How healthy is your data integrity? According to many B2B revenue leaders, their data and methods of measuring key performance indicators could use a checkup:
- 44 percent of companies cite CRM data quality as one of the top contributors that leads to forecast inaccuracy.
- On key sales metrics, 75 percent of companies lack company-wide definitions.
- Only 9 percent of marketers are “mostly” or “completely” confident that their data meets their standards.
There are a couple of common problems that revenue leaders face when it comes to their sales and marketing data quality.
B2B Companies Often Lack a Strategic Data Model
A data model is an undervalued component to an organization’s foundational go-to-market (GTM) tech stack.
In addition to the tools themselves (such as the CRM, marketing automation platform, or sales enablement software), there is a large amount of data captured in those tools that can be used to build efficient strategies.
What is a go-to-market data model? Think of a data model as the underlying blueprint and source of truth to your GTM strategy: What data points need to be captured and measured through your tech stack so your revenue leaders can make informed business decisions? Do you have a singular definition of the data points that need to be tracked? Are your teams aware of how that data flows into your systems?
For example, Gong is often used as a sales coaching tool, but the recorded sales calls can also be used as voice of prospect data that your teams can use to refine messaging, prioritize the product roadmap, or create new content.
The problem, however, is that revenue leaders often purchase tech stack tools before defining their data model. It results in poor standardization of data, confusion around data input, and overall frustration with reporting.
Other times, when the sales and marketing teams have thought about the go-to-market data model—it was thought about separately. The sales team handles its own data model in their tools, and the marketing team manages their data in separate tools. The result? Siloed data that makes alignment and reporting difficult.
Poor Data Hygiene and Data Integrity in the CRM
As B2B companies scale, the desire to fit a square peg into the circle hole also grows. The systems and processes that worked at an earlier stage are often not sustainable as the number of customers serviced and prospects reached increases.
What does poor data integrity look like in a growth-stage company?
- Overly customized fields with lack of standardization
- New changes to the CRM with new sales and/or marketing leaders
- A heavy reliance on sales and marketing teams to input data correctly and upkeep data quality
- Lack of parameters or change control so the CRM is exposed to changes, rendering historical data obsolete
Steps to Elevate Your Go-to-Market Data Integrity
What are the steps that you can take to elevate your go-to-market data integrity so that your revenue engine is fully aligned?
Treat customer acquisition/new revenue acquisition holistically
Having your sales, marketing and customer success teams work together creates transparency and streamlined movement toward the strategies that each team is responsible for.
Many companies are now appointing revenue operations teams that oversee the alignment between all parts of the organization’s revenue acquisition, including sales, marketing, product, and customer success.
Hire a business analyst early
In the early stages of a company’s growth, the person who often handles the CRM technical implementation is usually head of marketing or sales. As the company grows, the leadership team usually hires a CRM administrator who has the junior technical skills to manage the CRM but may not have the analyst skills to provide in-depth analysis into that data.
A business analyst—especially if they report to a revenue or go-to-market team—can take responsibility for reporting insights into the data that’s captured in the tech stack, so that the sales and marketing leaders can make efficient business decisions.
Ensure cultural sanctity of data
There is a culture component to elevating your data integrity that requires the entire company to place importance on data management and integrity. Holistically, everyone has to believe in it and be advocates for treating the go-to-market data model as the source of truth. Once the company is aligned on making a strategic definition of the data model, all decisions can flow from there.
The importance of a go-to-market data model can not be overstated; being intentional and strategic about the way data is captured in your go-to-market tech stack can directly impact the strategies your team chooses to invest in.