Understanding Recommendation Engines and their Potential in Fundraising

By Carolina Pelegrin Cuartero 
Data Scientist at joint systems


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Recommendation Engines in a Nutshell

Recommendation engines are advanced data filtering systems that use behavioral data, computer learning and statistical modelling to predict which content, products, or services a customer is likely to consume or engage with. Therefore, recommendation engines are not just giving us a better picture on our users’ interests and preferences; they can also enhance the user experience and engagement.

Recommendation engines, like any other data-driven method, need data to be applied. While no specific amount of data is required, data does need to contain high-quality interactions, as well as contextual information about users and items, to be able to create good predictions. Examples of high-quality signals are all kind of data that clearly states the user’s preference, like explicit ratings, reviews or likes to a specific product. While high-quality signals are preferred, implicit signals like browsing history, clicks, time spent reviewing a product or purchase history can also be used – those are more abundant but can be noisy and hard to interpret, since they do not need to indicate a clear preference.

In order to extract the relevant information from a dataset, data filter methods are used. Different filtering methods exist, including collaborative filtering, content-based filtering and hybrid filtering methods. 

• Content-based filtering methods use product information to recommend (new) items to a user, which are similar to the items that the user liked or bought in the past. It is an interesting set of methods, since they just need the purchase or feedback history of the customer to work – no customer data is needed!
• Collaborative filtering methods use behavioral data of similar customers, to predict what a customer will like in the future. Customer similarity is not necessarily based on socio-demographic data, but on product preferences.
• Hybrid methods combine both types of filtering methods to overcome the limitations of each approach, while creating more accurate recommendations.  

Why do recommendation engines matter for NGOs? - Possible Use Cases ...

NGOs, like any other organization, are rapidly undergoing digitalization processes. They are increasing their online presence and communication, using online platforms like websites, social media channels or emails, to raise awareness, inform donors about their projects and news, as well as to run online fundraising campaigns. Traditional NGOs also rely heavily on offline communication campaigns like letters, postcards, or even booklets that contain the last news and projects ongoing in the organization.

Even though NGOs campaigns may be slightly adapted to different audience groups, we are far from using the donors’ interests in our communication campaigns. Using recommendation engines could help us do data-driven decisions about who to contact, with what content and even what kind of donation to ask for, all based in previous donation behavior. This can potentially improve our fundraising results while keeping donors engaged and supportive towards our projects. 

Challenges

While we know that recommendation engines may help us better address our donors, there are some challenges that we need to take into consideration, like the potential lack of high-quality data and the presence of noise and bias in the datasets.

• Potential lack of high-quality data. Interactions with a NGO take the form of single donations or commitments, as a result to specific campaigns or addressed to specific projects and topics. Unlike the typical e-commerce products, the donor does not rate those projects in any way; therefore, just data in form of implicit signals is available, which makes the prediction more complex.
• Potential lack of information. For instance, about what communication channel does each donor prefer.
• Potential bias in interactions. Bias in interactions can negatively affect the performance. Data bias may include issues like:
  • Default bookings or the fact that, unless specified, donations may be booked to  a default campaign (for instance, the campaign that was previously sent to the donor), without knowing exactly if the donor was interested on that specific campaign.
  • Selection bias or donations towards those topics that are presented in the campaigns.
    
  • Temporal bias, or the fact that many donors may just interact (donate) at specific points in time. A clear example are donations at Christmas times – those are most likely related to the point in time, and not as much on the topics presented in the Christmas campaigns. 

Conclusion

Although some challenges exist (mostly related to the quality and availability of data), we still believe that exploring recommendation engines’ methods and applying them to our data, would help us to better understand our donors’ interests, which ultimately could keep donors engaged and supportive towards our projects.

Are you interested in recommendation engines? Do you think that they can help you better understand your donors’ interests? Contact us and let us explore your data together!