How Predictive Analytics Can Help NGOs

Predictive analytics can deliver the insights you need to help ensure your non-profit’s success. By uncovering patterns and trends hiding within your datasets, you can easily identify donors who may be willing to give more, or determine which programs offer the best return for your investment. And that means you can focus your limited resources — and efforts — on those areas where they’re likely to do the most good.

NGOs, also known as non-profits here in the U.S., fulfill a very important role as they seek to accomplish social good. They are in a unique position that allows them to see social need and react to it in ways that often times have more impact than other organization’s efforts could. Vault is looking to apply the science and art of measurement and data analytics to help NGOs accomplish their various missions, and we believe that if applied correctly, analytics can make a huge difference in NGO effectiveness

We’ve broken down the process of how to use analytics for NGOs into three categories, summarized below. We feel that it presents a systematic and practical approach to foster performance management and measurement in these organizations.

Measurement

The first hurdle that must be crossed is that of measurement, of taking the time and effort to measure work and progress and collect it in a database for further analysis and presentation. There are several reasons why it is important for an NGO to measure its efforts:

-Make sure time, effort, and money are being used where they need to be
-Gain ability to prove that you are accomplishing and fulfilling your social mission
-Gain ability to show that donor and sponsor funding is being used effectively

There are a few things to keep in mind when implementing a measurement strategy. First – it is important to not only measure the end goal, but also the incremental steps that lead up to that goal. Let’s say your organization’s mission is to decrease the number of diabetics within a specific demographic in your community. Measuring the % decrease in diabetes withing this population over a given time period is great, but it doesn’t tell the whole story. Ask yourself, what are the incremental steps leading up to the lowered diabetes rates? Perhaps one is the amount of exercise the average person in the demographic is getting on a daily basis. Perhaps another is the amount of sweets or fatty foods the average person is consuming per day. As you attack these issues that lead to diabetes, measure the improvement in these areas. Then people get the whole story of where your efforts have helped reduce each aspect of the larger problem – and you can find out which efforts are the most effective at getting rid of this problem.

Second – make sure and measure regression rates. Too often we stop the measurement once the problem is solved – once we have lowered the diabetes rate, in this case. But how many of those people, after we stopped working with them, have regressed into having diabetes? This is sometimes an alarmingly high number, and when regression rates are high, that means all the work we performed to lower the diabetes rate in the first place has gone to waste. If you see through measurement that the regression level is high, it’s time to implement some strategies and efforts into keeping the solution in place – that is, not losing ground once you’ve attained it. It’s often a lot easier to keep the problem gone than to go back and fix it again. This allows you to really fulfill your mission, in a lasting sense. It wastes less resources because you retain the ground you’ve gained. And donors and sponsors will be excited by the fact that you can show that your solution is a long lasting one.

Analytics

Once we have measurement strategies in place, now we have lots of data on our hands. Analytics is the process by which we extract useful intelligence from this data. There are many methods of doing this, whether it be through visual analysis techniques, statistics, predictive models, etc. (specific ways on how to do these types of analysis will be the topic of subsequent posts) Many people think that analytics is a task that is beyond their abilities, but many times even simple analysis will result in sufficient intelligence that you can use to do your work smarter.

One of the most important things to remember in doing analysis is the principle of segmentation. This means looking at the data in smaller pieces, rather than in aggregate. For instance, if you want to know who your most effective workers are, break down the data to show you the hours each worker put in, and the changes in the incremental metrics we discussed above that occurred as a result of their work. Maybe you want to know which types of donors consistently give high sums to support your work – break them down by demographics, or by income, or by age, or any other variables to get a view of what your ideal donor looks like. Then you can target more of these kinds of people in your donation campaigns.

Presentation

Not to be forgotten is the element of presentation. Once you have the data and all the analysis, you need to be able to present the intelligence you’ve found to others in a way that they understand, and in a way that will cause a change in their behavior. The intelligence from the analytics is there so that you can be more effective in your work; however, if no one understands it, nothing will change and it will be useless. There are a few easy guidelines to follow in presenting analytical information so that it sticks:

– Relate the numbers to something people understand (Just saying the number 416 can be somewhat abstract, but if you say instead “the number of people that fit in a Boeing 747” the number becomes real and concrete)
– Only show the necessary elements of analysis to get your point across (many times you’ll have to go through a lot of analysis to get a few golden nuggets of intelligence, and our tendency is to want to show off all of the work we did to get there. The problem is, the process is not important to the people you are talking to. What’s important is the results and intelligence, so just focus on that.)
– Keep it simple (showing too many variables on a graph, or just plain too many graphs, causes more confusion that it does clarity)
– Relate the analysis back to what concerns your constituents (Your focus should always be on solving the problem, and the analysis is only important insofar as it helps you to do that. Focus on what solves the problem for the constituents)

Hopefully this small outline gives you a framework that you can use in thinking about how to implement analytics into your organization. In the coming posts we’ll be discussing more in depth how to do each of these three points.

The challenges of fundraising:

Around the world, donors are already crucial to the function and sustainability of nonprofit organizations. Individual donors now represent almost 99 percent of nonprofit funding in India,2 80 to 95 percent of nonprofit funding in the US4 and 53 percent in Europe.5 But donor giving is undergoing dramatic changes. While the number of donors has remained steady, the average gift they give is much lower. As donors struggle with tightening their own household or corporate philanthropic budgets, they are more selective about which causes they will support. With fewer dollars available for giving, donors may seek a perfect fit with a cause before making a commitment. And when fewer donors are receptive to giving, the competition for charitable dollars increases.

In this environment, it is increasingly important for nonprofits to not only increase the number of donors, but increase the donation amount as well. In addition, they must find ways to reduce the high costs of donor processing and correspondence, and accelerate the turnaround time for funds availability. Most importantly, nonprofits must not invest their limited time, effort and expense soliciting potential donors that will likely never contribute. All of these challenges require a more effective strategy for the overall task of donor engagement. Fundraising encompasses a wide range of capabilities within a nonprofit organization. These include tracking and managing a complex array of donors, members and volunteers; anticipating which donors are most likely to give; building loyal donor relationships for repeat giving; identifying which donors will provide the biggest returns; creating campaigns and donation request levels to appeal to different donor types; understanding which communication channels are most effective; knowing when donors should be solicited and when they should not to avoid saturation; and deploying limited resources more cost-effectively

Predictive fundraising analytics 

Forward-looking nonprofit organizations are now using predictive analytics to improve donor engagement and returns on fundraising efforts. Predictive analytics helps these organizations unlock hidden insights within their data so they can: • Identify prospective donors • Understand and anticipate donor needs, behaviors and preferences Retain donors Attract ideal d o nors Increase contributions 360o Donor Experience • Know where to deploy donor resources for the biggest returns • Predict which donors are most likely to donate, how much they will give, and when they would likely donate • Determine the most effective messages and channels for solicitation (such as email, phone, direct mail or others) • Optimize the frequency of donor contact to maximize contributions • Anticipate when staff should provide additional attention to a specific donor

What is predictive analytics? 

Predictive analytics uncovers patterns, trends and associations hidden within all types of data to help predict future outcomes, solve problems and guide smarter decisions. Commercial businesses across many industries use predictive analytics to understand their customers and build stronger, more profitable relationships. These capabilities are also used by nonprofit organizations to gain similar benefits with their donors. Predictive analytics uses advanced algorithms to analyze donor data and deliver a 360-degree view of individual donors. These analytic results provide detailed insight into the needs, preferences and behaviors of donors. Predictive models can be created which enable nonprofits to anticipate how donors will respond to certain campaigns, which contribution amounts they would be likely to give, when they should be solicited and when they should be left alone, which communication channels they prefer and much more. By deploying these insights to decision makers and frontline systems such as call centers or direct mail initiatives, nonprofits can significantly increase the effectiveness of donor campaigns and strategies. And because predictive analytics learns from every donor interaction, it can also help to build more loyal relationships over time and provide an “early warning system” of donors that may be dissatisfied and require extra attention. Predictive analytics also helps nonprofits prioritize their resources based on anticipated returns and thereby reduce the costs of donor management. Organizations can determine which donor targets, messages and channels will yield the best results. The wasted effort and expense of low yield donor processing and correspondence can be minimized.

Four steps for using predictive analytics for fundraising:

So exactly how can nonprofits use predictive analytics to carry out their donor management strategies? There are four basic steps that follow an analytical process: align donor data, predict what donors want, personalize donor interactions, and integrate what you learned back into the process to optimize your future predictions. 

Step 1—Align: Integrate donor data The first step and the foundation of this process is to align your existing raw donor information. Donor data from all sources and systems across your organization, including spreadsheets, surveys, databases and social media, can be integrated within a single solution. With IBM predictive analytics solutions, it is not necessary to create a separate data warehouse to store this consolidated data. The predictive analytics software can access data from disparate sources and perform the required analysis on your desktop PC or a server.

This data does not have to be “perfect” before you move forward with your analysis. Because this is an ongoing process, you will have many opportunities to improve and refine your data with future iterations. Although these volumes of information are already available within many nonprofit organizations, they are often unused or not used to their full advantage. By accessing, organizing and analyzing this data, you can unlock valuable insights that you can put to good use. Some key data elements you should focus on in order to ensure success in the following steps of this process include: • Demographic data such as age, income, occupation, family status, business and personal relationships • Campaign data such as contact history, responses and donations, and results of test campaigns • Opinion data captured from donor feedback in social media, emails and surveys that provides insight into donor needs and preferences • Any other structured or unstructured (text) data regarding donor activity

Step 2—Anticipate: Predict what donors want The information you consolidated can now be analyzed by predictive models that help you understand and anticipate what donors want and will do next. These models use predictive analytics to determine ideal donor segments, score the data and predict the likelihood of future events. For example, you could use predictive models to determine how likely it is for an individual donor to respond to a marketing campaign. Or predict the most effective actions that will build long term, profitable relationships with donors. Along with predictive modeling, another key capability of this step is decision optimization. Once your predictive models tell you how a donor will likely respond, decision optimization tells you how to use that information most effectively. For example, outreach managers would not only know which donors are likely to provide the biggest returns, but also which precise messages or campaigns to implement in order to maximize the success of every donor interaction. 

Step 3—Act: Personalize donor interactions Now that you know the best actions to take, the next capability is to personalize interactions with donors by integrating those insights into your operational processes and systems. For example, you could integrate predicted donor responses into your direct marketing programs. Individual donors would receive direct marketing offers that appeal to them and your organization would not waste time or expense targeting donors that have no interest in a particular solicitation. You can also use predictive donor insights to guide the actions of your donor outreach representatives. With at-a-glance, aggregated donor information, your employees will know which donors may be dissatisfied and need a little extra care, and where to focus their retention efforts. In this way, personalizing donor interactions can help improve loyalty, boost response rates, reduce marketing costs and maximize contributions. And because analytics provides the capability to predict the likely amount of donor contributions, you can further customize solicitations to ensure that they will increase a donor’s value and giving over time. The predictive intelligence you gain from this process can also be sent to upper management via dashboards and scorecards to guide their decisions and strategies. 

Step 4—Optimize your predictions Predictive analytics isn’t a linear process. With each iteration you gain new insights from donor responses. That valuable source of information can now be integrated back into the analytical process to continually improve future performance. By adding more data sources to your analysis over time, and refining your existing sources, you can significantly enrich your donor view and sharpen the accuracy of your predictive models. And with direct analytical insight into the results of your donor initiatives, you can isolate KPPs, or key performance predictors, that will guide your efforts moving forward. This capability provides insightful data on what worked and what did not within your marketing or campaign initiatives. You are then able to anticipate what you can do next time to gain better results, reduce costs and improve overall efficiency.

Conclusion 

Nonprofit organizations need to improve their fundraising capabilities so they can become as efficient and effective as possible. Predictive analytics provides an effective way to understand and anticipate donor needs in order to increase the success of fundraising and marketing campaigns. Using this technology, nonprofits can gain a significant return on investment by increasing donor contributions, reducing costs and building stronger donor relationships over time