Join Us For Our Next Webinar!
The Link Building Graveyard: What to Bury & What Still Wins
Join Michael Johnson, CEO of Resolve, and leave with a keep-or-cut list you can take straight into 2027 planning.
