3 Tips For A Proactive, Data-Driven Approach To Growing Revenue (From A $200M+ ARR Leader)
August 16, 2024
Vice President of Strategy & Operations
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Sales and Revenue Operations
Vice President of Strategy & Operations
It’s been nothing short of exhilarating to be a revenue leader these last several years. From the economic downturn to the introduction of AI into every facet of business, revenue organizations now face an altered status quo. Prolonged pressure on buying cycles has created more thoughtful buyers, the surge of AI has unlocked new value opportunities, and revenue teams—the profit drivers of every business—find themselves at the forefront of the shift.
As someone overseeing sales at a $200M ARR brand in fast-growth mode, I wanted to share three learnings I’ve taken away over the past couple of years and how Docebo’s go to market teams are leveraging them to drive success amidst today’s evolving market.
1. Predictability, productivity, and proactivity all matter—but serve different roles
- Predictability. Predictability hinges on making internal workflows repeatable and precise. Revenue teams need to be constantly assessing customer health and identifying early warning signs of escalation or churn. And AI-driven solutions play a key role here—putting signals from customer interaction data at the heart of the revenue operating rhythm, helping onboard customers, and delivering immediate value across sales, customer success, and marketing. Leadership relies on these insights to make informed strategic decisions.
- Productivity. Managing productivity means measuring various metrics accurately, from the company level to individual reps. Technologies that understand all customer interactions allow for a strategic approach to assessing key metrics like customer acquisition cost (CAC) and lifetime value (LTV), and make clear where to increase investment or initiate specific initiatives.
- Proactivity. And finally, proactivity involves looking for opportunities to consolidate processes to drive efficiency across GTM functions. By asking how to achieve end results more efficiently, whether through reducing steps or implementing new software, leaders can quickly inform key initiatives to drive efficiency.
2. Using AI to power workflow automation and intelligence
AI is a significant driver of efficiency, with its true value extending beyond generative applications to tools trained on models relevant to specific applications. Over time, companies will find better AI use cases by training on these models or partnering with specialists. AI can surface insights on prospects and customers, convert complex interactions into measurable business intelligence data, and automate engagement across the entire customer lifecycle. This leads to better decision-making based on intelligent trends and enables smarter pipeline management.
3. Buyers are hyper-diligent and hyper-focused on consolidation
As buyers become more ROI-driven, they’re doing extensive upfront research on solutions.
By the time they engage with a company, they have read reviews, absorbed thought leadership, and consulted industry contacts. This diligence requires sellers to support the buyer’s journey effectively. Additionally, buyers seek to consolidate tools, avoiding multiple solutions that serve similar roles. Efficiency is key—so tools that streamline processes and minimize the number of platforms used are preferred.
When evaluating new technology, rep productivity is a primary focus. Seventy percent of reps’ time is spent on non-sales activities, so the goal is to maximize their sales time. Solutions that handle administrative tasks with high accuracy and allow reps to focus on high-value sales activities are central to an efficient tech stack. More software does not necessarily yield more efficiency; the aim is to alleviate administrative burdens and enable reps to concentrate on selling.
