Written By: Russ Artzt
Revenue Operations (RevOps) is gaining traction in companies that want to realize revenue growth and improve customer engagement. Given the newness of RevOps and its relative complexity, analyst firms and other industry thought leaders are offering frameworks to facilitate RevOps implementation. As businesses embrace these frameworks, they would be wise to consider the role of data in the success of the framework’s vision.
RevOps represents a synthesis and transformation of existing business activities that has the goal of fostering strong revenue growth and customer retention. RevOps achieves these objectives by aligning sales, customer success and marketing operations across the full customer life cycle. Growth arises from the improved operational efficiency of all three groups. Other benefits include better collaboration between teams and more predictable business performance. The paradigm saves each department from excessive operational and technical overhead as it speeds up each group’s workflows.
RevOps frameworks provide guidance on how to think about RevOps and then how to go about the actual, challenging work of turning the idea into a reality. The martech analyst firm Topo, for example, offers a thorough framework that contains six core categories of thought and action:
- Strategy — Developing a RevOps plan in alignment with revenue objectives, with the objectives of organizing and mobilizing the revenue organization. The strategy forms the foundation of the RevOps program at a company. It’s the roadmap everyone should follow to get to the operating state of RevOps they envision. A strategy is important, even if it evolves during the implementation, because, without it, no one will know what they’re supposed to be doing or what success will look like.
- Process — Designing, managing and tracking the company’s end-to-end revenue processes. This analysis and subsequent mapping of revenue processes is an essential step to success because it forces all stakeholders, from different groups, to recognize how revenue occurs in the business. From this insight, each group can work toward RevOps with a knowledge of how they contribute to revenue.
- Workflow — Putting the strategy and process designs to work using manual and automated processes that create an interconnected revenue process. This is where ideas meet reality.
- Data — Ensuring that data will enable the managing and optimizing of the end-to-end revenue processes developed in the previous stages of the RevOps project.
- Analysis — Monitoring and measuring success of the RevOps program on an ongoing basis, across the revenue lifecycle.
- Technology — Devising and deploying the technology stack required for the execution of RevOps.
These categories may be used once in the building of a RevOps program or repeated iteratively as RevOps launches and evolves within a business. The iterative approach is probably more realistic, as few RevOps initiatives launch in perfect condition. They need change, sometimes fairly soon after their start. The framework provides an effective basis for continual improvement.
Data’s Place in the RevOps Framework
Data is a foundational element of RevOps. Data is itself a category of the RevOps framework, so it deserves a great deal of attention just from that perspective. However, almost every other category of the framework is affected by data structure, process and quality—either implicitly or explicitly.
For example, the Topo framework includes a “Data Decision Framework” in its RevOps Strategy category. This refers to how a company will decide on business actions it will take when specific conditions are met. For example, the company might decide to hire a new sales rep for each monthly incremental addition of 500 Marketing Qualified Leads (MQLs). The MQL is a data-defined metric. Without good quality data, agreed upon by the stakeholders, it would be hard to get accurate counts of MQLs.
The process and workflow categories relate to data quality insofar as they require the three groups in RevOps to come together and agree on data schemas and quality criteria for their newly joined processes. Data normalization is important here, for instance, because it creates uniformity for customer data fields as they cross over from one domain of RevOps to another.
Measurement and analysis also rely on data quality. This is where data strategy and governance, which are part of the framework’s Data category, come into play. In order for data to be useful in analysis, it needs to be of high quality. There cannot be duplicate records or missing information. Data enrichment can augment the customer data so it produces more meaningful RevOps reporting outcomes.
The Technology category of the framework has to deal with data directly. Here, as the various work groups of RevOps come together to integrate their separate systems, they must necessarily eliminate their respective data silos. Differences in data schemas and standards between groups have to be resolved. The handoffs of data between systems rely on data normalization and uniform schemas for customer data.
RevOps has arrived. Each company will have to judge for itself whether it’s the right move, though it certainly appears to be a source of revenue advantage. The frameworks, such as the one provided by Topo, are helpful in giving direction and specific guidance to businesses that are undertaking a RevOps program. Throughout, data has an important role to play. The more attention RevOps stakeholders pay to data quality, the more successful they will be in their efforts.