The sale may be considered the end of one chapter, but it is just the beginning of another in the long story that is a customer relationship. The shift from a volume-based mindset to precision-based targeting is equally critical for existing accounts. The lifecycle marketing function at Intentsify leverages buying group intent data to fundamentally transform how we think about account engagement, moving beyond individual-focused efforts to orchestrating multi-threaded campaigns across the buying (or renewal) committees.
The Lifecycle Marketer’s Guide to Growth and Retention with B2B Intent Data
For Brian Ewing, our Director of Lifecycle & Product Marketing, this visibility means his team is no longer just reacting to MQLs; “we’re proactively building relationships with the right people at the right time.” Here is his playbook for turning intent signals into customer growth and churn prevention.
The Foundation: Strategic Account Planning
Effective account planning begins with a deep understanding of the buying committee. We meet with our sales leadership to map out the structure of the buying committee, and then we use Intentsify to identify and track each persona’s research behavior.
For a mid-market SaaS company, the committee might include the CMO, VP of Demand Gen, Marketing Ops, and a Finance stakeholder. We set up intent models that capture the unique research patterns of each role. A marketer might look at “ABM platforms” and “pipeline velocity,” while IT is researching “marketing technology integration” and “API capabilities.”
Identifying Upsell and Cross-sell Opportunities
For existing customers, having granular, solution-specific intent models is where the real revenue acceleration happens. We build models around our different product modules and advanced features.
A clear expansion signal is generated when a current customer’s buying group suddenly starts researching topics related to a product they don’t currently use—for example, if a client only using our core platform is suddenly researching “programmatic advertising” or “connected TV advertising.”
We also track new personas from existing accounts showing up in our intent data. If we’ve been working primarily with demand gen and someone from product marketing or sales enablement suddenly starts researching our solutions, that’s an opportunity to expand our footprint within the organization.
How Can Churn Prevention Be Achieved with Intent Data?
Churn prevention has become one of our team’s top priorities , and intent data is the crucial component of our early warning system. We’ve created specific “risk indicator” intent models that monitor for several red flags.
While researching competitors is an obvious signal, the subtle ones are equally important. Red flag searches include:
- “Marketing Automation Alternatives”
- “How to Calculate Marketing ROI”
- “Reducing Martech Stack Costs”
These searches all indicate potential dissatisfaction and signal the need for an intervention.
Actioning Negative Intent: The “Save Play”
Beyond competitive and cost-cutting research, we track a second, critical signal: a drop-off in engagement. We’ve identified that when engagement from multiple personas within a customer account drops off simultaneously, it often precedes churn by 60–90 days.
Therefore, we track both positive intent (active research) and negative intent (absence of engagement) across the entire buying committee. When these patterns appear, we immediately trigger a “save play.” This might include:
- Executive outreach
- Additional training
- A business review to re-establish value
See how our teams use this exact framework to build buying groups, surround accounts, and power pipeline in our eBook, “How Intentsify Uses Intentsify: Volume II.”
FAQs
How do you distinguish between an expansion signal and a churn risk signal using intent data?
An expansion signal is typically triggered by a customer’s buying group researching topics related to a product or feature they don’t currently own (e.g., searching for “programmatic advertising” when they only have a content syndication solution). A churn risk signal, conversely, is triggered by research related to alternatives, competitive comparisons, or cost-cutting (e.g., searching for “reducing martech stack costs” or competitor names).
How does using intent data churn prevention proactively differ from a reactive customer success strategy?
A reactive strategy only responds after a customer reports dissatisfaction. Intent data churn prevention is an early warning system that monitors subtle signals and drops in engagement from multiple personas that often warn providers of a customer that may soon churn. This allows you to launch a “save play” before the customer decides to leave.
What is the value of tracking a new persona from an existing customer account?
A new persona, particularly from a different department like product marketing or sales enablement, researching your solution indicates an expansion opportunity. It suggests that a new part of the organization is exploring how your product can solve their challenges, giving you a chance to expand your footprint within the organization.
How do you ensure sales and customer success teams act on these lifecycle intent signals?
We integrate these signals directly into their existing workflow and make them a workflow trigger. When a churn risk signal is hit, it automatically triggers a “save play” alert to the CSM or Account Manager with the exact details of the research, prompting them to act immediately and re-establish value.
