3 Common Data Quality Challenges That Undermine Sales Forecast Accuracy

Author: 
Steve Rietberg and Craig Riley, Gartner

In uncertain economic times accurate, timely, and actionable sales forecasts are more important than ever. That is why it is concerning that despite sales operations leaders’ best efforts, Gartner’s latest State of Sales Operations survey found that less than 45% of respondents indicated sales leaders and sellers in their organization have high confidence in forecasting accuracy. This level of skepticism can be a serious problem since it often results in sales leaders and sellers taking action based on intuition instead of evidence which can lead to bad decisions and ultimately hurt commercial performance.

A combination of real and perceived data quality issues often drive skepticism from sales leaders and sellers. Poor data quality is a real concern that can have far-reaching effects and unintended consequences. Sales leaders who rely on incorrect forecasts may over- or under-invest in certain regions or business units, or provide inaccurate guidance that ultimately gets relayed to external investors. Sellers who lack confidence in sales forecasts may decide to follow their gut and not pursue deals that analytics suggests they should, resulting in missed opportunities and reduced revenue.

Working with clients we’ve found that while poor data quality is a common problem, there is some good news. In most cases, the steps to improve data quality and generate more accurate and trustworthy sales forecasts are within sales operations’ control. Specifically, there are three common factors resulting in low data quality that sales operations leaders should address.

Poor CRM Adoption and Discipline

Poor data quality is inevitable when sellers and managers do not follow data management standards and are not disciplined in the frequency and completeness of their data entry efforts. Changing this behavior starts with sales operations working with sales, marketing and service leaders to decide what needs to be captured, when it needs to be recorded, and who will be responsible for it. These discussions should follow a ‘less is more’ approach by starting with specific use cases for each required input since discipline becomes harder to maintain as the number of inputs grows.

Next, sales operations must standardize and streamline methods of capturing key information on a deal. Sales operations ultimately decides which CRM fields are mandatory for sellers to populate. Using mandatory fields ensures that desired attributes are captured, but when overused, it leads to sellers circumventing the system by not recording updates or not entering some deals at all.

Validation rules must be used to ensure minimum data quality at the point of data entry. For example, account and opportunity records in a CRM system can be designed to prevent nonsensical combinations of field values, such as a lost deal with 50% probability to close.

Sales operations should not rely solely on mandatory fields and validation rules to improve CRM discipline. In addition, they should promote good discipline through good visibility. By providing more granular visibility of the pipeline – including highlighting the attributes that managers really need to conduct seller conversations – sales operations can enlist the seller and the manager to help enforce data quality standards.

Finally, sales operations leaders should create positive feedback loops by demonstrating how accurate CRM data enables sellers to succeed. As CRM adoption gradually improves, sales operations leaders must leverage sellers’ inputs and demonstrate how their efforts contribute to analytics that will help improve their commercial success -- and ultimately variable pay -- by highlighting at-risk deals and helping prioritize opportunity pursuit.

Insufficient Data Governance

Accurate forecasting cannot be achieved if the organization is prioritizing the wrong data management activities or delivering analytics that don’t support the business’ top priorities. A successful sales analytics capability requires a governance program that aligns with business objectives and addresses the organization’s most pressing data and analytics management requirements. This oversight requires a cross-functional governance team that includes the head of sales, head of sales operations, IT leadership and leaders from marketing and other stakeholder groups.  The governance team provides funding and resources to support the analytics program, provides executive sponsorship, steers analytics projects and agrees on success measures for the overall program.

In addition, effective data governance policies should designate data stewardship within each business function. Power users within stakeholder functions are often enlisted to monitor and affect data quality within their respective teams. These data stewards inherit data quality metrics and targets from the governance body and influence other users within their teams to ensure data quality standards are achieved and sustained.

A data governance body must meet regularly and engage data stewards and other leaders to ensure analytics are aligned with business objectives.

Infrequent Data Quality Inspection

Data quality processes must be developed for users in each system that captures sales-related data. Leaders, data stewards and system users should continually monitor data quality metrics such as the number of duplicates or unpopulated fields. Leaders and data stewards must be empowered to identify opportunities for continuous improvement. The data governance body must have a process for evaluating such recommendations and determining appropriate responses.

Even the best data governance programs can inadvertently allow incomplete or inaccurate data to enter CRM systems. Whether newly formed or longstanding, sales and marketing leaders should ensure their data quality inspection policies cover more than just the systems and inputs but also the people and processes that are essential to the collection and dissemination of sales data. 
These practices will go a long way to helping improve data quality issues and producing more accurate sales forecasts. Sales operations leaders are in a unique position to facilitate the successful application of all three.

Steve Rietberg is a senior director analyst and Craig Riley is a senior principal analyst in Gartner’s Sales practice.