Data Management: The Key to Strong Revenue Operations

Data Fuels Growth...Again!

Do your account-based marketing (ABM), segmentation, market sizing, and other go-to-market planning efforts seem to resemble a guessing game plagued with manual data manipulation and evaluation? You’re not alone! Revenue operations — the convergence of marketing, sales, and customer service — aka RevOps, isn’t necessarily easy to get right, and while there may be many reasons that this is the case, most of them are ultimately related to your organization’s data and how it’s managed.

RevOps is a framework that focuses on organizational alignment to drive revenue growth. RevOps aligns revenue teams — marketing, sales, customer service, and executive functions — in a combined effort to establish an agile, data-driven decision framework throughout the entire customer life cycle. At the core of this framework is data. According to Gartner, 75% of the highest-growth companies will have a RevOps framework by 2025.

Traditionally, these functions operate separately despite best efforts at collaboration. As a result of these silos, data is managed independently, making the quality, completeness, and accuracy of it vary across the board and making it difficult to get to the truth. With a RevOps framework, however, you can deliberately plan and execute a unified data strategy to support the end-to-end perspective all go-to-market teams need to grow revenue.

Pillars of Data Management for RevOps

When thinking about a RevOps framework, these are some of the important facets of data management that you should consider to strengthen your approach:

  1. Digital transformation journey maturity — RevOps success is very much aligned with an organization’s digital transformation journey. At the early stages of a RevOps initiative, you should focus on the organization’s readiness to progress into a unified process, with data driving effective decision-making. But before you pass “go,” what is your current data state? What are the gaps that exist? Which ones should be prioritized? Who should participate to build and execute workflows?
    Formulating answers to these basic questions will help you identify the baseline and goals for digital transformation. It’s a chance to bring different functions together to implement RevOps. Understanding the current state of these functions and their priorities is non-negotiable. For more information on this, read Every MDM [master data management] Initiative Should Start With a Collaborative Workshop.

  2. Data quality standards — Articulate a minimum grade or requirement given your established and planned use cases before data is allowed into your ecosystem. For instance, to drive accurate segmentation, what data will you need, considering that your current data collection process may only requires a company name and country in your marketing and sales tech stack? Should you require the entire address as well? Another example would be determining what it means to have a complete contact record. Be overt with your data quality requirements. Your data quality framework, habits, and compliance will be the foundation of your data health measures and maintenance.

  3. Data enablement — It is important for your users to import or input data into the shared RevOps data environment, so make it easy for them to put quality data into your process. Here are some examples:
    • Integrate customer (internal/external)facing and operational process applications with controls such as “search before create” features to prevent duplicate data and enhance the validation of new data coming in.
    • Data changes constantly, so it’s imperative to periodically refresh your commercial data with the latest market data through a trusted and vetted third-party data provider like Dun & Bradstreet.
    • Automate data integration by using application programming interface (API) connectors to link data sources to the business applications that need them. Using API technology in lieu of manual and batch processes streamlines data provision, eliminates human error, and improves timeliness of data delivery.
    • Use validation tools. For example, Regex, a validation tool within Salesforce that enables required fields and ensures the user-entered data is actionable by being complete and in the correct form (e.g., that phone numbers are always entered in a consistent format). Create consistency across your full stack.
    • Be considerate. Make sure your teams understand what is expected in every field. A good percentage of bad data is caused by not being clear about what information we are trying to capture. Use tooltips (simple hover-overs) to make it easy for anyone who enters data to get it right from the start.
  4. Strategic referential data — Ensure that your RevOps process includes a healthy marriage of internal and external data. Internal data (inside-in approach) provides an articulation of your company’s customers, transactions, vendors, and even potential partners. However, relying solely on internal data for RevOps activities such as market sizing, prospecting, and segmentation could provide a myopic view when defining your market. In short, you don’t know what you don’t know. Bringing in third-party referential data (i.e., an outside-in approach), will widen your aperture and give you a more accurate audience for ABM and go-to-market activities. Third-party data added to your RevOps strategy, such as that from the Dun & Bradstreet Data Cloud, may give you a more accurate view of your market while also increasing the accuracy of your internal data with added enrichment.

  5. Data stewardship investment — Data does not manage itself. Data stewardship practices execute the rules and adjustments needed to keep data in check with the appropriate use cases it is expected to serve. Data stewards bring scalability to RevOps so that your sellers can sell and your marketers can market — they should have the freedom to leave the data management function to the data team.

  6. RevOps governance council — RevOps is an evolving practice, especially with shifting variables such as technology and stakeholder behaviors. You will need compliance with rules and processes and the ability to provide adjustments to areas that might need fine tuning. Representatives from the sales, marketing, customer service operations, and participating executives should partake in such a council, particularly if the RevOps model is cross-functional and distributed among leadership teams.

Data + RevOps Drives Strategic ABM

Although the RevOps framework is still new to many, the principle behind it is not: cross-functional collaboration. Data is the thread that passes through and makes this collaboration not only possible, but also productive. In practice, data from the marketing, sales, and customer service operations needs to interact within the context of the customer life cycle. To ensure productive growth patterns, the RevOps framework needs to be a deliberate strategy and, as noted in many points above, data management is key to all this.

Foundational RevOps data is the driving force behind a well-executed ABM program: The endeavor produces quality leads and these leads turn into prospects and then customers (sales). A sale brings about customer service interaction, which can produce cross-selling opportunities and so on. Without deliberate data management strategies that shine a spotlight on data quality, you’re leaving your RevOps initiative to chance.

Being deliberate about your data management helps align teams and strengthen your RevOps framework.