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The good old days of Web 1.0
A world without the WWW is hard to imagine for most of us - although it is not that far away in the past. I get a little nostalgic when I think of setting up my first email address back in 1999. Back then the world was like this (ok, this is quite some time ago ..).
Some 20 years ago, the overall number of websites around was ridiculously low for today´s standards. To get any idea about how a page was performing, web marketers had to rely on things like the notorious visitor counters or wrangling log files created by web servers. The latter was definitely a hassle as access to these files was required in the first place, configuration file hacking or programming was necessary, only static reports etc.
Data-driven Digital Fundraising today
Nowadays the common denominator for most organizations having a digital presence is definitely running a website. In a context of marketing and sales as well as fundraising, this page often acts as kind of hub to which other digital channels and communication activities point. Running a website requires sound management. And as management is in essence a data-driven discipline, an evidence-based approach also is key in a web context...
This is where Google Analytics comes into play. It has the capacity to answer questions like these for online fundraisers and decisions makers in general:
Google Analytics is free in its base version which suits many use cases of websites. For big data applications, there is the even more powerful tool called Google Analytics 360 as part of the Google Marketing Cloud.
All you need to set up Google Analytics (free version) – apart from a website you have the ownership for of course – is a Google Account and the possibility to embed the little Java Script code snippet that does the magic. This can either be done by yourself or a web developer. Frameworks like Wordpress or Weebly (where this blog runs on) make life even easier through assistants. For the ones interested in what is happening under the hood, this infographic might be interesting.
Google Analytics in a nutshell
Google Analytics is embedded in an ecosystem together with tools like Google Tag Manager, Google Adwords etc. This is particularly the case on in a more advanced and differentiated online fundraising context.
Google analytics offers a lot of potentially insightful reports out of the box which can be used from day one. The main areas are:
Learn more
There is a plethora of resources from Google and other providers to take a deep dive into Google Analytics. These are a few:
Advanced Analytics @ Google Analytics
Google analytics can moreover also be interesting in a broader analytics and data science context. Think of the following uses cases:
To cut the long story short, analysts, data scientists etc. working in a fundraising context should - in addition to the digital fundraising experts taking care of the website and digital channels - care about digital data analyses in general and Google Analytics in particular. As 2020 just started - let´s get cracking! :-)
All the best and keep in touch as asykourdata.co or firstname.lastname@example.com.
When we started this blog some three years ago, data science was widely seen as a mere buzzword. The interest in the concept seems to be here to stay – and grow steadily. Google Trends shows how the interest in the search term Data Science has developed in the last three years:
Data Science is here to stay
We think that Data Science is more than just a fancy term for statistics and agree with the popular blog KDnuggets: Data science is about creating value through data and supporting digital transformation of other processes in a company such as marketing, customer service, production etc. We believe that the positive impact of advanced analytical methods is something that can be generated across industries and is not limited to the corporate sector. Earlier this year, we discussed the adoption of advanced analytics by nonprofits. Given the already existing relevance of data science, we asked ourselves what 2020 might bring - and found some interesting hypothesis on the web.
Take everyone onto the Data Science Journey
According to towardsdatascience, some 100 papers on Machine Learning were published in 2019 on a single day. This reflects that Data Science as a whole is here to stay. Hand in hand with increasing presence comes certain differentiation. The mentioned blog sees a trend towards specialization among different roles in data science. On the one hand, there are experts on bringing models into production and providing the necessary infrastructure. On the other hand, there are people involved in investigative work and decision support.
The footprint of Data Science is getting larger as models are becoming an indispensable part of business operations. This implies the ongoing challenge to further increase model performance, the possible need for model retraining or rebuilding as well as continuous levels of support for model stakeholders.
We mentioned before that Data Science is essentially about turning data into value for the respective organization. This value creation is, according to towardsdatascience, not only dependent on the "physical technology" consisting of algorithms and data flows. The "social technology", i.e. effective lines of related communication and decision-making or executive awareness (or even better, a basic understanding provided by interesting in-house-trainings in Data Science) are at least as important.
People and Tools are needed
Data Science is done by Data Scientists. According to a study by IBM, the demand for Data Scientists will grow by some 28% until 2020 (compared to 2017). Some might go as far as to call Data Scientists the "sexiest job of the 21st century" – like Harvard Business Review did back in 2012. Regardless of any labels, it can be expected that the perceived shortage of expert staff will remain in 2020 both across industries and on a global level. The good news is that further developed self-service tools will gradually improve the ease of data preparation, exploration, visualization and modelling.
Natural Language Processing
Most people think of structured information in rows and columns when they hear the term "data". In fact, an unbelievable large amount of unstructured data, i.e. texts, speech, sounds and videos are produced every single day. This also applies to different forms of personalized data and general customer communication. A powerful approach to make most of unstructured data is so called Natural Language Processing. It is essentially about classifying texts in categories, sentiments, similarities etc. What happens under the hood is that characters are translated into numbers and further processed by models such as Neural Networks. Breakthroughs in Machine Learning and emerging libraries like Tensorflow have drastically increased the possibility to apply NLP models to unstructured data.
Data Privacy and Security as relevant constraint
There is no data science without data. The "raw material" for analysis and models is often personal data, be it from customers or donors. Particularly in a European context, the public has become more aware and careful regarding the ownership of personal data. The ongoing challenge for any kind of organization involved in data science is to keep highest data security and protection standards, aligned with best practices and being transparent upon customer request. If organizations stick to that, there is no need to become paranoid about data protection at the same time.
What is it that fundraising nonprofits can do or learn about Data Science in 2020?
We think that a classic quote by Mark Twain gives valuable hints into this direction:
The secret of getting ahead is getting started. The secret of getting started is breaking your complex overwhelming tasks into small, managable tasks and starting on the first one.
No matter how far away you see yourself away from applied and sophistiacated Data Science, it definitely will pay off to be even more data driven in 2020 and beyond. As we outlined earlier this year, there still seems to be a competitive edge in the industry for "analytcial NPOs" (see our blog post for facts and figures in this regard if you are interested. Do not hesitate to ask experts or organizations you trust for guidance - also joint systems will be happy to help throughout 2020. :-)
We wish you merry Christmas holidays and a good start into a happy, healthy and successful 2020.
David Weber and Johannes Spiess
Factfulness: Ten Reasons We're Wrong About the World -and Why Things Are Better Than You Think by Hans Rosling (2018)
Our very first blogpost on askyourdata.co from January 2017 was inspired by Hans Rosling's fact-based worldview and the tons of high-quality material and data the Rosling's and their fellows had piled together at gaprminder.org. Sadly, Rosling passed away in February 2017. One can go as far as to call Factfulness, Rosling's last book he had written with his son and daughter-in-law, his legacy. Factfulness is in essence a plea against seeing today's world in a "binary", black-or- white, rich-or-poor manner. One reflection of this are four generic income levels Rosling suggests instead of "rich and poor".
This is what Bill Gates, a personal friend of Rosling and fan of Factfulness says about that: Hans compares this [..] to standing on top of a skyscraper and looking down at a city. All of the other buildings will look short to you whether they're ten stories or 50 stories high. It's the same with income. Life is significantly better for those on level 2 than level 1, but it's hard to see that from level 4 unless you know to look for it.
The main part of the book is structured along ten human instincts (e.g. fear instinct, size instinct etc.) that prevent us from fully perceiving the world as well as gradual developments and improvements as they are. Factfulness is not a mere 350-page-stream of undifferentiated optimism and positivity. Rosling names the threats and risks humankind is confronted with (climate change, global economic crisis etc.). A fascinating level of differentiation is maintained from the very first to the very last page.
Thinking Fast and Slow by Daniel Kahnemann (2012)
The fact that Daniel Kahnemann won a Nobel prize for economics in 2002 does not make this book a good read per se. In Thinking Fast and Slow, Kahnemann takes us on a tour de force through human thinking and decision making. The sheer quantity of insights Daniel Kahnemann and Amos Tversky, his main research partner who deceased in 1996, developed over time is breath-taking. Kahnemann shows us that there are essentially two ways of human thinking. The first system is fast thinking which happens almost automatically and instinctively. This is the predominant way of thinking. The second system is the one of slow thinking. It is capable of rational thought and conscious decision making but takes more of a person's energy and concentration.
Kahnemann and Tversky have coined the research on cognitive biases. For instance, the two conducted the first study on the anchoring effect that drives the perception of figures and decision making on a subconscious level. This can serve as an interesting baseline for fundraising appeals (e.g. use large numbers in texts to increase average donations). Thinking Fast and Slow is the lifetime achievements of two of the most influential contemporary psychologists within one book. The hardcover is a 450page+ read, i.e. not something for a rainy autumn weekend alone. I can recommend the audiobook, particularly for a commute. There is an extensive amount of related material on the web.
The Black Swan: The Impact of the Highly Improbable by Nassim Nicholas Taleb (2015)
Nassim Nicholas Taleb's book has been a global bestseller of popular science since it was first published in 2008. Also Daniel Kahnemann refers to Taleb in Thinking Fast and Slow and admits that his thinking at a later stage of his career was strongly influenced by Taleb.
Although the book has also be criticized by renowned reviewers for its partial lack of empirical evidence, intensive use of anecdotes and messy structure, we find it a worthwhile read. The key themes of the book are limitations of predictions and the occurrence of presumably "impossible events" – just like black swans were to Europeans before they were first sighted in Australia.
Taleb was a successful Wallstreet trader and quantitative analyst before becoming a successful author. This is why he argues that the unexpected is not only key to the understanding of financial markets but of history as a whole. This "holisitic" attempt by Taleb might be aiming a bit too high. However, The Black Swan is a thought-provoking and entertaining read.
Visual Display of Quantitative Information by Edward Tufte (2001)
The history of data visualization goes back many centuries. In a world of growing "datafication", the visual display of quantitative information is growing more and more important. We have dedicated a whole section of blogposts to this exciting topic.
Edward Tufte's book Visual Display of Quantitative Information is a highly valuable read for everyone that has to visualize data and communicate insights. Tufte advocates a quite radical "less is more"-approach when it comes to visualizing data. This means for example that, with insufficient data, a simple table might be preferable compared to creating any chart. If charts are used, no drop of ink should be wasted as Tufte continuously proclaims clarity, precision and efficiency in data visualization.
The book is not only insightful for producers of quantitative information but also for recipients, i.e. basically everyone reading a management report, newspapers, blogs etc. Data visualizations can sometimes tend to distort the underlying information in order to be more persuasive. This is what Tufte calls the "lie factor", i.e. the size of effect shown in the visualiation in relation to the effect size in the actual data. Visual Display of Quantitative Information is a very recommendable extension to every analyst's , data scientist's and information designer's library.
Are there any books, papers, webinars and talks you can recommend for long and dark evening? Don´t hesitate to post comments and / or stay in touch with the team of askyourdata.co.
All the best and read you soon,
David Weber, BA
Data Science & Analytics
"If your only tool is a hammer, then every problem looks like a nail." – Unknown.
Today's data science landscape is a great example where we need hammers, but we also need screwdrivers, wrenches and pliers. Even though R and Python are the most used programming languages for data science, it is important to expand the toolset with other great utensils.
Today's blog post will introduce a tool, which lets you leverage the benefits of data science without being native in coding: KNIME Analytics Platform.
KNIME (Konstanz Information Miner) provides a workflow-based analytics platform that enables you to fully focus on your domain knowledge such as fundraising processes. The intuitive and automatable environment enables guided analytics without knowing how to code. This blog provides you a hands-on demonstration of the key-concepts.
Important KNIME Terms
Before we start with a walkthrough of a relevant example, we need to declare some of the most important KNIME terms.
Nodes: A node represents a processing point for a certain action. There are many nodes for different tasks, for example reading a CSV-file. You can find further explanations about different nodes on Node Pit.
Workflow: A workflow within KNIME is a set of nodes or a sequence of actions you take to accomplish your particular task. Workflows can easily be saved and used by other colleagues. Even collapsing workflows into a single meta-node is possible. That makes them reusable in other workflows.
Extensions: KNIME Extensions are an easy and flexible way to extend the platform's functionalities by providing nodes for certain tasks (connecting to databases, processing data, API requests, etc.).
Sample Dataset and Data Import
For demonstration, we are going to use a telecom dataset for churn prediction. This dataset could be easily replaced with a fundraising dataset containing information about churned donors. Customer or donor churn, also known as customer attrition is a critical metric for every business, especially in the non-profit sector (i.e. quitting regular donations). For more information on donor churn, visit our previous blog posts.
Consisting of two tables, the dataset includes call-data from customers and a table about customer contract data. While using some of the information available, we will try to predict whether a customer will quit his subscription or not. Churn is represented with a binary variable (0 = no churn, 1 = churn). For visualization purposes, we are going to use a decision tree classifier, although there are probably even better classification algorithms available.
First, we are using an Excel Reader and a File Reader to import both files. To make things easier, we use a Joiner node where we join both tables based on a common key. The result is a single table now ready for exploration and further analysis.
Feature Engineering
Feature Engineering is the process of analyzing your data and deciding which parameters have an impact on the label we want to predict – in this case whether a customer will quit or not. In other words: Making your data insightful.
But before we look at correlations between the label and the features, a general exploration of our data is recommendable. The Data Explorer node is perfect for some basic information. One thing we notice is that we need to convert our churn label to a string, in order to make it interpretable for our classifier later. This can be done with the Number to String node. Now it's time for some correlation matrices. We are able to see some correlation between various features and our churn label, whereas others do not correlate really. We decide to get rid of those.
Model Training
Now let's start with the training of our model. But before we can do that, we need to partition our data into a training- and test-dataset. The training-set (mostly around 60-80% of our data) is used to train our model. The other part of our data will be used to test our model and to make sure it has prediction power. We can verify this with certain metrics. In this case, we will set the partition-percentage to 80%, which seems to be a good amount. This data will be fed into our decision tree learner.
After some computing time, our finished decision tree looks like this:
In order to make the model reusable and available for predictions with new datasets, we can save it with the PMML Writer node for later use. PMML is a format for sharing and reusing built models. If we want to, we can read the model later on with a PMML Reader node to make predictions with a new, unknown dataset. But before we use our model on a regular basis, we need to evaluate it with our test-dataset, which we split earlier.
Model Prediction and Evaluation
Now, testing our new model and evaluating its performance is one of the most important steps. If we can't be sure that our model will predict right to a certain extent, it would be fatal to deploy it. So we feed the Decision Tree Predictor node with our test-dataset. This lets us see how the model performed.
We have certain metrics within our KNIME workflow to fully evaluate it. First, we are using a Scorer node to get the confusion matrix and some other important statistics. Our confusion matrix gives us a little hint about threshold tuning, but the accuracy with 84% looks already pretty good. Our model predicted 29 cases as 'no churn', although they were actually 'churning'. This number is rather high, so we should consider tuning our model parameter.
Next up is the ROC (Receiver Operating Characteristics) Curve. It maps True Positive Rates and False Positive Rates against each other. One of the results is the AUC (Area under the Curve) which adds up to a very good score of 0.914. A score of 0.5 (the diagonal in the chart below) represents prediction without any meaningfulness, because it means predicting randomly.
Additional metrics would be the Lift Chart and Lift Table, but an explanation would be beyond the scope of today's blog. We think it's time to summarize and draw a conclusion.
Too good to be true?
KNIME is a powerful platform, which provides various possibilities to extract, transform, load and analyze your data. However, simplicity has its limitations. In direct comparison with various R packages, visualizations are not as neat and configurable. And the 'simplistic' approach to data science limits possibilities in some way or another, requiring the user to have a thorough understanding of the data science pipeline and process. Further, most real life cases are more complicated and need more feature engineering and analysis beforehand – creating the model itself is mostly one of the smallest challenges.
Nevertheless, we think that KNIME is an awesome tool for data engineering / exploration and workflow automation (and building fun stuff with social media and web scraping). But if you are looking for complex models supporting your business decisions – KNIME won't probably be the platform you are searching for.
We hope you liked this month's blog post and we would love to get in touch if you are interested in achieving advanced insights with your data or just want to dive deeper into the topic. If you want to know what Joint Systems can offer you concerning data analytics, this page will provide you with more information.
Using Open Data for Fundraising
In last month's blogpost, we referred to the impactful Economist article stating that data is the new oil. One interpretation of this metaphor is that data can be seen as "fuel" for today's economy. This also applies to the nonprofit sector. The reference to oil does, however, not necessarily mean "digging for data" implies high costs in terms of acquiring and collecting it. There is quite some open data around which might be useful in a fundraising context. We see a broad range of possible use cases when it comes to open data in a fundraising context:
We will rush trough two hands-on examples to illistrate how to obtain, process and visualize open data with possible value for fundraising decision makers and analysts.
Example 1: Visualizing income data on regional level
Geographical disparities can be of relevance in the context of certain fundraising practices such as events or contact to High Net Worth Individuals (HNWI). Some regions are "wealthier" than others in terms of the respective average income levels. This information allows some conclusions about overall fundraising potential in a respective area.
An aggregation level that we find useful and a good "common denominator" are the so-called NUTS regions the European Union uses. NUTS sounds like an English acronym, however, it is a French abbreviation and for Nomenclature des unités territoriales statistiques, in other words regional stat units.
The European Union´s Statistics Office is called Eurostat. They offer a huge database that can be accessed online and is free of charge in most cases. We download not only data on the regional distribution on the level of NUTS2 areas but also use the specific R package eurostat. NUTS2 areas are quite "intuitive" in our eyes. In Austria, for instance, they reflect the federal provinces whereas Germany with its 16 provinces is split into 38 regions. For reasons of completeness, we show you how we searched for respective income data in the code snippet below. Our code is by the way inspired by this recommendable tutorial on eurostat.
Code Snippet 1.1: Load libraries and search for data
The query above shows us 3 tables that contain income data. We decided to use the table with the index tgs00026, it contains data on disposable household income on regional level.
Code Snippet 1.2: Obtain both income and geospatial data
We now have two dataframes, one for the income data with a regional variable and one for the actual geospatial data. We merge the two and dive into the visualization immediately:
Code Snippet 1.3: Merge datasets into one and visualize data
As we already used a little French today, we are now able already to say voilà as the overview visualization we were striving for is finised and presentable:
Example 2: What about large companies and their CEOs?
The name Forbes might ring a bell if you think of listing the wealthiest people on the planet. Forbes also publishes data on the largest companies on an annual basis. We signed up at the platform data world - which we can also recommend - and obtained the 2018 dataset for the 2.000 largest corporations (here - signup is necessary). Luckily this data contains country information and also names the respective CEO - but step by step. As we are working with a flatfile, we did some data prep in it before loading it into R; we end up with a dataset that contains the following variables:
Code Snippet 2.1.: Read the file and select one country
So far, so good. We now have a condensed dataframe in R that contains the Dutch corporations that are listed in the Forbes 2000, i.e. 22 firms. We prepare a bubble chart with the company revenues and profits on the axes. The market value (mostly in shares) shall be reflected by the acutal size of the bubble.
Code Snippet 2.2.: Draw the bubble chart
This is our result:
We can also spot a cluster of companies with high profit volumes in the lower right of the bubble chart. This cluster contains "big names" like Unilver but also contains firms that are not as widely known:
Even though it might not be so easy to meet some of the CEOs immediately, it might be worthwhile researching whether those big and powerful corporations have CSR departments, foundations etc. one can get in touch with.
This was it for this month´s post. We hope you stay "open" over the summer break not only towards data but also our blog :-).
It was in 2017 when the renowned magazine The Economist wrote: "The world's most valuable resource is no longer oil, but data."
We set up our blog askyourdata.co in the same year. The topics of our articles vary from illustrative applications of models to fundraising data, discussions of topics like Artificial Intelligence for Fundraising to data visualization. The common denominator for our content is the context of charitable non-profit organizations. We think that the tools and methods of advanced analytics and data science can contribute to the effectiveness and efficiency of fundraising organizations. This is why we started wondering whether there are empirical findings on the state of data science and advanced analytics in data science. Good news: We found results that are both recent and interesting.
Data Science starts with data. Back in 2016, the American software company EveryAction surveyed some 460 professionals (presumably mainly in the US; the complete study can be downloaded here) from non-profits about their habits, culture, and outlook on the state of data at their organizations. Some key findings:
A bit more than two-thirds of the respondents conduct ad hoc or (ex post) hindsight analysis (descriptive, diagnostic) whereas the remaining third has stepped into advanced analytics (predictive, prescriptive, AI / cognitive). Having mentioned the term "advanced", one should not forget about the speed of developments. What might have been considered "advanced" a few years ago might turn into the "standard level" sooner or later.
The probably most interesting insight from the survey is that respondents with more advanced analytics capabilities reported higher effectiveness than others on three important metrics (Improve performance against mission, Achieve operating efficiencies, Improve staff productivity). In other words: Non-profits with deeper data capabilities see stronger impact, transparency and decision making.
Like the aforementioned study by EveryAction, the IBM survey asked for primary barriers to advancing data and analytics. These are:
What about data science in the non-profit sector? We did not come across specific studies on the state of data science in the non-profit sector in general or fundraising more particularly. However, we had a closer look at the industry segmentations in two major data science surveys.
JetBrains polled more than 1.600 people involved in Data Science in the US, Europe, Japan and China (download full study here). One of the questions asked for the industry for which the respondents analyse data. The non-profit sector ranked quite low (5% compared to Accounting / Finance / Insurance and Science with both 16%). This finding can be seen as consistent with the results of the aforementioned studies that diagnose scope for advances in the field of non-profit analytics.
The presumably most comprehensive study on the state of data science and machine learning is the annual Machine Learning and Data Science Survey conducted by the platform kaggle.com. Kaggle surveyed almost 24.000 people in October 2018, the results are therefore quite recent. In line with open data thinking, the raw data containing the survey results can be downloaded from the site, there is also a data analysis competition attached to it.
The non-profit related results in a nutshell: Of some 24.000 respondents, only a tiny fraction of 0.79% (189 respondents) answered that they work or recently worked for an employer in the non-profit industry. Regardless of the fact that the sample might not be fully representative across industries, this figure shows that data science is still in its beginnings in so-called third sector.
When it comes to the regional distribution of non-profit data scientists, it is striking that a third of respondents that said that they work for nonprofits reside in the United States. India is the runner-up with some 10 percent. The numbers of the remaining countries with respondents is quite evenly distributed.
Our conclusion: Non-profit representatives and decision makers are largely aware of the potential benefits that use of advanced analytics and data science might imply for their organizations. Building competencies, structures and systems is often challenged by scarce resources (most prominently budget but also expertise). The answers regarding the presence of data scientists surveyed by JetBrains and Kaggle reflect this state. The good news is: Nonprofits need not feel alone in their advanced analytics endeavours, as outlined by IBM who advocate establishing an insight ecosystem.
If you are interested in building your own insight ecosystem, you might wish to learn more on what joint systems can offer in this regard.
Let us stay in touch! We wish you a nice rest of spring.
Visualizing data is an integral part of analysts´ and data scientist's day-to-day life. Visualizations are not produced for the sake of beauty and design – at least not exclusively. One could say the common denominator for data visualization is to make it easier to process information for the human brain and therefore for the recipients. This might lead to better decision making (this is what is often called actionable insights), meaningful storytelling (e.g. in the area of data journalism in general but maybe also particularly in the context of NPOs) and the increase of so-called data literacy. One may like it or not but we live in a highly quantified society which means that often also "non-quantitative" professionals across industries are required to consider data.
The final product that analysts are asked for by recipients are often charts and infographics. Visualizations also play an important role in the course of data science projects. In line with CRISP DM thinking, it is often data visualizations that help develop the so-called data understanding. Modern tools such as good old Excel or more integrated and holisitc solutions like Power BI make it possible to process large amounts of data from different sources with relative ease and in short time. We can therefore draw a preliminary conclusion: The need for visualization of data will persist and steadily grow, modern tools make life significantly easier in this regard. But what does it actually mean do come up with good data visualizations?
The good news is: There are various sources and thinkers one can turn to get inspirations and recommendations in the context of data visualization and information design. In this month's blog post, we will take a close look at the work of Edward Tufte. Tufte is an American statistician and professor emeritus of political science, statistics, and computer science at Yale University. He is one of the most influential contemporary thinkers in the field of information design and data visualization. The New York Times went as far as to call him the "Da Vinci of Data" in 1998. More than 35 year ago, Tufte published the first edition of The Visual Display of Quantitative Information which has become a classic on statistical graphics, charts, tables. Tufte is also known for some easy to remember quotes such as:
"If the statistics are boring,
then you've got the wrong numbers."
Tufte has coined the idea of Graphical Excellence. Graphical Excellence means the efficient communication of complex quantitative ideas towards recipients. This requires clarity, precision and efficiency. What does efficiency mean in this context? The viewer should be given the greatest number of ideas in the shortest time with the least ink in the smallest space. You could say this is the application of a minimalist and "less is more" philosophy in the context of data visualization.
Graphical excellence is the well-designed presentation of interesting data - a matter of underlying data, of statistics and of design. One could say in data visualization, data is not everything but without the appropriate data, everything is nothing. Data and the messages derived from it have to be correct. Tufte uses the term integrity in this regard. There is a plethora of sources on how to lie with statistics. Data has to be relevant for the respective viewer. As mentioned above, Tufte went as far as to say that if the statistics are perceived as boring, then you've got the wrong numbers.
When it comes to design, Tufte suggests the following things that graphics should do:
"Design cannot rescue failed content!"
... is another striking quote by Tufte. We tried to put togehter an interesting (hopefully!) slideshow with some "evergreen" data visualizations and inspiring works from the recent past.
Have a look, enjoy and read you next time!
Recently, the average media consumer could get the impression that Artificial Intelligence (AI) is the next big thing and that it will coin our daily lives in the near future. Numerous developments are currently discussed in newspapers and magazines, TV debates, blogs etc. Some of these developments are said to bring about significant, sometimes even radical changes to different industries, be it health, finance, production etc. The question is:
Will charitable fundraising remain unaffected by these big shifts and everything will stay as it is? We have our doubts but definitely see the need for a differentiated view at the same time. This month's blog post will provide a quick introduction to artificial intelligence and reflect on it current and possible future role in the context of fundraising.
What is Artificial Intelligence
So, what is Artificial Intelligence? Even a quick web research delivers numerous definitions of which many show certain overlap. Let us proceed with one definition by digital evangelist Ray Kurzweil who said that AI ist the art of creating machines that fulfil tasks that – if they were carried out by humans – would require intelligence. The pictures that come to people´s minds should not be underestimated when it comes to a broader understanding of AI. It can be assumed that many think of so called strong artifical intelligence when they hear the term AI. Strong artifical intelligence implies by definition machines that are actually intelligent just like Data from Star Trek, C3PO from Star Wars or Bender from Futurama. The real-life form of artifical intelligence is so called weak artifical intelligence, machines that show intelligent behaviour to some degree. Weak AI essentially means rule-based systems that have capacity for machine learning.
AI history in a nutshell
The beginnigs of AI go back to the early days of modern information technology. You might know Alan Turing from your studies and/or the movie The Imitation Game (showing how Turing and his fellow experts significantly contributed to the Allied victory in WWII). He suggested a test now named after him to find out whether the respective opposite is a machine or a human.
In the same year, Isaac Asimov wrote the novel I, Robot and suggested the Three Laws of Robotics. Asmiov´s work is a good example of the overlap between technoclogical advances in AI and their dramatization in popular culture.
From the beginning oft he 1950ies onwards, AI technology evolved gradually, It was, for instance, as early as 1974, i.e. 45 years ago, when the Stanford AI lab introduced the first prototype of a self-driving car. A growing audience saw Deep Blue beat chess world champion Gary Kasparov in 1997. A bit more than a decade later, in 2011, Siri, Google Now und Cortana were introduced almost at the same time.
What about the future? There are developments than can be forseen. Sooner or later self driving cars will be introduced, translation algorithms as well as image and text recognition will gradually become better etc. When it comes to the long-term perspective, there are different positions. Elon Musk, from Tesla for example goes as far as to term AI as a potential threat to the existence of the human race. Andrew Ng, a machine learning evangelist, says that fearing the rise of killer robots is like worrying about overpopulation on Mars.
What are the functions of AI?
To put it in an anthropomorphic, i.e. human-like sense, AI can nowadays do the following:
From a more functional perspective, AI capabilites can be summarized as follows:
It has to be moreover noted that AI is not a monolithic and distinct technology but a bundle of different technologies, methods and algorithms. This is reflected by the different sub-areas:
Although AI is an ambiguous field, there are certain research areas and big topics that can be identified:
What AI is able to do - and how it might change the game
Many think tanks, consultancies and companies deal with the future potential and development of AI across industries. A quite recent study by Mc Kinsey deals with the impact of AI on different sector and functions.
AI can do an impressive lot of things nowadays, be it agriculture, medicine, finance, law etc. Business Insider have put together a compact list with some 50 examples, all with a link for further reading.
Expectation levels are definitley high when it comes to the potential of AI– which brings about the risk of exaggerated hopes and fears at the same time. Inspiried by a blogpost on datasciencecentral, a blog we can really recommend, we find it worthwhile to reflect on 6 common myhts regarding AI:
So what? The case of charitable fundraising organizations
To put it very generically, FR organizations can be seen as entities that link needs (be it children at risk, endagered animals or the environment) with supporters. As a consequence, there are three major pillars – the need, the oranization and the supporters (or, to be exact, the communication and interaction with them) in which AI might contribute.
The need
We found three inspiring examples from different organizations how AI – also at small scale and in a hands-on manner – can contribute close to the need of charitable organizations.
The organization and its processes
This is the "internal" view and it is therefore hard to find promiment good practice examples on the web. Generally speaking, AI has the potential to help improce payment processes, fulfilment processes, etc. These processes are often rather "generic" to a certain extent and therefore comparable to the profit sector – which is an opportunity when it comes to available tools, expertise etc.
Donor Communication and Fundraising
Many topics we have covered in this blog so far, be it churn analyses, donor clustering, dealing with unstrcutured data, data visualization etc. can be attributed to artificial intelligence in a broader sense and can be potentially applied in afundraising context. AI in fundraising might therefore mean using advanced algorithms and data science methods for donor data.
There are also other applications of AI in todays fundraising context.
The American version of Amazon's Alexa is now able to trigger donations through voice commands. Numerous organizations have started using chatbots to enhance their touchpoints with donors.
So, what to do now? Public Enemy already rapped "Don't believe the hype" in 1988. So, should the fundraising sector lean back and watch other industries chasing after supposed AI innovation? We do not think so - but instead of thinking about big investments in the first place, we find it recommendable gradually start building know-how, run tests and create prototypes at a smaller scale. If you need support or resources, get in touch with a reliable partner that knows your organizations and whom you have a sustainable relationship with. As far as conferences, blogs, books etc. about AI are concerned, there is nothing that holds one back - so why not start dealing with AI for fundraising in 2019?
Speaking of 2019: As the year is still quite young, we wish you and your colleagues a happy, healthy and successful new year!
P.S. for joint systems clients: This blog post is an extract of a keynote I have delivered recently. Please do not hesitate to get in touch if you are interested in diving deeper into the topic or learning about our service and product portfolio.
Have you heard about Giving Tuesday? Giving Tuesday is the equivalent to Black Friday and Cyber Monday – but for charitable donations. It was started in 2012 during the American Christmas and holiday season. Thanksgiving is traditionally the fourth Thursday in November, so Giving Tuesday is four days after, i.e. in late November or early December. This year, Giving Tuesday was November 27th, 2018. Having started some years ago, not only the amount of funds collected in the US has continuously risen but also the popularity and perceived relevance of Giving Tuesday for fundraising organizations all over the world. So, is Giving Tuesday a global movement already? We decided to investigate this by analysing Twitter data using the statistical software R.
Obtaining data from Twitter
To scrape data from Twitter, one has to apply for a Twitter developer account since July 2018 and create a Twitter app which has to be approved by the platform. You of course also need a Twitter account for that. We went through this quite simple process in which certain information on one's plans have to be provided.
We used the package rtweet to set up the connection from R to Twitter. After having successfully registered a Twitter app, it is recommendable to embed the necessary credentials (Consumer Key, Consumer Secret, Access Token, Access Secret) in the code.
After having established the connection, we used the command search_tweets to obtain the tweet data about Giving Tuesday. We started with a search number of 100.000 tweets without retweets. Due to restrictions of the Twitter programming interface (API), setting the parameter retryonratelimit to TRUE allowed for re-connects and stepwise data download.
Code Snippet 1: Get rtweet package, connect to API and obtain data
We ended up with 53.653 records in the data frame rt. As the R package in combination with the Twitter API allows the data extraction of the last 6 to 9 days, our data pretty much reflects the Twitter activities regarding Giving Tuesday 2018.
Drawing upon our initial question, we now turned to researching how "international" Giving Tuesday has become. The package rtweet allows the extraction of longitude and latitude data for the respective tweets. This then enabled us to draw a world map of tweets. A warning message told us that no such data could be obtained for 51.000 of the 53.000 tweet records. Regardless of that and assuming that the 2.000 tweets for which we got the data are somewhat representative, we used the package leaflet for a plot. It is visible, that tweeting about Giving Tuesday predominantly happens in the US and the UK.
Code Snippet 2: Map
In contrast to scarce geographical information, language information was mostly complete for the tweet data and therefore allows us to the analyse the international relevance of Giving Tuesday even better. The vast majority of tweets is English. Apart from some 500 tweets with undefined language, French and Spanish are the runners-up.
Code Snippet 3: Barplot languages
What about the timing of the tweets? We extracted the creation dates of the tweets and visualize them in a barplot. Non surprisingly, the 27th of November, i.e. Giving Tuesday itself shows by far the highest number of tweets. There is also notable activity the day before and after with some 6.000 tweets.
Code Snippet 4: Barplot Dates
In order to analyse the virality of the Giving Tuesday tweets, we measured and plotted the number of retweets. Our plot below shows that some 38.000 tweets did not get retweeted at all, some 8.000 received 1 retweet and so on (up to 6 retweets).
Code Snippet 5: Barplot Retweets
We then extracted and ordered the respective data to find out which one was the most viral tweet in the dataset. It is by Fred Guttenberg, an American activist against gun violence. His 14-year-old daughter Jaime Guttenberg was killed in the Stoneman Douglas High School shooting on February 14, 2018. This is his tweet:
Our example shows on the one hand that social media analyses can be conducted relatively easily, on the other hand we saw that Giving Tuesday is about to become a truly global phenomenon. So prepare for Giving Tuesday 2019 which might fit well into your Christmas fundraising. Speaking of Christmas:
On behalf of joint systems I wish our dear readers a very merry Christmas 2018 and a good start into a successful, healthy and happy 2019 - read you there! :-)
Clustering means grouping similar things together. In this month's we take a closer look at how the so called k-means algorithm as a clustering method might help to develop an even deeper understanding of established donor segments.
Many fundraising organizations segment their donor bases to enable target-group oriented communication and appeals. Working with segments also allows the development of more differentiated analyses on group migrations and income development. The example organization whose data we are playing around with has an up-and-running segmentation model that evaluates the payment behaviour of donors in terms of Recency (when was the last payment), Frequency (how often payments are made) and Monetary Value (sum of donations). This RFM method is commonly used in database-related direct marketing, both in the profit and non-profit sector.
We take a closer look at the best donor group in the following. The segmentation model already tells us that the group under scrutiny consists of "good donors" in terms of payment behaviour. In this regard, the segment as such can be seen as a homogenous group. Can a clustering algorithm like k-means generate additional insights regarding the "inner structure" of this group?
K-means is an unsupervised machine learning algorithm. Unsupervised learning data comes without pre-defined labels, i.e. any kind of classifications or categorizations are not included. The goal of unsupervised learning algorithms is to discover hidden structures in data. The basic idea of k-means is to assign a number of records (n) into a certain number of similar clusters (k). The number of clusters k is either pre-defined or can be jointly defined by the respective analyst / fundraising manager.
The following walktrough was inspired by Kimberly Coffey's blogpost on k-means Clustering for Customer Segmentation. It is a highly recommended read and can be found here.
Data extract and preprocessing
Our example organization has an established RFM-based segmentation model that yields 4 core groups. We defined the "best" of those groups to be subject for a k-means clustering attempt. The dataset we extract is straightforward in the first place as it contains the unique identifier, the Recency measured as number of days between December 31st, 2017 and the day of the last donation per person. Frequency reflects the number of payments for the respective record in 2017 whereas Monetary Value shows the donation sum.
K-means clustering requires continuous variables and it works best with (relatively) normally-distributed, standardized input variables. We therefore apply a logarithm (log) to the variables and standardize them to avoid positive skew. Standardizing variables means re-scaling them to have a mean of zero and a standard deviation of one, i.e. aligning them to the standard normal distribution. The following code snippet illustrates the loading and transformation of the data.
Code Snippet 1: Load packages and data, then transfrom data
This is how our dataset looks like the aforementioned transformation. Columns 5 to 7 contain the logs of Recency, Frequency and Monetary Value, whereas the standardized (z-)values are in columns 8 to 10.
A quick exploratory view
To intially dive into the data, we plot the log-transformed Monetary Value as well as log-transformed Frequency of donations and use the log-transformed Recency for colouring by using the code in Code Snippet 2.
Code Snippet 2: Exploratory plot of RFM variables
What is striking is the high general density of observations on the one hand (which is due to the large amount of data) and the different shades of blue that reflect certain heterogeneity in terms of Recency.
Running the K-means Algorithm
We now turn to running the k-means algorithm. The following code contains a loop that runs for a number of j clusters (in this example 10). It writes the cluster membership on donor level back into the dataset, creates two-dimensional plots (see example for 3 and 7 clusters below) and collects the model information.
Code Snippet 3: K-means clustering
These are two output examples of the code using 3 and 7 clusters.
So how do we choose the "optimal" number of clusters now? The graphs below both aim for the detection of the number of clusters beyond which adding a cluster adds only little additional explanatory power. In other words we look for bends in the so-called elbow chart. In our example it looks as if this would be at 4. The same decision could have also been made or at least influenced by a business decision regarding the "feasible" number of clusters. Adding additional clusters always adds explanatory power, however, in practice 4 groups are easier to handle (e.g. in the context of a direct marketing test) than 10 or more clusters.
Results and interpretation
Let us now take a closer look at the results. Clik on the picture on the left to get to an interactive 3d-graph of the 4-cluster solution for which the R-code can be found below. The 4-cluster solution yields 4 ellipsoids aiming to reflect the areas with high observation densities for the clusters. These ellispoids should contribute to the ease of reading the graph, the actual observations are still represented by differently coloured dots just like in the 2-dimensional plot we used for exploration. The three "upper clusters" in the picture share a comparable level of Monetary Value and Recency. The dark blue ellispoid stand out of the three as it reflects higher Frequeny. The lower ellipsoid reflects observations that rank relatively low on all of the three RFM variables (remember, the higher the recency, the "worse" - knowing that we are working with a dataset of good donors). The video below contains a fixed-axis rotation.
Code Snippet 4: 3d graph
So, what can we conclude from our clustering attempt? K-means is a widely-used and straightforward algorithm that can be applied relatively easily in practice. It is, however, worthwhile to dive into the underlying concepts of the algorithm and consider the related diagnostics (variance explianed, "withins"). Due to the derived possibilites in terms of data visualization, results can be directly communicated to fundraising decision makers. These decision makers should be involved in the process at an early stage. Although there are "objective" measures for the numbers of clusters, application-oriented considerations (e.g. for further analyses of test designs) should not be left out.
We hope you liked this month's post and wish you a nice beginning of autumn. Do not hesitate to share, comment, recommend etc. Read / hear / see you soon!
Februar 2020


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It's been a crazy week, thanks to it being yet another competition week, coupled with family stuff (Spring Break!), final copy edits for ONE HOT SECOND, and a car that has the hiccups. When it rains, it pours, right? I didn't get Teaser Tuesday in (too overwhelmed to remember it until Wednesday, heh), but I've got Thursday's throwback post ready and waiting. ^_^
To celebrate the conclusion of The Earth Angels miniseries with DANGEROUS ANGEL's release on March 31st, I'm sharing bits and pieces of the series from the three previously published books. Here's the opening chapter of WOUNDED ANGEL, the third book from the four-part miniseries. Ella Little may have been the most complex heroine I've ever created, which means she's one of my favorites. How else can I feel about a character when I had to consult a psychologist just to make sure she was pieced together right? :D Enjoy!
Chapter One
"Do you want to be helpless? Are you happy with the idea of being someone's prey? If so, quit now and let the pain win. Quit, I say! But, if you refuse to be a victim, let's do another five reps!"
Deaf to the heavy metal beat thrumming through the kickboxing gym, Ella Little crouched in front of the long black bag she'd already beaten to within an inch of its life, and went through the drill for what felt like the millionth time. The combination was a tricky one—three sharp left jabs and a fast uppercut right, then into two side knee strikes, aiming for the ribs or kidneys. Next was a flurry of half a dozen close-in body punches and an elbow strike before a front kick to gain space, then finishing off with a pivoting roundhouse kick to the head.
Balance, speed, power. A kill shot in each hit. In order for it to be effective in real life, putting deadly intent behind each hit was the way it had to be trained.
Within The Body Electric's cavernous kickboxing training room—or the Doom Room, as the more dedicated gym members called it—people were dropping like flies. No surprise there. The trainer was Jacob Braun, perpetually rabid and an agent of Mossad before deciding to retire to Chicago to torture its unsuspecting residents. He was in fine form this blustery March evening; with his eyes bulging and veins popping, he looked like a man who'd missed his daily dose of anti-psychotics. His salt-and-pepper hair was shaved so that it was little more than bristles, and he didn't need the microphone headgear as he screamed his peculiar brand of motivation while his mad eyes swept the room for easy prey. Ella hardly noticed her co-worker and friend as he pounced on a flagging gym member, far more intent on letting the power sing through each fluid punch and kick.
Though she loved her job as a personal trainer and self-defense instructor at The Body Electric, this was Ella's favorite part of the week—kickboxing with Jacob. Where else was it legally possible to let her violent flag fly, and even better, have it encouraged? The front kick snapped out, zinging all the way up to her hip, and she couldn't stop the fierce smile. That one would have buckled an opponent's knee, no problem, and that knowledge shot satisfaction through her system. That was what this class was all about—survival. Survival of both Jacob's wild-eyed instruction, and survival of the darkness that could swallow a person whole.
The kind of darkness that had once done its best to swallow her.
Man, she was tired.
To keep her mind off the lava-hot burn of lactic acid in her quads, biceps and deltoids, Ella tightened her pink-gloved fists and let her gaze wander to her neighbors. One of them was bent at the waist with his hands on his knees in a position that showed he was either in the process of regaining his breath or about to hurl. Another was barely managing to lift her foot high enough to reach the bag. Jacob would make them pay for their weakness.
Her bag jounced with the impact of her kick, and its movement coincided with another bag off to her right and ahead a few rows. Someone else was determined not to incur Jacob's wrath, if the bag's violent dancing was any indication. Her attention drifted to the man working the bag over like it had done him dirty, and her well-oiled rhythm skipped a beat.
He was a newcomer to their gym, but certainly no newbie to working out. She was sure he was new, because even if the traumas in her former life hadn't locked her emotions up inside and thrown away the key, she still would have noticed this living testament to testosterone-infused eye-candy the moment he walked through the doors. It was impossible not to notice him. The expanse of his shoulders bared by a tank top could have given Atlas a run for his Titan-y money. The rest of him was just as spectacular; he was a well-proportioned giant in a world that appeared to be toddler-sized by comparison. Like her, his black hair was damp with sweat, and long enough to tumble in waving abandon over his forehead. His sharply angled brows hooded intense eyes that even from this distance she could see were the color of onyx. There was a leanness to his face that granted him a hungry look, with high cheekbones shadowing concave cheeks darkened by a five o'clock shadow, and the faint sneer snarling his upper lip would have done an Elvis impersonator proud. If anything, he looked like he wanted to tear his punching bag a new one and was ready to do it with his bare—
"Are you enjoying your daydream, Ella?"
Her attention snapped back to reality so hard she thought it might have made a noise. With a sinking heart she realized that while she was checking out the vision of sweat-slick masculine perfection a few bags over, Jacob had prowled toward her with all the accuracy of a shark aiming for blood in the water.
"No daydreams here, Jacob." With renewed focus she attacked the bag in hopes of impressing him. "Just working the reps."
"And do you know what rep we're on?"
"Of course I do." She could bluff with the best of them.
"What number?"
Shit. "Four?"
A vein pulsed down the middle of Jacob's corrugated forehead. "You're on your sixth rep, which must mean you feel I'm not working you hard enough. Do you think I'm not working you hard enough, Ella?"
"Thank you for the suggestion. Everyone, Ella feels we should do more, so that's what we're going to do. Five more reps, double-time!"
It was a wonder she didn't fall dead under all the lethal stares.
At long last the torture came to an end. The man who had been bent over stumbled from the room while a few others simply dropped where they stood. Ella didn't bother to look around to what the newcomer was up to; in all probability he was wishing her six feet under like the rest of the class.
"I take it you really like kickboxing?"
In the process of toweling off her face and wishing she could strip out of her high-necked, long-sleeved black compression shirt before she passed out from heat exhaustion, Ella whirled around. Every nerve kicked into high gear as she zeroed in on the man she'd noted earlier, now only a handful of feet away. First Jacob, and now a stranger had snuck up on her. That went against her main survival rule of always being aware of who was around her. This could not be allowed.
"I suppose." Snappy comebacks weren't a consideration when her touchy ideals of personal security were compromised. In a world that was far more dangerous than it appeared, getting caught off-guard was an absolute no-no. "Sorry about the extra reps."
His smile was a slanted work of art, designed for the sole purpose of staggering the planet's female population. "Don't be. I need the work, and this was a fun way to do it."
"I don't think fun would be the word everyone would use to describe Jacob's advanced kickboxing class."
"It's all in the motivation. Take me, for instance—I'm not happy unless I'm pushed to my limit. Not that I'm a big believer in limits."
Somehow this wasn't a shocker. "I think a few people in class hit theirs."
"You didn't. You were hitting just as hard at the end as you were at the beginning."
So he'd noticed her. Ella had no idea if this was a good thing or bad thing. "I'm a trainer here at The Body Electric, certified in strength and conditioning, self-defense and sports medicine. If I can't take whatever Jacob dishes out, I don't deserve to work here." Then she closed her mouth with a click. Good grief, it must be her evening for breaking personal rules. Information meant power, and personal information gave power over her to people who had no right to it. Yet she'd just offered up a cartload of her new life to a perfect stranger like she didn't know any better. It was like she'd forgotten every stay-alive lesson she'd picked up in the past two years.
Who knew she was such a sucker for a pretty face?
"A personal trainer, huh?" His knockout smile widened, and she made herself look to the task of folding her towel before she blurted her Social Security number and cup size. "That explains it. No wonder I couldn't keep up with you."
Ella had to bite her tongue to keep from assuring him that he'd kept up just fine. If she did that, he would know she had been watching him, and that would lead to a conversational brick wall. She didn't want him to know she'd been aware of his existence. She didn't want to notice him, period.
Disgusted with her tangled thoughts, she tried to appear professionally aloof as she back-pedaled in the direction of Jacob, snatching up the gym bag that doubled as her bug-out kit. Packed with all the essentials she'd need in an emergency that covered everything from earthquakes to zombie attacks, she never went anywhere without it. "Take a few more of Jacob's classes and you'll be up to speed in no time. Have a nice evening."
"Bye now." As she turned away, she winced at the faint wisp of the South rolling through the words, overcoming her carefully crafted Chicago-Midwestern monotone. Not good. Tiny imperfections like that might not mean much in the grand scheme of things, but those little things added up. If she wasn't careful, she'd find herself having to pull up stakes before someone found her.
Out of the corner of his eye, Nate watched as the woman made her retreat. She looped her large gym bag's shoulder strap across her body, all the while talking to the bristle-haired sadist who'd ground his kickboxing class into the dust. She was an animated little thing, gesturing with graceful hands as she offered up what looked to be a heartfelt mea culpa with big, dark puppy eyes.
Pretty eyes, certainly. Too bad they weren't the color he remembered.
Bent over his own bag, he wrapped his towel around a holstered semi-automatic pistol, usually secured at the small of his back but now hidden in the bottom of his gym bag. But his attention never veered from his target, watching her out of the corner of his eye and cataloguing every detail. Aside from her gender, her height was about the only accurate thing about her—somewhere around five-six or five-seven. Her build was vastly different from the woman he'd met two years ago. This woman was built like a long-distance runner—all long, lean limbs and finely toned muscle covered in form-fitting workout gear. According to his memory, the woman he was looking for had more meat on her bones. That was definitely his personal preference.
And when she'd looked at him, there had been no recognition. He'd done his best to catch her off-guard, but when her gaze had met his there had been no telltale reaction at all. At least, not on her end. He, on the other hand, was still recovering.
Keeping her in his peripheral view, he rubbed an absent hand over his brow. The way she'd nailed him with that unwavering, bring-it-on regard sent an electrical jolt of hot awareness through his system, momentarily short-circuiting his brain. Beauty he could handle—she was a knockout, no doubt about it—but the power to throw him for a loop with a single glance was flat-out stunning. There was nothing about her that he didn't find appealing; he even liked the lyrical way she moved, as though gravity didn't affect her as much as it did everyone else. The way she wore her hair, boyishly short and asymmetrical, made him want to brush the long black bangs out of her face so he could look into those puppy eyes.
Again, pretty hair. And again, wrong color.
Maybe he was wrong. God knew it wouldn't be the first time.
The kickboxing instructor and the woman headed toward the heavy double doors that led back to the brightly lit main reception area, the hub of the sprawling fitness center. In unhurried movements he did the same, slinging his bag over his shoulder as he went.
"…never forget, you don't have to worry about that."
"Then what was the first lesson I taught you, girl?" The older man's voice, rough from screaming like a bloodthirsty maniac for the past hour, was now calm enough for Nate to pick up on a faint accent. Something foreign, exotic. "Always be aware of your surroundings. Be an animal. They have better instincts than humans. This is what you must be."
"I know that." The woman's voice came as a surprise—cold and hard as steel. Of all the things that didn't quite mesh with the woman he was looking for, that deadly tone was the one thing that fit like a glove. "Stupid me, I allowed myself to feel safe in your class."
"You are safe when I'm around," came the automatic response. "Naturally, you will be safe here. But this lapse…"
"It's unacceptable. I agree." Then she stiffened before she shot a deliberate glance over her shoulder, pinning him to the spot—proof that she was more aware of her surroundings than her companion. "Ah… Sorry. Are we blocking your way from getting out of here?"
Translation—stop lurking behind us, creeper. "Actually, I was hoping I could speak with you. I assume that as a personal trainer, you take on private clients?"
It was an impulse. But asking while in front of the older man, someone she clearly trusted, seemed like a decent idea so he rolled with it. When she paused and looked him over as if he were a side of beef that may or may not be rancid, Nate cursed his foundering instincts. God, he hated flying blind like this. Six months since he'd lost his inner compass and he still hadn't gotten used to running on normal human instincts.
Maybe he was wrong about her.
"My schedule is pretty packed and I only train on-property. If you're looking for concierge service, you'll need to talk to another trainer."
He allowed himself a small sigh of relief. At least it wasn't a no. "I don't even know what concierge service is."
"Jacob here offers in-home personal training sessions, as well as dietary and lifestyle guidance. If that's what you're looking for…"
"In-house is good." It was a cautious way of doing things, with no chance of putting herself in a position where she'd be alone and at the mercy of another. A smart move for any woman, but he liked how this wary trait fit his profile. If only he could see her back, he'd know for certain that she was the one.
She gave him another head-to-toe sweep, and for no reason at all his skin began to heat. "Do you have a particular area you want to target?"
"My focus is to beef up my training for the Chicago marathon. My endurance sucks." Did Chicago even have a marathon? He was almost sure it did, though he couldn't imagine anyone voluntarily running in a place that seemed as frozen over as Santa's backyard. "I've been working out on my own but I've hit a plateau. When you told me what your specialty is, it seemed like a sign from above."
Her friend, Jacob, grunted a sound that could have meant anything before he took his leave through the heavy doors. Nate watched the older man go with a surge of two-toned relief. Not only did his absence signal that Nate had been deemed harmless, but now that he had her all to himself he had a sudden urge to take her off to someplace one hell of a lot more private than a gym doorway.
She watched Jacob's retreat as well before she turned her attention back to him. "The marathon is in October, so…seven months from now. Plenty of time to get your stamina up to the level you need, as long as you're serious about this."
"Trust me, I'm serious about everything I do."
"I don't trust without a very good reason."
He searched her face, trying to superimpose the memory of a shattered woman, pale from blood loss and near death, over the woman who stood before him now. There were many differences, but…yeah. She could be the one. "I'm guessing I'll have to work hard to earn that trust."
"And maybe not even then." She dug through her packed duffle bag, a bulky nondescript thing as understated as the rest of her outfit. To his eyes, everything about her whispered at trying to be invisible. "Here's my card with my email and the number for The Body Electric—just leave a message for Ella. If you're really serious about this, give me a call or drop me an email that includes your contact info so we can figure out a schedule that works for both of us. Though I should warn you—as packed as my schedule is, it's entirely possible I can't accommodate the times you'll be free to train."
"I'll make sure our schedules will mesh…" He looked down at the card. "Ms. Little."
"Ella, please. And you are…?"
"Nate da Luca. Call me Nate."
"Call me when you're ready." And before he could offer a hand she was through the doors, while his hand inexplicably tingled at the lost opportunity to touch hers.
By the way, something REALLY AMAZING also happened this week along with all the other craziness. "March Madness" for the romance world is starting again—the annual DABWAHA tournament has begun! Out of the thousands of romance novels published throughout 2013, 64 titles have been chosen to go head to head… and my novella, STARTING FROM SCRATCH was one of them! More on this on tomorrow's post. ;)
Blurb for WOUNDED ANGEL:
Book three of The Earth Angels
A descendant of the Angel of Vengeance, Nate da Luca was gifted with an uncanny ability to find things. It made his job as a detective a breeze—until he learned the hard way that some things should remain hidden. After that, his powers vanished, along with his belief in himself. Which is going to make tracking down Gabriella Littlefield for his latest client a challenge.
Personal trainer Ella Little paid a hefty price for her life—now all she wants is to live it in peace. Then a sexy hulk of a man turns up in her gym, reigniting desires she thought she'd left behind along with her real name and hair color.
Desires she can't deny even after she discovers Nate's no stranger to her dark past.
Before he can convince her the attraction is mutual, Nate's going to have to earn Ella's trust. But a demon is playing for keeps in the world of humans, using Ella as bait, and the last thing they have is time…
Go back to the beginning with Nobody's Angel, available now!
63,000 words

subset: commoncrawl


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at the INN
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Lymphatic drainage: Stimulate the lymph system to work more efficiently and help move the lymph fluids back to the heart. Assist your body in clearing sluggish tissues of waste and swelling by freeing vessel pathways. Help retrain the lymph system to work better for long-term health benefits. $115
Thai herbal compress: A selection of therapeutic herbs, including plai, mangosteen, ginger, turmeric and lemongrass, wrapped in a muslin compress, steamed and then applied to the body in gentle pressing, circular and rolling movements. Designed to relieve pain and inflammation soothing muscular aches and swelling. $135
Available 1 Hour & 45 minute Massage Treatment of your choice:
Hot Stone, Thai Herbal Compress: $175 Deep tissue & Aromatherapy $175
All other Massage Treatments $165
Couples Massage
Enjoy one of our Massage Treatments in the company of your loved one.
Experience the luxurious comfort of our Oasis Couples Massage Room.
For an appointment, please call:
778-514-5444 or 778-514-5462
Open House every morning from 9am to 10 am.


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Recent Projects
Good Shepherd Lutheran Church Addition and Remodel
Billings, Montana
We have successfully completed numerous worship facility projects, including new construction of Atonement Lutheran Church and King of Glory Church; the addition and remodel of King of Glory Church, American Lutheran Church, St. Agnes Church in Red Lodge, First Presbyterian and the St. Patrick Co-Cathedral… just to name a few. We are excited to be part of this new chapter for Good Shepherd.
South Big Horn Hospital
Basin, Wyoming
The project consisted of the construction of a new 18,446 SF hospital addition. Work included site clearing, site utilities, paving, site demolition and a partial replacement/remodel of the existing hospital. The general construction included a typical footing and foundation wall structure with a slab on grade.
learn more >
Divide Bar & Grill
Billings, Montana
Single story bar and restaurant with kitchen. Exterior patio with two fire pits and pergolas. learn more >
Antelope Court Apartments
Havre, Montana
The project consists of constructing five 6-unit buildings, single story structures with 30 apartments. Each has an open carport. 24 of the units are fully ADA accessible. One maintenance shed, basketball court, garden space and a play structure are also included on the property.
Sage Court Townhomes
Sidney, Montana
The project consisted of ten townhouses with two identical 5-unit buildings. Each side included two 3-bedroom units and three 2-bedroom units. Each two story unit has its own attached 2-car garage and was designed and built under the IRC as a townhome with individual water, sewer, gas and electrical service.


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Silverado Memory Care's Adult Child Support GroupName: Silverado Memory Care's Adult Child Support GroupDate: May 14, 2019Time: 6:30 PM - 7:30 PM CDTEvent Description:
Join us for an opportunity to share your experiences in providing care for someone with memory impairment. Connect with other families who understand the emotional and physical challenges you face.
Complimentary adult flexible care is also available for your loved one with memory care needs.
Adult Child support groups take place the 2nd Tuesday of each month.
RSVP to Jill Aeschliman, LMSW
Call: 512-280-2030 or
Email: firstname.lastname@example.com


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We've got a cluster of 75 Win2k3 nodes at work in a coarse grained compute cluster. The cluster is behind a mountain of firewalls and resides in its own VLAN. Jobs of all sizes and types run on the cluster and all of the executables running are custom-made.
(ed: additional notes on our executables) The jobs range from 30 seconds to 7 days in duration, and may contain one executable or 2000 sub-jobs (of short duration). Obviously we are trying to avoid the situation where our IT schedules a reboot during a 7 day production job.
We have scheduling software which accomodates all of the normal tasks for a coarse grained cluster and we can control which machines are active for submission, etc. If WSUS was in some way scriptable (or the client could state it's availability for shutdown) we could coordinate the two systems and help out.
Currently, the patch schedule is the Sunday after Super Tuesday regardless of what is running on the cluster. We have to ask for an exemption every time we want to delay patching a machine for a long running production job. Basically, while our group is responsible for the machines we have little control over IT's patch schedule.
- Is patching monthly with MS's schedule sane for a production Windows cluster?
- Are there software hooks in WSUS where we could say, "please don't reboot just yet"?


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Whenever you are attempting a project like this, always test it out on a scrap piece first. When using different pigments, be sure to let one dry before adding the next color.
What to Use
Use water-based acrylic paints designed for leather because they are more flexible than ordinary acrylics. If painting the entire surface of the leather, check to see if your leather goods are vegetable-dyed, as they absorb the paint better than leather goods tanned by other methods.
Not sure if the leather goods are vegetable-dyed? Test a scrap piece to see how well it absorbs the paint.
What to Do
Begin by cleaning the leather goods, using a solution of one ounce of leather bleach to one pint of water, applying the solution with a soft brush or wool dauber. You can also lightly sponge the leather with water prior to painting in order to ensure a more even absorption of color. Don't soaked it, just get it damp.
Applying the Paint
You can paint over a large area of the leather goods or just a small part for highlighting effects. If you want to do the former, paint over the leather first with a water-diluted solution of the paint. In this way, you'll prepare the surface of the leather goods to receive the undiluted paint.
Use one part paint and one part water, and apply in broad, even strokes with a wool dauber. Add more water if the leather is not absorbing the solution, and more paint if the color is not showing sufficiently. Two coats of the solution should be applied, leaving sufficient time between coats for the paint to be absorbed but for the leather to still be somewhat damp and not dry. Try this out first on a scrap piece of leather and take your time.
Applying Undiluted Paint
Now it's time to apply the undiluted paint using a brush or wool dauber, and broad even strokes. For small areas that you wish to highlight, use undiluted paint and a small paintbrush. To prevent the paint from cracking, it's important to flex the leather goods throughout the drying process. Do this between coats of paint as well.
Using an Airbrush
Another option when applying paint is to use an airbrush. A double action airbrush allows you to control the flow of the paint and the air with one finger and avoids the initial blob of paint emerging with a single-action airbrush. You can strain the paint to remove any flecks by using a piece of pantyhose. If you over spray, acrylic paint can be easily removed while it is still damp.
Tools to Use
* Water-based acrylic paints
* Leather bleach
* Wool dauber
* Paint brushes or airbrush
* Vegetable-dyed leather goods and water-based acrylic paints work best
* Clean the leather goods with a solution of water and leather bleach
* Apply two coats of water-diluted paint, with sufficient time between coats for the paint to be absorbed
* Apply undiluted paint
About The Author:
*** Hey! Need To Know More About Leather? *** Here's How to Get Your Answers Quick and Easy Click Here ==> http://www.leathersecrets.com


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Phew! I was worried that the smear campaign against Cardinal George Pell mounted by the pigs at the Vatican trough would persuade Pope Francis to water down Pell's plan to impose proper accounting procedures on the Curia. But today Francis published the legal framework for the reform and – well, I can't improve on the reporting of the Vatican correspondent of Crux website, Inés San Martín:
Pope Francis decided the future of his financial reform on Tuesday, issuing a new legal framework for three key oversight bodies that largely confirm the authority of the man he put in charge of his clean-up operation, controversial Australian Cardinal George Pell.
The decision came in the form of a new legal framework approved by the pontiff for the Vatican's three financial oversight bodies that he created in 2014: The Council for the Economy, the Secretariat for the Economy, and an independent auditor general.
Despite mounting calls from some quarters of the Vatican to rein in Pell, such measures are largely missing from the new statutes, which were signed by Pope Francis on Feb. 22 and became effective March 1.
The only major concession is that while the Secretariat for the Economy has been confirmed as responsible for procurement and personnel, it will not administer Vatican real estate. That function, which had been assigned to Pell's department last year, will be returned to another Vatican department.
In general, the results are likely to be taken as a show of confidence in Pell at a time when the 73-year-old prelate had found himself under mounting fire.
Since his arrival in Rome one year ago, Pell had ruffled feathers by moving aggressively to implement new transparency and accountability measures, including publicly disclosing the presence of assets that he claimed had previously been hidden by various departments.
Let's be quite clear why Pell is 'controversial' and 'embattled'. It's nothing to do with bogus claims, which I discuss here, that the Australian enjoys a lavish lifestyle. He most certainly does not.
Cardinal Pell is embattled because, from now on, Curial officials will have to account for their spending. He's brought an end to a culture of fiddling your exes which makes 20th-century Fleet Street look like a Presbyterian knitting circle.
I caught the tail end of the hacks' jamboree and – how shall I put this? – not every restaurant bill submitted was as strictly business-related as the expenses form implied. But at the Vatican you usually didn't have to submit any receipts at all.
How we young reporters would have enjoyed that modus operandi! And now spoilsport George Pell wants to see proof that Church money is being spent on spreading the Gospel as opposed to foie gras over crisp squares of toast. He'll take a lot of flak for this 'Anglo-Saxon' puritanism over the next few months. But it's too late. He's won. I do feel a bit sorry for the restaurants near St Peter's, though. How will they survive now that the old guard at the Curia have to dig into own pockets for that second bottle of Chianti?


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On July 1, 2014, Canada's new Anti-Spam legislation comes into effect. This means that we cannot send you electronic messages of a commercial nature without your express consent. These messages could include important information to parents and guardians about a broad range of activities, including but
not limited to yearbook sales, field trips and travel opportunities, milk and pizza sales, student photos and fundraisers.
By signing this form, you are giving your consent to receiving electronic messages from the school, the Catholic School Council and/or Ottawa Catholic School Board (OCSB) that may include information of a commercial nature. Please note that you may withdraw your consent at any time and the OCSB will not share your email address with any other organization.


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Little Bird Told Me Lewis and Fitz Rocking Horse with FREE Delivery
Lewis is a handsome rocking horse, guaranteed to be well loved by boys and girls alike. Featuring a traditional argyle diamond fabric in stunning shades of blues, greys and gold, Lewis is as classical as he is handsome. Tucked inside his pocket is his friend Fitz, a huggable rabbit with crinkly ears and a rattle in his tummy. Lewis comes with a super soft scrunchy faux suede mane & tail and a textured velour saddle & bridle.
The charming Lewis rocking horse from Little Bird Told Me carries his lovable little friend Fitz wherever he goes.


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The Local Plan is the key document describing how our Borough will develop for about 20 years into the future. It covers all aspects of communal life in the Borough, including the provision of infrastructure (schools, water etc), the provision of an adequate road network, measures to boost the economy, steps to address social inequality and the provision of housing and much more.
The key elements of the planning system are :
Following years of work, involving several consultations, GBC approved a plan on 22 November 2017 for submission to the Secretary of state. The Submission Local Plan: strategy and sites (2017) will be the subject of an Examination in Public (EiP) by an independent Planning Inspector beginning on 5 June 2018.
GBC anticipates that the new plan will be adopted by the end of 2018.
In addition to the submission on strategy and sites, GBC has to prepare policies on development management to replace those in the existing 2003 Plan. It has also to bring forward proposals for the Community Infrastructure levy which will be the subject of a consultation and then be submitted for independent examination.
For more information see GBC's Local Development Scheme and GBC's dedicated Local Plan website.


text: 1912Staver 908 Posted November 7, 2016 The 1902 build date seems suspect, engines were for the most part still quite primitive in 1902. Something in the 1908 - 1912 range seems more likely. Nationals were quite advanced but if this engine really dates to 1902 they would have definitely been at the top of the game, and easily on par with the best from Europe. Greg in Canada 1 Share this post Link to post Share on other sites


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Warming Up Asana
Warming Up – Oxygenating the Blood, Improving Circulation and Promoting Flexibility on the Spine Yoga Exercise
Stand with your feet slightly apart, inhale and gently pushing the pelvis forward, bend your body slightly backward. Stretch your arms. Exhale and bend your body forward until the hands touch the floor, continue inhaling up and exhaling down. Try to keep your knees straight when you go down. Every time you lean back push the pelvis forward while contracting the buttocks. This will protect you back.
Exhale down, inhale up. Exhale down, inhale up.
This asana increases the oxygen in the blood and is very invigorating, keeping the spine and the muscles of the back young and flexible. In fact your whole body goes into heat, including the tendons.
See it Live:


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Would You Recognize Your Sister if She Were Homeless?
Eye-Opening Experiment from New York Rescue Mission Will Make You Cry
Editor's Pick
That the homeless can become "invisible" in the eyes of passersby is a sad reality, but the phenomenon becomes all the more depressing in this moving experiment for New York Rescue Mission, created out of Silver & Partners and directed by Smuggler's Jun Diaz.
The video shows interviews of people pouring their hearts out about their loved ones -- sisters, cousins -- even spouses. All the while, it intercuts scenes of those very same loved ones "transformed" into homeless people and planted on the street. While they're in their disguises, a hidden camera captures how that sister, cousin and husband who said they loved them so much, pass them by without a blink of an eye. The family members, not surprisingly, are moved to tears when they realize what they've done.
"Unlike most lighthearted fare we've come to expect from hidden cameras, this one had quite an emotional impact on our subjects," said director Jun Diaz in a statement. "The belief in this project was absolute and certainly helped alleviate the considerable anxiety I felt when I had to reveal the hidden camera footage to these people."
The video directs viewers to the Make Them Visible website, created out of Reactive, which shows the real faces of the homeless, beautifully shot in black and white, and encourages people to share the story and donate to the cause.
- Date
- Apr 23, 2014
- Brand:
- New York Rescue Mission
- Client:
- New York Rescue Mission
- Agency:
- Silver & Partners
- Chief Creative Officer:
- Eric Silver
- Creative Director:
- Howard Finkelstein
- Communications Strategy Director:
- Lachlan Hall
- Producer:
- Joel Jeffords
- Interactive Associate Creative Director:
- Justin Pedone
- Business Director:
- Cristina Costantino
- Production Company:
- Smuggler
- Director:
- Jun Diaz
- Executive Producer:
- Patrick Milling Smith
- Executive Producer:
- Brian Carmody
- Executive Producer:
- Allison Kunzman
- Producer:
- Ian Blain
- Director, Photography:
- Mike Berlucchi
- Editing:
- Now Corporation
- Editor:
- Jesse Reisner
- Assistant Editor:
- Jessica Farmer
- Executive Producer:
- Nancy Finn
- Audio House:
- Sound Lounge
- Mixer:
- Tom Jucarone
- Executive Producer:
- Vicky Ferraro
- Music:
- JSM Music
- Composer:
- Joel Simon
- Finishing/Titles:
- Suspect
- Executive Producer:
- Robert Appelblatt
- Executive Creative Director:
- Tim Crean
- Flame Artist:
- Victor Melton
- Producer:
- Juan Handal
- Website Design, Development:
- Reactive
- Chief Executive Offier:
- Carl Panczak
- Art Director:
- Ash Leech
- Project Manager:
- Liesl Pfeffer
- Project Manager:
- Ranise Jackson
- Senior Developer:
- Ken Park
- Front-End Developer:
- Dixon Cheng
Need a credit fix? Contact the Creativity Editors


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Support the Monastery
rulet oynamak Prayers for Wisdom
follow As our community assesses our situation and need for ongoing support, we pray for the wisdom to plan and make decisions that will lead to the fulfillment of our Camaldolese charism. We ask your continued prayers for wise stewardship of The Monastery of the Risen Christ as we venture together into the future.
20 super hot slot oyna Our Situation
zarlı casino oyunları The Monastery is located in San Luis Obispo near California's southern central coast. We are at 250-feet above sea level in the Coast Range Mountains and over three miles from the nearest city. The tranquility and incredible beauty of our location was cherished by the monks who founded the Monastery over twenty-seven years ago, and is equally cherished by the current monastic community and our many retreat guests and day visitors who visit from all over the world. Our isolation and beauty, however, come at a cost. We are in many ways like a small village, responsible for generating much of our own electricity, providing our own water, maintaining our roads, maintaining our buildings and infrastructure, and depending on satellite communications for our connection to the outside world. While most of our maintenance work is done by our own live-in workers, which helps to significantly lower our overhead costs, we still face significant, non-negotiable costs such as water, propane for individual living quarters, automotive fuel, satellite hardware and service, food delivery services, and numerous other recurring costs necessary for the daily operations of the Monastery.
We support ourselves through retreat donations, sales in our gallery, book and gift shop, sales of our delicious fruit and date nut cakes, and through the generous donations of our supporters and friends. Thank you for considering making a donation to support our work and ministry. Wishing you peace and all good things!
Direct Contributions
Contributions may be made online and securely through PayPal.
Click on the Donate button in the left column or click here.
Contributions, payable to The Monastery of the Risen Christ, may be mailed to:
The Monastery of the Risen Christ
PO Box 3931
San Luis Obispo, CA 93403
Contributions are deductible for federal income tax purposes under Section 170 of the IRS Code. A formal acknowledgment of your gift will be forwarded to you to support your deduction.
Annual Giving Campaigns
You may designate the Monastery to be the recipient of your contribution to the United Way or corporate matching campaign at your place of work. Legal documentation of the status of the Monastery as a Charitable Institution under federal law may be obtained by writing:
PO Box 3931
San Luis Obispo, CA 93403
or call: 805-544-1810
Bequests, Legacies, Trusts, Transfers, Gifts
You may designate the Monastery as the recipient of a bequest, charitable trust or gift. Bequests, legacies, devises, transfers or gifts are deductible for federal estate and gift tax purposes under Sections 2055, 2106 and 2522 of the IRS Code. If you are interested in a contribution of this kind, please write:
PO Box 3931
San Luis Obispo, CA 93403
or call: 805-544-1810


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We can't deny the fact that internet is the most powerful medium. For your Business to stay connected with the clients it is important to have a user-friendly website.
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We help you build logo, flyers, advertisement banners for your business. Our creative team can make your ideas turn to reality.
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Crazy Claire: Toddler Distraction Techniques
|a key
So I've started locking doors.
I lock them from the inside with the door open, shut the door from the outside, and when I need to get back in I use a coin to unlock them. It's a process, but it keeps her out of one or two places she shouldn't be.
|Claire's School Station
We are still working on Claire's busy ways . . . or I am say I am working on controlling my reaction to her busy ways. It can be very frustrating when I finish cleaning up one mess and find out she has climbed into the pantry and dumped out a box of spaghetti or got her paws on a pen and drawn on herself and other objects. I think I just need a little vacation. Either that or a baby straight jacket.


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How Photographs Are Sold is a new book from fine art photographer Alain Briot.
"The book features information from photographers who earn a living from their artwork, as well as from others who sell their artwork to further their passion rather than as their main source of income. It also includes examples from Alain's personal experience selling his fine art photographs."
How Photographs Are Sold retails for $36.95; however, it is currently available for $25.86 on Amazon.com.