by Christina Köck
Data Scientist at joint systems
What is Next Best Action?
Who does not sometimes wish for a mentor who always has an appropriate advice? In general, humans are very good at rating known situations and making the right decisions, but in case of new circumstances or a huge amount of aspects to consider, good advice is priceless. In marketing and similarly in fundraising, one-on-one support is very effective, but not possible in most cases. As companies and organizations were growing, mass marketing became the state of the art, trying to sell as many items to as many customers as possible. Customer received overwhelming amounts of advertisement and offers, which can be described as a “content shock”. Since the rise of machine learning and the possibility for sophisticated data analysis, companies try to stand out by putting the customer in the center. This is done by trying to predict what the customer wants and needs. New technologies should advise marketing specialists what to do next in order to satisfy every customer individually – supported by enormous amounts of data. This concept is called Next Best Action (NBA) or Next Best Offer (NBO) and can evolve into a useful fortuneteller in case of effective implementation. The benefits of the concept are reduced advertisement cost and increased customer loyalty.
How can NBA systems be developed?
The goal, structure and architecture of a NBA system strongly depends on the environment and purpose it is used for. Questions to answer before the development are for example:
After goals and actions are defined, the general structure of a NBA system can be summarized as follows:
The process of step one - filter- is rather rule based, depending on what actions or offers make sense for a customer. For instance, there might be products, which cannot be offered more than one time and therefore their offer will be excluded from the action list after purchase. The same applies for the third step: Typically, the actions with the highest benefit should be chosen but it depends on the user of the system, whether more than one action is taken or the actions need to be filtered again. A reason for a second filtering step could be that only one single action of a certain category is chosen even though two actions from the same category show very high benefit values. As it turns out, most NBA systems are based on proven business rules and requires a well-defined framework before the most complex component – the rating of the actions - can be developed.
A rule-based system requires a lot of experience in the domain and takes time to develop because it is all done by hand: Rule like “rate action xy high if the customer already bought from the category ab in the last two years” can be data driven if they are based on previous findings. Most companies already use such business rules for marketing. The scoring does not need to be metric (with numbers), as shown in the picture below. The system is not adaptive and needs to be adjusted, if the circumstances change. Rule-based systems are characterized by extreme simplification, since the real, very complex interrelationships cannot be completely cast into rules. The needs and the reactions of a customer will change depending on the previous action, which is very difficult to represent in a rule-based way.
If a low or intermediate level of machine learning is considered, relatively simple scores and predictions can be incorporated into the system. The simplest approach would be to calculate the probability for all customers to react positively to an action (propensity). To just predict which action might provoke a positive reaction might not be suitable for predicting long term customer loyalty. Several scores and probabilities can be combined to achieve this goal. For example, the propensity can be combined with a prediction of long term customer engagement. The reason for this is that on one hand no action is useful if the customer does not like the action (propensity), but on the other hand the actions also need to have a benefit for the organization (prediction of customer engagement/success for the organization). The scores can be weighted according to their importance and any number of further adjustments can be made. Typically, such a system will still be embedded in several predefined rules. Similar like factors for weighing scores (by multiplication), a higher importance of a score can be expressed by adding meaningful numbers to the score.
Disadvantages are that still a lot of experience in the field of use is needed. Scores are required for all actions and they need to indicate the benefit or propensity to the same extent.
The system could automatically adjust to a certain extend if the scores themselves are linked to current data and regularly monitored.
Full Deep Learning Models require more abilities that are technical, experience with deep learning and an appropriate technical environment. The difference to the previous systems is that these models are black box-models without the necessity for the users to tweak and define the rules and scores themselves. While intermediate levels of machine learning require a programmer to intervene to make adjustments, in deep learning the algorithms themselves determine whether their decisions are right or wrong. Internally, the algorithm will calculate scores for each action but will determine the best calculation and weighing itself. Rating systems in this category can range from a deep neural network for predicting benefits of actions to reinforcement learning models, which do not need a lot of training data in advance but can learn implicit mechanisms by trial and error. As long as these systems are fed with current data, they are adaptable. Disadvantages of this approach are the complex development and reduced interpretability due to the fully automated process.
In our view, George Box´ good old quote “All models are wrong, but some are useful” is also worth considering in the context of NBA models. Whether and how these approaches suit the needs of respective (nonprofit) organizations should be evaluated holistically. If you are interested in getting to know more about the concept of NBA or even think about implementing it in your SOS association, please do not hesitate to get in touch with us.
Non-profit organisations (NPOs) use offline-marketing strategies to both attract the attention of potential donors as well as collect donations. With mostly donors from “aging” generations reacting to direct campaigns and because those campaigns are slowly decreasing its impact, NPOs are investing into new ways of collecting donations, including online platforms. Not only online media has the potential to reach a wider profile of users; also, other advantages exist, including traceability, the option to gather user-level data and the fact that customers can potentially be better addressed over online channels. In addition, online media have proven to have a greater effectiveness in terms of customer conversion than traditional advertising.
With increasing inflation and life costs and shrinking marketing budgets, it is crucial for NPOs to understand their customer interests and behaviour to better and more effectively address them. If talking about online marketing platforms, this would translate into study what online sources (like email links, YouTube ads or paid search) are the ones bringing more potential donors to our websites.
Each person accessing a website to actually buy a product or donate something, may have visited the website before to research about the (donation) product, before finally deciding on converting or making a donation. Obviously, it is interesting to know the influence of each online source in a final donation through a website; this is known as the attribution problem and can be solved by using attribution-modelling techniques. Different attribution-modelling techniques exist, which try to predict the importance of the different marketing channels in the total conversion of customers (or donors). While advertisers tend to use very simplistic, heuristic ones, academia has focused on more complex and data-driven methods, which have been proven to obtain better results.
Using data from the visits and donations done during the year 2021 on the website of a SOS organisation, we studied the effect that different marketing channel sources (i.e. "from where do users access a website") had on donations. Analysis were done on Jupyter Notebooks using Python and two different modelling approaches:
After doing some first data exploration and filtering data representing an “abnormal” visiting behaviour (visits to the career site and those done on #GivingTuesday), we applied both attribution-modelling methods.
From our analysis, it could be easily concluded that different online marketing channels do have different effects on donations and donation revenues.
However, and to our surprise, minimal differences were found between LTA and MC results for both, estimated number of donations and revenue per channel, probably caused by a “special” behaviour of donors: as it seems, most of them will visit the website just once during the year and decide, on the fly, if they would like to donate something. This overrepresentation of paths with just one touchpoint causes both heuristic and data-driven methods to assign the same (donation) value to the channels.
While results are similar, we find that working with data-driven methods as Markov Chains still has advantages, including the fact that:
Do you want to know more about our results and insights? Are you also interested in a similar study? Do you have online data but are not making use of it?
Get in touch with us! 💻📱📧
We at joint systems can help you get the most out of your data 🙂😉
Some 3.5 years ago we discussed the state of data science in the nonprofit sector in this blog post. The world has significantly changed since then, however, being an insight-driven (nonprofit) organization is more imperative than ever. So, what is actually the status quo of data science and analytics in the nonprofit sector?
The most comprehensive survey on the state of data science and machine learning is the annual Machine Learning and Data Science Survey conducted by the platform Kaggle.com. In 2021, almost 26.000 people took part all across the globe. The participants were also asked about the industry they currently work in, Luckily, survey designers had added "Nonprofit & Services" as an option for the mentioned industry-related question. This enabled us to download the full survey response dataset from the Kaggle website. Using a global filter to focus on the responses from the nonprofit sector, we managed to put together this dashboard:
Back in 2019, when we last blogged about the status of data science in the nonprofit sector, we had already started our joint journey with our customers and partners. Still, we are continuous learners. However, together with our clients, we managed to write numerous success stories on how data science and analytics can make fundraising more efficient and successful. If you want to learn more, please go ahead and browse through the free resources we offer on our platform analytical-fundraising4sos.com or watch the video below for some inspiration.
We wish you all the best in these turbulent and challenging times. Let´s keep in touch and jointly make the most of fundraising data!
The world has become - and maybe always was to an extent - a volatile, uncertain, complex and ambiguous place (VUCA). This has turned the communication of complex topics and interdependencies into an urgent need. Data is ubiquoutous but let alone it is useless unless converted into information and ultimately knowledge. This is where the concept of Data Visualization or more holistically ideas from the field Data Storytelling can make crucial contributions. According to Dr. Jennifer Aaker, an American behavioural scientist at the University of Stanford, stories are remembered up to 22 times more than facts alone.
Often, data storytelling is simply considered an effective data visualization. In fact, the practise of creating data stories is a structured approach with the goal to communicate data insights as an interplay of the three elements data, visuals and narrative. Creating convincing narrative visualizations not only requires the skills of data analysis experts but al so the knowledge from designers, artists and psychologists employing certain techniques, following specific structures, frameworks and using tools.
The creator of the visual may not be able to put his or her intention into explicit knowledge since successful data visuals are often “a matter of taste” and creating representations of data visually hence a subjective process . This suggests that communicating data driven insights to decision makers requires data storytellers, who are skilled in the “art of data storytelling" which can best be learned through best practises. Data Storytelling is essentially about an "art and science mindset".
Lisa Oberascher, data analyst and alumna of Management, Communication & IT at MCI Innsbruck recently finished her bachelor thesis about Data Storytelling. Lisa conducted interviews with data practicioners and put together a great Data Storytelling cheat sheet.
The respective PDF can be downloaded here:
We wish you a great summer and happy data-storytelling :-)
Data is becoming more and more pervasive across industries. Analytics has come quite some way in recent years. A growing number of organizations have implemented analytics solutions and started exploring the potential of data science. With continuing technological advances and accelerating digitization, it is not always easy to overview the current developments in advanced analytics and data science. This end-of-year post tries to provide readers with information in a nutshell on contemporary issues in analytics and data science from a nonprofit and fundraising standpoint.
The infographic below is a "one-pager" for decision makers, analysts, data scientists and anyone interested. We differentiate between the topics that seem to be here to stay and relevant trends that should definitely be considered. In addition, we drop some hyped buzzwords that might be topics for the future and are worth observing. Please feel free to download, share, comment etc.
It has been a challenging but also inspirational year for many of us. We wish you and your dear ones a happy and peaceful Christmas 2021 and a good start in a successful, healthy and happy 2022.
These are our Christmas wishes in dozens of languages 🎄!
All the best and see you in 2022!
The effects of the COVID-19 pandemic acted as an accelerator for digitalization in terms of processes, services, or whole business models. Digital technologies are transforming the economy and are becoming ubiquitous. An increasingly widespread application of algorithms is decision-making in businesses, governments, or society as a whole. Algorithms might, for instance, determine who is recruited and promoted, who is provided a loan or housing, who is offered insurance, or even which patients are seen by doctors. Algorithms have become important actors in organizational decision making, i.e. a field that has traditionally been exclusive to humans. As these decisions often have an ethical dimension, the delegation of roles and responsibilities within these decisions deserves scrutiny. This is where Coporate Responsibility comes into play ...
Luckily, as social nonprofit organizations work in the interest of the common good in one way or the other way, Corporate Responsibilty tends to be rooted in the "DNA" of nonprofits. At the same time, algorithms have also made their way into the sector of fundraising nonprofit organization as we had already highlighted in specific a blogpost from 2019. Compared to other contexts such as human resource management or the labour market (see for example this critical discussion of the algorithm used at the Austrian Labour Market agency "AMS"), the consequences of algorothmic decision making in the context of fundraising nonprofits will tend to be rather harmless. However, in the light of technological advances and the need for nonprofits acting as as active members of modern society that have a voice, NPO decision makers should be aware of the big picture in terms of "Ethical AI".
Implications and Challenges
In the course of scrutinizing the ethics of algorithms, not only considering the algorithms themselves but also their actual implementation in software and platforms should be scrutinized. Two groups of concerns can be identified in terms of the ethical challenges implied by algorithms. there are epistemic concerns on the one hand when evidence provided by algorithms is inconclusive, inscrutable, or misguided. On the other hand, there are normative concerns related to unfair outcomes, transformative effects and traceability. These normative concerns have in common that they are related to the actions derived from algorithmic results. In a nutshell, the mentioned concerns can be summarized as follows:
So what? Three things nonprofit decision makers can do (at least)
Any questions or input? Let´s keep in touch!
We wish you a smooth start in a hopefully pleasant and successful fall of 2021.
All the best!