Accurate sales forecasting is vital for effective decision-making and resource allocation
Bad data will hinder portco’s ability to make informed decisions. Are they aware of the connection between sales stages, the buyer’s journey, and forecasting accuracy? In this post, we connect the purpose of both journeys and their impact on forecasts. At its conclusion, you will have key insights into overcoming the challenges of insufficient data.
The crucial connection between sales stages and the buyer’s journey
Sales stages are a prospect’s sequential steps from Lead-to-Customer. Each stage represents a milestone in the buyer’s journey. Each stage should have straightforward entry and exit criteria. When combined, they encompass the entire process a buyer goes through. In practical application, this allows teams to tailor their approach and messaging to the buyer’s needs. For example, a prospect might seek specific content during the early stages. Teams can provide relevant information, nurturing the prospect until they’re ready to move forward. The data captured between sales stages also informs the probability of an opportunity. If built into the customer relationship management (CRM) software correctly, the data will reinforce a process to identify buying behaviors.
Another example is determining what’s driving the prospect in their decisions. A well-aligned sales process enables reps and their managers to articulate the details. This includes understanding each buyer and their influences on the deal. Understanding this relationship ensures sales teams can effectively guide prospects through the sales funnel. A common misalignment occurs when sales stages reflect the internal procedure alone. This will always skew a forecast, leaving your portcos missing their number month after month.
Real-world example: Bringing the relationship between portco forecasting and data to life
“Bad data” is an abstract phrase. It has many possible definitions. We will focus on data intended to provide insights and the likelihood of a sale. Bad data originates from various sources throughout a Go-to-Market organization. Most commonly, outdated information, duplicate records, or user entry errors. This will have a detrimental effect on forecasting, causing misguided decisions and missed opportunities.
Consider a significant investment of resources based on a data-backed trend of increased demand.
The reports show new contacts being added to the database. There’s an uptick in late-stage opportunities in the CRM, and sales cycle length is trending down. This could all indicate success. However, you discover the data was partial or, worse, misrepresenting reality. Reps hold opportunities outside the system until the last minute. They wait because of the cumbersome CRM process. This artificially decreases sales cycles and, if not remedied, prevents any valid pipeline insight. The business development representatives (BDRs) are loading in sheets of contacts from a database. This produces noise and inflates leading indicators of outreach. We see both scenarios all too often.
It’s essential to identify bad data in your portcos as soon as possible.
We recommend regularly auditing your CRM and how well the system supports your Go-to-Market process. Implement validation rules or automation to require only the necessary information. Align data capture so the CRM only prompts for what is available during that stage. Also, don’t underestimate enablement and training. A well-aligned enablement function is measured by critical data captured during a sales process. This is valuable to both the firm’s and the team’s individual success. Improving data quality and forecasting accuracy requires a combination of best practices, alignment, and technology. Below are a handful of tactical opportunities you should consider for each portfolio company you support.
- Establish clear guidelines for collecting, storing, and updating data to ensure consistency and accuracy.
- Align sales stages with the buyer’s journey. Refine sales stages to reflect the buyer’s journey step-by-step accurately. This makes tracking progress and anticipating the next steps more accessible.
- Leverage third-party resources. Use credible external sources to supplement your data. Enriching contacts and accounts fills in gaps used for targeting and personalization. Using tools like this improves the understanding of market and customer behavior.
- Utilize technology: If the CRM has forecasting capabilities, deploy it. Use this to analyze data and generate accurate predictions where the data is captured.
By prioritizing data quality, your portcos can be more confident in their numbers. Clean data allows the sales teams’ work to be more predictable and aligned with their buyers:
Reaping the Rewards: 3 Benefits of High-Quality Data to Go-to-Market
- Improved decision-making and resource allocation
- Increased sales effectiveness and customer satisfaction
- Strengthened relationships with internal stakeholders.
We’ve uncovered the importance of data quality in forecasting.
It’s time to assess current practices and prioritize data quality in your sales process. Are your portcos’ forecasts reliable? Are you confident in their data’s accuracy? Enhancing your sales process through technology and training is vital. Prioritize data quality and best practices to make informed decisions, boost sales effectiveness, and drive growth. Don’t let bad data be an obstacle – embrace accurate forecasting to elevate business performance. To swiftly address these challenges and amplify your success, schedule a call with a Revenue Operations expert. Harness-tailored insights to transform your forecasting accuracy and business outcomes. Your journey to precision forecasting begins with a call.
