3 reason why Non Profit Organizations still need Data Analytics
The key feature that distinguishes non-profit organizations (NPO) from other commercial companies is their business incentive: instead of being revenue or profit-driven, the NPO may think more about how to bring value to those in need, and eventually benefit the society as a whole. As a result, some people may believe there is nothing NPO can do to increase their “revenue” — or donations — that they just need to be there, wait for kind people to make contributions, and finally send the donations to targeted people.
In addition, some people believe that all their money donated should go directly to the people they want to help and as a result, they may argue that it is critical to allocate part of the donations to foster a data analysis team, just like the way they react when seeing NPO promoting themselves through paid advertising.
However, is data analytic that useless to the NPO?
The answer is NO.
In fact, 78% of the NPO with advanced analytics announced that they achieved higher effectiveness in their mission performance, based on an IBM Executive Report published in 2017. NPO also need to optimize their operation by more effectively allocating the limited resources and getting more resources from the public, and data analytic can help them to achieve this goal, just like how it helps the other profit-driven companies.
What Data Analytics can bring to NPO
Data analytics can benefit the NPO in the following 3 ways:
1. Understand what your customers need
It is hard to identify customer preferences just through human observation. Indeed, it is inefficient and costly to ask the customer’s interests, especially when sometimes the customers cannot clearly tell you their preferences themselves. With automatically collecting relevant data through a well-established database, analytics through computers, which is very effective when dealing with a huge amount of data, can help you to identify the key-driven factors that matter, for example, the factors causing a higher customer sign-up and/or lower churn rate.
In addition, data analytics can help to dig out the inherence connection between different features that are not easily observed. As a result, the organization may explore more opportunities through cross-selling. For example, the data analytic shows that the customers attracted through email promotion are more likely to make contributions to the Women categories. This connection may not be easily noticed through human observation. In this case, the organization may choose to show some women success stories on email promotions.
Data analytics can help you to understand what your customers really want. Through better resource allocation based on the recommendation given by data analytics, you can better serve your customers and attract more public resources.
2. Quantify the outcomes and make them more comparable
People tend to believe in figures since numbers are more measurable and comparable. If you just tell others A is better than B, people may doubt the reasons behind it and wonder whether the comparison is subjective and biased. However, if this conclusion is supported by data and numbers, it will be more persuasive. For example, “among our customers, 70% will choose product A, and 80% these customers will also come back to purchase A again within 3 months, while product B only has 10% return customers.” may be an effective way to illustrate why you can draw the conclusion that A is better than B.
Therefore, data analytics can benefit organizations by quantifying the outcomes to make them more comparable. Showing the percentage of customers who choose your organization twice or more to donate may be a good way to prove yourselves and attract new customers. Furthermore, through appropriate visualization, data analytics can even do better. By being presented the right graphs, the audiences can understand the rationale behind and the story the data wants to tell them more easily. Data analytics can serve as strong supporting evidence in decision-making.
3. Forecast and better prepare for the future
Data analytics is a continuous, iterative exploration and an investigation of past business performance to gain insight and drive business planning for the future.
— Grant Thornton
At the same time when finding out the leading factors for some key metrics such as customer sign-up and cancellation, the data analytic team also build the models based on historical performance. With these models that take many influential components into consideration, the organizations can make better predictions and cope with future uncertainty.
Furthermore, data analytics can also monitor the performance and financial health that once there is a huge discrepancy between the actual outcome and the prediction, the organizations will notice immediately and take actions to check abnormality and try to fix the problems on a timely basis.
Be aware of the trips when using data for the decision making
Now you may have a rough idea about how NPO can benefit from data analytics and cannot wait to have a try immediately. However, please keep in mind that on one hand, due to its never-be-tired nature, the machines can perform better in data processing and calculation by avoiding common human mistakes. On the other hand, not as intelligent as human-being, the algorithm can only perform what people tell them to do without thinking whether it is reasonable.
For example, in one of the cases I studied before, the data analytic team found out that the default acquisition category had the largest numbers of customer sign-up, indicating that there was no need for breaking down into small subsets of categories. However, at the same time, it showed that the cancellation rate of the default category was also the highest, indicating that the default category should be removed. The team was confused by these contradictory results, and through further investigation, they finally found out the reasons: some customers did not see there were other categories that could be chosen when signing up, but later when they noticed, they just canceled the subscription and rejoined with the category they like, as the cancellation and sign-up procedures were very simple. This phenomenon might attribute to the larger numbers of customer sign-up and higher cancellation rates simultaneously. After filtering out such “useless” entries and improving the user interface, the insights from the data analytics are more meaningful for making the correct decision.
In all, data analytics can also benefit the NPO from many aspects, such as more effective resource allocation, more interpretable and comparable results, and better prediction for the future.
However, there is still a long way to go when an organization wants to become data-driven. The hardest part for NPO to apply data analytics is not whether they could recruit appropriate data scientists, or whether they have the resources to build a data analytic team. The difficulty lies in whether the organization will rely on the results of the analytics, and set data analytics as one of the formal steps in their decision-making process. Perhaps engraving data analytics into the organization’s strategy is one of the best ways to apply data analytics everywhere in the organization.
