Revenue operations materialized to proactively drive alignment amongst business processes and look at the entire customer lifecycle. This holistic approach naturally merges marketing, sales, and customer success teams. Yet, with so much oversight, the infrastructure for revenue ops needs to be built on quality data and a structured reporting process.
At our most recent San Francisco Meetup, panelists Ben Trombley, Nima Asrar Haghighi, and Tobias Muellner addressed the organizational structure of revenue ops and how AI is influencing data collection and reporting activities. Joshua Hanewinkel moderated the discussion, highlighting the need for centralized data and exploring how AI is transforming workflows for revenue ops teams.
Building the Team
The idea of revenue ops is still fairly new and can easily be confused with merging responsibility between departments - so what exactly does a revenue ops team do? While some structure is dependent on your particular company, our panelists agreed on many of the same priorities:
Work cross-functionally between teams to collect and analyze data
Ensure everyone is using the same data set
Mitigate any siloing effect by creating a business function that reports data across all teams
The common theme here is data. In order to collaborate with teams and run reports that drive strategic decisions, revenue ops needs to be the house of all things data, providing intelligence to support business strategy. As the owner of your dataset, revenue ops enforces a single source of truth and works cross-functionally to evaluate the overall health of your business and drive long-term growth. "Ultimately, revenue ops is increasingly the data person," said Ben Trombley, Co-founder and CTO of DataFox.
Defining Data-Driven Metrics
Taking a data-driven approach to revenue ops allows teams to specialize in a certain business function without contributing to the siloing effect. By having everyone derive value from the same data set, it becomes easier to monitor long-term goals with multiple stakeholders - but it's important to be data-driven for a reason.
"Does data-driven mean that you are looking at the data? Does it mean that you are spending a lot of time on data? Or does it mean you take action based on the data? You have to make sure that any KPIs, any reports, any piece of information you are spending time to collect and report on is actionable and you are able to use it." - Nima Asrar Haghighi, Sr. Director of Demand Generation & Marketing Operations at MuleSoft
Defining exactly what you will measure and how you analyze data is important to create consistency and transparency across teams. As the owner of all things data, there's incentive to create an ecosystem in which everyone thinks in terms of revenue ops. Muellner explained, "everything that we do - be it marketing ops, be it sales ops - we build it from a revenue ops mindset and we build into it." With everyone thinking about the data (where it comes from, how it's being used) you create a narrative that will naturally contribute to the dissolution of data siloing within your company.
Data quality is the backbone to any successful AI strategy. Without a quality data foundation, even the most powerful AI tools won't be able to supply you with actionable analysis. Similar to the computer science term - garbage in, garbage out - if flawed data is the input then flawed data is the only output. Essentially, the quality of output is determined by the quality of input, which makes it necessary to have impeccable data quality to begin with. Executed correctly, AI has the power to transform your revenue ops team by automating and scaling team workflows.
"AI automates the grunt work that goes with doing research, so it frees people up to focus on what they're good at - building relationships, reaching people on the phone, driving strategy, socializing across an organization. AI won't replace any of that but it can replace all of the work that goes into manually researching company data and trying to determine what indicates a good fit. We can use AI to give you the right data on an ongoing, constant basis. Allowing you to iterate on your strategy." - Ben Trombley, Co-founder and CTO of DataFox
Automating your data collection process enables you to work at scale and run more accurate analysis with constant, ongoing data enrichment happening in the background. By using AI to source company data, you seamlessly transform your targeting strategy with the addition of real-time signal data to supply actionable intelligence on when a target company exhibits best-fit characteristics. Using 68+ types of signal data your sales team is armed with contextual outreach initiatives and never misses a good deal.
Successful revenue ops teams are using AI to drive actionable strategy across their entire organization, providing an overall efficiency that forces you to value data and empowers business teams to work smarter. Make sure you aren't at risk of being left behind. Learn more about how DataFox uses AI-powered data to enrich and maintain company data in real time.