Why Soft Credits Should be Excluded from Primary Fundraising Analytics

Soft credits are widely recognized in the nonprofit world for their value in acknowledging relationships and influences within donor networks. They allow organizations to appropriately recognize individuals who play critical roles in facilitating donations but who don't personally write the check. However, when it comes to foundational fundraising analytics—such as RFM (Recency, Frequency, Monetary) analysis, donor scoring, or lifetime value calculations—it's crucial that soft credits are carefully excluded from the core dataset. This isn't to undermine their value; rather, it's to ensure analytical accuracy, clarity, and the validity of actionable insights inferred from your data.

The Hidden Pitfalls of Including Soft Credits in Primary Analytics

1. Inflated Metrics: Including soft credits as if they were actual financial transactions inflates the frequency and monetary contributions of individual donors. For example, John Smith donates $5,000, and his wife, Mary, receives a soft credit. If both transactions are analyzed identically, your records falsely suggest $10,000 in contributions or two separate contributions of $5,000 each.  Such errors distort donor scoring and segmentation, ultimately influencing predictive analytics calculations.
2. Double Counting: Fundraising analytics depend on precision. Soft credits inherently create multiple entries for a single donation event, increasing the risk of double-counting. This confusion can lead to overstated revenue projections, misallocated resources, and erroneous strategic decisions.
3. Distorted Donor Segmentation: When RFM analyses include soft credits, donors with many soft credits but relatively few direct financial contributions may incorrectly appear as high-value donors. This misclassification makes donor segmentation inaccurate, which can significantly weaken personalized communication and strategic targeting.  Highly influential donors may be important, but they should be analyzed within a different context.
4. Overestimation of Lifetime Value (LTV): Accurate donor lifetime value analysis depends heavily on actual financial contributions. Mixing soft credits into the primary analysis falsely elevates the estimated LTV, making strategic forecasting unreliable and potentially leading to ineffective allocation of stewardship resources.

Why Soft Credit Analysis Should Stand Alone

Soft credits are important, but they are a parallel analytic. The best practice is to maintain dual metrics: one for fundraising dollars raised (hard credit) and one for fundraising influence or network impact (soft credit).  You should look at the data through different lenses.  Soft credit analysis can give you a 360-degree view. For example, a donor might have $5k in actual gifts and $20k in influenced gifts – you see both their personal giving capacity and their network value. This prevents the pitfalls of double counting money, while still quantifying the otherwise invisible.  Analysis of a donor’s network value should trigger different actionable insights and potentially recognition and stewardship approaches than analysis of their hard credit giving alone.

By keeping the analyses separate, you will avoid the potential to distort your core fundraising analytics that are generally considered most valuable and that can produce the most actionable insights.  The solution lies not in abandoning soft credits entirely, but in implementing a more strategic approach to how they're tracked and analyzed.

Recommended Best Practices for Managing Soft Credits

Rather than ignoring soft credits entirely, nonprofits should adopt a structured, dual-analysis approach:

• Maintain Clear Data Segregation: Record soft credits distinctly in your database, with clear tags differentiating them from direct contributions. Make sure every member of your analytics and fundraising teams understands the difference.
• Conduct Parallel Analyses: Regularly perform two analyses: one strictly financial, excluding soft credits, and another relational, explicitly focusing on soft credits. Each serves a distinct strategic purpose—financial analysis for budgeting and forecasting, relational analysis for understanding influence and donor networks.

• Develop Influence Metrics: Create specialized metrics for soft credits, such as "influencer scores" or "relational impact scores," separate from financial scoring. This strategy helps quantify relationship value without compromising financial analytics integrity.

Bringing Balance to Your Fundraising Strategy

Recognizing donors and their networks is vital, but accurate, actionable analytics must remain a priority. By excluding soft credits from primary fundraising analyses and managing them separately, organizations ensure their analytics remain precise and strategically valuable. Such an approach provides clarity in financial management while still acknowledging the powerful role relationships play in successful fundraising.

In conclusion, soft credits are an essential part of any robust nonprofit analytics program—but they deserve their own analytical space. Treating soft credits separately preserves the integrity of your fundraising analytics, enabling smarter decisions, more accurate projections, and ultimately, greater fundraising success.