Despite the nonprofit sector’s current focus on operational performance, efficiency and effectiveness, many organizations continue to rely on techniques that provide little to no quantifiable insights into performance improvement. To generate measureable results that also support mission achievement, organizations are turning to data analytics.
Data analytics yields meaningful patterns in data — financial and nonfinancial — that can describe performance, and predict and guide improvements. Analytics is a multidisciplinary approach that simultaneously applies statistics, computer programming and operations research to quantify performance and provide a clearer picture of what is working and where improvement is needed. Analytics can help you validate trends, pinpoint root causes of existing issues and take a comprehensive analytical overview of organizational performance as a whole — within programs and their underlying business operations. More importantly, analytics can be predictive as well as real time, illustrating future possibilities to enable decisions about transforming performance.
Data analytics is a continuous, iterative exploration and an investigation of past business performance to gain insight and drive business planning for the future. The results of data analytics can be used to streamline operations, increase cost efficiency, determine and optimize financial margin by program, model and forecast performance (e.g., membership trends, donor trends, resource needs and revenue expectations), improve the budgeting process, and enhance overall mission effectiveness. With a true understanding of business performance based on data, statistical methods and predictive modeling, data analytics allows management to concentrate on the fundamental objectives of the organization and find ways to enhance mission achievement.
Steps to instituting data analyticsPrepare your organization for optimal results:
• Invest in technology (e.g., data warehouse and analytical tools) to capture desired data and create correlations.
• Establish performance metrics beyond financial measures and agree on mission-driven indicators.
• Acknowledge the human element and the importance of effective change management.
• Build collaboration into the process in order to view the organization as a whole rather than as a collection of departments and interests.
• Develop or acquire personnel to bring data analytics and business intelligence skills into your organization.
• Commit to act on the trends and insights discovered through data analytics.
• Create a cross-functional steering committee that can set aside other biases in order to act on the analysis.
• Dedicate organization-wide focus to an ongoing data analytics program, as opposed to conducting a one-time exercise.
Results can be financial, programmatic, constituent building and sustainable
As an example of how data analytics can be used to strengthen an organization’s membership recruitment and fulfillment operations, consider how membership targeting and member maintenance has traditionally been done — buying lists of names in the target market and sending communications deep into that list, as well as the existing membership base. Contrast this with an enhanced approach that effectively leverages metrics in a cross-functional analysis. Instead of hoping for a yield from a static list that offers few additional insights, with the use of data analytics, various factors, activities and characteristics are analyzed in concert to determine how they relate to and affect each other. This enhanced approach enables organizations to generate an optimal number of engaged members in a fiscally prudent manner. Using data analytics, planning exercises can take into account a more complete set of factors — e.g., membership outreach yield, cost per outreach, market penetration, demographic factors and changes, membership engagement, and membership product cross-selling. These factors are brought together in a solution to improve targeting, new member sign-ups and member renewals, as well as to identify future member needs and services — all while keeping the cost and return on investment in mind.
Donor building and constituent outreach are fundamental building blocks of revenue for many nonprofits. Similar to enhanced practices in membership development as cited above, organizations are tapping into data analytics to improve donor yield and engagement. Conventional approaches for improving the success of these operations are giving way to donor analytics that extrapolate donor demographics (e.g., financial, geographic, political and gender). The analytics compare the demographics to various donor offerings (e.g., one-time gift, recurring, corporate or product-based purchasing) to determine the right fit for each individual. Generating donations no longer involves going as deep into your snail mail/digital mailing list as you can afford; it now involves determining the right product for the individual, and the organization’s desired yield and margin for each product and donor target. Further, when that outreach occurs, what is the optimal format for doing so? Monitoring open rates and click-throughs is one thing; doing so while comparing graphical layouts, subject lines and timing becomes an altogether different analytical exercise in order to optimize yield and engagement. Similar analysis can be applied to other forms of outreach (e.g., in-person events) to determine what truly generates a return and which activities should be continued. For many years, the label of friendraising
was adequate cover for events that did not generate a net positive return; now it is necessary to assess an event’s indirect and direct impact to determine how and if the activity should continue.
Outcomes can be demonstrated for planning and competitive advantage
Beyond reducing the cost of operations and programs, performance improvement can be measured by mission-related factors and increased constituent outcomes by program. Another factor driving use of data analytics is heightened interest in program outcomes. Constituents and funders are demanding proof that programs are successful and improving the lives of constituents. In response, nonprofits are utilizing data analytics techniques and models to assess service outcomes and report mission-related achievement. Organizations that can exhibit a clear return on investment are at a significant competitive advantage when it comes to obtaining external funding.
While data analytics can assist organizations that are struggling with their bottom-line financial performance, analytics can also benefit financially stable entities by illuminating budget-neutral changes that can deliver better programmatic outcomes. The traditional approach of measuring financial performance solely by financial metrics — such as operating margins, and growth rates in revenues, expenses, and specific categories of expense and revenue — is limited in that it does not link financial performance to operational performance. Data analytics helps organizations enhance operational as well as financial performance by identifying and integrating financial metrics and business activities that have a significant impact on programmatic outcomes. Improving financial results while expanding program offerings and improving the success rates or outcomes of programs is true performance improvement, which can be achieved when financial and nonfinancial data are analyzed using statistical and predictive modeling techniques.
Assessing and measuring budget trade-offs related to investments in program sprawl (i.e., when an organization currently has too many programs, many of which may be underutilized or stray from the organization’s mission) is where data analytics also shines. Data is aggregated, analyzed and explored across many dimensions — for example: at-risk populations, programmatic factors (such as funding restrictions, per-capita rates versus service efforts, and volunteer service providers) and program type (such as housing, health services and workforce training). To help the organization rationalize program offerings, predictive analytical models can use past financial and outcome performance of programs to analyze, on a program-by-program basis, the impact from changes to specific funding criteria, socioeconomic and demographic changes, program visibility, shifts in government program priorities, interagency collaborations and program delivery models. With so many factors to assimilate, data analytics’ holistic and objective fact-based approach can help navigate difficult — often political — waters and enhance performance in a financially sustainable manner.
Satisfying social advocates, donors, funders, dedicated volunteers and committed board members while balancing program costs and outcomes requires a data-driven focus. Therefore, organizations are prompted to make decisions that are solidly based in fact versus emotions as they scrutinize program performance and investments in new programs amid the challenges of a competitive marketplace. To do so effectively, organizations must strive to understand their current program model by looking beyond the traditional metrics of program deficits, staff efforts and numbers of people served. To deliver truly improved performance and the best data for deciding on program investments and divestment, they must expand the data to include program efficacy, quality of program outcomes versus program efforts, salary-cost-to-revenue ratios by program, number of service efforts (hours and staff) provided by other area agencies in the same programmatic fields, relative cost of programs by agency, and program outcomes by other agencies in the same programmatic field.
In analytics, human factors are as integral as data
In the end, data analytics comes down to the human element. The organization’s leadership needs to champion the desire for a better, more integrated decision-making process based on an understanding of the relationships between business drivers, programmatic outputs and predicted financial performance, and how each sustains the organization’s mission.
To perform meaningful analytics, databases and technology tools are critical. However, this is not simply an IT exercise — at its core, data analytics involves people and their commitment to the mission. It is essential to involve a variety of perspectives to ensure fair representation of stakeholders. The traditional analysis functions of the finance, budget, program, membership and fundraising departments need to be reassessed, and new skills introduced for analyzing different types of data. Collaboration is needed among various stakeholders to determine what data should be gathered and analyzed, and how analytics will be interpreted and converted to performance insights. Leadership is needed from the executive team to determine, in collaboration with the board, how decisions derived from business insights will be applied throughout the stakeholder groups, and how opinions about facts and data will be synthesized into action. This approach requires an organizational commitment to change.
The highly competitive nature of the nonprofit sector, increased scrutiny by stakeholders and changing demographics of our nation create an environment in which improving financial and nonfinancial outcomes cannot be achieved based solely on the conventions of past practices. The true power of data analytics lies in establishing a dedicated, ongoing program that enables nonprofit organizations to gain insights into their operations and improve performance going forward.
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The State of the Not-for-Profit Sector in 2016