By Lindsey Meyl, Vice President, Revenue Operations, iManage
Within the finance sector, revenue growth has become a more complex undertaking in recent decades, especially as subscriptions and other recurring revenue models take center stage. It’s not just fintech platforms with SaaS offerings getting in on the game – it’s the asset management firm selling premium advisory services, the brokerage selling a monthly research newsletter, and so on.
This shift reflects not only technological disruption but also changing investor expectations, as markets increasingly reward firms with predictable, recurring income streams that demonstrate resilience and scalability.
Unfortunately, this makes the old playbook for revenue growth something of a dinosaur. The linear marketing funnel used to be: guide prospects to acquisition, close the deal, and move on. But in today’s recurring revenue economy, acquisition is just the proverbial opening bell.
To sustain revenue growth, financial services companies must shift away from the linear funnel towards a “bow tie” model. On the left side sits customer acquisition. On the right, lies the real compounding factors: adoption, retention, and expansion. That’s where customer lifetime value is maximised.
Making a pivot to this new framework, however, requires a disciplined data strategy – one that integrates data collection, enrichment, and AI-driven activation. Without it, financial companies will be unable to successfully reshape their revenue operations for this new era.
Gather the data
What might this data strategy might look like in the real world? Take the example of a company building an “account propensity to buy” model aimed at unlocking growth on the right side of the bow tie.
The foundation of the model is data. Which customers historically convert at the highest rates? What patterns define their journey? And where are they now in that progression?
Answering these questions means assembling a mosaic of signals. Some of these signals are external, such as search activity or digital footprints on the web. Others come from direct engagement, like site visits, webinar registrations, or email interactions.
Then there are conversational insights from meetings with the account team, where customer priorities and objections surface in real time. For instance, an institutional investor might notice that clients are increasingly voicing worry that more established markets are overpriced and that emerging markets might be undervalued. That’s not just chatter – it’s a cue to tailor outreach and refine offerings accordingly.
Finally, situational signals – leadership changes, mergers and acquisitions, or regulatory shifts – can alter a client’s operating environment and reshape their appetite for new solutions. For example, how does something like FINRA complicate the landscape for a client and necessitate a change in their “business as usual”?
Individually, all these signals are fragments – but taken together, they form a dynamic picture of intent and opportunity.
Layer in context, then operationalize
Of course, raw inputs are only half the story. Context determines value. Which signals deserve the most weight? Which are noise? Calibrating the “propensity to buy” model is as much art as science, requiring iterative refinement and optimisation of the AI algorithm.
Once tuned, the strategy must be operationalised. That means embedding insights into workflows so that sales, marketing, customer success, and partner teams act in concert. The payoff is sharper alignment: the right people focusing on the right accounts at the right time – and the financial implications are significant.
Win rates improve because effort is concentrated where it counts. Bottlenecks in the revenue cycle become visible – whether it’s a six-month stall between evaluation and purchase, or a year-long lag in upsell conversations. Identifying these inefficiencies is akin to spotting working capital trapped in the system: once freed, growth accelerates.
It’s worth noting that the benefits of this intelligence extend to the customer as well as the companies. Customers experience more relevant, timely engagement. Instead of generic – or even worse, tone deaf – outreach, they receive solutions tailored to their evolving needs. That tailored approach strengthens relationships and reduces churn – paving the way for ongoing success in the recurring revenue era.
A strong foundation makes smarter revenue operations possible
Financial companies are now living in a world where the selling never truly stops. A thoughtful data strategy ensures teams remain synchronised and aligned on priorities, with AI models providing the predictive power to help maximise revenue.
Before they reach that stage, however, financial enterprises must first master the basics. That means collecting the right data at the right touchpoints, ensuring it is clean and reliable, enriching it with external intelligence, and governing it properly.
Get these foundational elements right, and the organisation is positioned for growth. Neglect them, and it won’t matter how many teams or AI tools you throw at your revenue operations. A robust data strategy is, simply put, the only viable path forward.