Jeff Saenger is the VP of Customer Success at BoostUp where he leads customer success and service strategy. He is a senior executive with a high degree of experience in building, scaling and optimizing high performing global service teams at enterprise B2B SaaS companies.

Accurate contract renewal forecasting has become a critical capability for achieving sustainable revenue growth in the subscription economy. With the rising cost of customer acquisition, businesses are heavily focused on maximizing the value of their customers through renewals and extensions.

Advances in technology combined with the importance of service revenue generation have led to a shift in the way companies organize contract renewals. A survey of technology companies found that 72% of organizations have a dedicated team that deals solely with service contract renewals. 

By mastering the renewal forecast process, companies can retain high-value customers, identify timely upselling opportunities, and maintain a predictable revenue stream - important prerequisites for long-term success.

What is Renewal Forecasting?

Contract renewal forecasting is the process of predicting the revenue expected from existing customers through contract renewals, upsells, cross-sells, and expansions over a specific period of time. Although it is similar to general sales forecasting, renewal forecasting has distinct nuances. 

Unlike new customer business, where the focus is on acquiring new customers, renewal forecasting is about retaining and growing an existing customer base. This requires a deeper understanding of customer health, engagement levels, product usage, and historical purchasing behavior.

Additionally, the renewal forecast definition must take into account potential revenue losses due to downgrades, shrinkage, and churn. It's a delicate balance of minimizing revenue erosion while maximizing expansion opportunities. 

Accurate renewal forecasts not only influence top-line growth projections but also provide insights into the stickiness of products/services, customer success efficacy, and the overall health of a recurring revenue business model.

Foundational Elements of Renewal Forecasting

Accurate predictions require a solid data and process foundation. From centralized renewal pipelines to tracking the right metrics to leveraging predictive analytics, putting these core elements in place is critical to forecasting accuracy and accountability.

Centralized Data and Processes

Having a centralized renewal management system with automated data capture is table stakes for reliable renewal forecasts. Scattered data across multiple systems leads to blind spots in pipeline visibility. A unified renewal pipeline provides a comprehensive overview of all upcoming renewals and associated metrics such as renewal amounts, contract terms, and account context. 

Automated data flows ensure that this central repository is always up to date and contains the latest customer interaction details, usage signals, and opportunity edits.

This centralized approach enables efficient pipeline inspection, seamless handoffs between sales and customer success, and consistent forecasting processes across the organization. It eliminates the mobility and version control challenges associated with disparate spreadsheets while ensuring that all stakeholders are working with a single source of truth.

Key Renewal Metrics to Track

Even though renewal rate is the most important metric, several additional metrics provide full context for accurate forecasting. Upsell rates and expansion sales show the potential for growth within the customer base. Churn rates and shrinkage metrics quantify potential revenue leakage. Net Renewal Rate (NRR) summarizes these various movements in a unified view.

At the account level, health scores, product usage intensity, and customer sentiment data signal renewal risks and cross-sell opportunities. NPS, support ticket volumes, and onboarding milestones unlock predictive lead indicators. Ultimately, successful renewal forecasting requires triangulating multiple sources of truth - from hard numbers to leading qualitative indicators.

Consolidating this wealth of data into streamlined dashboards and roll-up reports enables efficient pipeline reviews and drives forecast accountability.

Leveraging AI/ML for Better Insights

As renewal forecasting becomes more data-intensive, AI/ML technologies are playing an increasingly important role in transforming signals into predictive insights. Machine learning models can accurately score renewal propensity by identifying historical patterns across numerous variables like product usage, customer tenure, sentiment analysis, and more.

This automated analysis quickly pinpoints at-risk customers, identifies cross-sell whitespace, and quantifies deal momentum - focusing human efforts on the highest impact situations. AI eliminates the tendency for past bias and quantifies perceived qualitative details to reduce forecast ambiguity. Continuous training of these models further increases forecast accuracy over time.

Mastering the Renewal Forecast Process

Renewal forecasting is an intricate process that requires aligning people, systems, and data. From meticulously setting up renewal opportunities to factoring in qualitative insights, multiple elements contribute to forecast accuracy.

Setting Up Renewal Opportunities

Establishing a standardized process for creating and updating renewal opportunities in your CRM is foundational. Leading practices include automating opportunity creation based on contract renewal dates and pre-populating fields like renewal amounts, product details, and key milestones. This eliminates manual effort and ensures comprehensive pipeline capture.

Defined fields capturing the likelihood of renewal, forecasting categories, and next steps drive forecast cadences. Closely integrating renewal opportunities with the account and contact records allows seamless handoffs between sales and customer success teams. Embedding triggers to auto-update opportunities based on usage, sentiment, and engagement data ensures pipeline hygiene.

Multi-year renewal constructs require unique considerations like revenue amortization schedules. Approval workflows facilitate deal desk oversight on larger, more complex renewals. Ultimately, mature renewal processes balance pipeline discipline with flexibility to adapt to unique customer situations.

Qualitative Forecasting Considerations

While historical data provides a solid baseline, qualitative factors add the essential color for precise renewal forecasts. Health scores aggregate multiple signals like product usage, support engagement, and survey feedback into a unified rating of retention risk. Automated artificial intelligence models can discern patterns and derive predictive health insights exponentially faster than manual efforts.

Customer engagement scores quantify rep interactions and identify situations requiring more high-touch nurturing. Executive relationships, project milestones, and pending expansions can materially impact the probability of renewal. Contextualized account notes from sales and customer success capture forward-looking insights. An experienced manager's judgment is still indispensable to overlay their on-the-ground knowledge.

Dashboards consolidating these qualitative details in conjunction with quantitative metrics empower fact-based forecast reviews versus gut-feel calls.

Driving Forecast Accountability

Even with solid processes and data, renewal forecasting can falter without a culture of accountability. A clear assignment of responsibilities is critical - whether it's that customer success is responsible for renewals, that sales is co-responsible for strategic customers, or that there are centralized renewal departments. Tying variable compensation not just to booking the renewal, but achieving against the committed forecast instills individual motivation.

A cadenced ritual of forecast reviews maintains organizational focus. Operational reviews should scrutinize pipeline quality, identify stalled deals, and facilitate constructive coaching. Executive reviews should tie renewal projections to topline goals, headcount plans, and investment decisions. Public pipeline reviews increase transparency and peer pressure for accurate forecasting.

Designed effectively, forecast accountability extends beyond an individual contributor exercise to fostering continuous improvement. Variance analysis identifies systemic issues that impact forecast accuracy. Early investigations proactively assess risk factors. Leading organizations view forecast accountability as a cornerstone of their renewal management.

Optimizing for Predictable Revenue Growth

While accurate forecasting is critical, the ultimate goal is driving predictable revenue growth through renewals and expansions. This requires purposeful strategies anchored on understanding customer behavior and tailoring engagement models accordingly.

Retention-Focused Engagement Models

Not all customers are created equal in their importance to your business. Implementing value-based segmentation models allows prioritizing engagement for your highest revenue and expansion potential accounts. Common segmentation criteria include:

  • Annual recurring revenue (ARR) tiers
  • Estimated customer lifetime value (CLTV)
  • Product/service line consumption
  • Industry/firmographic segments
  • Customer tenure/maturity stages

For your most strategic accounts, a Named Account Management model ensures hyper-focused attention from cross-functional pods including sales, success, support, and executive sponsors. Micro-segmentation allows nuanced playbooks to address unique needs. Lower-value segments leverage more scalable digital nurturing supplemented with effectively timed human touchpoints.

By concentrating retention efforts on your most critical customers, you can protect core revenue while expanding it.

Proactive Upsell/Cross-sell Management

Expanding revenue within an installed base is far more cost-efficient than acquiring new logos. However, misaligned outreach often undermines growth potential. Data-driven signals highlight optimal timing for initiating expansion conversations.

Product usage intensity and feature adoption curves identify the appetite for greater consumption. Customer lifecycle milestones like implementation anniversaries or growth triggers create relevancy windows. Automated engagement scoring quantifies rep mindshare and white space for incremental value delivery.

Equally vital is understanding the ancient "what's in it for me" from the customer's lens. Onboarding health, support incidents, NPS ratings, and retention risk scores collectively mold the context for separating real demand from fanciful projections. Unified customer intelligence surfaces these insights to tee up renewal managers and account owners on the ripest cross-sell and upsell opportunities.

Connecting Forecasts to Revenue Goals

While renewal forecasts provide important insight into future periods, their true value only becomes clear when they are directly linked to topline ARR and revenue targets. Mature organizations cascade corporate goals into commitments across sales, customer success, and renewals teams. Forecast reviews become checkpoints to harmonize these interdependent contributions.

Modeling renewal projections against new acquisition targets clarify overall growth expectations. Scenario planning quantifies the sensitivity of the NRR rate - if the renewals overperform by X%, new logos are required to adjust by Y%. This translates ambitions into concrete activities: Do we double down on expansion motions? Redouble retention efforts? Adjust quota allocations?

Ultimately, connected forecasts transcend their operational roots to become a powerful mechanism for organizational alignment. Shared goals unite disparate teams in prioritizing key actions. As a foundation for future growth, accurate renewal forecasting deserves a strategic focus from senior leadership to frontline employees.

Interested in knowing more about renewal forecasting in practice? Check out our webinar, Mastering Renewal Forecasting: Strategies for Sustainable Growth.